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

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

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.

Update on Status of Book-Writing Project

I should post an update. Almost five months have passed since the previous post in this blog.

Work continues on my writing of the book described in the chapter outline and on the statement of purpose page. This remains my top priority. I have completed first drafts of Chapters 1 and 2. These are written longhand, about which I will say more below, so they will not appear here until I type them in. That should happen within a few months. Drafts of Chapters 3–5 have already been posted here in earlier years. At present I am halfway through longhand drafting of Chapter 6.

This work is difficult and slow for me. Questions about order of presentation, voice, and style challenge me. I struggle with knowing how to write this book. Now, to my credit, I have written many shorter publications. In each of three length-categories (300 word letters to the editor, 600–1000 word columns, and 1500–4000 word papers) I struggled on the first few. But after completing those few I had learned how to write a piece of that length; thereafter I fell easily into such a project. This fuels my hope that I can learn how to write a book. But this is still my first.

I have read many books of advice for writers. A few months ago I decided to experiment with a strategy that I picked up from novelist John Irving. Irving tells that he writes his first drafts longhand — not on a computer. A computer makes it too easy, he says, to look back and improve a word or sentence. But at first-draft time careful sentence-level editing wastes time. My experience confirms this. Again and again I have spent hours crafting a paragraph, making it flow just as I want — only to delete that whole paragraph later on when the structure of my entire project finally came into view.

So I am drafting this book longhand now. I am happy with the change. I really need to press on — once through my entire outline — before I will have my arms around the whole of my subject. Then I will be posed, I hope, to make those decisions about: order of presentation, voice, and style.

The chapter outline which I posted almost three years ago was, as I now see it, a list of subjects, one subject per chapter, upon which the Resource-Patterns Model of Life sheds some light. But the subsequent drafting of those chapters has shown me that the amount I have to say under each of those subjects varies considerably. In a few cases I have come to see that what I have to say would be labeled better with a name different from the first name I chose. In some of the subjects I discover, once I start to write, that I have much more to say. But where does this bonus material fit in the outline? Perhaps, after completing drafts of those chapters in the present outline, I will draft another presentation of essentially the same material but following a different path into the subject.

Thursday, January 12, 2017

A New View of Macroeconomics

1. Introduction

What would macroeconomics look like in the Resource-Patterns Model of Life (RPM)? My book outline does not address this question. But RPM can model some aspects of macroeconomics as I will picture here with tabletop critters. In many ways RPM clarifies our view of human macroeconomies. The most noteworthy clarification may be the way in which prosperity results from learning new modes of cooperation to exploit large but previously unappreciated resources.

For brevity, I will assume the reader of this post has already digested one of the earlier descriptions of the tabletop critters, such as here or here. In this post I will emphasize principally the differences from that simpler model, adding what can make a model of macroeconomy. Our modeling technique will be the thought experiment, as we have used throughout with the tabletop critters, although in Section 4 we will mention several subjects which arise and promise great opportunities for computerized agent-based modeling.

The major difference here from the earlier models concerns the resources which critters can employ. Previously our critters had to have two resources, both water and sugar, and they had no use for any other resources. Now, while our critters still require a few essential resources they can make use of an array of other resources and they will live better if they can advance to use of those other resources. We will draw a model with ten distinct resources.

Figure 1 introduces the reader to the three symbols used in our figures. Notice that any resource is indicated with a triangle and a particular resource distinguished with a letter, ‘A’ to ‘J’, in the triangle.

Figure 1: Three entities in our model.

On the right hand side in Figure 1 we see a resource with a dashed circle around it. This dashed circle indicates a very important characteristic of a resource, being who “knows” about the resource. We modelers know about the resource since we put it there with expectation that the critters will eventually organize exploitation of it. But the critters have not yet “learned” about the resource enough to organize exploitation. We call this an “unexploited resource”.

2. Critters’ View in the Initial Condition

Figure 2 shows our first view of the initial condition. At this scale and in this timeframe we notice one feature: a population of critters living thinly scattered on the tabletop. Each critter must occasionally replenish its internal storage with each of a few essential resources. A critter dies if this requirement is not met.

Figure 2: A thin and impoverished population of critters.

We intend this initial condition to evoke images of our human past as hunter-gatherers, so we make it possible for a skeletal population of critters to survive by stipulating that fate sprinkles morsels of the essential resources randomly upon the tabletop. Those morsels dropped by fate are too small to be visible in this view. But we modelers should remember that at least some of the critters can survive in this region as long they keep on moving about and feeling for the small, randomly located morsels.

3. Modelers’ View of the Initial Condition

In Figure 3 we see the stage set for growth of an advanced macroeconomy. We have added large unexploited deposits of ten different resources. Those ten we will say constitute all the resources which the critters may require to build and maintain a populous macroeconomy.

Figure 3:The same population with opportunities they do not yet "know about".

Now perhaps these ten substantial resource deposits were present in the critters’ world in Figure 2. But the resources were not shown in Figure 2 because we wanted to show the initial primitive existence; the usual picture of a primitive existence does not recognize the potential productivity which may be seen in that environment by a visitor from an advanced civilization. The critters living in Figure 3 have not advanced any yet from their condition in Figure 2. But the point of the difference between Figures 2 and 3 is to establish in our modelers’ minds the opportunity which lies before our critters.

A few of these large unexploited resource deposits will be of the essential resources which critters require in order to survive; we may name these “life-essential” resources. The remainder of the ten resources may become of value to the critters after the critters have lifted themselves above the threat of starvation; we may name these “prosperity-essential” resources.

We have said that the ten new resources in Figure 3 are unexploited by critters. But to be more complete we may say that the ignorance of the critters concerning these unexploited resources need not be absolute. In this view of the initial condition perhaps some critters in the neighborhood of each of the unexploited life-essential resources have learned the location and return to that location when they need more of that life-essential resource. But no networks of trade in any resource have as yet developed on the tabletop. This is our starting assumption.

4. Modeling Needed to Illuminate the Growth of Cooperation and Productive Habits

Given our initial condition in Figure 3, it should be possible for the critters to grow a wealthy and thriving economy as pictured in Figure 4. I will say more about that Figure 4 end result in the following section. In this section I will mention some of the challenges which our starting point shown in Figure 3 presents to us agent-based modelers who aspire to demonstrate more specifically how the critters can advance to a prospering macroeconomy.

Figure 4: A thriving macroeconomy with resources discovered and exploited.

Obviously the critters could achieve macroeconomic success in short time if we modelers, while working in thought-experiment mode, endow each critter with human-like powers of perception and communication. But that much critter-empowerment cripples our best opportunity to learn from this model. If we work with only minimally-empowered individual critters then we must learn more about what can be accomplished through modes of cooperation among groups of critters. So we should note carefully, when we are working in thought-experiment mode, each new power that we assume the critters can employ (for more about the danger of anthropomorphism see this post). Ideally I would like to accomplish the whole development of the thriving macroeconomy in a computerized agent-based model, because such computerization requires the successful modeler to gain command of a large number of unforeseen and unforgiving facts (See the Introduction in Generative Social Science: Studies in Agent-Based Computational Modeling, 2006, by Joshua Epstein).

Another way in which we will need to limit the power of our individual critters will be in mobility. We do not want our critters to be so mobile within this landscape that an individual could satisfy its wants for all ten resources by traveling to each in succession. If critters were that mobile then they may have little to gain from trade. In the first critters thought-experiment I created this limitation in mobility by asserting simply that the distance between the two essential resources was farther than a critter could travel in its entire lifetime. But in a computerized agent-based model such a limitation would need more detailed specification.

Here is a list of topics that may arise as agent-based modelers strive to empower the critters in just such minimal ways as will enable them, through passage of ample time in the model, to advance from a condition suggested by Figure 3 to the prospering condition suggested by Figure 4.
  • language. What primitive signaling system might enable the simplest mode of trade?
  • money. Might one of the resources have properties that enable its use as a medium of exchange?
  • markets. Will particular locations become habituated meeting spots for traders?
  • investment in educating offspring. Will this give a comparative advantage to a subpopulation?
  • critter-on-critter raiding (cannibalism, theft).
  • moral restraint and law. In response to cannibalism and theft, will we see growth of protective alliances, that is in-groups and out-groups?
  • capital. My thought experiments with tabletop critters have not as yet produced analogs to capital in mainstream economics. Capital is certainly important, but I have only a few ideas on how to start modeling capital in an extension to the tabletop critters model. I welcome suggestions.
For an extended thought experiment on how critters can develop a network of trade between two essential resources, see the draft chapter on The Learning of Rules.

5. Result, a Thriving Macroeconomy

Figure 4 pictures the end result of our present thought experiment. We have a thriving macroeconomy with these features:
  1. The population of critters is much larger than in Figure 3.
  2. The members of this population enjoy a much higher standard of living than before, as evidenced by:
    • Almost no critter dies for want of essential resources. But malnutrition was the dominate cause of death earlier in Figure 2.
    • Individuals are able to get the essential resources they need for mere physical survival with only a small fraction of their available time-cycles.
    • Individuals can spend most of their time seeking fulfillments higher in Maslow’s hierarchy.
  3. The economy draws upon a wide range of resources (all ten in our picture) and not on only the few resources essential for bare survival.
In order to arrive at this favorable end we modelers employ our human powers of imagination and generalization to skip ahead over many difficulties (learning opportunities) such as those listed previously in Section 4. But I hope you might agree that it is reasonable for us to imagine such an end because we can assert that humans have made such an advance in our own history on Earth. We humans have advanced to our present prosperity in a large population from an earlier primitive and poor existence in a small population.

6. Constraints on this Thriving Macroeconomy

We have asserted that the critters’ advance into a thriving macroeconomy must be somehow possible. But we can also reason that the resulting macroeconomy must have certain limitations, which necessarily follow from the initial conditions that we assumed in setting up the Figure 3 starting point.

The prosperity we see in Figure 4 requires the resource deposits of Figure 3. Of course the critters also employed other gifts we modelers have given to them, such as their powers of calculation, perception, and action. But we note the necessity of resources to counter the tendency for some members of some populations to congratulate themselves too much. Some members may believe that their prosperity resulted entirely from their wise action. We modelers know however that both resources and focused behavior were necessary to reach the prosperous outcome.

The practices which enable the critters to achieve prosperity are dictated, for the most part, by the location of resources and the capabilities of the critters. These practices, or rules of social conduct, can not be made up by the critters themselves to satisfy some goal other than prosperity without, it seems, sacrificing some prosperity.

Every resource deposit is finite and may eventually be consumed. So our experiments within this tabletop critters model may in future show depletion of the large deposits (as depicted with triangles). But for the time being there seems to be enough challenge just to model discovery of the means of cooperation to enable critters’ first exploitation of these large deposits.

As a companion to that future aim of modeling resource depletion we may look ahead to modeling discovery of yet more resource deposits. That is, the step which we took from Figure 2 to Figure 3 may be repeated: Once again there may be larger and “deeper” resources awaiting discovery. These may come into “sight” of an adequately advanced set of critters. Our human history suggests such ongoing discovery of resources, the potential uses of which were unrecognized until our technology advanced to a certain level.

7. Concluding Notes

We have looked at a model of one macroeconomy. But our human world contains many macroeconomies, as many it may seem as there are nation states. This suggests a future direction for this research program. Interaction of many macroeconomies may raise the subjects of international trade, trade warfare, and real warfare.

In a related project I have proposed that most of our human wealth exists in the form of institutions, that is in persistent habits and expectations within the human population. That earlier proposal may seem to be challenged by the conclusion now asserted above, that prosperity requires resources. But the more complete view gives credit to both institutions and resources; institutions represent the habits and biases which enable people to exploit resources which are too difficult for individuals acting alone to exploit.

Our human history on Earth tells that we have made tremendous strides in achieving health and wealth for ourselves — while greatly increasing our numbers. Many theories attempt to explain this advance. But I believe there is little consensus favoring any one of these theories, so the door is open for new and improved theories. As such a theory, I offer the RPM and more particularly the model of tabletop critters sketched above. This model gives a framework for new sub-theories to explain particular aspects of human advance. Moreover the overall theory gives agent-based modelers a way to test such sub-theories.