Friday, October 19, 2018

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.

Darwinism Comparison

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 no 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.

Assumption 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.

Monday, July 23, 2018

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.

Wednesday, July 11, 2018

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.

Wednesday, January 10, 2018

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.

Monday, August 7, 2017

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



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.


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.


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.


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.


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 five 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.

Thursday, July 27, 2017

A New Theory of Life

This is a draft of Chapter 1 in the book outline


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

the universe has one or more dimensions.
living things
the universe contains living things, in order to give some interest to our model.
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. The concentrations of resources vary widely in size, from tiny (perhaps atomic or subatomic) to huge (galactic or larger).
time passes in the universe.  Resources and living things may move around with passage of time.

1.4.2 Properties of Living Things

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

Saturday, June 10, 2017

Stephen E. Toulmin, an Appreciation

I have become a fan Stephen Toulmin (1922–2009), philosopher of science. I learned of Toulmin in January when I watched this 3½ hour YouTube roundtable discussion on metaphysics.

After reading Toulmin's 1953 The Philosophy of Science: An Introduction, I felt sure that book was misnamed. No way is it an introduction. It is the deepest and most difficult book I have read in philosophy of science. Also, since I felt sure that he was saying something and that I might be able to understand some of it, I returned to page one and read the whole again a second time. He teaches me about what I am trying to do in this project of mine to write a book about the Resource-Patterns Model of Life. I would like to correspond with others who know Toulmin's work.