The Learning of Rules

This is a draft of Chapter 5, in the book outline

 

The Learning of Rules

We have established that living things (LTs) can live much better in many circumstances if they cooperate with each other to exploit large or difficult resource patterns (RPs). We have seen that this cooperation is accomplished through rules which govern the behavior of LTs. Those rules, in the early example with critters, were given to the critters by us human modelers. But that is a big and arbitrary assumption, that we can give rules to critters.

What if LTs do not have us modelers, or some other superior and overlooking intelligence, to give them rules? The higher ambition for us modelers is to craft LTs which can discover their own rules. Ultimately we would like to show how LTs of our creation can survive by finding whatever RPs happen to exist in any universe they inhabit. We want them to be survivors, able carry on without our oversight. If we modelers can achieve that, then we should have gained valuable insight into our own psychological existence.

This turns out to be a large and complex subject. In this chapter we will review some general assertions and we will look briefly in a few promising directions. But we will not develop complete answers to any of these questions; the area is still too big and unexplored.

1. Complications of learning rules

When we start to explore how the LTs in our models will learn the needed rules, many complications arise. Our quest becomes clouded, or sometimes divides into distinct programs, with each new complication that we encounter. In this first section of the chapter we will review eighteen of these complications.

1.1 Care and conscious attention must be exercised when giving new abilities to LTs

We human modelers need to be mindful of what powers we are giving, or assuming to exist, in the minds of our LTs. This is because we can gain insight when we acknowledge that critters from a given cast of development simply can not see or know things which are obvious to us humans. We can also gain insight when we discover that the gift of a simple mechanical rule can empower a population of critters to accomplish coordination which we would have thought requires significant intelligence. Because of this I appeal to the reader to notice carefully when assumptions are made about what a LT in a given thought experiment can sense, know, or think.

My preaching grows from my own experience. My own modeling within RPM has given me examples of failures to account for powers which I have assumed my LTs possess. So we all need to be on guard with these thought experiments. If we pay attention and leave a list showing each additional ability, we create the condition in which we may gain the most rewarding insights into our human mental and social experience.

Here we may recall an advantage of computerized agent based modeling, as contrasted with the thought-experiment agent based modeling which we commonly employ in this book. A problem arises because natural language words, in which we first consider and then later specify our experiments, commonly express fuzzy meanings. But when we strive to write computer programs to make computerized agents perform as we had imagined they would perform, our unconscious assumptions are exposed as I discussed previously. If you have not done any computer programming yourself, this will probably be attested by any computer programmer you know.

1.2 Rules generally not obvious

The rules are not always obvious. Consider for example the rules which humans have discovered for making iron. I do not know those rules myself so I feel sure I would never have thought of those rules if I suddenly found myself in a stone age setting. We can not assume that the rules needed will simply show themselves to the perceptions of some LT. We must be prepared to search for what is not obvious.

But this idea, that we will search for what is not obvious, might suggest that we do not know what we are searching for. In fact this is typically the case when the goods enjoyed in a later era were discovered through serendipity during the earlier era. No one would have searched for the way to make iron if that one did not know the great uses of iron. And no one could have known the uses of iron if someone had not already made iron.

In some cases, surely, we human overseers can conceive of the pattern of behavior which the LTs in our model will need to discover. In fact this is how we start one of our first thought experiments: with water and sugar laid out so we humans can see it and we know that our critters cannot see it. Then the value of work with RPM reveals itself as we challenge ourselves to give our critters just enough abilities so they can, through discovery of appropriate rules, exploit the RP we can see. But as we seek to model more complex situations there may be some resource patterns, such as make it possible to make iron, which even our human-level conceptions can not perceive, unless we call in a metallurgist.

Other rules, which humans have somehow discovered but which are not at all obvious to me, are: the rules for rotating crops, and the rules that give trading value to money.

Figure 1. The tabletop immediately after a large RP has been added to the initial condition, but before any rules have been learned.

1.3 Rules may appear in sets correlated with RPs

In our first example of rule-based cooperation, there were three rules which acted together to help critters exploit a single RP. In case you do not recall, those rules were:

• 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 resources that pass through your possession.

Our purpose here is to notice the bundling of those three rules together. Any one or only two of those three rules would not have been sufficient to assure the prosperity which the three together created. So perhaps we should expect new rules which we may discover to be similarly bundled in sets. Exploitation of a new RP may require adherence to most or all of the rules in a set.

1.4 Rule-sets may overlap

Next we meet another complication, being that these rule-sets may overlap. A particular rule may be included in more than one rule-set, may be applicable to exploitation of more than one RP. This redundancy may lead us into tension between redundancy and efficiency, as we try to design the minds (or the computer programs) of our critters.

Here I also mention the possibility that some rules, drawn from different rule-sets, may contradict each other. But we already have enough complications, so we will defer consideration of this issue.

1.5 Rules may vary with time

Another factor complicates the learning of rules: Rules may change with time. Rules to exploit a given RP can work, obviously, only so long as that RP lasts. In our tabletop thought experiments, a rule to carry water to the right may be beneficial to the community for 100 generations of critters. But when the large deposit of water runs out almost all of our critters will die unless we have given them abilities which have enabled them to discover a different RP, requiring different rules.

1.6 Plans: Rules in packages with time spans

In the simple model of tabletop critters, with which we started, decisions about how to behave were restricted to a single cycle. The whole rule-delimited decision-making process started again in the next cycle (or clock tick) of the model. But in our human lives we know that many of our actions derive from plans which exist throughout a span of time. A single meta-rule, such as to finish a task before sunset, commonly affects many moment-to-moment choices of how to act. So we see that RPM will need to be extended at some stage in advancing research to start exercising an ability to learn not only rules affecting immediate choices, but also rules which act as plans governing sequences of immediate-choice rules.

1.7 The rules take on meaning only within a structure

We may tend to forget that rules, to be effective, need to be expressed within a system that can interpret the rules. That is, if we focus single-mindedly on discovering the rules, we may lose sight of our assumption that there exists an entity which can read, interpret, and act upon, the rules we have discovered. Daniel Dennett writes of a similar oversight possible in genetics, in which the elaborate encoding of instructions within DNA cannot have any use without the other mechanisms in a cell which read and act upon those instructions. (“... the language of DNA and the ‘readers’ of the language have to evolve together; neither can work on its own. ... only the combination of information from the code and the code-reading environment suffices to create an organism.” Darwin’s Dangerous Idea: Evolution and the Meanings of Life (1995) by Daniel Dennett, a Touchstone book, p. 196–7)

1.8 Learning rules enables advance in level of life

In Chapter 4, Life in Levels we reviewed the evidence that life on Earth has grown in levels. We also met the suggestion that we humans, as we go about organizing our affairs with other humans, may well be in the process of creating the next higher level of life. The advance from one level to the next higher level may consist mostly of learning and adopting new rules.

1.9 Rules relate to the type of organization

At this point we should recall that organizations of LTs come in many types. In Chapter 4, we were introduced to an eight-type taxonomy of organizations in which we may categorize an organization by asking three questions about it. The questions address three qualities, asking whether the organization does or does not possess each quality.

1. member aware. Are the members (constituent lower-level LTs) aware of the organization?
2. self aware. Does the organization have a “headquarters” which can make some decisions on behalf of the organization?
3. encoded. Has the way to create such an organization been written down, or encoded, such that new copies can be created by following the encoding?

We should also be aware that the adoption by an organization of a new rule may help that organization to obtain one of the three qualities which we have used to categorize organizations. The adoption of a new rule might change the organization in a way that would cause us to change our yes-or-no answer to one of the three classifying questions. The change, if we think of it as an advance, would probably be from no to yes.

1.10 The learning of a new rule depends upon the kind of LT we have

A new rule might succeed only if the organization in which it is attempted possesses one or more of the three qualities we just discussed. A new rule suggesting, for example, that all members of an organization should be given a new bit of data (data which has become known in some part of the organization) may require that the organization is self-aware, that the organization has a list of its members and a way to communicate with them. Thus, learning of a new rule may require awareness of the qualities of the organization in which the rule may be adopted.

Reviewing these last two points, we have seen that the learning of a new rule by an organization may interact in two ways with the type of that organization:

• The learning once accomplished may change the type of the organization.
• The learning may be possible or meaningful only for particular type of organizations.

 

1.11 Learning of rules does not pause for us when a new organization is accomplished

Probably the process of growing level-to-level does not pause when a given level is reached. Our modeling stops the process, like a still photograph, so that we can study the process and give names to things we see. But we may need to remember that the process may flow continuously.

1.12 Any LT must already possess a sub-structure of rules

In RPM, any LT which comes to our attention is probably already following a set of rules. LTs require resources to survive. If they simply do nothing for an extended period they will die for lack of a necessary resource. So a surviving LT must at least have a rule to imbibe a resource when it can. A surviving LT probably also has a rule, or a complex set of rules, to seek opportunities to imbibe.

With the model of tabletop critters we start, as you will recall, in the initial condition with a set of critters already surviving, although poorly. As we have just described, these critters must be following rules, rules which we modelers gave the critters just so that we could start somewhere. We may need to recall this assumption at some stage of our future research.

1.13 A new rule may be optional

Generally the first overall rule for any LT will be to save its life if it can. Since we start most of our thought experiments with a population of LTs which we assume to be surviving, if not yet prospering, each member of that population is probably already gifted with basic rules which enable its survival in dire circumstances. So the new rules which we will give to LTs in our thought experiments will not usually override what the LT already knows instinctively to do in a dire circumstance, but will be applied in better times. A new rule, applied tentatively during better times, may be part of a valuable discovery, as we will consider more in sections 5 and 6. But a new rule will rarely override basic survival instincts.

1.14 A rule may be known by all or only a few

In our first example of rule-based cooperation among critters all the critters in the scene were given the same set of rules. But some rules could be successfully implemented with only a small number of the members of the organization given awareness of the rule. Indeed this follows from specialization in roles, and may suggest further ways to clarify and divide our quest.

1.15 A rule may be known by none of the LTs

We human modelers, having developed a believable story that the critters established a path of trade between water and sugar, may look down upon the critters and say that they have learned the rules. But this “learning” that we perceive is not at this stage perceived by any of the critters.

What is known to each critter in the line consists only of that critter’s memory of successful acquisitions of resources, that is memories of trades or finds in the neighborhood where it lives. No critter has the overview which we modelers enjoy. So no critter has capability to see that it lives in prosperous circumstances, prosperous that is relative to the distant cousins still living as hunter-gatherers nearby. No critter can imagine the RP which we modelers laid down to grow this model of critter-prosperity.

1.16 Rules relate to perspectives

The challenge which focuses this chapter, how will rules be learned, overlaps to some extent with the question addressed in a previous post: How can a LT develop a needed perspective? There is clearly a strong relationship. But for the present we will push forward with discovery of rules without getting entangled in the relationship suggested by development of perspective.

1.17 Rules may be rough at first, but improved later on

The issues which we will face may be divided between two subjects:

• learning rules for the first time, that is learning rules of any quality which are at least minimally workable, and
• improving rules to make them operate more efficiently.

To illustrate using the model of the tabletop critters, Figure 1 shows the initial condition immediately after addition of a RP. Figure 2 shows a crooked line of trade which may result after the first learning of rules. For a further explanation of this crooked line, see section 2.2.1.

Figure 2. A crooked line of trade which may result shortly after the first learning of new rules.

Figure 3 shows what we might expect after rules have been improved. In the remainder of this chapter we will focus mainly on the first learning of rules. Improvement of rules will receive more attention in the later chapter on Public Psychology.

1.18 Rules may or may not be inherited

We humans seem to inherit some rules as instincts or tastes. These rules, we suppose from the theory of evolution, served our ancestors and we inherited them. But other rules, such as the meaning of a word in a vernacular language, clearly must be learned anew by each generation. We mention this complication but, for now, worry no more about it.

Figure 3. An almost straight line of trade such as we expect after rules have been first learned and then later improved.

 

2. One way that rules may be discovered: Trade

In this section we will develop an example of one way that rules may be learned, through mutually beneficial trade.

We will use the model of tabletop critters, giving them both ability to trade and a large resource pattern; this is essentially the challenge which we described in Section 2.4.1. Our aim is to show how critters could get from the condition shown Figure 1 to the better condition shown in Figure 2. To some readers it may seem obvious, as it does to this author, that critters with trading ability would, given time, form a line of trade between water and sugar. But other readers may want more analysis. So, in case it does not seem obvious to you, in sections 2.1 and 2.2, I will give a longer explanation.

2.1 Review of initial condition

We start out in a situation much like the model’s initial condition. I will give a review of the initial condition in this section, but a more complete description may be fond in this post.

A sparse population of critters lives like hunter-gatherers on the tabletop. The critters need both water and sugar to live since their bodies consume some of each of these two essential resources in each increment of time. A critter will die if it runs out of either essential resource.

We give the critter a sense of location so that it always knows where it is on the tabletop, that is its X-Y coordinates. A critter also has a sense of touch in the area nearby around its body. Within this sense-area a critter can sense and identify another object which might be water, sugar, or another critter. But a critter has no sense at all of anything outside its sense area. It has no sense of sight.

In each increment of time (each cycle of the model) a critter decides how to act based upon:

• its internal state, that is which resource it needs most badly;
• external circumstance, meaning where it is on the tabletop and what if any other objects are it its sense area;
• relevant memories.

Most of the time a critter will decide to move since only infrequently will it find itself within reach of a consumable resource. If a critter decides to move the direction it chooses will be affected by two factors:

• It will move toward where it calculates it has a good chance of finding the resource it needs most. To do this, it will remember when and where on the tabletop it has previously found some of that resource. It will favor moving in a direction toward such a location, and it will favor recent memories over older memories. Perhaps, if it has many memories it will choose a direction toward an average location derived from those memories.
• Its direction will be somewhat random. If it has helpful memories and therefore can calculate exactly the location, where it would like to arrive after some requisite number of steps, it will still add some random variation around the direction it chooses for each step as it moves toward that target location. If, on the other hand, it has no helpful memories to guide its choice of direction, it will choose a random direction.

Life is possible in this initial condition on the tabletop because water is deposited by fate onto the tabletop in small quantities and at random locations. Similarly sugar is deposited by fate in other random locations. The deposits of a resource are small, in that the critter which first discovers one can probably consume the entire deposit in one cycle, leaving none either for another critter to find of for itself to enjoy on a later return trip to this location. But the deposits are usually large enough to supply the dietary requirement of a critter for that resource for many cycles. In this way a critter which has just discovered and devoured a deposit of one resource can now go off in search of the other essential resource. With luck it will find some of that other resource before it expires.

In the initial condition, given the circumstances we have sketched, it turns out that a critter cannot make much use of its ability to remember where it previously found a resource, because the deposits fall in random locations and are usually consumed entirely upon first discovery. A critter’s best strategy is probably to just keep moving about randomly and hope for good luck. But the critter’s ability to remember details about its previous finds will become crucial, next.

2.2 Extend the model with a Resource Pattern and new critter abilities

Now we add a large resource pattern as described previously. Recall that the water and sugar in this large RP are further apart than a critter can expect to travel in its entire lifetime, so no critter could flourish alone by journeying between water and sugar. The large resource pattern can be seen in Figure 1.

We also give new abilities to the critters. In addition to the abilities inherited from the initial condition each critter will be able to:

• calculate its days-on-hand for each of the two essential resources. For each resource, this involves dividing the amount the critter has in its internal store by the amount which the critter’s body consumes during each cycle (day). For example, if a critter has eleven units of water in its internal storage and if its metabolism uses one unit of water during each cycle, then it will calculate that it has eleven days-on-hand of water.
• compare the days-on-hand for each of the two essential resources and thus calculate terms under which it would trade. It would seek to trade a quantity of its more-plentiful resource for a quantity of its more-needed resource. Perhaps it will accept any terms which would extend its expected days of survival based upon both resources.
• display willingness to trade by displaying a symbol, readable by any other nearby critter, signaling which resource is offered and by implication which is sought.
• recognize another critter with opposite (compatible) trading aim
• negotiate, agree if possible, and complete a beneficial trade.

2.2.1 The critters can and eventually should establish a trading path
Now, with the large resource pattern in place and with critters given ability to trade, we will continue our thought experiment by predicting what we expect to happen in the neighborhood of the large deposit of water on the left.

Consider Figure 4. Around the large water drop we have sketched three regions. We will assume that a given critter might travel about as far as the distance between two regions. Critters can and will wander across these region boundaries which we imagine, but we will assume that no critter which starts in Region A could wander as far as Region C.

Figure 4. Regions around large deposit of water. The critters become water-rich, first in Region A, then B, then C.

For a critter who starts out in Region A (nearest the large deposit of water) we expect this critter may soon stumble into that deposit. It will remember the location and will return there in the future when water is once again its primary want. But this critter must still find sugar in order to survive. So, after the passage of some time, we expect that most critters in Region A will spend most of their time looking for sugar by foraging outward from the water drop.

The principal source of sugar in Region A continues, for now, to be what it was in the initial condition – the random small sprinklings of sugar dropped by fate onto the tabletop. But there is now an additional way that a critter might get sugar: through trade with another critter which it might meet. If that other critter has happened recently to discover an abundance of sugar but not water, it will probably agree to swap with our water-rich critter. That other critter will remember where it met our water-rich critter and will be inclined to return to this spot if in the future it once again finds enough sugar to be in a position to want to trade sugar for water.

Now consider the critters foraging in Region B. At the outset we expect that about half of them will be in greater need for water than for sugar; that is just a continuation of the way it always was in the initial condition. But after the addition of the large RP, we expect some Region B critters who have more days-on-hand of sugar than water will have met, when they wandered in the direction of Region A, a water-rich critter willing to trade. These two will complete a trade and remember the location. In this way after the passage of still more time we expect that Region B, like Region A before it, will come to be populated by critters who are relatively rich with water and who are seeking sugar.

With a similar story repeated for Regions B and C, we expect the passage of yet more time will generate in Region C a population more likely to be seeking sugar than water.

None of these critters which needs sugar more than water knows, we know, about the huge deposit of sugar far away to the right (in our picture), so the sugar-seeking foraging of critters outward from the water will occur in all directions around the water.

Now we assume that a similar thing will have been happening around the sugar, but with the resources reversed: In all directions outward from the large deposit of sugar will live a population of critters more likely to be seeking water than sugar.

Eventually, in the area between the water and sugar, a critter which happens to be bestowed with water which came originally from the large water deposit will meet a critter which happens to be bestowed with sugar from the large sugar deposit. A trade will occur. We will call this the first trade which is enabled by the large RP. The two participants in the first trade will remember the location and each will be disposed to return to that spot when once again it needs the resource it received there.

Of course those two original partners to the first trade may chance to meet again at that spot. But note that our critters are not loyal to particular trading partners; any other critter which randomly goes within reach of that first-trade spot when either one of the first-trade partners has returned may also benefit from a trade at that spot. For all critters in that region, a trip to that spot has a better chance of giving a successful trade than a trip to some other randomly chosen spot in the region. The first-trade spot seems likely to become a location remembered favorably by several critters in addition to the first two trading partners.

As we have described the circumstances leading up to the first trade, we might expect the location to be on the shortest line between the water and sugar, because a point on that shortest line would be more probable than other points in that area. But it is unlikely that the first meeting would occur exactly on that shortest line. We expect rather only that the trade will be somewhere in a more broadly defined area, as shown in Figure 5.

Figure 5.

We expect a regular trade route, engaging many more critters than the two partners in the first trade, may be established through the first-trade location, as shown in Figure 2.

2.3 Reflections upon this model

 

2.3.1 What we see, what critters cannot see
At the end of the above thought experiment, pictured in Figure 2, we human modelers can say that the critters have learned rules which enable their cooperation in trade. We can say this because of the evidence of prosperity that we can see:

• We see critters clustered in a line of trade. This is a configuration of critters on the tabletop which could not have occurred in the earlier initial condition, we know, because such a density must have something feeding it and the initial condition provided no such concentrated nourishment.
• At the ends of that line of trade we can see the supplies of water and sugar which we know support the line.
• Our modelers’ record keeping lets us see that critters in the line live longer lives than their unorganized kin who still live as hunter-gatherers in remote regions.
• Our oversight also informs us that critters in the line almost never die of starvation for an essential resource. But the fate of starvation is the most common death experienced by unorganized critters in remote regions, as we can see in our model’s data record.

We humans are able to see these things because we made this model, after all, so that we can cultivate and observe behavior in populations composed of individuals deliberately simpler than we are. We humans have advances senses, language, and powers of cognition.

But, while we humans can see prosperity gained among our critters, no critter can see any of the evidence of prosperity listed above. To understand why, recall that at this stage of the model development we have given the critters no more than a list of specific abilities, as outlined in sections 2.1 and 2.2 above.

If this is not already clear, consider any critter in the established line of trade. This critter cannot see or know about:

• it is in a line of critters, a line that extends between large deposits of water and sugar.
• it has a better life than unorganized critters, better that is than its ancestors had before this line of trade was discovered and better than its kin-critters still living in the outlying region
• it does not know, when it obtains a resource in trade from another critter, where or how the other critter got the resource. Such speculation is far beyond the calculating capacities we have given critters at this stage of development.

We conclude that perception of prosperity among LTs is not necessarily possible for all LTs. In this example the perception is possible for us humans but not for our critters.

2.3.2 The critters’ “learning of the rules” is distributed among the many critters in the line
What does it mean that LTs so simple-minded as our critters have “learned the rules”? To find the answer we use our modeler’s-eye view to look into the memories of critters. We find useful memories. A fortunate critter in the line has many memories of acquisitions which were recent and nearby. Whereas the less fortunate unorganized critter has fewer memories and those more remote in both time and distance.

So the information which gives rise to our perception that the critters have “learned rules” exists only in memories distributed among critters. We human modelers tend to think of the critters’ learning of rules as one accomplishment. Indeed – in our view – it is one accomplishment. But in the critters the body of information is divided among all the critters in the line, each critter possessing only a list of favorable memories of trades.

2.3.3 The organizational classification of this line of trade
We can now classify the line of trade based upon the discussion just completed. In section 1.9 above, as you may recall, we reviewed a way to classify an organization into one of eight types. The classification hinges upon three yes-or-no questions. Asking those questions about the line of trade, we see it is:

1. not member-aware as argued above. No critter knows about the line of trade.
2. not self-aware. Obviously the line of trade has no central office.
3. not encoded. There is no encoding, telling how to create a line of trade, which was created by or is accessible to the critters.

So the three answers are all “no”. But in the sections and chapters ahead we extend the abilities of the critters to discover and exploit resource patterns. There we will observe that these extensions lead us to answer “yes” sometimes, to assign the organizations so formed in different categories.

2.3.4 Rule-discovery through trade stands upon a sub-structure of rules
We should pause to notice an assumption we have made to reach this point. We gave critters ability to negotiate and complete a mutually beneficial trade. But this was a simplification. A trade would not happen all at once but rather would take place over a number of steps, as a sequence of choices and acts each of which was made based upon what the trading partner had done immediately before.

This complication would surely be discovered by a human modeler who was trying to create the effects we discuss here in the form of a computer program. A critter’s computations, needed to complete a self-serving trade, may well require steps and decisions of which the modeler had been unaware until the modeler realized his program could not achieve the desired result without specification of underlying and necessary behavior.

Even though the title of this chapter promises to explore how rules will be learned, here once again when we examine our assumptions we see that we are giving rules to the critters, rules which act at a lower level in the organizational structure. At this stage we will not pretend to know how critters might have learned these lower-level rules, if we had not made a gift of those rules. Rather we will grope ahead, working with concepts which I admit are vague for the time being.

3. Ethical Interactions

3.1 Ability to cheat trading partner

Now we will call into question one of the many rules which we have assumed necessary to complete a mutually beneficial trade. This is the rule to give what has been promised to the trading partner.

In the division of trade into a sequence of steps through time, it will usually occur that one partner gives a resource first and is thus for the moment exposed to risk until the other gives also in exchange what has been previously agreed. Thus the partner who gives second has an opportunity to cheat, to take what has been offered while not reciprocating.

Notice that if a critter did cheat in trade then the cheated partner would not gain a favorable memory of successful trade at that spot. The cheated partner would have no reason to return to that location in the future when it needed the resource which it had failed to obtain. So, without mutually beneficial trade the line of exchange would never form. If a line had formed and consistent cheating started within that line then the line would dissolve before long.

The only lines which continue, it would seem, are those with uniform or almost uniform fidelity. We may guess at this stage that some small degree of cheating would not destroy the line of exchange, but this raises complex questions beyond our present scope. Here we will conclude only that in order for a successful community of trade to form then the critters, when given ability to cheat in trade, learn a rule not to cheat in most opportunities.

3.2 Ability to overpower and eat others

Next we will momentarily consider an extension of the critters’ abilities. This extension, which will bring the model one step closer to our human experience, gives the critters power to prey upon one another: When circumstances allow one critter (or organization of critters) may kill and eat another critter and thereby gain water and sugar from the body of the victim. The body of a critter is, we see now, a resource pattern which may be discovered and exploited by other critters.

With critters in our model thus enhanced, suppose we start again with a situation such as pictured in Figure 1. It seems unlikely that critters will form a line of trade as shown in Figure 2, because any predictable density of critters would represent a resource pattern inviting other critters to return to that locale looking for critters to eat. Also, when a critter is eaten, its memories of favorable trades, the memories which gave rise to the line of trade between water and sugar, disappear from the tabletop along with the unfortunate critter.

So here again, as when we gave critters ability to cheat, we will give our critters ability to learn an ethic. This ethic, when learned, will deter critters from killing and eating each other in most normal circumstances. Once again then it will be possible for critters to discover a prosperous line of exchange, assuming we give them enough time during which they will test both using and not using this new ethical rule.

4. Population and Resource Accounting

Our discussion above shows that the presence of a large new RP is not sufficient for prosperity. Prosperity can follow only if the critters learn important rules. But still that large RP is necessary for the prosperity as can be shown by a type of accounting.

We can see this accounting by reviewing the example of the initial condition on the tabletop. As we told that story, fate dropped a morsel of water here, a morsel of sugar there, in spots selected at random. The rate of this dropping of resources limited the size of the population of critters. The body of each critter consumes a certain amount of water and sugar in each cycle. So, on average, the amount consumed by the entire population of critters per cycle must be less than or equal to the amount dropped by fate per cycle. The size of the population is limited, as this accounting shows, by physical facts.

Next, as our story of critters has developed, we added a large new RP with the water and sugar. For our present purpose, which is to learn how new rules to exploit a new RP may be learned, we can safely assume the large new RP is unlimited. We assume the large new RP will outlast our present experiments in rule-learning. Eventually of course the large RP may be consumed. That prospect, and the challenge it represents, lies outside the scope of this chapter, but it received some consideration in an earlier post.

5. Other Rule-Learning Paradigms

We have seen in the preceding discussion that rules may be discovered spontaneously by a population of mutually-respectful traders. Now we will turn now to consideration of other ways that rules may be discovered.

The other ways which we will consider here all require at least a little wealth on the part of our LTs. As we mentioned in section 1.13, a new rule may be optional. When a LT is near starvation for want of an essential resource — when it is most certainly not wealthy — then its behavior will probably follow primitive rules applicable in an emergency. In order to survive it must bet its life upon the most immediately promising pattern of behavior. But as we have seen previously there may be opportunities on the tabletop when large gains can be had if new rules can be discovered. As such, when LTs have at least a modest store of all essential resources, it will sometimes be the best strategy for the LTs to act in ways which increase their mid-to-long-term prospects of discovering and exploiting new resource patterns, that is of discovering new rules.

5.1 Rules may seem evident, or self-evident, to all

The first category we will consider will be a circumstance in which all the critters can sense the resource pattern. It will still be the case that a single critter cannot exploit the RP alone, because the water and sugar are too far apart, so prosperity can ensue only if these critters discover rules of cooperation.

For a specific example, assume that we start with trade-enabled critters such as we used in section 2.2. We then give each of the critters a sense of sight such that any critter can see the mountain (relative to the size of a critter) of water in one direction and the mountain of sugar in another direction.

Finally we give the critters these additional rules.

1. In fortunate circumstances, in which you are not in immediate danger of dying for want of a resource, pause to look around.
2. If you see large deposits of both water and sugar, note their directions.
3. Add a bias to the direction you will choose for your future wanderings, such that you will move eventually into some position between the water and sugar.

Once a few critters have so moved themselves between water and sugar it should not be long before mutually beneficial trades lead to a dense population between water and sugar, with members of this dense population all benefiting from both the water and sugar in large RP, as we see in Figure 3.

Let us pause to consider the question of whether the success, which we see at the end of this thought experiment, can be called the result of a conscious plan. We human modelers are conscious of a plan in at least one human head. I made up those additional rules, after all, to give the critters what it would take to bring about the critter-success I seek.

But none of the critters is conscious of the plan because consciousness is not among the capabilities which we have given them. The critters “minds” are simple computer programs. The critters follow the given rules without awareness, as a walking human takes each step without being aware of the rules which must be guiding his nervous system as it sends instructions to the many muscles involved. So we conclude that yes, this success resulted from a conscious plan in a human mind, but not in any of the critters’ calculating capacities.

5.2 Rules may be proposed

Next we will consider the possibility that rules may be proposed by one or more critters. Some few among the critters may have special gifts which enable them to propose rules to the larger critter community. For example the gift of sight which was enjoyed by all critters in the previous example might be given to only one of the critters. This one critter now can see the RP, the mountains of water and sugar.

While this seems like a step toward solution of the problem, it should be obvious that the perception in the sight of the one gifted critter must be translated into messages which will be communicated to and understood by many other critters on the tabletop. The critters need a language sufficient for this communication. We must give substantially more powers to the critters if they are to act this way to exploit the RP.

This need for language, while a most interesting and important subject, would require a large leap in the calculating capacity of the critters. It raises many questions outside the scope of this chapter. But it should be worthwhile to highlight a few points here:

• Our present scenario, with one critter gifted in a way that may be valuable to others, introduces specialization in our model. Specialization on a grander scale is of course what we humans do as we divide ourselves into professions and working roles.
• In order for specialization to work, trust may be needed. Critters without the gift of sight, if they are to follow the suggestions of the gifted one, must trust that the gifted one is telling the truth. But trust must be balanced with healthy skepticism because once again we see the possibility that one set of critters may exploit another set. A too-trusting critter, expecting to move into a position of prosperity may instead move to where it is killed and eaten.
• In an advanced application of RPM, LTs may pay each other for goods and services. Critters who want good advice about where to go might pay sighted critters for the information. Or sighted critters, if they should happen to be wealthy as well, may act as entrepreneurs, paying blind critters to move into positions which will prove profitable, and then reaping the profit.
• In other advanced applications of RPM, critters that specialize in carrying messages may help a larger community. These message carriers and the problems they encounter in RPM may help us humans gain fresh insights into the character of our own media.

 

5.3 Avocational choices

Humans who are wealthy have some free time. Wealthy people undertake many various avocations, we know, and some of these avocations might result in discovery of new rules. Notice that many of us acquire knowledge not for any immediate gain, but for the mere pleasure of it, we might explain. Then recall that plots of fiction sometimes show a human population surviving in the end because a single member had cultivated a unique expertise, and used that expertise to save the lot. Surely this sometimes happens in reality.

We suppose therefore that a population which contains many members with special knowledge, albeit farfetched, has a better chance of surviving unpredictable future crises. Thus we should not be surprised if our evolved human dispositions lead individual humans into various and odd-seeming avocations. We can see the variety of avocations as experimentation undertaking by the whole population. And, if our modeling with RPM ever gets so advanced that we have the option of imbuing individual LTs with distinct tastes for avocational pursuits, we might in this way unleash great powers for new rule discovery.

5.4 Variability within families

The propensity which we have just considered, for LTs to spend some of their wealth experimenting with avocations, may have a parallel expressed as variability within family groupings.

I will introduce this idea with an analogy to how humans invest their financial savings. We recall that:

• Among investment opportunities, there is typically a tradeoff between risk and return. Conservative investments which are quite certain to preserve their value also promise little gain. Risky investments, on the other hand, which may lose most of their value, also give hope of great gain.
• Among investors there is variability in the amount of risk they can tolerate in their portfolios. An investor who is only a slim margin above poverty, who has only one week’s pay to invest, should probably invest it conservatively, with low risk but high security for the principal. But an investor who has become much more wealthy, an investor who has already saved about ten years’ pay, may be wise to invest some in projects with both higher risk and higher expected return.

Now we consider a parallel in human families. Each child, I suggest, may grow up imbued with a characteristic motivation, a motivation to be either conservative or risk-seeking, or somewhere in between. Conservatism in this context will consist of adopting ways known to promise survival, that is to adopt the basic values expressed by the lives of the parents. A risk-seeking child, on the other hand, may feel less reverence for the values expressed by the parents’ lives, and may be motivated to break far away from the pattern of life demonstrated by the parents. The risk-seeking child will more likely fail but, if he finds success, my find high-returns.

Consider a single couple and the children which that couple may bear. The first child may be the only child and, as with the first savings, it makes sense for this child to be a conservative investment within this scheme I am suggesting. This first child will probably be imbued with the sort of conservative motivations just mentioned. But after our couple has a few children, or more, growing healthily, then the continuation of the family line seems secure. So then it makes sense for that family unit to invest in more risk-seeking children. That is, younger children in large families are more likely to aim their lives away from family tradition. Or at least this is what I believe I see in my own informal survey of the human families I have known: The oldest tend to be motivated conservatively, in the sense described, and the youngest tend to be risk seeking.

This applies to RPM in that we may vary the motives implanted in offspring. If and when we are modeling societies that can imbue their offspring with varying motivations, on this conservative to risk-seeking axis, we may do well during prosperous times to test more risk-seeking in offspring.

6. Charity may lead to rule discovery

Now we will consider charitable giving among LTs. We may expect giving to increase as the wealth of the givers increases. Charitable activity may contribute to the discovery of new rules through a process which shares features with the rule discovery we saw in section 2.

Consider this example. Suppose that a critter which lives near the mountain of water, and so has great wealth in ability to acquire water, has also been unusually fortunate recently in finding sugar. This critter, feeling very secure, enjoys some freedom of choice about how to behave during the immediate future. The critter may choose to spend some of its time carrying water (the resource which it can acquire in super-abundance) outward, away from the mountain of water, into the surrounding desert. This makes sense since sometimes, out there in the surrounding desert, one meets another critter near death for want of water. A charity-motivated critter feels reward upon giving water to another critter so needy.

This motive, or rule as we may call it, to give away a portion of the resource in greatest abundance, during times of prosperity, may be coded into the critter’s computer program by us human modelers who are trying to create a model of human charitable giving. Or if we are working in thought-experiment mode we might assert that this motive had been favored by natural selection and thus inherited by the critter in its genes.

In either case, suppose our critter aspires to help as many near-death-for-want-of-water critters as possible. Suppose that it has to decide which direction to travel as it heads away from the mountain of water in search of water-needing critters. I would expect it to find a higher density of such water-needing critters in the direction of the large deposit of sugar, because critters in that direction have a lower probability of dying for want of sugar. We suppose our charity-motivated critter remembers, as did the trade-motivated critter of section 2, where in the past it has met water-needing critters. So the charity-motivated critter will probably head away from the mountain of water in the direction of the mountain of sugar. But of course it cannot see the mountain of sugar or reason, as can we humans, about the cause of the greater density of water-wanting critters in that direction.

After passage of more time on the tabletop, our water-charity-motivated critter, when heading toward the mountain of sugar may meet another critter not motivated so much by want of water as by ambition to give away some sugar. That is, charity-motivated outreachers may meet somewhere in the middle between the two essential resources. Although our water-charity motivated critter was not seeking sugar at this time, it will remember the spot as a favorable location to visit in the future when sugar is again in short supply. These critters, first motivated only by charity, may thereafter fall into mutually rewarding trade. This story of charity-motivated discovery seems to have much in common with the trade-motivated story we developed in section 2.

7. Conclusion

We will conclude this chapter on learning of rules with a reflection upon the ways that LTs can acquire rules. Consider three levels of rule acquisition.

1. In the first level we modelers use our overview to see what rules will enable critters to find success, and we code those rules into the critters program. For example, in the initial condition we coded our critters to keep moving so they would have a better chance of stumbling onto the randomly dropped resources. For another example, we first demonstrated the power of rule-enabled cooperation by giving the critters the rules they needed to exploit a large RP: carry water right, sugar left, etc.
2. In the second level, which has been the emphasis of this chapter, we modelers again use our overview to see what rules the critters need. But in this case rather than code the rules directly into the critters’ programs or instincts, we give critters memory and calculating capacities which we, again using our overview, believe will enable the critters to discover the rules for themselves. This exercise places us humans in a laboratory in which we can learn important lessons about ourselves and other LTs.
3. In the third level we see our ultimate challenge. In this level we modelers do not perceive the next RP that our critters may discover and exploit. We strive to empower the critters to discover their own RPs, because either we modelers lack perceptual powers to see the RP or we cannot be with the critters, wherever they may be, to offer our guidance. In this we see that RPM seems to frame the epistemological problems of our human existence.

The meaning of 'proof': Answer to the quiz posted on January 25.

On January 25, I asked: What is the definition of ‘proof’? as this word is commonly used in mathematics.

My understanding of the meaning of ‘proof’

A friend of mine says that he knows a proof when he sees one. But ‘proof’, as this word is commonly used in mathematics, has no concrete definition. ‘ Proof ’ is an appeal to authority, an appeal to the judgment and instincts of other mathematicians.

Wikipedia has a page on mathematical proof which I believe supports my definition.

Why is this Relevant to the Resource Patterns Model of Life?

I will soon draft the chapter on Philosophy of the Critters, as this is the next chapter I will take up in the book outline. That chapter will suggest a meaning for ‘truth’ because such a concept may be needed in the thinking of critters.

To give a preview, recall the calculating capacity of critters in the initial condition. In this original form critters certainly have no concept of truth. In fact they have no concepts at all, in the sense of consciousness of a sensation or proposition.

But as we work with RPM we will give more powers to our critters, trying to learn for ourselves what capacities will enable the critters to handle ever more human-like problem situations. At one stage we will give critters ability to signal one another, a signal offering a mutually beneficial trade. But when we also give critters ability to overpower and eat one another, which is a natural-enough development, then a signal offering trade might be bait. As such, the critters’ minds will need a way to judge the trustworthiness of a proposal. When such a judgment of trustworthiness becomes so certain as to always be accepted, then a critter needs a concept like ‘truth’.

And a situation like that is probably where humans, in our history of development, first needed a level of confidence called ‘truth’. If I am correct, our idea of truth does not originate in the satisfying simplicities of logic. Logic would prove useful, in fact, only later in higher levels of development. ‘Truth’ originated in the demand to judge a sensation, idea, or proposition. 'Truth' originated in decision-making for survival, I argue.

We started out above on the subject of ‘proof’, not ‘truth’ into which I have digressed. But I hope the reader of this post will see kinship between the two meanings and thereby see how the preceding discussion of truth relates: Mathematicians need a word to signify their unquestioning confidence in a proposition. ‘Proof’ satisfies this need.

My Quiz for Math Professors

Student: I think I understand what you have told us, Professor. We must know our definitions. We must memorize the definitions, because proofs hinge upon definitions.

Professor: Yes, that is right.

Student: What is the definition of ‘proof’?


As with the previous quiz which I posted here, you may find my answer in the next post.

Philosophical Anarchism of Paul Feyerabend

During the past few weeks I have enjoyed reading Against Method by Paul Feyerabend (revised edition 1988). As suggested by the title Feyerabend argues for philosophical anarchism, for an anything-goes approach to scientific method. Feyerabend responds to the method of falsification advanced by Karl Popper by showing that no theory can stand under that ideal: Every revered theory of science stands in spite of some contradictory evidence! Practitioners who need to get on with their work mostly just ignore contradicting evidence, and Feyerabend shows this turning away from uncomfortable evidence is okay and even necessary for all sorts of advance. I find this very interesting and mostly convincing.

I differ however, if I have understood Feyerabend correctly, in a view suggested by the Resource-Patterns Model of Life (RPM, which is the focus of this blog). RPM starts out with an assumption — that resource patterns (RPs) exist concretely in our universe, and even though this is only an assumption I believe almost all people will feel confident building large structures upon this assumption. The challenge of living things (LTs) in RPM is to discover and learn how to exploit RPs. So for us humans (being LTs) our important discoveries about RPs are not found in a universe of anarchy. Rather the locations of RPs, and the physically possible ways to exploit RPs, are orderly. What we may discover pertaining to RPs is also orderly, an order extant in the physical facts of the extra-human universe. Feyerabend’s anything-goes method suggests to me a failure to recognize this order underlying what we humans learn about our surroundings.

But my difference just mentioned may boil down to almost nothing when I allow that we humans do not know what we will discover until after we have discovered it. If we could gain a God’s-eye view before we start a search then it would not be a search; we would know beforehand the necessary direction of search. So my objection has merit in that God’s-eye view. But for us here below who lack that view Feyerabend seems to have a good point.

Let me add that RPM provides an excellent platform for continuing development of the philosophy of mind to which Feyerabend has contributed. We start with tabletop critters which have minds (or more specifically computer programs) which we have specified to be just barely sufficient for their survival at a low level. We add a huge RP which the critters can never hope to exploit unless they can discover new modes of cooperation. Then we start giving the critters incremental senses and/or calculating routines, running the model to see which increments in ability enable a population of critters to discover and exploit the RP. In this quest we will face concrete examples of development of language, leadership, and lying. We will come face-to-face with what looks to us like a thought which exists not in a single critter but in a connected network of critters — a thought possible only in that higher level of life.

I hope my promises just expressed will show more clearly as I post drafts of the remaining chapters for my book underway.

Report of Conference on Social Science Simulation

On the recent weekend of October 29 – November 1, I attended this year’s conference of The Computational Social Science Society of the Americas, at Santa Fe, New Mexico. This Society emphasizes computerized agent-based modeling. So their work bears some relation to my project in this blog. My project, to give a clue to newcomers, is exposition of a new model of our human experience, the Resource-Patterns Model of Life (RPM). Below I will comment upon two of the papers presented at Santa Fe. The second of those papers led me to read a few papers by Robert Axtell. I will end my comments here with what I found in Axtell’s papers. Overall I found affirmation in the conference. Affirmation, that is, of the approach to modeling I use in this RPM project.

Mirta Galesic presented her paper with Daniel Barkoczi, “Social learning strategies, network structure and the exploration-exploitation tradeoff”. The “social learning” subject of this paper catches my attention because social learning could describe the main question of interest that arises in RPM. Evidently this paper is only a small part of a body of literature on social learning now available. This literature has a context which bears some relation to RPM, so it will need to be studied as research is undertaken on social learning within RPM.

Digression: Before proceeding with the remainder of this post let me tell that I struggle to understand what I am doing on this blog. In a sense my drive is clear: I am promoting RPM which gives a better understanding of some important facts of life. But I have paused over questions such as: What is a model? Does conjecture have a place in science? I want my methods of both science and communication to withstand scrutiny. So I am looking for guidance, looking for what I may learn from the experience of others — especially those who have labored to communicate new agent-based views of science.

With that said let me say that in RPM the agents and their world remain, at this stage, mostly a thought experiment. I have started to computerize the agents as I reported earlier, but that effort did not promise enough rewards at this stage to justify the time it would require. So I am presenting a thought-experiment agent-based model of economic and psychological life.

Matthew Koehler presented a paper “Exploring Organizational Learning and Structuring”. But for me the most interesting part of Matt’s presentation was a long digression with which he started. In this digression he talked about the degree of specificity of agent-based models, praising what he had learned from Robert Axtell’s papers (see below). Matt showed a table (unfortunately not in the available paper) in which one axis represented the degree of specificity. Thought experiments were shown at the low-specificity end of that axis. I was heartened to see this because it seems to endorse the usefulness, within a broad view of science, of thought experiments at the early stage of development of scientific theories.

Matt also offered a suggestion for how to make a paper about a computerized agent-based model more meaningful to a reader. This was to lay out natural-language sentences, the sentences describing the agents and their world, in the first column of a table. Then in the second column give a reference to the computer code, that is to the line numbers of computer code in an appendix with the paper. This linking of natural language to computer language would empower a careful reader to understand exactly what is meant by the unavoidably fuzzy meanings of natural-language sentences. It may be impossible for some readers to gain a comfortable feeling of comprehension without such specifics. That may be helpful for me with exposition RPM. In some cases I have told more detail than I had believed necessary at first. But still I do not know how much detail to spill.

Robert Axtell, cited by Matt Koehler, has written in two papers (see references below) about a helpful way to categorize agent-based models. He suggests models be evaluated based upon their correlation at two levels with the empirical world. The two levels are the micro level of the agents and the macro level of system-wide developments. In each of these levels correlation with the empirical world is ranked, ranked to be either qualitative or quantitative, with quantitative judged to be better. This categorization ranks a model with a number from 0 to 3, with 3 being awarded to models which correlate with the empirical world at both micro and macro levels.

To equate Axtell’s terms “micro” and “macro” with what we use in RPM, recall that RPM allows for consideration of life in four or more levels. Almost all of our attention in RPM will focus upon the psychological implications of how living things on one level may advance to the next higher level. So Axtell’s term "micro" might refer to any level n in RPM, and then "macro" refer to level n+1. Also, micro might refer to the critters and macro to the organizations of critters which form in response to resource patterns.

RPM would be ranked at the lowest level in Axtell’s categorization scheme, because it is not close to empirical quantification at either micro or macro level. But that is alright, because RPM is still in the early fuzzy-language stage of paradigm development.

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References

R. Axtell (2005). “Three Distinct Kinds of Empirically-Relevant Agent-Based Models”. Brookings Institute, 30 September 2005.

R. L. Axtell, and J. M. Epstein (1994). "Agent-Based Models: Understanding Our Creations". Bulletin of the Santa Fe Institute, Winter 1994, pp 28–32.

Daniel Barkoczi, and Mirta Galesic (2015). “Social learning strategies, network structure and
the exploration-exploitation tradeoff”. CSSSA 2015 link.

Matthew Koehler, Luciano Oviedo, and Michael Taylor (2015). “Exploring Organizational Learning and Structuring”. CSSSA 2015 link.

Steve Schapiro, Scenes of Art

Last weekend I attended an agent-based conference in Santa Fe, New Mexico. I expect to share a few notes about that conference in my next post. But let’s start with some serendipity. On my trip home I met Steve Schapiro, a photographer from Chicago. Chance tossed us together on two legs of the journey: the shuttle to Albuquerque then the flight to Chicago.

Steve showed me this. When you are eating a meal either alone or in company — stop — sometime during the course of the meal. Look at the scene before you on the table. The arrangement of food and implements will be a scene of art. It will be perfect. You cannot improve it. If you try to improve it by moving things around you will only break it. You will make it worse.

First Steve told me about this, then later he proved it. After I had mostly finished my airline snacks and cup of coffee, he stopped me to look at the tray-table in front of me. It was indeed a satisfying scene of art.

What a delight!

Of course I can’t leave this alone. I need to explain this phenomenon. Here goes my attempt.

The scene invites you to participate. A person looking into that scene may know exactly where he or she would start. When I look at that scene I feel an impulse in my arm to start an action.

One reason for this feeling is, I suppose, that almost everything in the scene was made for use with human hands. The table, utensils, napkin, and servings of food were all made for human touch.

Let us compare the scene of a partially completed meal with three alternative scenes:

meal just set
If we look at the table with everything set in place but the meal not yet commenced, we human observers might know how we would start but we are not sure we have permission. Is this place setting mine or someone else’s? Has the time to dig in been signaled? It would be improper if we started without these permissions. The scene does not quite invite my participation.

craftsman’s workbench
We might look at a midday scene on a craftsman’s workbench and, as in the scene of a meal in progress, see objects which were made for use with human hands. But if I am not familiar with that particular craft I do not have any sense of what I would do next. The scene does not invite my participation.

extra-human landscape
A natural landscape with no evidence of human existence may appear beautiful to my human eye but this sense of beauty differs from the art I sense in a partially completed meal. I do not know how or if I would start to do anything in this scene. It does not invite my participation.

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Why, you might ask, am I writing about scenes of art in this blog which has the purpose of promulgating the Resource-Patterns Model of Life? Psychology. The main interest which I find in RPM is its implication that we living things are probably biased to search for resource patterns. Such searches might be expressed as instincts or impulses. And this could possibly include our sense of art.

I could grope forward here to propose more specifically-worded ways that a sense of art may help LTs find RPs. But I prefer to encourage the reader to think in that way.


Addition: March 23, 2016
David Sloan Wilson seems to deal with a similar question. See for example Chapter 16 in Evolution for Everyone: How Darwin's Theory Can Change the Way We Think About Our Lives, (2007). Wilson looks into this question from a different model, a different perspective, although there are strong parallels.

Perspectives in the Resource-Patterns Model of Life: A Search for Externalities and Who Can See Them

People commonly “talk past each other”. Each person represents a different perspective. But we often fail to see this difference in perspective as we struggle to be civil.

The Resource-Patterns Model of Life (RPM) calls our attention to the need for specific perspectives and shows limitations under which living things must somehow develop perspective. By focusing our attention on the development of perspective, I would hope that RPM would make us more aware how our perspectives serve our specific interests.

My discussion here of perspective was stimulated as a side effect of another question. I suppose it may be helpful to compare RPM with older, well established models in physical and social science. In this vein I have started to compare RPM with models familiar in mainstream economics. I have looked in RPM for the some of the objects with which we are familiar in economics, objects such as “commodity”, “price” and “market”. Recently I came across “externality”, another object familiar in mainstream economics, and realized I had not identified externalities in RPM. Uncomfortable with my ignorance I explored the question. Most of what I found, it turns out, is about the development of perspective, so that became the main subject in this post.

Let me offer a definition of “externality” for those readers who have not learned how economists use this term. An externality is a side effect of economic activity, an effect upon parties not part of the economic exchange, parties that is who are external to the activity. For example the smoke which comes out the stack of an electric power plant is an externality if we assume that neither the producers nor the buyers of the electricity are motivated to care about the smoke. Normally we think of externalities as being negative. But externalities can also be beneficial, if for example your neighbors have a loud band playing in their back yard for a party – and you like the music. Wikipedia offers a longer definition of externality.

The first two perspectives

As we search for externalities in RPM we will use the model of tabletop critters. The critters, as you may understand if you’ve been following development of this blog, start out as dirt-poor hunter-gatherers. Then the critters gain prosperity as they gain new ways of cooperating among themselves, cooperating to exploit resource patterns in their environment.

Figure 1. A community of critters prospering without knowledge of either their relative prosperity or the causes of this prosperity.

Figure 1 shows such a prospering organization of critters, organized in a line of exchange between the model’s two essential resources, water and sugar. For review, the water and sugar constitute a single resource pattern (RP).

Externalities have not yet been mentioned in the development of this model, but when we ask if there are externalities which result from the cooperation among the critters in that line, we easily imagine that externalities could result from such enterprise. An externality could be added to the model if we wanted to experiment with its effects. The externality could be added in either of the two classes of models we might employ: (1) thought experiments or (2) computerized agent-based models. The externality might be smoke or trashing of the environment surrounding the line of exchange. It would impact other critters not in the line of exchange, or other living things also added to the model. Let us add such an externality. I have not attempted to show the externality in the Figures in this post, but please assume it is there.

Let us notice who is noticing this externality. We human modelers who have created this thought experiment can see the impact of the enterprise in the line of trade upon other life, i.e. the externality. Or at least we humans who have learned the meaning of “externality” can see the externality.

But the critters in the line of cooperation cannot see the externality. We can assert that the critters cannot see the externality because we created those critters as we created this model. We gave them a list of capabilities and those capabilities do not include capacity to sense the welfare of another critter, at least not in this early and not-much-extended application of the model.

My confidence that these critters can not sense or think some things grows from my computerized modeling. I have written computer programs which are the “brains” of such agents, thereby I can know what the critters can “sense” or “think” or “remember”. For more about the psychological capabilities of the critters see the draft chapter on psychology.

The need for a new perspective

The critters in that circumstance described above benefited from a RP because of a good fortune which lay outside of their control: the RP was there in their environment and we modelers gave them the rules of cooperation which would empower them to exploit that RP even if they could not perceive the RP.

But the critters would enjoy a still brighter future if they could:

1. perceive the RP which feeds them,
2. start to act in ways which constitute a search for the locations of other RPs like the one that feeds them.

Those two abilities suggest what I will define as “perspective” for our purpose here: Perspective is the ability of a living thing (LT) (or an organization of living things, see life in levels) to adapt its actions in response to some feature in its environment. The actions of the LT to which I refer may be either internal (thoughts or decisions) or external (physical movement).

Let us suppose that the critters of Figure 1 live in only a small part of a larger universe. Near the center of Figure 2 we see that same prospering community of critters from Figure 1, but now the scale is reduced to about one-third of the former size to show the larger surroundings. Now we can see that the universe of the critters contains many pairs of water and sugar, many RPs that is. In fact we see a larger pattern, a pattern of RPs. But, while our critters have happily colonized the RP from which they derive their sustenance, we humans can see that the critters could do much better if they could perceive what it is that makes their success possible (the RP and the rules we gave them) and start a search for other similar RPs.

Figure 2. The same thriving but unperceptive community of critters from Figure 1, but seen in the larger universe of their surroundings, a universe with many opportunities for extension of critter-life.

With a new perspective the critters may go on to occupy their corner of the universe. Figure 3 shows what this little world might look like with all the RPs being exploited, a consequence of the critters succeeding in developing the perspective we have suggested.

Remember that our goal as human modelers is to gain insights from RPM, insights about ourselves and our society. So we will proceed by extending the model, giving the critters new sensual and computational powers, trying to understand which additions are necessary to empower the critters in the model to take this next step toward mimicking our human experience.

Recall also that life exists in levels. In RPM a living thing can appear singular, as a single critter or single human. But an organization of living things can also be conceived as a single living thing, a LT on a higher level. Conversely, rather than look up in the order to larger living things, we can look down in the order to smaller living things: Any single living thing, such as a critter or human, can probably be dissected and discovered to be an organization of smaller living things, LTs on a lower level. The development of perspective, the ability of an organization to recognize and act upon an RP, is a key component of level-to-level advance.

Figure 3. The critters, having learned a perspective of their larger environment, have populated their larger environment.

How a new perspective might be developed

While I cannot predict how our critters (aided by much human tweaking) might eventually gain ability to develop the new, needed perspective, here I will briefly describe two broad avenues of development.

Avenue 1: Diversity in population along with specialization

Some of the LTs in an organization may have or develop special abilities. For example we might extend our model of tabletop critters such that some fraction of the critters are born with a rudimentary sense of sight, so they can detect a concentration of particular colors of light coming from certain directions, and we might also give distinct colors to the sugar and water.  So these critters gifted with rudimentary sight can directly sense the resource pattern which enables the prosperity of their community of critters. This new ability within the set of critters may constitute one step in development of the needed perspective. Other needed steps may include rudimentary signaling, or language, and induction or the ability to propose the existence of additional RPs.

Avenue 2: Systematic organization, spontaneous order, moral codes

This second possible avenue for development of perspective grows from spontaneous order, or the relatively new science of chaos theory and its kin. An entire population of constituent agents, when viewed as a single combined agent, may often exhibit behaviors which we humans could not have predicted from our knowledge of only the abilities of the constituent agents. A set of LTs within RPM may act in a way that enables it to discover and exploit neighboring as-yet-unused RPs, although we observing humans may not be able to explain how this happened.

In this avenue of development we may consider an analogy with our own human nervous systems. The psychology of a human being derives somehow from the interactions of millions of nerve cells. We could not predict the behavior of a human even if we could perfectly understand the behavior of the human’s constituent nerve cells, or so it is commonly asserted and I probably agree.

Reflections on development of perspective

Here are a few reflections on the development of perspective in RPM.

• We human modelers face a challenge to give the critters enough powers (senses, physical actions, and calculating or “thinking” power) so that they can develop the needed perspective. This challenge is large and difficult. It is probably more than I can accomplish. It may require the careers of a score of modelers.
• I find it difficult to write about this topic because I want to write clearly and concretely. But I am groping into a dark, unknown region. The words at my disposal serve poorly to convey either what I am finding or what my findings may mean. But then I suppose that this is the experience of any new science, of anyone who hopes to describe new concepts, which have not yet spawned their own terminology, with our existing set of words.
• In spite of the difficulty which clearly lies ahead for RPM, I claim that the structure provided by RPM is a big step forward. RPM narrows the problem of development of perspective, providing a resource-constrained framework within which to work.
• As noted above, we humans struggle to invent new language so we can discuss what we see in RPM. But we also notice that the critters in our model could use some ability to communicate. They could, we may imagine, discover the RP which empowers their present level of success if they could give signals to each other. A vocabulary with only a few meaningful symbols may add considerably to their ability to organize. I hope to learn more with subsequent research.
• Recall that this discussion about the development of perspective was stimulated by an observation about economic externalities: Educated humans can perceive an externality in RPM, but the low-level critters at the start of our thought experiment could not perceive the externality. Now, after we have discussed development of a higher-level perspective for our critters, in which perspective the critters can perceive the RP that sustains them, we may wonder if this higher-level critter can perceive the externality. No. We still have not given them anything like general overview of affairs in which an externality may be perceived.
• Wealth spawns philosophy and exploration, or at least that is an assumption I make about societies. The golden age of Greece would provide a first example among humans. Second, present American civilization has wealth which spawns both space exploration and my development of RPM. A third example is provided by the critters in Figure 1. They can obtain all the resources they need to survive with only a small fraction of their time-effort. As such they have resources which they may apply to the challenge of learning about their universe, and that exploration may yield discoveries which increase the probability of the long term survival of their descendants.
• One human speaker sometimes challenges another to explain what the other means by the word “we”. We humans often shift our base as we speak, sometimes speaking only for ourselves, sometimes speaking as representative of an already-extant group, sometimes hoping that the group suggested by the “we” will come together at some future time. I am trying to hone my skills in recognizing when I shift base while speaking, or when I hear another speaker shift base. I believe I have a long way to go in gaining this skill and I sense that I am not alone. But RPM may help us with this education, help us to see that “we” represents a specific interest. Critters which specialize, using abilities often helpful for the larger community, will probably signal with an implied “we”. Each perspective may have its own “we”.

Correlation between perspective and interest

RPM reveals a strong correlation between perspective and interest. A given perspective, if it either aids or promises to aid the expansion of a population, will probably be valued by agents in the population. Those agents have an interest in the perspective as I will argue here.

But let us start with our initial population. Recall the condition in Figure 1. This population of critters enjoyed a comfortable standard of living but at that stage no critter could perceive the RP which fed them, so no effort to find neighboring, similar RPs could have started. There were none among them whose wellbeing relied upon the perspective which developed later in our story. So obviously, at this preliminary stage, there were not any critters whom we might characterize as having an interest in the perspective not yet developed.

But after that perspective of the resource pattern was developed and used (as illustrated in Figure 3), then a much larger population was made possible by use of that perspective. It follows that the lives of most of that larger population depend upon that new perspective. Without the perspective their lives would not exist. We humans looking into the model from our perspective can reasonably conclude that those critters have an interest in the perspective. The perspective, having been developed and used to advantage, correlates with an interest among the critters.

A nuance may catch our attention. Consider the specialization paradigm which we described above (the first of the two avenues for development of a new perspective). Under specialization some critters may come to have or to control large amounts of saved resources, and those powerful critters might be expected to risk some of their savings in research which might yield a profitable new perspective. Research may be funded that is. So we may expect funding to give rise to another specialty, being researchers, critters willing and able to perform research for compensation. Thus researchers naturally have an interest in the search for new perspectives which provides their livelihood. So, in exploring this nuance we discover the effect of a new and not-yet-described RP. The funding of research, overseen by some powerful critters, becomes an RP for other critters, and this creates a distinctive inner niche where life – if following appropriate rules – may thrive.

Conclusion

Stimulated by “externality”, a concept familiar in economics, and by the question of whether externalities can be spotted in RPM, we have been sidetracked into a discussion of perspective. It has become clear, I hope, that an externality in RPM can be spotted by a human modeler educated in economics. But there is no way the primitive critters of the tabletop model can hope to “see” or “know about” an externality. We modelers know what calculating powers we have given to the critters, so we can know with considerable confidence what the critters cannot know.

We have lingered over the challenge presented by development of perspective. RPM lays this problem open to us, on a workbench as it were. In the example presented, we see how a population of critters could grow if the critters can develop a broader perspective. We see programs of research through which we may seek a deeper understanding of our own individual and group psychology, and these programs are given greater, realistic focus by RPM.

We humans often seem to blame other humans for being wrong on some point; we wish we could shout some truth into our opposites. But what we do not always see is that our opposites have a different perspective, a different interest. They are seeing a different RP or opportunity for organization.