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Towards Realistic Models for Evolution of Cooperation. LIK MUI. … about procedure …. Briefly go over the paper Clarify major points Describe simulations (not in paper). RoadMap. Introduction Cooperation Models Simulations Conclusion. . Evolution of Cooperation. Animals cooperate

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Presentation Transcript
about procedure
… about procedure …
  • Briefly go over the paper
    • Clarify major points
  • Describe simulations (not in paper)
roadmap
RoadMap
  • Introduction
  • Cooperation Models
  • Simulations
  • Conclusion

evolution of cooperation
Evolution of Cooperation
  • Animals cooperate
  • Two questions:
    • How does cooperation as a strategy becomes stable evolutionarily?
    • How does cooperation arise in the first place?
darwinian natural selection
Darwinian Natural Selection

“Survival of the fittest”

  • If evolution is all about individual survival, how can cooperation be explained?
  • Fittest what?
fittest what
Fittest what ?
  • Individual
    • Rational agency theory (Kreps, 1990)
  • Group
    • Group selection theory (Wilson, 1980)
  • Gene
    • Selfish gene hypothesis (Dawkins, 1979)
  • Organization
    • Classic organizational theory (Simon, 1969)
roadmap1
RoadMap
  • Introduction
  • Cooperation Models
      • Group Selection
      • Kinship Theory
      • Direct Reciprocity
      • Indirect Reciprocity
      • Social Learning
  • Simulations
  • Conclusion

group selection
Group Selection
  • Intuition: we ban cannibalism but not carnivorousness
  • Population/species: basic unit of natural selection
  • Problem: explain war, family feud, competition, etc.
kinship theory i
Kinship Theory I
  • Intuition: nepotism
  • Hamilton’s Rule:
    • Individuals show less aggression and more cooperation towards closer kin if rule is satisfied
    • Basis for most work on kinship theory
  • Wright’s Coefficient of Related: r
    • Self: r=1
    • Siblings: r=0.5
    • Grandparent-grandchild: r=0.25
kinship theory ii
Kinship Theory II
  • Cannot explain:
    • Competition in viscuous population
    • Symbioses
    • Dynamics of cooperation
direct reciprocity
Direct Reciprocity
  • Intuition: being nice to others who are nice
  • “Reciprocal Altruism”
    • Trivers (1971)
  • Tit-for-tat and PD tournament
    • Axelrod and Hamilton (1981)
  • Cannot explain:
    • We cooperate not only with people who cooperate with us
indirect reciprocity
Indirect Reciprocity
  • Intuition: respect one who is famous
  • Social-biological justifications
    • Biology: generalized altruism (Trivers, 1971, 1985)
    • Sociobiology: Alexandar (1986)
    • Sociology: Ostrom (1998)
  • 3 types of indirect reciprocity:
    • Looped
    • Observer-based
    • Image-based
indirect reciprocity looped
Indirect Reciprocity: Looped
  • Looped Indirect Reciprocity
    • Boyd and Richerson (1989)
indirect reciprocity observers
Indirect Reciprocity: Observers
  • Observer-based Reciprocity
    • Pollock and Dugatkin (1992)
indirect reciprocity image
Indirect Reciprocity: Image
  • Image (reputation) based Reciprocity
    • Nowak and Sigmund (1998, 2000)
social learning
Social Learning
  • Intuition: imitate those who are successful
  • Cultural transmission
    • Boyd and Richerson (1982)
  • Docility
    • Simon (1990, 1991)
critiques of existing models
Critiques of Existing Models
  • Many theories each explaining one or a few aspects of cooperation
  • Unrealism of model assumptions
unrealism for existing models
Unrealism for Existing Models
  • asexual, non-overlapping generations
  • simultaneous play for every interaction
    • c.f., Abell and Reyniers, 2000
  • dyadic interactions
  • mostly predetermined behavior
    • c.f., May, 1987 (lack of modeling stochasticity)
  • discrete actions (cooperate or defect)
  • social structure and cooperation
    • c.f., Simon, 1991; Cohen, et al., 2001
  • extend social learning
    • c.f., Simon, 1990
roadmap2
RoadMap
  • Introduction
  • Cooperation Models
  • Simulations
      • Nowak and Sigmund Game
      • Prisoner’s Dilemma Game
      • Simon’s Docility Hypothesis
  • Conclusion

nowak and sigmund game
Nowak and Sigmund Game
  • Payoff Matrix

C = 0.1

B = 1.0

  • Image Adjustment

A = 1

evolutionary pd game
Evolutionary PD Game
  • Repeated Prisoners’ Dilemma Game
  • Agent Actions:

Action = { cooperate, defect }

  • Payoff Matrix:
pd game agent strategies
PD Game Agent Strategies
  • All defecting (AllD)
  • Tit-for-tat (TFT)
  • Reputational Tit-for-tat (RTFT): using various notions of reputation
simple groups social structures
Simple Groups: social structures
  • Group structure affects members
    • Interactions, observations, and knowledge
    • Persistent structure
  • Groups actions
    • Observed indirectly through member\'s actions
group membership
Group Membership
  • Member agents
    • Have public group identity
    • Directly associated with one environment
  • Group Structure is a Tree
    • Least common ancestral node (LCAN)
    • Events occur with respect to a shared environment
shared environment example
Shared Environment Example

AgentsGroup

A1,A2 G1

A3,A4 G2

A5,A2 G1

A1,A3 G0

A5,A3 G0

groups organizations bounded rationality explanation
Groups/Organizations: bounded rationality explanation
  • Docility
    • Cooperation (altruism) as an explanation for the formation of groups/organizations
  • Why individuals “identify” with a group?
    • boundedly rational individuals
    • increase their survival fitness

(Simon, 1969, 1990, 1991)

conclusion
Conclusion
  • Reviewed 5 major approaches to study evolution of cooperation
  • Provided 2 main critiques for existing models
  • Constructed model extensions addressing the critiques
implications for computer science
Implications for Computer Science
  • Artificial intelligence
    • Benevolent agents are not good enough

(c.f., multi-agents systems)

    • Learning theory can be used to study evolution of cooperation
  • Systems
    • Improve system design by understanding the dynamics of agents
    • Accountability substrate needed for distributed systems
future plan
Future Plan
  • Extend the simple group social structure
  • Overlapping generations
  • Sexual reproduction
  • Extend social learning using realistic/robust learning model
modeling diploid organisms2
Modeling Diploid Organisms

One of 2 Child Chromosomes

Parental Chromosomes

simulation demo
Simulation Demo
  • Recall PD payoff matrix:
  • PD strategies: viewed as a probability vectors
    • Strategy: <PI, PT, PR, PP, PS>
    • TFT: < 1, 1, 1, 0, 0 >
    • AllD: < 0, 0, 0, 0, 0 >
    • AllC: < 1, 1, 1, 1, 1 >
    • STFT: < 0, 1, 1, 0, 0 >
simulation a search problem
Simulation: a search problem
  • Search Optimal PD Strategy
    • Search space: I, T, R, P, S probabilities
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