Philosophy in the Resource-Patterns Model of Life

This is a draft of Chapter 6, in the book outline, also available as PDF.

This chapter is divided into three major sections:

6.1 postulates of the Resource-Patterns Model of Life (RPM) explained.
6.2 models of mind suggested by RPM’s postulates.
6.3 language learning within the model.

6.1 Justification for Postulates of RPM

In Chapter 1, Section 4 we listed the assumptions upon which RPM builds. Now I think that a better name for those assumptions is postulates; hereafter I will usually refer to postulates. In this first section of Chapter 6, I will justify some of those postulates.

6.1.1 Basic Postulate: Living Things Exist in a Universe

This Resource-Patterns Model of Life starts in my experience as a human being. I find myself in life having senses, hungers, memories, thoughts, and hopes for the future. I am told and I believe that my life can continue only if I eat enough nourishment, and only if I avoid an array of threats to my life.

I have choices about how to act, and my performance in making these choices seems to affect my success. I would like to make these choices better. Often it seems, if only in retrospect, that I could make better choices.

Fortunately for me there are nutritional resources here on Earth, and I have been taught that I can fill my plate if I choose my actions appropriately. I also have many acquaintances and as far as I can tell I speak for them as well. We humans generally share such experience.

6.1.2 Postulate: Life Advances in Levels

6.1.2.1 Looking to smaller sizes

Now we take an important observation from biology. This has been spelled out in Chapter 4, and perhaps the reader should review that material. But, to review briefly, biologists believe that we humans and other large multicellular organisms grew on Earth from combinations of single-cellular organisms. A long time ago, perhaps a billion years, single-cellular organisms were the largest and most developed forms of life. Then through a long process which we seek to understand those single-cellular organisms combined in cooperating organizations which gave rise to us much-larger multicellular organisms.

Furthermore biologists report a still earlier advance which created the single-cellular organisms mentioned in the preceding paragraph. Those organisms, called eukaryotic cells, appear to be composed of numerous smaller and still more primitive components, called prokaryotic cells, such as simple bacteria.

Thus we see that life in our past has grown from one level to another. At the two higher levels (eukaryotic single-cellular organisms and multicellular organisms) we see that what we normally consider to be a single organism is actually an organization of a multitude of smaller and more primitive organisms. And the same sort of composition from earlier “living” parts may also apply to the lowest level which I have mentioned, prokaryotic cells.

One can speculate that such disaggregation could carry on to smaller and smaller levels. But I will not attempt such a claim. For our purpose here it seems sufficient to notice the two smaller levels which immediately precede our present level as multi-cellular organisms. From those two we may:

1. formulate some outlines of how life as we are given ability to observe it has grown to its present stage;
2. propose that such level-to-level growth continues now as we humans, and perhaps other multi-cellular organisms, organize ourselves in groups.

6.1.2.2 Looking to larger sizes

I do not suggest that every organization which we humans form constitutes a new Living Thing (LT), because most of our organizations are temporary and fall apart before long. Consider a parallel between organizations at our human level and organizations of single cells at the first smaller level. I suppose biologists could point out numerous organizations of single cells which do not constitute a new multi-cellular organism. Single cells may form millions of organizations among themselves, giving each of these organizations a “test run” we may say, before any one such organization displays all the attributes which we humans might recognize as an individual living thing at a higher level. Similarly on our level, I suppose we humans will form many, many organizations that fall short of the sort of viability which might be necessary to form a new LT on the next higher level.

Of course we will notice many types of organizations if we start to examine and compare them. I have proposed one possible way to sort organizations into eight types. This is based upon whether the subject organization has attributes which may be called: member-aware, self-aware, and encoded.

A skeptic may doubt that large organizations composed of many humans might be viewed as individual living things such as we humans experience ourselves to be living things. After all, we humans are conscious, it may be argued, implying that no other organization could enjoy such awareness. I have tried to answer such skepticism in Chapter 4. Let me add here that I believe our most advanced science still has not grasped what exactly consciousness is. Lacking ability to clearly define consciousness, as we do, I would be humble about any confident assertion that other entities must lack it. See more about consciousness in Section 6.2.1.2.

6.1.3 Assumption: Prosperity Is Good (PIG)

When we are working with LTs we normally assume that prosperity (a higher standard of living) among those LTs is a good thing, and that an increase in population along with an increase in prosperity is also a good thing. I am a human, a LT, and my immediate impression is that my prospering, and the prospering of other people, is good.

People who focus upon sustainability may disagree with the PIG assumption. But whether we RPM modelers agree or not, motives to increase prosperity may have been programmed into us through Darwinian selection. If those motives occurred in some individuals by natural variation, and if those individuals succeeded in leaving more offspring, then we see the evolutionary mechanism by which a bias for prosperity may be felt in most of us today.

This assumption gives shape to the whole thrust of this book. Throughout we seek to understand how new sets of rules, which enable prosperity, can be discovered and learned. We assume it will be a good thing if we can understand processes of discovering new rules and thus augment our human prosperity.

6.1.4 The Second Law of Thermodynamics

6.1.4.1 Postulate: We continue to live because we keep on finding new Resource Patterns (RPs).

A cornerstone of RPM comes from physics, as the second law of thermodynamics places limits upon life. The second law tells that perfect sustainability is not possible, on this Earth or within any bounded ecology, for a Living Thing (LT) or any population of LTs. It is not possible to recycle 100% of the energy and raw materials which a LT requires to stay alive. So for a LT (or any population of LTs) to continue living it must consume new resources, energy and raw materials, which come from somewhere outside the LT (or population), and eventually from outside the previously assumed bounds upon the ecology which hosts such a population.

At first look this physics of the second law confirms the views of doomsayers. In every snapshot of life which we may take, we can see that the LTs in this snapshot are consuming resources which are clearly finite in this snapshot. It would seem that life such as this cannot go on indefinitely. Such a doomsayer’s view is easily supported from observation and logic.

But another way of looking at the history of life on Earth shows no such gloomy outcome. We humans are the crowning product of that history, such as we like to see it, and we seem to be doing quite well. Our numbers are larger than ever before and each of us on average lives better than ever before. Yes, we use up the supplies of some resources. But we always seem to find other ways to satisfy our needs.

What could explain this seeming contradiction of the 2nd law? Here is what I assume must be the answer. Resources which we humans discover how to employ have been available here on Earth all along. But we did not recognize the potential value of such resources until we reached a level of technical sophistication which enabled us to exploit that value.

So, while a doomsayer may grasp onto the despairing long run implications of the 2nd law, a pragmatist may say that we humans should perhaps be more humble. We need not look to the everlasting and entire-universe-encompassing view. It may be enough, the pragmatist may continue, if we learn from the last three billion years of life on Earth in order to look ahead for only a shorter time, say one million years, as we anticipate our life in only this one galaxy.

There is of course no guarantee that our good luck in gaining access to new resources will continue. Ecological doomsayers might be correct. But in the sky I see the Sun and Jupiter, vast amounts of energy and raw material which we humans have yet to learn how to exploit. I assume that our human good fortune in increasing prosperity will continue for the foreseeable future.

6.1.4.2 Postulate: The universe must be patterned, or we would not be here.

The next implication which we draw from the second law is that the universe must be patterned, or we wouldn’t be here. We have seen that a living thing can keep on living if from time to time it discovers and imbibes a supply of each resource which is essential to maintain it’s life. This discovery of a resource will in many cases involve a LT moving to the location where a resource exists. As such, the LT must be either:

1. lucky, in happening to move where a resource lies at hand, or
2. knowledgeable, from either
   1. experience, in remembering the location of an unexhausted resource or
   2. education, having been taught where to seek a resource.

But can it be only luck (as just listed above)? We might imagine some circumstances in which a LT could survive with luck alone. This could occur when the resource was all around, predominantly present, so that a LT would just happen to bump into it so often that it would almost surely never run out; it would not need knowledge.

But it seems survival can’t rely upon just luck alone. Notice for instance that we assumed motion in the just-described situation where a LT comes into chance contact with a resource frequently enough to allow survival. But if an LT has an option to remain idle, that is to make no physical movement in any given cycle, then such an LT might adopt a strategy of remaining idle most of the time – and might very well starve to death. So in order to survive our LT in this example must not adopt that stupid idle, strategy, and must “choose wisely” to adopt a strategy of sufficiently frequent moving about.

We living things need to employ some knowledge of how to behave in order to arrive at those circumstances in which we are able to partake of life-essential resources. Said another way, we must adopt behaviors to be able to partake of enough life-essential resources in the environment in which we find ourselves.

Now, if you accept that conclusion, I believe that a corollary is that life-essential resources must exist in patterns in our environment. The opposite of patterned distributions would be random distribution. But if resources were distributed randomly then we living things could not make use of knowledge to exploit those resources; random behavior would be just as productive as knowledge-guided behavior. Since we know, I hope I can claim, that our knowledge serves our survival, I hope that I have your concurrence that our corollary is also true: Most of the life-essential resources upon which our lives rely are distributed not randomly but in patterns, patterns which enable our knowledge-guided behavior, Resource Patterns (RPs).

Here is another point which follows the above reasoning. Wherever we see a living thing we will probably be correct to assume that there must be one or more patterns of resources in that LT’s environment which sustain that LT. Or, there must at least have been RPs in the past sufficient to have developed and maintained the LT up to the time when we saw it.

6.1.4.3 Postulate: Living Things which we see must be “doing right”.

Since continuing life requires behavior patterned to exploit whatever RPs exist in the given ecology, we may conclude that LTs which we observe must, in an overall sense at least, be doing the right things in order to survive. This general rule will not apply to every observation of a LT during a prescribed span of time, as shown by the following exceptions to the general rule:

• A LT which has recently sated itself on a large meal may be able to survive a long time on its internal storage of resources.
• A LT may be young, having been born or launched into life with sufficient internal storage of resources to survive a relatively long time.
• A LT may be only one of a large number of offspring produced by a parent (or parents), in a dispersion of young into the environment in which only one or a few members of this dispersion need to “take root” and survive for the long term, in order to support the larger-view of life of the species of the parent(s).
• A LT may have been provisioned by a supporting organization to go on an exploratory mission during which it is expected to find no resources for itself, but only to bring back information which may prove helpful to the supporting organization.
• The RP which has sustained a LT for a long time may have been finally used up in a recent moment. So while this LT appears to be doomed, it is still surviving on previous productive behavior.

While such exceptions do exist we must be careful to allow the possibility that LTs which we observe may be living thus, in a temporary and unsustainable way. But still, generally speaking, if we observe a LT (or a set of LTs) over a long span of time and a variety of circumstances, at some point as we extend the scope of our overview we must be observing, in this scope, behavior aligned with the RPs at hand.

6.1.4.4 Postulate: The universe’s Resource Patterns must exist in a range of scales.

Recall that we see living things on at least three levels, and apply the conclusion we reached above in Section 6.1.4.2: that the existence of LTs strongly implies RPs which are exploitable by those LTs. We are thus encouraged to suppose that there are patterns of resources on different scales. Prokaryotes, before the time when many of them organized into eukaryotes, must have been sustained by exploiting one or more Resource Patterns (RPs) on a scale accessible to them. The same would apply to single–celled eukaryotic organisms. There must be larger resource patterns, or more difficult to exploit RPs, which gave rise to and sustained these larger organisms. Similar reasoning applies to the next higher level, to us multicellular organisms. There must be in our environment RPs with size or difficulty which placed them outside the reach of single cellular organisms, but which we larger organisms can exploit. Thus, evidence and reasoning support our conclusions that RPs exist on a scale appropriate for us multicellular organisms and smaller.

But what about larger-scale organizations? Can we assume larger resource patterns exist in our environment which may sustain the organizations of us humans which we are constantly testing? Of course there may be debate among us as to whether such larger RPs exist. If those larger RPs do not exist then all multi-human organization will fail. But there seems to be plenty of evidence that larger RPs do exist. Notice that for most of our recorded history several types of our human organizations (families, business firms, religious organizations, and states) have been forming and lasting long enough to suggest the existence of a sustaining RP. Recall the argument developed above that we may assume existence of a RP which sustains any long-lasting organization — whether or not we can perceive that RP. Furthermore, we can easily perceive large and difficult RPs which we have not yet gained organizational sophistication to exploit. Notice the sun, plenty of energy; notice Jupiter, plenty of raw material. To date we have used almost none of these resources. They wait for us, assuming we reach an appropriate scale of size and sophistication.

Concluding, we assert that RPs exist on a wide variety of levels, both smaller and larger than our human scale.

6.1.4.5 Postulate: Living Things have goals.

Here I will argue that LTs must have “goals”, or at least the behavioral choices of most LTs much of the time should suggest that they have goals. We have noted that LTs need to adapt their choices to the patterned availability of resources in their environment. They must do so in order to discover resources — in order to survive. So if we see a LT we can assume that it has in the past, unless it has been temporarily lucky, acted as if it wanted to discover resources. We could reasonably judge that it has at the very least this one goal of discovering resources.

If we are modeling LTs with computerized agents, what I am here calling a “goal” will be represented by rules which the agents use to select their actions. For more on that subject see Chapter 5, “The Learning of Rules”.

While we can expect a goal of survival to be evident in the behavioral pattern of most LTs much of the time, this goal will not always be superficially evident to an observer. When an LT is doing well, when it has an abundant store of life-essential resources, then the best chance of long-term survival may be served by experimental behavior. Experiments may increase the chance of discovering new as-yet-unexploited RPs. Such a discovery may increase the chance of survival either when presently exploited RPs are used up or in event of some future calamity.

But experimental behavior, perhaps by definition, will include some failing behavior. So outsiders, evaluating such observed behavior, may judge this failing behavior negatively. Failing behavior may be justifiable in the light of a given organization of LTs, when the larger picture and long-term future are considered. Taking chances in order to discover good RPs may necessitate some failures from behavioral choices.

While I hope I have convinced you that individual LTs must have goals, we should not necessarily expect all LTs in a given population to have entirely the same goals. Various goals which led to experimentation and dispersion may help a population of LTs in the long run.

6.2 Models of Mind: Overcoming the 2nd Law of Thermodynamics

As we have reviewed above, the 2nd law of thermodynamics challenges us to explain the existence of Living Things (LTs). We meet this challenge in RPM with a theory which offers to explain the increase in material order in some locations. Those locations, being the bodies of LTs, can be built and maintained with localized gating or control of the down-gradient flow of matter. Of course some order is dissipated overall in each interaction (according to the 2nd law), but properly chosen interventions can create, for a time, locales of increased order. The set of choices implied by these properly chosen interventions becomes the challenge which life must meet. Since we LTs exist, we may infer that life does indeed overcome the challenge. It is a challenge of information processing, a challenge of “mind”.

Within our modeling method, RPM, we divide this information processing challenge in two. The division takes place along the line established by our definition of a LT.

1. On the smaller side, by defining a LT we give only a general overview of information processing which must go on within the LT. So we modelers are challenged to produce a more detailed and insightful explanation of the information processing within these pre-existing LTs.
2. On the larger side, we modelers present an information-processing challenge to the LTs (and ourselves) in the initial condition: how can these LTs advance themselves through coordination of their activities in order to exploit a large but difficult RP?

We will address each side of this division below, in Sections 6.2.1 and 6.2.2 respectively.

6.2.1 Information Processing within Existing LTs

6.2.1.1 Flow diagrams of information processing

In this section we will look at how we might model a mind of one of our starting-level agents, by stepping through a series of diagrams which show an increasing level of detail.

For our first step we will simply observe a LT and notice that it seems to be living. It is producing observable actions. See Figure 1. This Figure shows one of our familiar critters, but for this discussion we will generally be thinking of our more general class of LT. This shows what exists, what we hope to model.

Figure 1. The motions of a LT, not entirely predictable, suggest internal information processing.

Recall the discussion in Section 6.1.4.3. Since we do observe a LT its life history must comply with the 2nd law of thermodynamics. This LT in our view must be doing something right, or many things right. Its survival to this moment shows that it (or its parents or its parent-community) is not acting randomly or otherwise stupidly. As we proceed in sketching a model of this LT’s interior decision making process, we should remember that we are trying to capture how this LT does things right in the world in which we see it in order to survive.

We notice that the LT depicted in Figure 1 does not always do the same thing. At different times it does different things. So we can be hopeful that this variability of action gives another clue of the 2nd law compliance, of something environmentally-smart about this LT.

Next naturally we notice circumstances in the surroundings of the LT. See Figure 2. We notice that these circumstances change from time to time. So we may propose that the different activities of the LT are responses of the LT to its different circumstances. As we start to study this possible correlation we try, naturally, to list the LT’s external circumstances which we suppose may be influencing its choices.

Figure 2. In trying to explain the LT’s motions we try to catalogue affective circumstances.

The next step in our effort to model how the LT is deciding how to act may be to start building a model of the information processing system which we suppose must be operating within the LT. For our purpose of modeling the LT’s information processing system, we may at first view that information processing system as a black box. See Figure 3. Now the circumstances (of the LT in Figure 2) come into the black box as sensory inputs. And the choices (actions of the LT in Figure 2) come from the black box as its outputs. Our use of a black box here shows that something, perhaps very complex, goes on inside the black box.

Figure 3. We call the LT’s information processing a Black Box.

Next we will start to diagram what we might suppose goes on inside the black box. We use diagrams of a sort used by computer programmers to show their top-level description of how a program operates. Three preliminary points should be understood:

• With these diagrams we are modeling the mind, and only the mind of a LT. Other parts of the LT which we assume exist, but which we do not specify here, are one or more senses (abilities to detect aspects of both the external and internal environments) and one or more abilities to act (to make some physical or bodily motion).
• These diagrams should be helpful for thought experiment agent-based modeling (TEABM) modeling as well as for computerized agent-based modeling (CABM). If we work at the shallower depth, of thought experiments, then we might proceed with our thoughts codified no more explicitly than as shown by diagrams such as these. If, on the other hand, we work on the more difficult CABM depth, we will still probably use diagrams such as these as our starting points, that is as our top-level description of what we aim to accomplish with more detailed computer programming.
• We should not forget that many agents at a given level may have among them many different designs of minds. That complexity may be addressed in the future, but at this early stage I will be presenting only one prototypical design as our starting point.

In Figure 4 we model what happens during one moment of the life of a LT by starting at the top and making one pass down through the five boxes. And, since an arrow loops from the bottom box back up to the top box, we model a continuing stream of moments which extends through the entire life of the LT.

Figure 4. We break down a LT’s information processing into these five steps during each moment of life.

In this diagram we include the important concept of learning from experience. We give memory to this mind, showing memory in the shape of a drum since that shape represents a database in computer diagrams. The LT can learn from its prior successes and failures.

The memory of this LT might be empty at the start of life, that is on the first pass down through the boxes. In that case memory could not help with the first choice. But in every subsequent moment there will be some memory, and increasingly more memory, which the LT may use as it attempts to choose an act which might bring it success given the current situation.

We say the LT decides what act to “attempt” rather than to “perform” because such a decision may fail. As the LT tries to perform the chosen act the outside world also acts. For example a decision to step forward may be blocked by sudden insertion of some physical barrier. Note that doing nothing, that is to make no outward move, may be among the acts which we build our LT with ability to attempt during a given moment.

We should keep in mind, as we go forward, that in order to survive the LT needs other things in addition to what we are now diagramming. These other things include:

1. The mind needs a good set of pre-programmed instincts or behavioral rules. These are needed in addition to memory in order to make productive decisions about how to act. The most important built-in rules aid survival by giving high priority to avoiding danger and finding food.
2. The environment must contain a pattern of resources which make it possible for a LT, such as we are creating it, to discover and exploit this pattern, that is to survive.

Next, we will use a diagram which adds explicit display of two functions which were only implicit in Figure 4’s broadly-described “Decide What Act to Attempt”. See Figure 5.

Figure 5. We extend the model by adding the possibility of learning new rules.

The two additional functions are:

• Box 3 Induce, shows an ability to build new rules after repeated experiences. This function operates more slowly, over extended periods of time, and is drawn with dotted lines to signify this difference of pace.
• Box 4 Categorize, which is a memory for storing and searching rules which may pertain to the situation of the moment. These rules, which may either suggest or require a particular action in response, are passed on to Box 5, Decide.

A line is drawn directly from Box 1 Sense to Box 5 Decide to allow immediate responses, like reflexes, in situations where there is no time to remember and consider anything.

It may be natural for a reader at this stage to ask what is the difference between the rules implicitly operating in Box 5 and the rules delivered from Box 4 to Box 5. For now we can speak only in general terms about this distinction while we remember that these box diagrams are only a beginning toward analyzing a process which, when more fully modeled, may require hundreds of boxes for display in such graphical form.

Finally, in this sequence of increasingly complex diagrams, we will add a box which represents planning ahead. See Figure 6 in which we have added, to the boxes in Figure 5, Box 5a Plan. The function performed in this box is to imagine the future. Before the LT decides in Box 5 what act to take in the present, this new Box 5a shows that the LT can guess what will happen if it makes a particular act.

Figure 6. We add ability to imagine a future, to plan ahead.

The outputs of Box 5a would resemble the outputs of Box 1. While the outputs of Box 1 represent the external and internal environments, as the LT is able to sense those environments, the outputs of Box 5a represent those environments as the LT is able to imagine them.

To produce a guess about what would happen in the world following this LTs imagined (or hypothesized) act, the process in Box 5a may consider that the external and internal environments would differ from the immediate situation, as recently sensed in Box 1, in ways such as these:

• the world will differ insofar as this LT’s hypothesized act (the input to Box 5a from Box 5) will have been performed.
• other LTs may have reacted to this LT’s act and thus changed the condition which this LT senses.
• physical processes underway in the world may have continued during the elapsed time between cycles, and the situation changed accordingly.
• one cycle’s worth of the LT’s internally-stored resources will have been consumed.
• and more. This list could be extended of course.

Because the output of Box 5a attempts to represent a future state of the world, Figure 6 shows the three output arrows from Box 5a feeding into the same boxes as the three output arrows of Box 1. After the processes of Boxes 2 and 4, results are fed back again into Box 5. There, in light of these imagined results, a decision may be made on what act to attempt, with this decided act sent to Box 6. Or such a decision may be postponed while another imagined act is sent to Box 5a, to consider another step ahead of planning.

6.2.1.2 An Explanation for Consciousness

I discover a possible explanation for consciousness in the preceding development of flow charts for the mental process of an existing LT. Notice these processes which coexist at Box 5 in Figure 6.

• What comes in: sensory inputs, memories, awareness of rules.
• What it can do: Imagine an immediate future which may follow from the next act of this LT, and also a long term future involving a series of acts by this LT.
• What it must do: Decide what act to undertake next.

I suggest that the combination of those processes describes what I experience as consciousness. In addition to offering an explanation of consciousness this way of thinking offers a definition of consciousness, being the combination of processes described above.

John Searle gives a similar definition (in a YouTube lecture titled Professor John Searle: Consciousness as a Problem in Philosophy and Neurobiology, Cambridge, 2014, near minute 5 of the 53 minute recording). Transcribed here:

Consciousness consists of all of those states or feeling or sentience or awareness. It is a set of processes that begin when you wake from dreamless sleep and it continues all day until you go to sleep again or drop dead or go into a coma or otherwise become, as we would say, unconscious.

Most other theorists who write on the subject of consciousness dive in without offering a definition. Probably consciousness is very hard to define unless we take this approach of a simple coincidence of processes.

Notice that this combination of processes in Box 5 seems to follow from our basic assumptions in RPM. If a LT is to survive it must process information in the way suggested for Box 5:  Moment by moment the LT must choose how to act in response to its sensory inputs and goals. So, if all LTs have such a process, and if this is consciousness, then it would seem that all LTs have a cousin of the sort of consciousness which we humans experience.

6.2.1.3 An explanation for the experience of dreaming

Building upon the above concept of consciousness, we may also find a rudimentary explanation for dreaming within the processes depicted in Figure 6. To find this explanation we would assume that when a LT sleeps some, but not all, of the information processes shown in Figure 6 shut down. We would guess that sensory inputs shut down for the most part, and also the abilities to act. But the imagining or planning function of Box 5a would continue to operate, along with the loops involving Boxes 5, 5a, 2, and 4.

6.2.1.4 Induction and Deduction in this model

Texts on philosophy and logic often mention induction and deduction. We may find rewarding insights if we search for these two ways of thinking in the model of information processing which we developed above in Section 6.2.1.1.

For readers not familiar with these terms, ‘induction’ refers to thinking from specific instances to general rules or observations. ‘Deduction’ is the opposite, thinking from general rules to specific implications. For example, suppose you move to a new region and during your first few months there it rains on Tuesdays but not on other days. You may think, “It always rains on Tuesdays but not on other days”. This is induction:
    (particular observations → general rule).

After that rule about when it rains has formed in your thinking, if you want to know if it will rain on a particular day in the future you may use the rule to make a forecast. You will think it will rain on that day if it is Tuesday. This is deduction:
    (general rule → particular statement).

Deduction
First let us find deduction in our model. We start with this general point about computer programs which give us a way of modeling the information processing of LTs. Computer programs can normally be seen as performing deduction in their operations, because a computer program is a set of rules which are followed rigidly step after step to produce an output. Of course the inputs to the program also affect the output, as well as the rules which constitute the program. But still, once given the inputs, the output is then usually calculated deductively from working the rules of the program on those inputs.

Now, to find deduction in our particular model, refer to Figure 4, and consider the step “Decide What Act to Attempt”. Here the program will follow rules in evaluating the present situation in light of relevant memories to produce a single decision on how to act. This is deductive process.

But to be careful here we should recall one of RPM’s foundational assumptions, being that LTs act non-deterministically. This assumption might challenge our view that the process of deciding upon an act represents deduction, because normally we would think that deduction, with given rules and given inputs, would always produce the same output. But not necessarily. Suppose a situation in which preliminary calculations have suggested not just one act but a set of acts all of which promise roughly equal hope in these circumstances. For such a situation the program might select one act randomly from that set. With some randomness inserted into the calculations, the LTs behavior may be both nondeterministic and deductive.

Induction
Now we turn our attention to induction as it may appear in our model. At first we might think that no induction is needed in our model. No induction was mentioned, after all, in Figure 4. And the calculation which we discussed above worked only with already-established rules (deduction) to produce its output. So we have not yet said why induction would be necessary or helpful.

Here is a reason for induction: Reality must limit the amount of information processing which a LT can do in a given moment. If memory has grown large with experiences then a large number of relevant memories, passed into the “Decide What Act to Attempt” step, could require a long time to compute. So if a LT needs to act quickly it will need some computational way to decide quickly.

For an example, suppose we have a critter as introduced in Chapter 2. It has only a simple mind such as in Figure 4. Suppose it has run dangerously low on sugar but now, in this moment, it senses that it has just come into contact with a portion of sugar. It faces no immediate danger other than the danger of starvation for want of sugar. It has to decide how to act in this moment.

Following the procedure which we have sketched above in Figure 4 our critter will:

1. search through all its memories, selecting those memories which resemble to the current moment;
2. evaluate the acts performed in those previous moments based upon the quality of the outcome at the conclusions of those moments;
3. wrap up this deductive comparison by selecting the one most promising act to perform in this moment.

This deductive process might take ten minutes, especially if our critter is mature and has accumulated many relevant memories. But there is risk in taking a long time to make this choice because in our critter’s world it is possible that another critter may consume the sugar while our critter is thinking.

Notice that the process of combing through all memories might discover that our critter has chosen the same act in every one of the previous moments which resembles this present moment. If that is the case it becomes obvious, to us modelers at least, that the lengthy process outlined above can be cut short if our critter has a way to recognize the circumstances of this moment as calling for immediate choice of a single act. So our critter has a better chance of survival if we can give it a way to learn a new rule for action in circumstances where quick thinking is important for survival.

How will this induction be done? There are many possible ways, but we should note:

• induction is not strictly logical. One general rule to cover all possible experiences with given circumstances cannot be derived from only a limited number of particular experiences learned in those circumstances. As such the inductive production of a new general rule always gambles with the possibility that the rule will be discovered to be wrong by some new experience. Induction requires a leap of faith.
• variety in method of induction, among the members of a population of LTs, is probably good, since this variety gives the population a better chance of having at least one member which produces a good rule for the given circumstances. That one member at least may survive and its progeny may gain dominance.

We may reasonably guess that the members of a population which seems successfully established in a given environment have induced a set of rules which reflect the features of that environment fairly well. These induced rules enable the LTs to decide efficiently how to act in conformity with their environment. The induced rules are a sort of representation of the environment, a representation created for the purpose of enhancing survival of the host LT in its environment.

Now we have described the need for induction in a model such as Figure 4, so this motivates addition of induction in Figure 5.

6.2.1.5 Interior perceptual map

In this section I will introduce the idea that the mind of a LT carries a perceptual map of reality. The purpose (or use) of this map will be to give the LT an interpretation of its sensory input data. Given any particular set of inputs the map produces a map reading. Map readings help the LT to know where it is and what is happening, and to decide what it should do.

In Figure 7 we see a representation of how most of us most of the time conceive of our relation with the Real World. That is, we LTs are separated from the Real World, but we have a direct view of the Real World. We see the Real World as it is and respond to it accordingly, or at least it seems that way to us.

Figure 7. We LTs normally imagine we have unmediated exposure to the Real World.

But as we study ourselves and our world we need to make new distinctions. Now we will divide the information processing capabilities of the LT. In Figure 8 we see:

1. the LT’s perceptual map of the Real World. It represents subconscious, automatic perceptions and judgements.
2. the conscious decision-making center of the LT which we will call the Homunculus.

Figure 8. Clarifying the division in our nervous systems between perceptions and consciousness.

Now we move on to Figure 9 where we combine the ideas from Figures 7 and 8. On the left near to us we see the LT divided into perceptual map and homunculus. The homunculus can “see” only its perceptual map. All of the homunculus’s inputs about the “real world” come from the perceptual map and not directly from the Real World.

Figure 9.
Showing our modelers' conscious recognition that a LT's perception of the external world is mediated by a Perceptual Map.

Some confusion may be caused by my use of the name “map” here, because in everyday human life we are conscious of using a map, such as we would be conscious of using a road map. But for our purpose in these discussions I want to separate consciousness, which resides in the homunculus, from all the nervous system processing which goes into perception, usually subconscious, which resides in the perceptual map.

You might also be confused by this division of a LT’s information processing capability into only two parts, map and homunculus, since in Section 6.2.1.1 we divided this capability into as many as seven parts, as in the seven boxes in Figure 6. But these are two separate models of a LT’s information processing capability, models invented for different purposes. Figure 6 may help a computer architect who is structuring the overall task of developing programs to mimic information processing in a LT. Whereas Figure 8 will help us as philosophers working with more general problems, such as the language-learning which we take up in Section 6.3.

Even though these two models represented in Figures 6 and 8 serve different purposes, we may gain some clarity by comparing them. How, we ask, do the seven boxes of Figure 6 relate to the two components of Figure 8? It seems clear that Box 1 Sense of Figure 6 would be mostly in the perceptual map of Figure 8, while Boxes 5 Decide and 5a Plan of Figure 6 would be in the homunculus of Figure 8. But the other four boxes of Figure 6 (Remember, Induce, Categorize, and Wrap up) cannot be so simply thrown into one or the other of the two components of Figure 8. The computation performed in each of those four Figure 6 boxes is, in most normal circumstances performed in the perceptual map. But in certain demanding circumstances consciousness, such as I experience it, enables me to examine what goes on in those four boxes, to question and perhaps override the outputs of those boxes. So the computation performed in those four boxes may be divided, in abnormal circumstances, between the perceptual map and the homunculus in Figure 8.

To say more about the purpose of this map, it is, like any other attribute of a LT, to enhance survival and reproduction. A LT can survive if the map-readings from its interior map provide good-enough input data for the LT’s decisions on how to act. Moreover those LTs which have the best maps for a given environment will probably produce the most offspring. After a few generations these “best maps” will be common in a population which, to our observation, seems to be surviving quite well.

Also we should be careful to think in evolutionary terms. The quality of the map is judged by success in leaving offspring. The quality is not judged by our human values. We cannot expect a LT to evaluate a given circumstance as we humans would evaluate that circumstance.

Perception is Interpretation
Most of us quite naturally trust our senses to bring us accurate impressions of our surroundings. And we might believe that these impressions, which we receive consciously from our senses, are true and unbiased. Our senses, we might believe, are free of any possible errors which might have been introduced by subjective interpretation of sensor data. But in what follows I question this presumed accuracy. We will consider only one sense, that of sight, but similar arguments can be made for other senses.

When I become conscious of seeing a face before me, that impression seems to come into my consciousness as a single, whole impression. But my perception of the face starts in my eyes, and we know that each of my eyes contains millions of sensor cells (rods and cones). Each one of these sensor cells is positioned on the retina so as to catch and respond to the light coming from a tiny fraction of the visual scene in front of the eye. So the single impression of a face before me must have been constructed somehow from the millions of output signals from individual sensor cells. A great deal of subconscious interpretation goes on in my nervous system between the outputs of the individual sensor cells and the single impression of a face which I experience in my consciousness.

Notice that my eyes sometimes deceive me. I see something – or think I see something – which turns out to be something entirely different when I have looked for more time or moved closer to get a better look. I assume that you have had a similar experience. What we can conclude from such experiences is that our visual perception works with sketchy data, with incoming light impressions which are insufficient to support a definite conclusion of the name or category which needs to be assigned to a pattern perceptible in the incoming light. But our survival as LTs requires that we choose acts based upon the best information available to us. An immediate, although possibly mistaken, identification of what we see will often enough give us an advantage that we should not be surprised that our sense of sight interprets whatever data it has, giving our deeper, decision-making nervous process such an identification.

Pixels of Map
The map’s representation of the world is not perfect, as we have just argued. We can use an analogy of pixels which, as you know, make up any modern digital image. The pixels in a digital image, while not representing the world perfectly, do hopefully give a sufficiently useful idea of the content of a scene for a viewer to derive a helpful understanding.

Considering the size of the pixels in this map, or the graininess of the representation provided by the map, the pixels will get smaller with experience. We might consider each new experience (each cycle of the model) as adding one pixel. Each new pixel in the map provides more detail in that area of the map. For an example which develops this idea see Section 6.3.4.1.

Notice that the addition of experience (of pixels) does not increase the size of the overall area represented by the map.

Concerning the overall area represented by the map, we will assume that the area of the LT’s map covers its universe of possible experiences, as suggested in Figure 9. The map is there to help the LT decide how to act in any circumstance it might encounter. This universal coverage exists from the first moment of the LT’s life.

But as I just described, the detail within the map improves with experience. And now we should clarify that the resolution of the map, that is the density of pixels, varies from one part of the map to another, depending upon the number of experiences in any sub-area of its world. The density of pixels will become high in the frequently recurring situations which a LT will encounter. On the other hand the density of pixels will be low in situations which the LT has never encountered, or encountered only a few times.

Ways other than experience to populate the map
Education: We should mention education as it may pertain to pixels in a LT’s map. In RPM models more advanced than we have yet developed senior LTs purposely educate junior LTs in safe teaching environments where the seniors can impart education-pixels, which would have useful resemblance to experience-pixels, in the maps of growing junior LTs. A senior might say “I hope you will never encounter (such-and-such) a dangerous situation, but if you do, this is how you must react…”. In this way a LT may be equipped to learn from the experience of its elders, with one or a few pixels implanted in regions of the LT’s map, even for situations completely unlike all the living experiences of this LT.

Inheritance: We will also mention the inheritance of instincts or dispositions which we humans and other fancy LTs get from birth. Instincts and dispositions may also be regarded as pixels in a LT’s map, pixels acquired not from direct experience but from some process at birth.

6.2.2 Information Processing Toward a Higher-Level LT

In the use of RPM, once we have established the initial condition, with a population of LTs surviving by feeding upon a barely sufficient RP, we turn our attention to discovering how members of that population can overcome the information-processing challenges of exploiting larger and more difficult RPs. This relates to our assumption that life advances in levels, as we developed in Chapter 4. The challenges of information processing for groups of LTs will be the subject of the next Chapter, on Public Psychology. Here I will make just a passing observation about these problems.

In a typical experiment in RPM we will try to show how the LTs of the initial condition can solve a single problem of coordination (for examples, see the challenges in Section 2.4). Then if we succeed, if we and our LT-agents can solve that problem, we will have demonstrated one of the abilities which a becoming-more-effective organization of the LTs will need. But this one ability must represent only a small fraction of all the advances which would be necessary for us to judge that the organization had gained all the abilities which, taken together, would lead us to judge that the organization had gained the status of a LT on the higher level. So in this way of modeling we may advance only one step at a time. We will need to make many such experimental advances before the combination yields a LT.

If that ambitious aim is achieved then we will be able to claim that our efforts have created the model of the mind of the new, higher-level LT. And, as we work on the humble steps toward achieving that aim, we can claim that we are building that model of mind.

6.3 Language

In the course of modeling how groups of LTs may discover modes of cooperation, RPM promises to show us many things about natural language, since language provides one of those modes of cooperation. By ‘natural language’ I mean a human language, such as English, as opposed to a computer language, in which programmers write specific instructions to be carried out exactly by a computing machine.

In RPM, the development of a set of mutually helpful signals may help our LTs coordinate their actions. Along the way we will encounter philosophical implications about the nature of language.

6.3.1 A Simple Agent-Based Language-Learning Experiment

I will start by describing a simple language-learning experiment (LLE), since this concrete example may help readers understand more abstract points I will make later on about language. Before we jump in, it is worth noting that this language-learning experiment is one which I have carried out on a computer. As such, this experiment differs from most of the other experiments which I describe in this book, since those other experiments have been carried out only as thought experiments (TEs), which require less specification of details.

Two simple agents, a consumer and a producer, exist in a computer program which runs in cycles. In each cycle:

• The consumer wants one of five commodities randomly, one of: wheat, oats, chocolate, beer, or nothing. But the consumer has no ability to get such for itself. The consumer can only act by displaying one of ten symbols: A,B,C,D,E,F,G,H,I,J.
• The producer can see the consumer's symbol and can produce any one of the five commodities. But at the outset the producer has no idea what the consumer's symbol might mean.
• Success is awarded to both agents when the producer delivers what the consumer wants.

Each agent has an internal memory which starts out empty but which remembers all past experiences. In each cycle each agent performs the following steps in sequence:

1. notices its input (commodity wanted for the consumer, or consumer's symbol for the producer),
2. looks in memory for previous experience with that input,
3. decides upon an act by repeating a successful experience, avoiding an unsuccessful experience, or by acting randomly if experience offers insufficient guidance,
4. performs the act and remembers the result, that is whether this combination of input and act succeeded or failed.

As you might guess the two agents in this experiment eventually discover a language through which they successfully coordinate their actions. In this language the consumer always displays a specific one of the ten symbols which uniquely correlates with its want, and the producer upon seeing that symbol always delivers the commodity the consumer wants. This accomplishment, with success in every cycle, has usually been attained within a few hundred cycles in my computer runs.

In case you did not guess that the two agents would eventually stumble upon a perfectly successful language, let me explain. It happens because there are only a small number of ways that each agent may respond to each of a small number of inputs.

• If by luck the producer delivers what the consumer wants then both receive positive feedback. In that case each of the two agents remembers its experience, the stimulus-act combination which led to this positive feedback, and each will forevermore repeat that act when presented with that stimulus.
• But we must recognize that at the outset bad luck is more likely than good luck. In the first cycle of the model there is only one chance in five that the producer will deliver what the consumer wants. But each agent remembers each failure. In succeeding cycles each agent randomly selects an act from among a smaller number of remaining possibilities. Eventually it has to happen that the producer will deliver the commodity wanted by the consumer.

6.3.1.1 Language Implications

With this experiment, with the producer and consumer discovering a mutually supporting set of signals, we can proceed to enticing philosophical speculation.

First we will be bold and call that set of symbols which come to be used regularly a language. Admittedly it is a simple language, but it provides a good starting point for our research program into more realistic language-learning experiments.

Concerning the meanings of words, we should notice why each symbol (word) comes into use. This coming-into-use happens because a symbol helps both of the agents accomplish mutually beneficial coordination of their actions.

• The consumer is aided by knowing a symbol which, when it feels hunger for a specific item, it can display to the producer with confidence that the producer will then deliver that specific item.
• The producer is aided by knowing which food, among those which it can produce, will win a reward for it when delivered in response to a particular symbol from the consumer.

So the coming-into-use of a word has everything to do with the needs and abilities of the consumer and producer (the agents in the model), but has nothing to do with the needs and abilities of other agents – including notably us modelers – unless the needs and abilities of those other agents somehow translate within the model to needs and abilities of the consumer and producer.

Another lesson we may take from this experiment is that we should not be surprised by the growth of natural languages. This growth can be seen to flow from the RPM assumptions. We modelers can expect growth of natural language where we arrange the following:

• Make sure the agents have: (a) memory, (b) tendency and ability to repeat favorable acts and avoid unfavorable acts, (c) ability to display various signs to one another.
• Place agents in an environment where there is a large or difficult RP, exploitation of which is beyond the capacity of an individual but within the capacity of an appropriately organized group of the individuals.

If we structure the experiment appropriately we will expect the agents to discover cooperation which employs their signals. They should discover a cooperative resource-exploiting language just as surely as a computer could find a needle in a haystack if we give the computer sufficient abilities and enough time.

Creation of a new organization, but not a new LT
At the completion of this experiment, with the two agents acting together in harmony for mutual benefit, outside observers may perceive the pair of agents as two halves of a single entity. Whether an observer perceives the two trading partners as a single entity may depend upon the priorities of the observer or upon what the observer has been trained to see.

In the jargon of RPM we may call the cooperating pair an organization, which we see as resulting from any degree of cooperation, but not as a LT, which we see only if an organization has all the properties of a LT which we listed in Section 1.4.

Precise meanings are not realistic
In this consumer-producer language-learning experiment, the words have exact meanings. ‘F’ for instance might come to mean oats, exactly and for ever more. But, as we all know, in the real world our natural language terms can take on a variety of meanings. Later in Section 6.3.4 we will run through a language-learning experiment in which words can and will always have fuzzy or ambiguous meanings.

6.3.2 Language Stories

6.3.2.1 “Wha?”, “Duh!” story

Now I make a point with a little story.

During the night at Empower Designs the IT guy installed an update to the software used by the staff. First thing the next morning Rachel arrived at her cubical and, as she started up her computer, Bob arrived at his neighboring cubicle and likewise touched the start button on his computer. Soon Bob heard Rachel say “Whaaa?”

A minute later Rachel heard Bob carry the conversation forward, “Duh!”

Both employees are having difficulty with something in their environment. Both have expressed frustration, but what is it about? Are they having the same problem? We do not know yet, of course, and neither do they.

But I think this exemplifies the way in which many of our productive conversations start: Using terms which are so broad as to be almost meaningless we reach out to someone who might have a problem similar to our own. As a conversation continues we find more specific terms with which to share our experiences.

I want this example to throw cold water on the idea that our language should be precise. Of course we strive to reach ever more precise understanding of our problems, but vague words and vague sentences help us find standing as we step toward clarity.

6.3.2.2 The ambiguity almost universally inherent in natural language

Consider a three-word sentence, for example “Bill hit Bob.” Suppose this sentence exists in a context where each word has five possible meanings: there are five “Bills”, five meanings offered in the dictionary for “hit”, and five entities called “Bob”. Then our three-word sentence has 5x5x5 = 125 possible meanings.

Suppose Susan sees something which she describes as “Bill hit Bob”. What Susan saw aligns exactly in our context with one of the 125 possible meanings. The other 124 possible meanings describe something else — which Susan has no intent to convey. Suppose Alex hears Susan and his mind goes to work, subconsciously, trying to make sense of her three-word sentence. Alex’s mind has 125 options, all of which might make sense to Alex at the conscious level. But the meaning delivered up in Alex’s mind, from the subconscious level to the conscious level, will be the one meaning of the 125 which makes most sense in Alex’s subconscious calculation. Suppose we ask Alex if he understands what Susan said. He might answer “yes” if his mind has been able to achieve a reasonable level of confidence in any one of the 125 possible meanings.

Will the one meaning which Susan saw be the same meaning which Alex comes to understand? Of course we do not know. We need more information to answer that question.

6.3.3 Formation of verbs

In this chapter we deal with nouns more than verbs, but we will speculate a little bit here about formation of verbs.

In Section 6.2.1.1 we have suggested a model of information processing for a LT that decides upon an action for a given circumstance by searching memory for actions taken in previous, similar circumstances. This search of memory might produce evidence that particular acts produce predictable effects. This change of state of the world might be represented as a triple:

prior state → my act → present state.

This triple has much of the semantic content of a verb. Verbs as such might be used in planning activity (Box 5a of our Figure 6).

A verb, as just suggested, might represent change in the world during a single cycle of our model, but a verb might also represent a sequence of changes brought about during many cycles. A sequence of steps may often be required to achieve a single objective during the life of a LT.

Imagine for example a village of primitive people live in huts in a clearing in a forest. From the clearing there is a path which leads through the forest down to a stream. As such the overall activity of ‘go to the stream’ may be broken down into three steps: go out of the hut; walk across the clearing to the path; walk down the path to the stream. This sequence, having been accomplished one or more times successfully, may be remembered as a single set of connected act-choices through which a single objective may be achieved.

Such a set of act choices may later come to be named among the villagers with a single verb meaning go-to-the-stream. Such a name could be selected almost arbitrarily from among signals available to villagers, since what matters is that agreement on the meaning can be discovered or created. The mapping between signal and set-of-act-choices can be invented on the fly by advanced LTs such as we imagine our villagers to be.

In this way a single word, a verb, can come into use and come to mean to each villager what it means to that villager. Of course different villagers may have different sub-acts, somewhat different connected sequences of act choices, to accomplish the same goal (go to the stream). For instance, occupants of different huts must take different routes from their huts to the path since each hut must be in a different spot. But success and reuse of the single word (verb) depends upon the success of the overall sequence and not upon detailed equivalence in the component sub-acts.

6.3.4 Formation of a Single Noun

6.3.4.1 Language-map thought experiment (TE) learning wheat vs. non-wheat

We will now build a thought experiment which suggests how we LTs might learn the meaning of a noun. In this experiment we will name our agent the “producer” since this agent has a role somewhat like the producer in our earlier experiment in Section 6.3.1. Sometimes our producer will be asked to select “wheat” from its environment, other times it will be asked for “non-wheat”. It will be trying to learn the meaning of only this one word, wheat, as distinguished from everything else.

The world in which our agent, the producer, lives
We give the producer a two-dimensional world from which it will be given opportunities to select objects. In this world objects are depicted by black dots and the character of an object, whether wheat or non-wheat, is determined by the object’s location. See Figure 10. The area of wheat is shown by an oval. Everything inside the oval is wheat, everything outside is non-wheat. But only we modelers know about or can see the oval drawn on Figure 10. The producer cannot see that oval and will learn the character of an object only after making a selection. The producer will be trying to learn which area in the world correlates with wheat.

To keep this simple, the distinction shown by the line of the oval is absolute: Thus object A is definitely wheat, even though it is near the line, and object B is definitely non-wheat.

Figure 10.
Producer’s Real World with black dots representing objects, both wheat and non-wheat.

We work with an image we call the map
We will use the idea that a LT has a map of its world in its calculating capacity and we will show how an agent might use its map to converge upon an understanding of the word “wheat”. You may recall that when we introduced the idea of a map, in Section 6.2.1.5, we emphasized a distinction between the Real World and the LT’s map of the real world. But that distinction, while crucial for some insights, would add unnecessary complexity to our current thought experiment. So we will use a map which is rectangular like the Real World and which we assume represents the real world perfectly enough for our purpose in this experiment. The map, which will keep a record of the producer’s experiences, starts out empty as shown in Figure 11, showing no experience at all.

Figure 11. Producer’s map at the start of cycle 1.

The cycle in this TE
Once again in this experiment our producer makes a choice during each cycle. At the start the producer has no experience and can hypothesize only that wheat may exist anywhere in its world. But as cycles pass the producer can form and then improve a hypothesis about which area of its world contains wheat.

Each cycle consists of these steps:

1. The producer gets a request for one of two types of objects: wheat or non-wheat.
2. The producer is given three options selected randomly (by the operator of the model) from among all the objects in the world. There is no guarantee that any one of these three may satisfy the request.
3. The producer may update its hypothesis concerning what part of the world contains wheat, if, given what it learned in the previous cycle, an updated hypothesis will help with the current decision.
4. The producer chooses one of the three options.
5. The producer learns the actual type of the object it selected. It writes this new knowledge into its memory for the location. Thus for the future it knows what kind of object to expect at this location.

Now we run the experiment
We will operate this model for a few cycles while we observe what is going on from the viewpoint of the producer’s homunculus. That is, we observe the producer’s map.

In the first cycle at the outset the map is void of experiences, as in Figure 11. We assume that the producer receives a request for wheat and then, as shown in Figure 12, the producer is given three choices among which it must choose one in hope of finding wheat.

Figure 12. Three choices offered to producer in cycle 1.

We will assume that the producer chooses (guesses) the object farthest to its left and then learns that object has turned out to be non-wheat.

So after the end of the first cycle the producer’s map will show one experience as in Figure 13. The producer knows that an object at the location of the red dot was non wheat. We will use red dots to show non-wheat experiences.

Notice that the two objects not chosen by the producer in cycle 1 are forgotten. There is now no trace of them on the map, since the producer has learned nothing about the objects at those locations.

Figure 13. What the producer knows after Cycle 1.

Figure 14. The producer’s options in Cycle 2.

Now in cycle 2 assume that the request-symbol is “wheat” once again. The producer’s three choices for cycle 2 are shown in Figure 14 as black dots, along with the single red spot of knowledge so far. The producer will try to use this single spot of knowledge to decide which of the three dots might be wheat. We will suppose that this producer forms a hypothesis — that its world is divided in half, down the middle, with wheat on the right and non-wheat on the left. See Figure 15 which shows a vertical dotted line where the producer guesses the division occurs. This guess makes sense of what it already knows and also helps it to decide what to do in the present cycle.

Figure 15. During cycle 2 the producer hypothesizes that non-wheat lies left of the dotted line and wheat lies right of the dotted line.

So the producer will choose one of the two objects on the right side of this dividing line. We will suppose the producer chooses the object at the far right. Once again the producer receives negative feedback, since this turns out to be non-wheat. So the producer starts out in cycle 3 with the knowledge shown in Figure 16. Each red dot shows that an object selected from that area was non-wheat.

Figure 16. What the producer knows after completing two cycles.

Figure 17. In cycle 3 the producer is offered the three objects shown with black dots.

In the beginning of cycle 3 we will assume that the producer is asked for wheat once again. The three choices offered in cycle 3 to the producer are shown as black dots in Figure 17. Figure 17 also shows the two red dots of non-wheat experience as well as the vertical dotted line representing its previous guess about the layout of its world.

Assume that the producer now guesses that only the upper right-hand quadrant of its world contains wheat. This quadrant is marked off with dotted lines in Figure 18.

Figure 18. In cycle 3 the producer refines its hypothesis of which area (the upper right) contains wheat.

In conformity with this guess about the way things are, the producer chooses the one dot which lies in the upper-right quadrant. This time finally the producer has made the correct choice. It learns that wheat was found at the location chosen. In Figure 19 we show the producer’s map as it exists at the start of cycle 4, with two red spots indicating where non-wheat has been found and one green spot indicating where wheat has been found.

Figure 19. What the producer knows after three cycles.

Now that we have completed three cycles of examples showing how the producer’s map gradually gains experience, let us step ahead to the end of 25 such cycles. See Figure 20. In this condition, with 20 dots of experience showing non-wheat and 5 dots of experience showing wheat, the producer may hypothesize that the rectangle shown in dotted lines delineates the part of the world which contains wheat.

Figure 20. Step ahead to the end of cycle 25 to see both the producer’s experience and hypothesis of the area of wheat.

Claims at conclusion of this thought experiment
(1) No matter how much experience the producer gets it never attains perfect knowledge of what is wheat in its world. The producer never hypothesizes an area for wheat which entirely equals the oval with which we modelers defined the wheat area; there will always be cases near the border of the oval upon which the producer might err in its guess about the identity of an object. The producer’s knowledge of the meaning of wheat will always be ambiguous.

(2) The experience of the producer in this TE seems analogous to many human experiences of learning the meanings of symbols. Any human agent sent out to acquire an object, whether named with only a single word or specified with a sheaf of documents and pictures, may fail to make a choice which satisfies the principal who sent him.

But of course the chance that the human agent may satisfy his principal increases with the amount of working-together experience shared by this agent-principal pair.

(3) We may say that the producer always has an operational definition of wheat in that it will always act to deliver its best guess. It is never stumped.

(4) With enough experience our producer can get very good at selecting wheat in its world. It might go one million cycles without a single error! But we modelers must be careful about how we describe this accomplishment. We might imply that the producer has more capabilities than it actually has. We humans are biased to quickly and easily perceive things for which we already have names in our natural language. And we are likely to use those names when we talk about those things, talking either to ourselves or others.

For example, we modelers may “see” that our producer has learned a good working definition for wheat. If we say “it has learned a good working definition for wheat” then it may seem a natural step for one of us humans to ask the producer to tell its definition of wheat. But this shows the danger of anthropomorphism. In our outline of this TE, we have given the producer no capacity of language! It certainly does not have any way to receive (to sense) our word “definition”. True, it will have built a good set of lines, or rules for selection of objects in its world, but we have given it no capacity to summarize and describe these rules. So we see a stark contrast in these two views of the mental capacity of our producer, between acting as if it knows the definition of wheat and being able to produce a definition in some language of “wheat”.

(5) Further this producer has no notion of truth. The primitive operations of this producer select an object which best matches some geometrical constraints on a plane. Nothing in the calculating capacity which we have specified for our producer requires it to form a statement which can then be submitted to a logical truth test. This producer, operating at its primitive level, has not yet come close to needing a fancy concept like truth.

Self awareness, or more specifically an ability in the producer to recognize about itself that it has discovered a rule, may come only much later in the advance of nervous system processing.

(6) In the computational processing of each of our producer’s cycles we modelers can recognize computations which we may label as induction and deduction. We can see induction when the producer first hypothesizes which area of its world contains wheat by drawing a line to divide the world, and further induction in the later cycles when the producer draws or moves dividing lines to focus its hypothesis more closely. We can see deduction when the producer employs this current-best hypothesis in choosing one of the three candidate objects offered in a given cycle.

6.3.4.2 Confusion when two try to learn the same “fact”

In the TE just completed in Section 6.3.4.1 we saw how a good working idea of the meaning of a word such as wheat may be gained by an agent we called a producer. Now we will run a similar TE with a different producer which we will call the “other producer”. The other producer will differ from the first producer in the ways that it:

• chooses a single object when more than one object remains possible after the range has been narrowed by the current hypothesis for which area contains wheat.
• revises its hypothesis for the wheat area with new experience.

We will see how this different way of thinking leads the other producer to a somewhat different understanding of wheat.

Figure 21. The other producer’s map at the start of cycle 1, showing no experience.

We assume that the other producer starts out with no information in its map, as shown in Figure 21.

Figure 22. The other producer’s three options in cycle 1.

At the start of the first cycle the other producer gets a request for wheat. Then it is given three choices, the same three choices as the first producer got, see Figure 22. But while the first producer guessed that the spot farthest to the left represented wheat this other producer guesses the spot on the right.

This guess turns out to be correct. So the other producer starts cycle 2 with the knowledge shown in Figure 23. The green spot represents a location at which wheat has been found.

Figure 23. Showing what the other producer learned in cycle 1.

Since this other producer has satisfied a request for wheat in its first cycle we will assume that in the second cycle it gets a request for non-wheat, and it is given the three choices which are shown in Figure 24.

Figure 24. In cycle 2 the other producer gets these three options.

In order to use what it has learned, with its one spot of information, this other producer hypothesizes that the world may be divided into the two classes, wheat and non-wheat, by a horizontal line as shown in Figure 25.

Figure 25. In cycle 2 the other producer hypothesizes that wheat lies in the upper half of its world.

Then, since the hypothesis places non-wheat in the lower half, it guesses one of the two objects in the lower half; it guesses the object at the right. That turns out to be correct. It is non-wheat. So our other producer starts cycle 3 with knowledge depicted in Figure 26.

Figure 26. Showing what the other producer knows at the end of cycle 2 (and the start of cycle 3).

In cycle 3 the other producer receives a request for non-wheat, and it is given the three choices shown in Figure 27.

Figure 27. In cycle 3 the other producer is given the three options shown.

The new information learned in cycle 2 fits with the hypothesis formulated from what was learned in cycle 1, so the other producer has no reason to move the horizontal dotted line or to draw another dotted line. It chooses the one option which satisfies its hypothesized rule. This is also non-wheat. So again our producer has succeeded and it enters cycle 4 with the knowledge represented in Figure 28.

Figure 28. What the other producer has learned after three cycles.

Now, as we did with the first producer, we will jump ahead again to end of the 25th cycle. We will assume that our other producer has repeatedly revised its hypothesis about which area of the world contains wheat. See Figure 29 which shows 25 dots of experience and the dotted lines to show the other producer’s current hypothesis concerning which area contains wheat.

Figure 29. Step ahead to the end of cycle 25 to see the other producer’s experience and hypothesis of the area of wheat.

Now we can see that the two producers’ ideas (or hypotheses) of wheat differ somewhat. In Figure 30 we see the two areas hypothesized by the two producers, copied from Figures 20 and 29, along with the true area which we modelers established by definition, copied from Figure 10.

Figure 30. Our two producers have learned much but have different ideas of wheat and still might err when selecting wheat.

Claims at conclusion of this thought experiment
(1) To the extent that this LLE models how we learn words in natural language, it is natural and inevitable that different people will have different ideas of the meanings of words.

(2) The difference between these two producers’ hypotheses of the area for wheat has come about because the two producers have different ways of thinking. Even in identical circumstances sometimes they make different choices. As a result they accumulate different experiences with which to make future choices.

(3) Yet the difference between the two producers will narrow as they each accumulate experience because, as we constructed these TEs, judgement concerning whether a chosen object is wheat or non-wheat comes from a standard (the oval) created by us modelers.

(4) We, as users of natural language, should understand that other users of natural language always have their own individual needs, abilities, and experiences, so unless we have completed the perhaps impossibly difficult task of learning of those others’ minds, we should not believe that we perfectly understand the natural language expressions of others. Rather, we should understand that our grasp of the meanings of others’ natural language expressions is limited by the scope of our own needs, abilities, and experiences. We may be able to act as they would wish in response to their statements, but we have to be careful in assuming we understand why they said what they said.

6.3.5 Concluding claims about language learning

These LLEs get their significance from RPM’s approach to realism, to a realistic ontology.
Some readers may judge the above language-learning experiments to be simple and insignificant, so I want to emphasize how the meanings of these experiments are amplified by the context of RPM. If you accept RPM’s postulates outlined in Sections 1.4 and 6.1 then you can see how agents in our world find themselves, like the agents in our LLEs, in circumstances in which they gain a clear advantage if they can learn to successfully signal one another. RPM lays down a circumstance in which we should expect LTs given appropriate powers to develop mutually helpful signaling.

Good enough understanding
One result which follows from RPM’s postulates is that signals between agents do not need to be perfect. All the agents need is that their attempts at communication succeed frequently enough so that the overall benefits of those attempts exceed the costs. Words can and probably do have fuzzy meanings because that is what works in the practical world of RPM. Figure 31 summarizes this argument, showing that our understanding may be good enough for various particular circumstances if we understand each other only partially.

Figure 31. We manage to survive with good enough understanding.

6.3.6 Future Directions for Language-Learning Experiments (LLEs)

Rewards should come from exchange with other agents, as well as from the environment.
The LLEs presented in this chapter have included us modelers in that we:

1. set the standard for what learning was to be accomplished by the agents in the experiments, and
2. provided feedback to the agents concerning the how we judged their behavior.

While this involvement has enabled us to create a few simple but instructive LLEs, future LLEs will do well to remove us modelers farther from the action in the model.

We could create LLEs in which agents are:

1. motivated by their own hungers or ambitions (hungers or ambitions which we modelers have built into the agents, but which we modelers do not control directly because these hungers or ambitions arise in response to ongoing circumstances in the model), and
2. rewarded by what they gain in exchange with other agents (as they learn to coordinate their actions with other agents in order to harvest from resources which we modelers have placed in their world).

Let the agents learn which classes of objects deserve distinctive names.
Furthermore we modelers could learn by removing ourselves from specifying the objects for which the agents need to learn names. In the experiment of Section 6.3.1, you may have noticed, we modelers set up the experiment so that the agents would learn one symbol each for each of the five types of objects which we made available. We did not know what symbols would become associated with the objects, but we knew there would be five distinct symbols for the five objects which we built into the experiment. We could step closer to realism by building an experiment with these components:

• agents with hungers (metabolic requirements) which may be satisfied in varying degrees by different resources found in the world
• resources which are distributed in the world in concentrations which sometimes vary gradually from location to location
• agents capable of communicating with a substantial variety of symbols.
• agents restricted in their individual capabilities so that cooperative ventures are likely to benefit at least some of the cooperators.

With such components we may expect to see signaling develop but we may be incapable of guessing the size of the vocabulary which ensues.

For examples of these issues, consider these TEs with generalized need for food.

Example TE 1

As we LTs interact with the world, there will be many objects in the world which we cluster together in our thoughts because those clusterings have some common feature as seen from the viewpoint of the LT.

When for example I feel hungry I think of food. Many varieties of food may serve this need of mine. I search, not for the one specific double cheeseburger which I may purchase but rather for any of the many particular meals which I may subsequently find before me. I could not perform such a generalized search without having some sort of conceptual category which encompasses all the possibilities for the meals which I might eventually consume.

Example TE 2

Imagine agents with some similarity to the consumer and producer in Section 6.3.1. Suppose the consumer can feel hunger for grain and this hunger might be satisfied by either wheat or oats, so that a different symbol may be discovered to convey this more-general wish, and both producer and consumer rewarded by the environment when they accomplish productive signaling for this circumstance.

Extending this thought experiment, it may happen that the producer gains access to rice and that the consumer would find rice to satisfy its hunger for grain, even though the consumer does not even know about the existence of rice.

Although the producer does not know under what circumstance if any he will be rewarded for supplying rice to the consumer, as time passes a circumstance may arise in which the producer’s best available choice is to try passing rice to the consumer — and see what happens.

If the consumer favors the rice it might want more and thus repeat the signal which had previously gained rice from the producer, but the producer, finding a different set of objects available in its world in the present cycle, may choose to deliver an object which previously had been favorably associated with the consumer’s symbol. So we have a situation in which the two agents would do best to learn a new signal for rice, but also to modify the meaning of grain to include rice.

6.3.7 Conclusion: Ambiguity in Language is Unavoidable but Often Helpful

These TEs exemplify what I claim is a general truth. We LTs, in roles as consumers, routinely experience needs for which we are ignorant of all the specific ways in which a given need may be satisfied. And, when we are in roles as producers, we routinely have options for actions which, given particular inputs, may bring us rewards; but we don’t know till we have more experience or some sort of informative guidance. Not only the experimentally-simple producer and consumer, but also we fancy human LTs, live in circumstances which require us to signal with terms which have, for our initially and necessarily ignorant positions, many possible meanings – general meanings.

Life by Trial and Error

A new look at assumptions
in the Resource-Patterns Model of Life

Can a living thing survive simply by trial and error? In the 1970s this question started my thinking which has grown into the subject of this blog. If “yes”, if life does succeed by trial and error, I could see back then how to start modeling the process of life. I would start by writing computer simulations in which little agents could roam around in a computer-simulated environment. The little agents would represent Living Things in the real world. The brains of these little agents would be computer programs which I would write, programs which tested strategies for survival using trial and error.

I loved computer programming so I started writing those programs. I also started scanning scientific literature to see if anyone else was working in the same track. This project gripped me and, even though I could spend only part time on it, it shaped my quest for further education up until 2013. Then in retirement it became my full-time project.

Only last month did I realize what I had done 40 years ago. In my eagerness to start modeling I had jumped right over the question in the opening sentence above. I had assumed “yes”:

Life can find ways to succeed simply through trial and error.

So now I had better take stock. In this post I will expand upon the consequences of that assumption.

Reasoning to Further Assumptions

Before I jumped in I believed that I could probably succeed in modeling life by trial and error. There is an obvious strategy: A Living Thing (LT) must try a variety of acts, remember the success or failure of each act, and use this memory in choosing future acts. See the figure below.

Information Processing in a Living Thing

But notice that this strategy for information processing within a LT can work only if the environment surrounding a LT offers a possibility of survival. The rewards offered by the environment to a LT which can learn must outweigh the costs such a LT must incur from errors as it experiments with how to behave in this environment. That is, the environment must contain sufficient resources, and the resources must be distributed in a way which may be learned by at least some of the LTs trying to survive there. The resources must be distributed in patterns which may be exploited by the LTs.

So we must have:

• Living Things with memory;
• environmental Resource Patterns (RPs) which may be learned.

But, as we advance toward creating a working model it becomes clear that we can specify more attributes of our Living Things. In addition to memory, our model of LTs must have:

• senses to pick up clues from the environment;
• ways of acting to harvest from the environment;
• an internal store of essential resources sufficient to carry a LT through a time of learning which must include some failures;
• ongoing consumption of resources which have been imbibed, since a LT needs fuel to continue living;
• a bias to favor choosing acts which will probably lead to success in discovering and exploiting new RPs;
• a bias to do some act — even any random act — before too much time has passed, to avoid starvation which must result from prolonged idleness.

It was through reasoning like this that I arrived at the basic assumptions for this model of life. I have listed these assumptions in my presentations of the model (for example see Section 1.4).

One feature of this model stands out when it is compared with other models: In this model Resource Patterns are of paramount importance. This observation helped me decide the name which I have used for this model, being the Resource-Patterns Model of Life (RPM). But I may change the name after some more reflection. Perhaps the name should reflect the prior underlying assumption which I have just noticed, the assumption of life by trial and error.

Comparison with Darwinian Evolution

This Resource-Patterns Model of Life shares some important similarities with Darwinian evolution. In both theories there is trial and error. In each theory there is (1) a mechanism for generating unpredictable variants and (2) an environment which passes judgement on those variants.

But the two theories differ in the range of variants which may be tested within the theory. In Darwinian evolution these variants are limited, as I understand it, to biological traits or species. Whereas in RPM we may also test variants in:

• single acts of behavior by a LT;
• adoption of bias by a group of LTs;
• transfer of life from one celestial body to another.

RPM, we see, is a more general theory than Darwinian evolution. Some of this generality comes from RPM’s application to any size of LT.

RPM also provides a platform to model a set of LTs which cooperate to form one higher-level LT. In many circumstances this higher-level LT will be capable of exploiting a RP which none of the smaller, constituent LTs would have been able to exploit alone. In this case the learning which goes on among the constituents will pertain to how they interact with one another. RPM then becomes a platform for modeling development of language, social instincts, and exploitation of one group by another.

In RPM the failure of a choice does not necessarily lead to death or failure-to-reproduce of the LT making that bad choice. A failure of choice leads, rather, to memory of the error, so such a choice may be avoided in similar future circumstances.

RPM may be seen to encompass Darwinian evolution by saying that the inheritance of attributes in Darwinian evolution is a way that a species (seen as a single LT) remembers what it has learned. Darwinian evolution is one possible mechanism of learning bodily design. Whereas RPM opens study of a broader set of ways to learn.

Basis in Thermodynamics

Living Things must eat if they are to survive. This will seem obvious to most readers without further scientific justification. But, for readers who want deeper science, the necessity to eat can be explained by the second law of thermodynamics. The second law asserts that in every process of energy exchange some useful energy is lost as heat. This means that no machine or LT can carry on forever with its initial store of energy.

A car must occasionally be given gas. A LT must occasionally be given more food energy. But notice the difference between machines and LTs. While cars have us LTs to fill their gas tanks, we LTs have only ourselves to get more energy. How do we LTs manage to get new supplies of food and energy for ourselves? This puzzle, presented to me by one of my mentors during 1973–74, made me think that maybe trial and error could suffice in a system which could remember. As such the second law of thermodynamics underlies my whole RPM project, and the second law is one level deeper than my assumption described above that life can work by trial and error.

Assumptions Might Be Wrong

In quick review, the assumptions in RPM include:

• Every LT must eat (from the second law of thermodynamics).
• A LT might succeed if it has capacity to learn by trial and error.
• The environment must contain appropriate RPs.
• A LT must have physical abilities to sense and act (in addition to the information-processing capacity to learn).

I admit that one or more of these assumptions might prove wrong one day. Notably, advances in quantum physics might overturn my views of energy and order.

As an engineer I am willing to believe the second law of thermodynamics; it works after all in our human experience to date. But as a philosopher I remain skeptical about the final verdict on the second law. The second law seems vulnerable because it stands upon concepts like energy, matter, time, and information — concepts which may be scrambled by a new and deeper cosmology.

In RPM I build upon the assumptions outlined above to reach a number of socially important conclusions — as you may see elsewhere in this blog (I suggest you start with the Statement of Purpose page). While I allow that the entire structure of RPM is vulnerable, I believe nonetheless that RPM should prove valuable for many of our present purposes.

Life vs. the Second Law of Thermodynamics

Editorial note: I wrote the paragraph below in my ongoing effort to finish the draft of Chapter 6, about philosophical implications, in my book outline. The paragraph makes a point better than I've made it before. As such I offer the paragraph here as a standalone post.


As we have reviewed before, the 2nd law of thermodynamics challenges us who would explain the existence of Living Things (LTs). The Resource-Patterns Model of Life (RPM) meets this challenge with a theory which offers to explain the increase in material order in some locations (the bodies of LTs), with localized gating or control of the down-gradient flow of matter. Some order is dissipated overall in each interaction (as required by the 2nd law) but properly chosen interventions within the overall flow can create, for a time, locales of increased order. The set of choices required to make these properly chosen interventions becomes the challenge which life must meet. Since we LTs exist, we may infer that life does indeed overcome the challenge. It is a challenge of information processing, a challenge of “mind”. RPM provides a platform in which we observe and experiment with this information processing.

The promise of inter-level learning

I am in the midst of rewriting a draft of Chapter 6, which is about the philosophical implications in the Resource-Patterns Model of Life (RPM). As such I come up against an assumption which I have been making — about of the benefits of inter-level learning. But what, you probably ask, is inter-level learning?

The meaning of life in levels

To begin, you need to understand “level”. You need to understand what I mean by the assumption that life grows in levels. We humans know that our bodies are composed of cells. Also, biologists tell us that long ago (perhaps one billion years) single cellular organisms were the fanciest forms of life on Earth. So single cells organized somehow to form larger organisms, larger Living Things (LTs). We say that life grew from the level of single cells to the level of multicellular organisms such as ourselves.

But there are more levels than those two we have just mentioned. If we look down the scale, we see that the larger single-cellular organisms (called eukaryotic cells) seem to have grown from many still-smaller and more primitive organisms (called prokaryotic cells) like bacteria and the organelles found in eukaryotic cells.

Since we humans start our exploration on the level where we live, we can think of ourselves as level N. Then we think of single-cellular organisms as level N-1. And we can think of tiny bacteria as level N-2.

Now suppose we try to look up the scale of levels, toward level N+1. Notice our human organizations: families, businesses, churches, and states. We make these organizations as we attempt to find advantageous cooperation among ourselves. Our strong social instincts show, I claim, that we strive continuously for better organizations. Most of our attempts at organization fail. But sometimes we succeed, and when we do succeed those successes are copied and multiplied (Darwinism at the level of memes).

I would not say we had reached level N+1 until one of the organizations which we create possesses all the properties of a single autonomous Living Thing. Those properties, as you may recall from Section 1.4.2, include: senses, memory, resource consumption, calculating capacity (ability to decide), and ability to act.

So I assume that life has grown in levels in the past, and continues to grow now toward the next higher level as we humans organize our affairs. This is what I mean by the assumption that life grows in levels.

The meaning of inter-level learning

Sometime after we have become accustomed to this life-in-levels view, our attention may naturally focus upon the growth from one level to the next. The levels are interesting and worth recognizing, but the really interesting part for us scientists must be the growth from one level to the next.

Now, to introduce inter-level learning, consider these two questions:

1. Under what process, what set of steps, did single cells become organized to produce a multi-cellular organism such as a human with senses, memory, calculating capacity, abilities to act, etc.? That is, how did life grow from level N-1 to level N ?
2. How might we humans better coordinate our activities to achieve successful families, businesses and states, organizations which help their constituent members to live better? That is, how might life grow from level N toward level N+1 ?

The idea of inter-level learning suggests that the answers to these two questions may have similarities. If we knew all the answers to question 1, above, some of those answers may help us to find answers to question 2. Similarly, if we have learned some of the answers to question 2 from our direct experience as humans in organizations, then that knowledge might help biologists who are trying to grasp how single cells took the first steps of coordination in groups.

My proposal, that inter-level learning may be possible, assumes that there is some structural similarity between the challenges faced by LTs on two different levels in the hierarchy of life. I may be overreaching in this assumption, since I have not started to seek empirical evidence in support of the assumption. But, in support of this assumption, we may notice that the general assumptions of RPM (See Section 1.4) apply at any and all levels, while making no distinctions between levels. So, within the model suggested by those general assumptions of RPM, nothing suggests that the challenges faced by LTs on one level must be different from the challenges faced by LTs at a different level.

But neither, of course, do those general assumptions imply that that the challenges must be the same at two different levels. The Resource Patterns (RPs) which beckon growth above any given level may demand development of organizational capabilities which differ from the organizational capabilities needed on a different level. Also differing from level to level will probably be the capabilities of the LTs available to start organizing upward from that level. For example, probably we humans bring to our efforts of organization a different set of inherent capabilities than eukaryotic cells brought to their challenges of organization; but this assertion needs support from knowledge we do not yet have about the abilities of eukaryotic cells.

One example of inter-level learning

In the draft of Chapter 5 we ran through a thought experiment in which tabletop critters developed a line of exchange between large deposits of the two essential resources, water and sugar. This thriving organization of critters could exist without any of the critters knowing about the RP. No critter knows where its trading partners get the excess of the resource which those trading partners are willing to trade away. Each critter, in order to find what it needs, has learned only how to behave and trade locally. Yet the sum of all this local knowledge adds up, in the perceptions of us human overseers, to a thriving trade route.

If you accept those conclusions of that thought experiment which starts from the level of critters, then you may join me in supposing that a similar condition can exist on the level of humans. We individual humans, it seems to me, are for the most part incapable of comprehending why we live so much better now than our ancestors lived 5,000 years ago. The perhaps surprising idea that we humans could stumble into great wealth without any of us comprehending how or why it happened gains support, I claim, from inter-level learning, from the thought experiment in Chapter 5.

Concluding reflections

Now I have completed my description of inter-level learning. In what follows you may find a few reflections on the subject.

On the concept of Life in Levels

Our ability to perceive that life has grown in levels depends, of course, upon our definition of a living thing. According to that definition we perceive a living thing when we see an organization which has all the properties of living things which we listed in our initial assumptions.

We can find many examples of organizations which are not living things because these organizations lack one or more of those properties which taken together define a living thing. I would say that all of the organizations which we humans have built to date fall into this lesser category. A state, for example, can sense, remember, decide, and act in many ways, but cannot reproduce itself with predictable success. A corporation which operates a chain of fast-food restaurants, for another example, can reproduce in part by starting up a new restaurant location. But such a corporation probably lacks the ability to reproduce itself entirely, as a whole new corporate structure.

If we assume that life will eventually continue its growth from our human level N to a higher level N+1, I guess that we humans have barely started that growth; we have progressed only a small fraction of the way from level N to level N+1. To support this guess, notice that the complexity which we can see in the organizations created by us humans remains triflingly small when compared with the complexity we can see in a human body composed of organized cells. On that scale of complexity, it would seem that we humans have only started our long journey toward level N+1.

It is worth noting, when we consider the vast complexity of a human body, that most of the cells in a human body carry the same DNA, the same set of instructions. I find it frightening to consider the analogous situation in an organization composed of humans. In that analog humans would lose much of the individuality which we now enjoy. The humans would all have the same set of rules coded into their minds, or something like that.

On intelligence

When we consider eukaryotic cells, in light of their accomplishment in having organized themselves to make us humans, we humans may suspect that eukaryotic cells possess a considerable measure of intelligence. Indeed since, as just noted above, eukaryotic cells seem to have accomplished a much greater feat of organization than we humans have yet accomplished, perhaps we should humbly conclude that eukaryotic cells are more intelligent than we humans. But how could we know? I believe we have no good definition of “intelligence”. Experts on intelligence, I have heard it said, inform us that intelligence is what is measured by an intelligence test. In other words, they don’t really know what intelligence is.

Our stupidity about what we mean by “intelligence” is confirmed, I believe, by the assertions made in recent centuries by some of our fellow “intelligent” humans, assertions that intelligence is a uniquely human trait. So other mammals lack intelligence in this view. But experiments with many animals and even, I have heard, with cells, have shown increasingly that those others possess some of what we now recognize as intelligence.

If we were to display some intelligence of our own, in a quest for evidence to prove that others lack intelligence, I think we would have to start that quest by learning the language of the others which we propose to test, so that we could quiz them in a language which is meaningful to them. I believe we humans are now making our first clumsy steps in that direction, but we have far to go.

As such I will claim that we have no way of knowing just how intelligent a single cell may be, and we are not qualified to assert that cells could not have been intelligent enough to build us multicellular organisms.

Continuing this line of thinking, we humans should admit that we are capable of seeing only a few of the levels of life assumed by RPM. In addition to our own level we can see perhaps two levels below and one level above. But we certainly are not capable of seeing way to the bottom of the levels which RPM suggests may exist.

When advances in our instrumentation bring us evidence of new levels of smallness, evidence of new entities smaller than any previously known to us, we are not yet in any position to start quizzing the intelligence of those entities. First we would have to learn their language, if they have one. So it seems possible to me that the levels of life reach down into quantum mechanical realms. Although I will make no such claim, note that particles at the subatomic level exhibit two of the properties we attribute to LTs, being abilities to act and to act non-deterministically.

Having admitted our poor ability to see more than a few steps down the scale of life-in-levels, we should also admit poor ability to see up the scale. To my perception it usually seems that we humans are at the top. But how much trust should we place in such a perception? One of the lessons which I hope will be taught by RPM is that perceptions grow to serve particular orders as encouraged by the existence of real, or at least plausibly proposed, RPs. Other perceptions, beyond those so needed, present no justification for their growth in RPM.  So my perceptions, as a human, serve my development and survival at the human level. Surely, as should be suggested by the history of the development of science, I am surrounded by orders which I am not capable of perceiving at my stage of development. Higher levels of life may exist beyond my ability to perceive.

Consciousness: an Explanation and Definition

What is consciousness? Many have weighed in on this question. Recently I discovered a possible explanation for the rudiments of consciousness. That explanation, presented here, differs from others I have seen (notes below).

You will need to understand the context, the kind of being in which this “consciousness” occurs. Living Things (LTs) are assumed to exist in a general model of life which I am describing. You do not need to know much about that general model (which I call the Resource Patterns Model of Life) except to understand the requirements imposed upon Living Things, and the capabilities given to them. Living Things:

• must consume some resources in order to survive.
• have a chance to survive by finding and exploiting resources in their environment.
• have one or more senses, bringing in information about their surroundings.
• can act by choosing, in each moment of time, one of the acts possible for them as given by their makeup.

As such, I believe you will agree, a Living Thing must have some sort of internal information processing system. This information processing system uses inputs (senses), along with any wisdom or intelligence it can muster, to produce outputs (its choices of how to act in each moment).

Now we step beyond this brief outline of inputs and outputs to propose a somewhat more detailed model of the process inside one of these information processing systems. See Figure 1. Notice this summarizes activity in only one moment of time. The process repeats itself. After finishing the final box on the right it returns to the first box on the left, again and again throughout the life of the Living Thing.

Figure 1. Information processing in a moment of life of a LT. Does Box 5 suggest our experience of consciousness?

Except for the following points, I will hope Figure 1 explains itself to you.

• Boxes 2 and 4 are each drawn in the shape of a drum. This shape is borrowed from computer notation where it signifies a database in which data can be stored and searched. Each of these databases may be empty, at the start of life anyhow, but may contain a large amount of data, an amount which could grow throughout a lifetime.
• After Box 1, when the immediate situation is sensed, but before box 5, when the LT decides what to do, the LT may remember (Box 2) previous experience which resembles this current situation and it may categorize (Box 4) this situation as governed by rules which either suggest or require particular responses. But the line directly from Box 1 to Box 5 allows for quick reflexive decision when there is no time to be careful.
• About Box 3, the process shown in the middle, this works on a slower schedule. It is not moment-by-moment, in step with the other boxes in the chart, but occurs over longer times. Gradually, with “intelligence” which I will not pretend to describe here, a LT may reflect upon its experience and decide to behave differently in the future.
• In Box 5, I say the LT decides what act to “attempt” rather than to “perform” because such a decision may fail. Between Box 5 and Box 6 the real world acts. For example a decision in Box 5 to step forward may be blocked by sudden insertion of some physical barrier, an unexpected event to the Living Thing, .
• In Box 5 a LT may decide to wait, to make no outward move in the current moment.

The activity in Box 5 includes the building blocks of primitive consciousness, I propose. What is consciousness, in its simple form, if not these four concurrent processes?

1. a current sensing of inputs (input from Box 1)
2. recognition of familiar objects and processes (input from Box 2)
3. awareness of rules, of “shoulds” (input from Box 3),
4. decision about how to act. That is choice of an act from among a known set of possible acts (output from Box 5).

This proposal has developed, effectively, a definition of basic consciousness, being the concurrence of the four processes listed. In my exposure to date most scholars who address the subject of consciousness seem to proceed without seriously attempting a definition. It is perhaps very hard to define unless we approach the question from this other direction, as I here propose.

Once again, notice the context of a Living Thing. The context makes this description of consciousness stand apart from other descriptions I have seen. A Living Thing must consume resources in order to survive. A Living Thing may be able to accomplish this consumption by responding to its circumstances with appropriate choices of actions. A basis of consciousness, as defined above, almost falls out from this context, from the information processing necessary between senses and choices.

---

Notes: Other sources on consciousness

1. Daniel Dennett, Consciousness Explained, (1991). I read this book over 20 years ago and remember only a few of its contributions. But I do not think I owe credit to Dennett for the insight expressed above.
2. Interviews with several scholars conducted by Robert Lawrence Kuhn on the YouTube channel Closer To Truth. Search that channel on consciousness. So far I have watched a handful of these (as of January 2018). My view shares much with the view expressed by Rupert Sheldrake.
3. Other YouTubes by scholars on consciousness. My watch history shows about 15 such views during the past two years. Feel free to ask if you need more specifics. Again, I recall none to which I believe I owe credit for the insight above.
4. [added July 17, 2019]  John Searle speaking at CRASSH, Cambridge, UK, 22 May 2014, “Consciousness as a Problem in Philosophy and Neurobiology”, YouTube.

Tabletop Critters and Other Examples

This is a draft of Chapter 2 in the book outline

Tabletop Critters and Other Examples

Now we will see how the Resource-Patterns Model of Life (RPM), which was outlined in Chapter 1 Section 1.4, can apply to our understanding of living systems. We start with two brief examples: a world with two continents, and a green plant. Then we will gain familiarity with tabletop critters. These examples, as you will see, provide a fruitful basis for thought-experiment agent-based modeling (TEABM), the most fruitful basis for ABM which I have employed.

 2.1 Introductory Examples

2.1.1 A world with two continents

Suppose there is a planet which has two continents. The first, a frozen polar continent, gets 99% of the planet's precipitation, but is so covered with glacier that only a few blades of grass grow during the warm week of summer. The second is a vast, warm desert, with fertile soil but no water. Notice the possibility for agriculture if fresh water can be transported from one continent to the other.

Figure 2-1: A world with two continents, promising agriculture

Suppose that this agriculture, if achieved, could support a population of one billion humans for the foreseeable future. But suppose that at present, with no agriculture, only ten thousand humans live on this planet, and they live near starvation in scattered bands.


Now obviously the task which we see, which promises vast wealth in the form of crops, cannot be achieved by any one of the humans. This task requires companies, or whole industries, of ice carvers, shippers, and farmers. But, equally obviously, the humans can achieve it if they organize and combine their efforts appropriately, each doing a small part of the whole task.


This is the kind of challenge which we consider in RPM: A small population of poor living things (LTs) could grow greatly both in numbers and wealth if they coordinate their activities appropriately. RPM gives us a workbench, so to speak, upon which we open up these questions of whether and how coordination might be achieved in circumstances resembling the challenges facing hunter-gatherer level humans on the planet with two continents.

2.1.2 A green plant, with its millions of cells in roots, leaves, and stem

The environment in which these cells live has a resource pattern: above the ground there is abundant energy in sunlight and below the ground there is abundant water; but the distance between these two necessary resources is too great for any of the cells, acting alone, to exploit. This situation is akin to the world with two continents. But we see in the plant that the needed organization has already been accomplished. The plant is an organization in which each cell plays a part. Without participating in the scheme of the plant probably few of these cells could have survived in this environment.

2.2 Tabletop critters

 

2.2.1 Initial condition

Tabletop critters provide the model of LTs which we will use most. With this model we can frame important questions about life.


Imagine a flat surface, perhaps a tabletop, upon which some tiny, perhaps one-celled, critters live. These critters need both water and sugar to live, and this tabletop upon which they find themselves is basically a desert. The wind blows and occasionally deposits a few molecules of water or sugar at random, unpredictable locations on the tabletop. Figure 2-2 shows how we will picture the three types of objects on the tabletop.

Figure 2-2: Our way of picturing critter, water, and sugar

The water and sugar provided randomly by the environment just barely enables the critters to survive and reproduce themselves — provided of course that they keep moving about so they chance to find the small deposits of water and sugar.

In Figure 2-3 we see the same three types of objects to which we were introduced in Figure 2-2, but on a smaller scale so that we can see a larger area of the tabletop. We see more considerable distances between the critters and the resources they need to survive, so it is easier to imagine the near-starvation struggle of the critters to discover resources. Figure 2-3 thus represents what we call the initial condition on the tabletop.

Figure 2-3: The initial condition on the tabletop

 

2.2.2 Opportunity

Suppose that onto this tabletop fate places a drop of water at some spot, and a crumb of sugar at another spot. See Figure 2-4. Once again we have zoomed out when compared with the previous drawing (Figure 2-3). I drew Figure 2-4 on a smaller scale to show the larger area of the tabletop affected by this large new resource pattern. Now the critters have been reduced to looking like small spots; the original wind-dropped spots of water and sugar have fallen completely out of this view because they are too small to be visible; but the new drop of water on the left and crumb of sugar on the right are huge compared to the critters.

Figure 2-4, We add a large new resource pattern. Water on left, sugar on right.

Suppose that the distance between water and sugar, a centimeter, is much further than any one of these critters can travel in its entire lifetime, but suppose that the critters do have ability to pick up raw materials, carry them for small distances, and then drop them again. So the critters have the physical capability of establishing a line of exchange between the water and sugar. To see this capability, suppose that we give the critters some rules of behavior such as these:

• If you sense water on the left, carry it to the right and set it down.
• If you sense sugar on the right, carry it to the left and set it down.
• If you get thirsty or hungry, help yourself to what you need from the materials that pass through your possession.

Following these rules, those critters who were lucky enough to start out somewhere between the water and sugar should thrive after passage of some time. These lucky critters will no longer die because of starvation, and they will reproduce more. A dense population of critters will come to live in a line of mutually cooperative exchange between the water and sugar. See Figure 2-5.

Figure 2-5, A population of critters prospering by trade between water and sugar

2.3 Reflections so far

 

2.3.1

Looking back now over our three examples (the world with two continents, the green plant, and the tabletop critters), I hope you may notice similarity. The hunter-gatherer people in the world with the two continents are in a situation like that of the critters in figure 2-4: both live impoverished in a world which promises plenty if they can cooperate. And the cells which make up a green plant already seem to have achieved a large degree of mutually beneficial cooperation toward which our critters in Figure 2-5 have taken a promising step. In all three examples a resource pattern may be exploited by the living things which succeed in discovering rules of cooperation.

2.3.2

I wrote above that rule-based behavior could lead to productive cooperation among critters. But you may wonder what exactly I mean by rule-based behavior. So, to tell more, we have designed our agent, the critter, with capability to perform a number of different acts. These acts include: move a single step in any chosen direction; take into internal storage a portion of a resource (water or sugar) to which the critter finds itself adjacent; set back onto the tabletop a portion of a resource taken from internal storage; reproduce (divide in half); do nothing (make no outward move); and perhaps other possible acts which we have empowered our critters to perform.

In each increment of time as our model runs each of our critters can perform one and only one of these acts. The critter’s “mind” has to choose one of these acts; this after all is the use of its “mind”. But how will the mind choose? This is where rules such as those we have mentioned will play a part. Rules narrow the choices among which the critter may choose in a given circumstance. Sometimes a rule may allow only one single choice. Other times a rule may prohibit a choice or set of choices. Also a rule may favor or disfavor a choice, without outright command, changing only the likelihood of that choice among the set of possible choices which may be developed by the critter’s preliminary "thought".


Thus rules, embedded in the thinking of our critters, can guide the development of outcomes produced (or experienced) by groups of critters.

2.3.3

The rules are not arbitrary. The rules work because they help LTs exploit a resource pattern (or an environmental feature) which is bigger than any of the LTs, and which none of the LTs can change. So the environment in which the LTs live determines the rules more than the LTs themselves. The LTs contribute to formation of the rules only to the extent that the LTs have capabilities which – if organized into cooperative wholes – make exploitation of the RP possible. The LTs cannot make up the rules simply to serve the whims of the LTs.

It is not clear if or how the LTs can discover the rules which will lead to their flourishing:

• In the world with two continents the humans needed to learn all the practices (rules) which could lead them to prosperity. But we cannot tell clearly and simply how they might accomplish that learning.
• In the green plant, the cells already practice delimited (rule bound) specialties in an order of mutually beneficial cooperation. We do not know how these rules became established. But of course biologists work to elucidate this mystery.
• On the tabletop the critters advanced from the poverty suggested by Figure 2-4 to the prosperity of Figure 2-5 because we gave them rules. But could critters have discovered such rules themselves without our help? This question expands and becomes the subject of this book.

2.3.4

We should remember that life was possible for our critters from the outset on our tabletop. It is not generally required that an opportunity for improvement of life must be exploited. Life could go on as before in most cases. And when a group of critters succeeds in advancing, by exploiting a RP, there are likely to be some critters from the initial population who are left behind by this advance. You may have noticed that I drew a few of these in Figure 2-5, still surviving in a thinly scattered population away from the thriving center of RP exploitation.


We humans who live well in cities are aware that in the hinterlands, away from our fruitfully organized lives, live many people in a style which we remember, or our parents remembered. We think, perhaps correctly, that we could always choose to return to that poorer way of living.

The availability of such a choice becomes important in our agent-based modeling, because the attempt by one critter, or a group of critters, to prosper by discovering and exploiting a previously unexploited RP, need not be a life-or-death gamble. Most attempts to advance to greater prosperity are launched from a way of living to which the attempting LT may fall back, if necessary.

2.3.5

Our modeling will generally follow the example of tabletop critters which we pictured in Figure 2-4, in that a population of agents will be modeled as living in an environment in which we modelers have posed an opportunity as a problem for the population. Some, or possibly all, agent-members of the population can advance their success in life if those members “learn” to work together.

2.4 Challenges for our critters

We have seen that organizations of critters can leap ahead in prosperity if the members in each given organization follow situation-specific rules as they choose how to act. Further, we should see that the challenge of learning what those rules need to be is the study which falls open before us as we examine life through our RPM. But before we step further into that study, here we will see a few of the difficult life-advance challenges which we modelers may present to our critters.

2.4.1 Challenge 1

In Figure 2-4 we have already presented our first challenge, but then we gave the critters rules to overcome the challenge. Suppose we do not give the critters rules. Can the critters somehow learn new rules themselves, rules analogous to the rules we modelers provided which enabled the critters' leap to wealth shown in Figure 2-5?


In order to introduce a new symbol in our graphics we redraw Figure 2-4 in Figure 2-6. A dashed line has been drawn around the water-sugar pair of resources. It signifies that the critters have not yet learned enough to significantly exploit that RP. But of course we modelers know about it. We put it there after all. The dashed line enclosure reminds us modelers that the RP is evident, for the time being, only to us modelers and not to the critters.

Figure 2-6, A dashed line shows that a RP is unexploited.

When we see evidence of organized exploitation of an RP, as in Figure 2-5 we will usually consider that the critters have discovered that RP, although we must later on examine more carefully what mental states and processes might constitute such “discovery”.

2.4.2 Challenge 2

Figure 2-7, A world with an unexploited opportunity

In Figure 2-7 we see two resource patterns, arranged vertically this time. The RP on the left has been discovered by critters and is being exploited. These successful critters on the left must have rules which differ from the rules which helped our critters in Figure 2-5, simply because of the up-down rather than right-left orientation of the RP. But this difference does not affect our present challenge.

Instead, in this challenge we ask: Can anything which has been "learned" by the critters on the left help them to discover the similar RP on the right, and help them to discover it more quickly and with less prolonged, accidental learning (covered more completely in Chapter 5) than was required by the critters that first learned to exploit the RP on the left? This is a complex question which I will not pretend to answer in definite terms. But throughout the remainder of this book we will work toward answers.

You might notice that in Figure 2-7 we have once again reduced the scale of our drawing a little bit (we have zoomed out) so that we can show this challenge which involves a larger region of the tabletop.

2.4.3 Challenge 3

Figure 2-8, Different RPs require different rules.

In Figure 2-8 we see the starting point for our third challenge. The world has two resource patterns:

• on the top the resource pattern (consisting of both water and sugar) is oriented horizontally;
• on the bottom the resource pattern is oriented vertically.

Both RPs have been "discovered" by the critters as we can see, since in each RP a dense population of critters lives in what we can only explain as a line of trade between water and sugar.

I have tried to show a considerable distance between the top RP and the bottom RP in order to make it seem unlikely that critters would develop a line of trade between those two RPs. But let us assume that occasionally a critter might somehow make the long journey from RP to RP. Or we mischievous modelers might pluck a critter from one line of trade and drop it into the other line of trade, just to see what would happen.


What would happen? This is the challenge. But, short of all the work which we might do to bring this challenge to a computerized agent-based model, we can think and say things such as the following:

• If somehow a critter found its way from one community to the other and then tried to become a productive member in the new community by following the rules which it had learned in its original community, it would fail in this effort. For example, suppose a critter that has learned to carry sugar to the left (in the upper resource pattern in Figure 2-8) somehow finds itself in the other line of trade (in the lower resource pattern) where physical reality requires that sugar be moved up, not left. This critter's effort to be a good citizen by following the rules it has learned will introduce waste, not help, into the new community.
• Where critters discover rules which enable those critters to live better, those rules are dictated by the physical realities of the critters' nearby environments. Each new resource pattern may possibly introduce a requirement for a new set of rules. So even though we might think of our critters as constituting a single biological species, our critters must be capable of conforming to various sets of behavioral rules, rules as dictated by physical circumstances beyond the control of any of the critters.

2.4.4 Challenge 4

Continuing the direction we started with Challenge 2, in Figure 2-9 we see one community of critters thriving at one RP in a world with many (albeit just six in Figure 2-9) similar and unexploited RPs. The challenge now concerns not just one neighboring RP, but a pattern in many neighboring RPs — a pattern of resource patterns.


In case it is not obvious I will say that we have zoomed out again, once again showing a larger piece of the world in which our critters live.

Figure 2-9, Can critters exploit a pattern of resource patterns?

We can start with this challenge, obviously enough, by trying to imagine how real living systems might have solved the problem. We might imagine that within that single thriving community there might be some variability among that population of critters. Let us imagine two types:

1. The first type has only the attributes which enabled their ancestors to follow rules of cooperative exchange which resulted in this community. Members of this type do not necessarily know even that they live in a successful community. We modelers have given them no such sensual or calculational capabilities.
2. The second type have a variation, an addition to their attributes. This variation makes it more likely that they or their offspring will recognize a worthwhile gamble in sending a provisioned party of explorers off in the direction of one of their world’s unexploited RPs.

We expect then, after the clock of life has run for a time, that the other six RPs will come to be inhabited by thriving communities of critters descended almost entirely from Type 2, not Type 1. Type 2 will dominate in this world because of what we can see as natural selection.

Such a conclusion to our thought experiment draws much from Darwin as I understand that theory:  variation followed by natural selection. But it also adds an explanation for the selection mechanism. It suggests how nature selects: by affording much greater reproductive opportunity to variants able to exploit the available RPs.

As you must have guessed, I intend this modeling with critters to suggest explanations for some of our human experiences, as we will be seeing.

2.5 A more detailed look at the critters

Now that we have had our first introduction to the critters and to a few situational challenges which we can pose in the model of critters, we will back up a bit to take a more detailed look at what these critters are and can do.

I developed the additional details that we will review here as I wrote a computer program to implement a CABM of tabletop critters. As I said in Section 1.5, the labor of creating computer models forces a modeler to make many model-specifying decisions which the modeler had not dreamed necessary beforehand. While the challenge of programming agents to achieve a desired society-wide observable can be insurmountable, the effort is always educational.


One critter property, which I promptly discovered required my judgment, was the distance a critter could travel in any time increment. I decided this maximum step-size of a critter should be roughly equal to the diameter of its body (the yellow oval), as show in Figure 2-10.

Figure 2-10, definition of critter step size

Also I decided a critter may move in any direction in the plane provided its body does not collide with anything (a resource or another critter’s body). To preserve the visual clarity of the model, objects are not allowed to pile on top of each other in the plane.

Next, while in thought-experiment mode I had assumed critters could sense other objects on the tabletop nearby or adjacent to the critter’s location. But in computer mode this must be defined specifically, so I decided the seven rays extending outward from the critter's body suggest the sense area. See Figure 2-11 in which the dotted oval shows this area. My CABM critter can sense the presence of another object which lies at least partially inside its sense area but not anything outside that area. So in this picture the critter can sense the spot of sugar but not the drop of water.

Figure 2-11, definition of critter sense area

A critter can attempt to consume a resource which it can sense in its sense area. For this purpose of consumption it is not necessary that the critter move any closer to the point where its body is touching the resource. A critter cannot consume a resource outside its sense area. In Figure 2-11 the critter can attempt to consume the sugar but not the water.

A critter does not always accomplish what it decides to do, as we saw first in the assumptions outlined in Section 1.4.2 under “ability to act”. In each increment of time it decides upon an action to undertake, then it attempts that action. But larger fate determines whether and how much the critter's attempt succeeds. For example, two critters may move in one time increment to where both can sense a single water drop. Both may decide to consume the whole drop with their next act. See Figure 2-12.

Figure 2-12, Showing why a critter does not always succeed in its chosen act.

In the picture above, both critters attempt to imbibe the water drop at time n+1. But obviously both cannot succeed. So the program running the model plays the role of Fate and somehow decides how to allot the water in the drop.

2.6 Clarifying the Initial Condition

Shifting back now to thought-experiment mode, we have asserted that we start with an initial condition in which a small population of critters just barely survives by foraging for water and sugar. For each individual critter, there is no steady and certain source of water or sugar. Instead a small portion of water or sugar appears now and then, randomly dropped into the world. These resources come as gifts from fate perhaps, or are carried in by the wind. In any case the critters' only hope of survival comes from moving about almost continuously in hope of encountering water or sugar. The critters are hunter-gatherers. Death because of starvation for either water or sugar is their most common fate. But fate can also be good sometimes. Sometimes a critter finds enough water and sugar to enable it to reproduce. So the population hangs on — barely. In our initial condition, the population of critters is probably near the maximum that the environment can sustain, given the rate of influx of resources.

But in my effort of computerized agent-based modeling (CABM) I found this idea, of a stable population just barely hanging onto life, difficult to achieve. The best I got was a population which cycled in number between small, approaching and sometimes reaching extinction, and large, with many more foraging critters than could be sustained with the program's set rate of sprinkling new resources onto the tabletop at random locations.

I suppose that I could approach closer to achieving the initial condition, as we described it for our thought-experiment ABM, in computerized ABM — given time and impetus. But, at this still early stage of use of the resource-patterns model of life, the promise of more thought experiments outweighs, in my thinking, the promise of CABM. We carry on in thought-experiment mode, for the most part.

A New Theory of Life

This is a draft of Chapter 1 in the book outline

Introduction

A New Theory of Life

1.1 Puzzles

Life defies the second law of thermodynamics. Or at least that defiance is suggested by a preliminary understanding of the second law. There is no perpetual-motion machine. Every system, including any machine or any living organism, when considered by itself alone and separate from the outside, must consume the usable energy with which it starts, must eventually run to a stop or die. Yet life carries on, for billions of years now so far as we know.

This puzzle was one of the many things I learned from my Ph.D.-engineer mentors during the most educational year of my life. In 1973, when at the completion of my B.S. in electrical engineering I had failed to gain admission to any medical school, momentum in that same med-school direction combined with luck got me a job as a Bioengineering Research Assistant at Harvard Medical School in Boston. John L. Lehr, the youngest of these Ph.D. mentors, told me about the second-law/life puzzle. The puzzle settled in my head as one of the things I wished I could understand better, as one of the questions guiding my curiosity.

Over the course of many years I formulated my own answer to the second-law/life puzzle, and that answer is the subject of this book. But there is much more here.Other deep and difficult questions have perplexed me. Two of these are:

1. Why does planning succeed for some of our human organizations and not for others? To explain, most economists now believe that socialism on a national scale is doomed to fail. The failure of Soviet communism supports this belief. But planning and central control of smaller economic organizations, such as retail stores, seems to succeed. If planning can work for businesses, why does it seem doomed to fail on the national scale?
2. In elections in the US, the residents of cities tend to vote for Democrats, or representatives from the left, while rural residents tend to vote for Republicans, representatives from the right. I do not believe that either side can fairly be dismissed as stupid or evil. So what explains this undeniably consistent trend?

Once again I have discovered workable answers to such questions, after such questions have resided for decades in the back of my thinking. The theory presented in this book shows a reader how to answer those questions, and how to explain the answers.

1.2 Hypotheses and computer loops

When I was about 30 I wrote down another question, or actually a tentative hypothesis, which promotes the idea of ‘hypothesis’ to center stage. I had been wondering, how does my mind work? How does any living thing choose its actions so as to survive rather than perish?

Extending from my own mental experience as well as I am able to observe it, I decided that maybe, probably, every one of my thoughts and actions was simply a hypothesis. I can never be totally sure of my thoughts, and any action which I make, even an action in which I had previously felt great confidence, might fail for some reason I had not anticipated.

Yet I succeed often enough to have survived thus far in spite of the refutations which life, and greater circumstance, deliver to me and my hypothetical thoughts and actions. And this survival in spite of possible error seems to characterize the existence of other now-living life forms. The explanation for this survival seems to lie in relationship between the living thing and its environment. If the good hypotheses are rewarded handsomely or frequently enough by the environment, and the bad hypotheses are punished mildly enough by the environment, then the living thing which lives by testing these hypotheses has a chance to survive. If the living thing has a way of remembering its successes and failures, and if the environment has some regularity, then a living thing given some mental or calculational capacity, in addition to memory, may improve its adaptation to its environment.

I wanted to test this theory, that the key to life might be simply a strategy of repeated trials guided by a growing memory, in computer models. I had loved computer programming since I had first experienced it, using Fortran, as an undergraduate in 1970. So it was easy for me to see how I could write a beginning try-and-remember loop, to model a very simplified life form. I was excited by this idea as I started a graduate program in computer science in 1982. After two and one half years in that program I passed the doctoral written exam, gaining admission into the dissertation phase. But try as I might I was unable to find, in that department at that time, enough faculty members who felt interest in my proposal and who would serve on my dissertation committee. And unfortunately I felt little excitement for any of the research projects going on in that department, projects which I might have joined to find a dissertation topic and faculty adviser. I took a leave of absence from that graduate program to take a job as a carpenter, thinking I would return to the program after one year.

But the environment provided an unanticipated positive reward for my shift to carpentry. I was soon answering many calls from friends and professional acquaintances who, learning that I was carpentering, wanted me to work on their houses. During the next few years I completed many remodeling jobs, which kept getting bigger, and I got two North Carolina licenses: as building contractor and plumber. I was able to start a business of designing and then building spec houses.

So I never finished the Ph.D. in computer science. Without planning I had stumbled into a way of making a living which, I realized, was better for me than if I had completed the Ph.D. I had more power over my own choice of direction than if I had become a professor. And my dissertation topic lives on! This book is my dissertation and you, reader, a member of my committee.

1.3 Other questions answered by this theory

In the section which follows this I will finally get around to stating my theory in concise terms. Unfortunately those concise terms may not prove evocative for you without the coaching offered in later chapters. So before we take the step of introducing the theory in concise terms, I will offer more encouragement for you to stay the course by listing additional important issues which this theory can enlighten. Stick with me until you learn how to think within this theory and you will be rewarded with deep and powerful ways to answer these questions.

• Why do people talk past each other?
• If anger is bad, something that mature people learn to suppress, why is it so common, so instinctive?
• If humor or, more specifically, laughing together is good, why are some people so commonly injured by someone else’s joke?
• If, as Darwin suggests, evolution of life as we know it proceeds by variation followed by natural selection, how does nature select its survivors?
• What hope exists for the future of our human race?

1.4 Assumptions of the theory

Now, having laid out the promise of fruitful new understanding which a reader of this book may gain, we will steer into the presentation of the model. What follows attempts to give a formal and systematic presentation of the model which, for a reason which should soon become clear, I have named the Resource-Patterns Model of Life. We start with an outline of the assumptions which underlie the model.

Our basic assumption is that Living Things exist in a Universe. So now we will review our assumptions about that Universe, and then about Living Things.

1.4.1 Properties of the Universe

space and time
The universe has one or more dimensions. Time passes in the universe.

living things
The universe contains living things.

resources for present living things
The universe contains raw materials and energy of the sorts required by living things.  Most of these resources are distributed in concentrations, i.e. in patterns. Such patterns of resources give rise to the prospect that living things may discover and exploit these patterns.

resources for future living things
Patterns of resources vary widely in size, from tiny (perhaps atomic or subatomic) to huge (galactic or larger). Smaller patterns support microscopic life which we can see, while the larger patterns lie beyond the reach of present humans. 

1.4.2 Properties of Living Things

senses
Living things can detect certain aspects of their surroundings.

ability to act
Living things can attempt to act in particular ways.  Many such acts involve motor or muscular movement.  But other possible acts might be to wait idly or to calculate without moving. Living things can act to imbibe resources or to reproduce themselves. Note however that a living thing's choice to act in a particular way does not guarantee that the attempted action will succeed.  Each attempt by a living thing to act might succeed or fail, depending upon circumstances.

purpose
Living things have goals.  Typical goals might be to imbibe the resources necessary for life, to reproduce, or to gain security.

memory
Living things have some ability to store a record of their experiences.

calculating capacity
Living things can “think” about how to act.  Typically this calculation might consider: (1) the present state of the environment as determined through senses; (2) present goals; (3) present store of resources; (4) memory of prior similar experiences.

resource consumption
Living things necessarily use up some of their store of resources in each increment of time.  The amount of consumption may depend upon the action undertaken.

resource storage
Living things can store some of the resources which are necessary for their lives.  Typically living things can store an amount of each resource sufficient for multiple time increments, so that living things can spend some of their time in actions other than imbibing.

nondeterministic choice of actions
Living things will employ their memories and calculating capacities to guide their choices of actions with as much "wisdom" as they can muster. But they will commonly find themselves with no definite knowledge about how to act. So, in order to avoid starvation which will certainly come if they remain idle, living things will sometimes guess how to act, selecting an act at random if need be.

life in levels
Living things are usually composed of a large number of smaller entities which in turn appear to be individual living things on a lower level. But we can look in the other direction as well, to higher levels. As we humans go about coordinating our affairs with others on our level we are testing organizations which, when these organizations become successful, acquire some of the properties of living things as listed above. Through such coordinating action we humans may eventually create organizations with all the properties of living things on a higher level. Such organizations would in fact be individual living things on that higher level, in view of this model.

Obviously these assumptions have been designed in sympathy with humans.  The living things could be us humans.  The universe could be the Earth.  But the model allows us to look at other implementations, at other “living things” and other “universes”.  These other implementations will ring with suggestions about our existence as humans, about our social orders.

Here I have listed many assumptions.  But I highlight one of the assumptions in the name which I have given to the entire model: the Resource-Patterns Model of Life. This one assumption about the distribution of resources leads to valuable and useful suggestions. Our social orders often reflect patterns of resources in the universe. And the direction which resource patterns impart upon our social orders has been overlooked by social science to date, as far as I have been able to determine.

Hereafter I will commonly abbreviate: Living Thing as LT; Resource Pattern as RP; and Resource-Patterns Model of Life as RPM.

1.4.3 Operation of the Resource-Patterns Model of Life

The resource-patterns model gives its user a way to think about certain sorts of problems. After we accept the above-listed assumptions, we may deduce from those assumptions a set of guiding principles which I outline here under four points.

1. Living Things survive by finding and imbibing resources.  If LTs don’t find enough resources their numbers will decrease.  If LTs find abundant resources their numbers can and probably will increase.
2. In each increment of time each LT has a range of choices about how to act.  Probably most of these possible actions will be useless in that these actions will not contribute to the effort to imbibe resources.  So a LT needs to narrow its range of choices.  This focusing of choices is the principal requirement of the LT's calculating capacity.
3. Any particular supply of a necessary resource must be finite, assuming that this supply has been discovered by LTs at a particular place and time.  This supply can be exploited only until it runs out.  Ongoing life therefore requires an ongoing discovery of new supplies of necessary resources.
4. Cooperation may help LTs to exploit some RPs.  Consider three cases:
   1. Some resources are abundant but far away, too far away for a single LT to exploit.  But such resources might be exploitable if a number of LTs combine in a linked network of trade.
   2. Other resources are near at hand but too difficult to extract without specialized tools or knowledge.  Such resources might be exploited if specialized LTs cooperate.
   3. Some resources may be extracted only through an effort which continues during a span of time. It makes sense for individual LTs to participate throughout that span of time only if the environment is stable and predictable.  The environment can become more predictable if the future behavior of other LTs becomes more predictable, if the LTs can somehow form rules of cooperation.

Thus, if a set of LTs can discover modes of cooperation, that set of LTs may flourish in an environment where a similar set of LTs, but without cooperation, would perish.

1.5 RPM uses Agent-Based Modeling

In the past few generations, with the increase of availability of computers, a new method of modeling for social science has grown called agent-based modeling ABM. The approach we will use in RPM can be called ABM.

ABM uses a number of small-scale entities called agents which interact with each other and their environment for the study of larger-scale consequences. The modeler adjusts the capabilities of the agents, either individually or in whole groups, and then models the passage of time by dividing time into moments or what we will sometimes call cycles. In each cycle each agent responds to its circumstances and acts within its capabilities. All together, with numerous agents acting during numerous cycles, group- or society-wide consequences often become evident to an observer with a society-wide viewpoint, to us modelers that is.

As you have probably anticipated, the LTs, which we have described as parts of RPM, are agents, typically, in our modeling.

Now we will compare two ways to implement agent-based models: computerized agent-based modeling (CABM); and thought experiment agent-based modeling (TEABM). Both of these methods of modeling have been used by me in developing my current understanding of RPM, and both methods influence my assertions in this text.

For some people, such as I who love computer programming, computerized ABM is very inviting and enthralling. RPM simply begs for CABM since it lays open many tantalizing problems which may be modeled by application of a little programming wizardry. But experience has shown that these computer models routinely get tangled in complexities which are not evident, for the most part, until a modeling project is underway. Nonetheless that difficulty can be a blessing: CABM promises to teach its practitioners a great deal about the assumptions which our human minds tend to make as we think about events. Thus CABM promises maximal education for human modelers – but at an expense in time and effort which is probably prohibitive for most aims of research.

The thought-experiment method of modeling, on the other hand, allows its practitioners to leap into any thicket imaginable. With minimal preparation the thought-experimenter can start to explain – at least to the satisfaction of the thought experimenter – what has happened and what will happen in that thicket.

Almost all of what I present in this book may be described as the results of thought experiments. So I should not discredit this method too severely. Einstein, after all, used thought experiment with great success. I believe thought experiment is appropriate and perhaps necessary in the early development of a subject. Eventually, if my RPM subject remains viable, terms will be defined and ways of measuring will be discovered. More exact science should ensue, including much fruitful CABM.

Given that the subjects we will discuss in this text will employ the method of thought experiment almost exclusively, I must warn, both the reader as newcomer to RPM and myself as leading modeler, of the limitations which anthropomorphism brings to our modeling. We will be modeling both the internal “nervous-system” workings of individual living things and the social interactions and thereby the achievements of groups of LTs. It will be too easy and tempting for us modelers, when setting a LT/agent in a problem situation, to assume that the LT/agent can sense and think as one of us humans can sense and think. But when we do that we rob ourselves of the power in RPM – to learn bit by bit about how our human minds and social interactions grow of necessity in the problem situations which we will model. We need to learn to notice carefully when we give extra powers to the LTs in our though experiments; probably we should write down these powers in a tabulation which we keep. This discipline will regain for us some of the teaching power which we set aside when we decided to model in thought experiments rather than in CABM.

1.6 About the Approach Taken in this Book

Here we will pause to consider the approach I have decided to take in writing this book for you.

Compare two styles of book: a college textbook for an introductory course in a subject; and a page-turner which keeps the reader engaged in development of its subject. Most of my previous experience with writing has emphasized the second style: I have tried to catch the reader’s interest, perhaps with reference to some issue on everybody’s mind today, and then to make the flow of development hold the reader’s attention through to the end.

Unfortunately, since I assume you would rather read a page-turner, I believe that my first book on this subject should cover the ground once. I aim to lay down the canon of RPM; I believe this should be my priority at this time. After I have completed this responsibility I will be able to imagine that my readers have a copy of this reference text on their shelf. Then I expect I may spill out many pieces of writing which are both more engaging, because related to an issue of immediate interest, and enjoyable for both reader and writer. In these subsequent texts I may refer often, for fuller explanation of some concept, to this canon on your shelf.

1.6.1 Choice of Language: Everyday Language vs. Specialized Terminology

Again and again I have faced a choice as I have attempted to communicate the essence of this new scientific model. Should I employ the everyday term, a word which will be widely understood, to name a specific concept within RPM, or should I invent a new model-specific term, the meaning of which must be learned by a newcomer?

As  you might expect I have tried to take the easiest path. This is to use the familiar term – that term with which indeed I first labeled the concept in my own thinking – in a narrowed way, in a new way with particular meaning within RPM.

This use of everyday English terms has a downside of course. A reader who has not labored to learn the RPM-specific meaning of an everyday term found in my text probably has no chance of understanding my meaning, but this newcoming reader may quickly and easily gain an erroneous impression of my meaning.

There is I believe no shortcut. A newcomer to a discipline must learn the particular ways of thinking, ways employed by seasoned practitioners, associated with the discipline-specific terminology. This remains true whether the terms are reemployed familiar words or newly-contrived for RPM purpose.

To help the reader with this learning of model-specific terms I have added a glossary at the end.

1.7 Outline of Contents to Come

Chapter 2, Tabletop Critters and Other Examples
Here we will see a few easily grasped examples of how RPM can explain and predict group-based behavior. Then we will look in more detail at a model of very simple LTs living in a difficult – but still very promising – environment. I will call these LTs “tabletop critters”. Our modeling here is done entirely in thought-experiment mode. Tabletop critters are the workhorses in my modeling to date.

Chapter 3, Activity and Abilities of the Critters
Here we will take a closer look at the individual agents, or critters, in our agent-based modeling. We will list more particularly what a critter can sense and do. We will consider critters at three stages of development
1) with enough abilities to probably survive in the initial condition
2) with enough additional abilities to exploit a first simple RP which we offer
3) with further abilities which – as we need to study – may enable a set of critters to spontaneously discover and exploit a RP.

Chapter 4, Life in Levels
We will review the evidence that life on Earth as seen by biologists has grown from level to level, for example single-cellular to multi-cellular. Then I argue that a similar growth, to a next higher level, goes on among us humans as we organize ourselves into families, businesses, and states. Our modes of social cooperation are complex and seemingly related to RPs.

Chapter 5, The Learning of Rules
LTs succeed in their lives, both as individuals and as members of organizations which succeed, by behaving under the influence of rules – rules which must somehow be acquired. While in early and simple cases we modelers can implant rules into our agents, this chapter starts to look at the complications which naturally arise as we modelers try to empower our agents to learn their own rules. One subject which arises here is preprogrammed or instinctive ethics as expressed among prospective groupings of LTs.

Chapter 6, Philosophy in RPM
RPM gives us a new way of looking at many longstanding questions of philosophy. The agent based modeling we do in the context of RPM opens the workings and motivations of modeled LTs, on a workbench so to speak. We see the stages of development for a need for ‘truth’. We see how agent-to-agent signals become a working language. In the agents of advanced thought experiments we see a combination of computable routines which, taken together produce effects which we would be challenged to distinguish from consciousness.

Chapter 7, Public Psychology
In RPM we see survival and reproductive fecundity awarded to whole organizations of LTs which succeed in cooperating among themselves to exploit RPs. So we naturally expect most LTs, being descended from these populous organizations, to have biases and instincts of their forebears which helped to develop and maintain those organizations. Our thought-experiments lead us to expect many forms of group-think, anger, deception, suspicion, and even humor.

Chapter 8, Public Policy
A particular category of organization of LTs, which we call “government” or “a state”, can be distinguished. Restraints or commands, which we humans call “law” are gathered under control of such an organization, and are often considered to be “public policy”. In RPM we can see public policy as a particular application of public psychology.

Chapter 9, Conclusion
We review the new contribution to social science made by RPM, with RPs which need to be discovered by as-yet-unknown methods. Cooperation, in as-yet-unknown ways, may be discovered without planning or foresight by any of us LTs. This way of modeling social life gives us great challenges and opportunities for understanding ourselves better.