Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler...

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Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University Pullman, WA 99164-4910 USA [email protected]

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Information (1) Intrinsic component of all physical systems? Flows, but cannot fill up an empty container –Claude Shannon developed techniques for measuring the information rate of a source and the capacity of a channel –Others argue that information itself has “zero dimension” and that therefore, like contrast, symmetry, correspondence, etc., cannot be “located” (Gregory Bateson)

Transcript of Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler...

Page 1: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Information Flows in Computational Approaches to the

Evolution of Cooperation

Timothy A. KohlerDepartment of AnthropologyWashington State University

Pullman, WA 99164-4910 [email protected]

Page 2: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Plan

• What is information?• How information functions in various

computational approaches proposed to understand the problem of human cooperation

– Simple, easy-to-understand systems– Structure of such problems is—or can be recast to become—why people

forego immediate personal gain to pursue prosocial, longer-term goals that might include achieving a sustainable but still high-payoff interaction with the environment

Page 3: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Information (1)

• Intrinsic component of all physical systems? • Flows, but cannot fill up an empty container

– Claude Shannon developed techniques for measuring the information rate of a source and the capacity of a channel

– Others argue that information itself has “zero dimension” and that therefore, like contrast, symmetry, correspondence, etc., cannot be “located” (Gregory Bateson)

Page 4: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Information (2)

• Just as mass is a reflection of a system containing matter, and heat is a reflection of a system containing energy, organization is the physical expression of a system containing information.

• Just as energy has as one of its fundamental attributes the capacity to perform work, information has as one of its fundamental attributes the capacity to organize things.

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Information (3)

• Information is the raw material which, when information-processed, may yield a message. Upon receipt of a message, the message must once more be information-processed by the recipient for the message to acquire meaning.

• There are many different kinds of information-processing systems (ISPs); for example, the thermostat is a mechanical ISP; computers are electronic ISPs; brains are neurological ISPs.

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"Lineages" or "flow structures" of information occur in three domains that form a nested hierarchy (Goonatilake

1991):• the genetic;• the neural-cultural (including individual

memory and cognitive process, which I consider useful to separate from culture and its transmission);

• and the "extrasomatic" (artifacts, especially information storage devices).

Page 7: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Cooperation & Sharing

• Two large classes of contemporary approaches to understanding altruism:– unselfish behaviors are really disguised selfish behaviors– truly unselfish behaviors have been able to evolve

through genetic or cultural group selection

• A behavior is altruistic when it increases the fitness of others but decreases the fitness of the actor.

Page 8: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Kin Selection• Genes are the fundamental units of reproduction,

and we are only vessels for our genes• We therefore ought to be molded by evolution to

assist our closest relatives preferentially• Philip Morin et al. (1994) have demonstrated that

such cooperative behaviors among chimp males as defense of territory and political support to achieve alpha male status seem explicable through principles of kin selection

• Unarguable, but incomplete

Page 9: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

(Direct, Pairwise) Reciprocal Altruism

• Classic IPD: role of information extremely constrained

• In “memory-1” strategies such as TFT, the only information one player has about the other is whether that player cooperated or defected in the past move.

Page 10: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Prisoner’s Dilemma

Column Player Cooperate Defect

Row Cooperate R=3, R=3 S=0, T=5Player Defect T=5, S=0 P=1, P=1

Notes:Payoffs to row player shown first in each cellR= R eward for mutual cooperationS= S ucker’s payoffT= T emptation to defectP= P unishment for mutual defection

Common values for these payoffs are shown, but others are possible ifthey satisfy:T > R > P > S, andR > (S + T)/2

Page 11: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Lindgren & Nordahl

• possibility of the initially neutral evolution of strategies with longer memory

• for example, a memory 2 strategy would remember both an opponent's and self's prior moves, allowing for the appearance of more complicated strategies

• longer-memory strategies have an advantage

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Page 13: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Characteristics of Pairwise Reciprocity

• In 2-person games cooperation may emerge through selection in evolutionary games if pairs of individuals interact for a sufficient number of times, often leading to something generically similar to a Tit-for-Tat strategy

• Populations composed of unconditional defectors can resist invasion by reciprocators unless there is some degree of assortative pairwise interaction

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Problems with Pairwise Reciprocity

• When groups are larger than 2 are formed at random, however, and the play is structured as an n-way prisoner's dilemma, reciprocating strategies become increasingly less likely

• Groups of 32 expecting to have about 1,000 interactions must have initial frequencies of some 70% reciprocators for cooperative strategies to increase (Boyd and Richerson 1988)

Page 15: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Indirect Reciprocity

• Pairs of players who encounter each other infrequently

• Changes information that players have about each other in an important fashion

• One well-known formalization due to Nowak and Sigmund (1998)

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Nowak and Sigmund (1)

• Random pairs of players are drawn from a population, one of whom is a potential donor and the other a potential recipient

• Donor can cooperate and help the recipient at a cost c to himself, in which case the recipient receives a benefit of value b (b>c)

• If the donor decides not to help, both individuals receive zero payoff

Page 17: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Nowak and Sigmund (2)

• Each player has an image score, s, which in some games is visible to all players

• If a player chosen as a donor chooses to help, her image score increases by 1; if she chooses not to help, her image score is decreased by one unit (the image score of a recipient does not change in either case)

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Nowak and Sigmund (3)

• Players have various strategies for “helping” and decide to help based on a threshold k: if the recipients image score (s) is greater than k they help, if not, they don’t

• Players with the highest scores (the payoffs from playing, not the image scores) at the end of a round produce offspring in proportion to their scores

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Nowak and Sigmund (4)

• When everyone's image can be seen by each player, and when there is no mutation, the population is quickly dominated by players with a k=0 strategy (they will cooperate with anyone who has an image score of 0 or better)– the most discriminating of all the cooperative strategies

• Under mutation and selection, strategies cycle endlessly

Page 20: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.
Page 21: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Indirect Reciprocity (concluded)• If players have information about the degree of other

players' propensities to cooperate, and make judgments about whether to cooperate based on that "reputation," cooperation is easier to sustain, even in large groups, so long as these "reputations" are widely known

• Some recent empirical work (Wedekind and Milinski 2000) suggests that people do not necessarily focus their altruistic acts on those who have been kind to others, calling into question the mechanism proposed here

• Cognitive burdens?

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Costly Signaling (1)• Some similarities to previous approach: signals that

are costly to the emitter constitute information used by a recipient in choosing an action, such as whether or not to cooperate with the signaler

• But here, signal is connected to some underlying but poorly observable quality of the signaler which nevertheless is of importance to the receiver

• Also, signaler is providing a public benefit through his or her signal—for example, turtle hunting among the Merriam (Smith and Bliege Bird 2000)

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Costly Signaling (2)

• Prosocial—or any other type of—signaling is a Nash equilibrium if—– low-quality types pay greater marginal costs for

signaling than do high-quality types– other group members benefit more from

interacting with high-quality than with low-quality types

– other group members benefit more from interacting with high-quality than with low-quality types (Smith, Bowles, and Gintis 2000)

Page 24: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Costly Signaling (3)

• If the reason that indicator traits of underlying qualities are often "prosocial" (enhancing the well-being of members of Ego's social group beyond his or her immediate kin) is that pro-social traits are valued in and of themselves (since they signal the signaler's value as a potential ally) then the line between CST and indirect reciprocity based on image scoring is extremely fine

Page 25: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

“Apparent” Altruism (Concluded)

• Except perhaps for indirect reciprocity through image scoring, any of the pathways to cooperation considered above employ mechanisms that would be as available to other animals as to humans

• Seminal contributions to reciprocal altruism and costly signaling were by biologists (Trivers, and Zahavi and Grafen, respectively)

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Strong Reciprocity and Multilevel Selection (1)

• Relies on a cultural information channel• A form of altruism that benefits group

members at a cost to the strong reciprocators, and involves a predisposition to follow a social norm to cooperate with others and punish non-cooperators– Defined to contrast with reciprocal altruism which is

considered "weak reciprocity" because it is so dependent on high probabilities for future interaction

– Result is truly unselfish behavior, not disguised selfish behavior

Page 27: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Necessary Requirements for Group Selection:

1. There must be more than one group (there must be a population of groups);

2. Groups must vary in their proportion of altruistic types;

3. There must be a direct relationship between the proportion of altruists in the groups and the groups’ fitnesses (groups with more altruists must produce more offspring);

4. Groups must be isolated for at least a portion of their life cycles; however, their progeny must be able to mix or compete in the formation of new groups.

Given these conditions, group selection can be effective if:

6. The differential fitness of groups (the force favoring the altruists) must be strong enough to overcome the differential fitness of individuals within groups (the force favoring the selfish types).

—Sober & Wilson 1998:26

Page 28: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Strong Reciprocity and Multilevel Selection (2)

• Imagine a group of foragers, largely unrelated, in which agents can either work alone, or work cooperatively in a group, which in general is more rewarding.

• Output is shared equally by all agents. • Agents may shirk, which reduces the output

to be shared– it is advantageous to the shirker– if there were no policing of free riders, even complete

shirking would promote a member's fitness.

Page 29: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Strong Reciprocity and Multilevel Selection (3)

• Group is small enough that members can be monitored, and if detected shirking, may be punished, at some cost to the punisher.

• Punishment consists of a shirker being ostracized from the group.

• Ostracized agents work alone for a period before being readmitted to a different group.

Page 30: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Strong Reciprocity and Multilevel Selection (4)

• Groups consist of two types of people: – reciprocators who work and always punish

shirkers when they see them, even though there is a cost to doing so;

– self-interested individuals who maximize their fitness and therefore never punish, and work only to the extent that the expected cost of doing so is less than the expected cost of being punished.

• Punishment consists of a shirker being ostracized from the group.

Page 31: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Strong Reciprocity and Multilevel Selection (5)

• Analysis of fitness consequences of reasonable parameter settings for the costs and benefits shows that a stationary equilibrium can be expected composed of– 70% of the population in groups (and therefore 30%

not in groups);– these groups composed disproportionately of

reciprocators. • Population as a whole should be composed of

65% reciprocators, but the groups should stabilize with 70% reciprocators.

Page 32: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Strong Reciprocity and Multilevel Selection (6)

• To the extent that the authors hazard a historical reconstruction of how such processes might have played out in the Pleistocene, they favor the idea that "the cognitive and affective traits required to fashion, learn, detect violations of, and wish to uphold social norms may be genetically transmitted, while the content of the norms (and in particular the linking of nonshirking and punishing) may be culturally transmitted" (Bowles and Gintis 2001:16).

Page 33: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Strong Reciprocity and Multilevel Selection (7)

• Useful to draw on culture—our capacity for which differentiates us from the rest of the animal kingdom—to explain our propensity to live in highly and flexibly cooperative but largely unrelated groups—a practice that likewise distinguishes us from other animals, at least in degree, and does much to explain the success of the species.

Page 34: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

How do these models map into the prehistory of human

societies?

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Page 36: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Extremely Slow Growth in H. erectus

• erectus populations in Asia by 2 mya• linguistic capabilities of these hominids will

probably never be known in detail. Evolutionary psychologist Robin Dunbar (1996) suggests that language is used in three different ways: – formulaic usages with little semantic content; – “gossip” for passing social information; – and symbolic language of the sort employed in

a presentation like this.

Page 37: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Language uses (continued)

• Of these, the second by far dominates our everyday uses, and greatly facilitates the integration of social groups.

• Among primates, grooming is a key mechanism for maintaining social relationships, and grooming time appears to increase linearly with group size among the Old World monkeys and apes.

• Neocortex size relative to total brain size likewise scales with mean group size.

Page 38: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Language uses (continued)• “If we interpolate the predicted group size for

humans based on our neocortex size into the relationship between group size and grooming time, we find that humans would have to spend something in the order of 40 per cent of the day engaged in grooming in order to maintain … cohesion” (Dunbar 1996:383). – The earliest steps towards the development of

language, therefore, may have been as substitute verbal grooming, or gossip, mitigating this time crisis

– Seems unlikely that language would have evolved beyond that point in this period

Page 39: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Pre-sapiens (concluded)

• Of those mechanisms for cooperation discussed here, which would have been available to these populations?

• Certainly any but perhaps "strong reciprocity," which may require greater symbolic abilities to understand and teach the social norms on which it depends

Page 40: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Acceleration of population growth with the appearance of anatomically

modern humans• 120 to 35 ka for this process (Klein 1992)• Leading components probably include

– longer juvenile dependence and lower juvenile mortality

– increased provisioning of females by males with the development of stronger male–female bonds and more exclusive mating patterns (Foley 1992); nuclear families?

Page 41: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Leading components in transition to anatomically modern humans (cont.)

• Longer period of juvenile dependency within the various families of the band provided a more variable, and more often one-to-one enculturation process that would lead to more rapid innovation than the strongly conservative many-to-one enculturation inferred for earlier times.

Page 42: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Archaeology of the Upper Paleolithic

• Aggregation sites of the later Upper Paleolithic suggest the existence of social units that are too large for the participants to be related genetically, or perhaps even to be known to each other through regular face-to-face contacts. – Such aggregations, even if only periodic, would have

been promoted by (and may not be possible without) the existence of cultural norms dictating appropriate behavior (Chase 1994).

Page 43: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Archaeology of the Upper Paleolithic (cont.)

• Many of the Upper Paleolithic symbolic activities seem to be ritual in nature, and ritual enforces, and reinforces, such norms.

• Language may have existed prior to such symbolic activity—Dunbar’s model predicts that “social language” (gossip) should emerge well before this time—but these symbolic activities must have been carried on through fully symbolic language.

Page 44: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Archaeology of the Upper Paleolithic (concluded)

• Shared, symbolic norms may have very tangible consequences for individual and group survival.

• Richerson and Boyd (1992:69–71) explain the origin of culture as due to its success (when coupled with individual learning and genetic inheritance) in providing a selective advantage in environments that are neither too constant nor too variable from generation to generation.

Page 45: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Upper Paleolithic Societies

• Success of these populations in part due to the greater cooperative possibilities for large groups made available by strong reciprocity.

• If so, then all the mechanisms for cooperation considered here would have been available to human populations by 35 ka at the latest.

Page 46: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

Neolithic Societies

• What then accounts for the next feature of interest in population graph, the large increase in rate of population growth associated with Neolithic societies?

• Problems of organizing larger settlements and societies remind us that Goonatilake's third lineage of information—"extrasomatic" artifacts, especially information storage devices—has not been exploited in the accounts of cooperation presented to this point.

Page 47: Information Flows in Computational Approaches to the Evolution of Cooperation Timothy A. Kohler Department of Anthropology Washington State University.

External Information Storage

• Artifacts such as tokens that allowed storage of economic information

• Institutions that provided a framework for the organization of people in ways that cross-cut kin networks

• eventually including stratified societies that radically changed the egalitarian assumption of all the models presented above

• All were of selective value in societies that had grown beyond the size that could be maintained solely by the mechanisms discussed earlier.