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Evolutionary Game Theory Iterated PD, TFT

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Evolutionary Game Theory Iterated PD, TFT Evolutionary Game Theory ISCI 330 Lecture 18 1 ISCI 330 Lecture 18 Outline • A bit about historical origins of Evolutionary Game Theory • Main (competing) theories about how cooperation evolves • PD and other social dilemma games • Iterated PD, TFT, etc. • Evolutionary Stable Strategy (ESS) • N-player PD (and other games) • Simpson’s paradox and the role of assortment 2 ISCI 330 Lecture 18 Evolution by Natural Selection • Lewontin’s principles (from Darwin) – 1) Phenotypic variation – 2) Differential fitness – 3) Heritability • In Evolutionary Game Theory – 1) Population of strategies – 2) Utility determines number of offspring (fitness) – 3) Strategies breed true • Frequency-dependent selection – One of the first examples is Fisher’s sex ratio findings – Introduces idea of strategic phenotypes 3 ISCI 330 Lecture 18 Ritualized Fighting opponent’s behaviour Dove Hawk V / 2 Dove actor’s 5 0 behaviour V (V- c) / 2 Hawk 10 -5 • V = 10; c = 20 • The rare strategy has an advantage (i.e. frequency dependent selection) • Hawk-Dove, Chicken, Snowdrift, Brinkmanship •If 0 < c < V, then game is PD instead 4 ISCI 330 Lecture 18 Main Theories: Evolution of Altruism • Multilevel Selection ∆Q = ∆QB + ∆QW (Price Equation) • Inclusive Fitness/Kin Selection – wincl. = wdirect + windirect ∆Q > 0 if rb > c (Hamilton’s rule) • Reciprocal Altruism ∆Q > 0 if altruists are sufficiently compensated for their sacrifices via reciprocity (ESS) 5 ISCI 330 Lecture 18 Additive Prisoner’s Dilemma (PD) Actor's Fitness (Utility) opponent’s behaviour C D contributes b contributes 0 w0 + b – c w0 – c C actor’s sacrifices c 4 0 behaviour w0 + b w0 D sacrifices 0 5 1 • w0 = 1; b = 4; c = 1 6 ISCI 330 Lecture 18 Non-Additive PD Actor's Fitness (Utility) opponent’s behavior C D contributes b contributes 0 w0 + b – c w0 – c C actor’s sacrifices c (+d) 3 0 behavior w0 + b w0 D sacrifices 0 5 1 • w0 = 1; b = 4; c = 1; d = -1 7 ISCI 330 Lecture 18 Main Theories: Evolution of Altruism • Multilevel Selection – Predominate models are in terms of public good • Inclusive Fitness/Kin Selection – Predominate models is in terms of individual contributions (b and c) • Reciprocal Altruism – Predominate models in terms of iterated PD (iPD) 8 ISCI 330 Lecture 18 Evolutionarily Social Dilemma Games • What features do Hawk-Dove and the PD have in common? – Cs do better in CC pairs than Ds do in DD pairs – Ds do better than Cs in mixed pairs • Given 4 utility levels (1st, 2nd, 3rd, 4th) how many 2-player, symmetric games are there that capture this idea of “social dilemma”? • With a partner, find these other games. Can you name them? 9 ISCI 330 Lecture 18 6 evolutionarily interesting “social dilemmas” • How do these games compare in terms of – Nash equilibria? – Pareto optimality? – Is it better to be rare or common? • Consider populations of strategies rather than 2-players • Relative vs. Absolute fitness 10 ISCI 330 Lecture 18 Common EGT Assumptions • Population of strategies • Replicator equations – Number of individuals of a certain strategy in next generation depends on: • Average fitness (utility) of individuals with that strategy • Which depends on frequency distribution of strategies •Often assume – infinite populations where replicator equations give proportion of strategy (scaled by average fitness) – continuous (or discrete) time – complete mixing (random interactions) – strategies breed true (no sex or mutation) 11 ISCI 330 Lecture 18 Iterated Prisoner’s Dilemma • Robert Triver’s concept of reciprocal altruism • Robert Axelrod’s tournaments – Every strategy plays every other one • Or at random for evolutionary experiments – On average 200 rounds – Final score is cumulative payoffs from all rounds – Can condition current behaviour on any amount of history • opponent’s and actor’s 12 ISCI 330 Lecture 18 Conditional Strategies • Anatol Rappoport’s Tit-For-Tat strategy – Unless provoked, the agent will always cooperate – If provoked, the agent will retaliate swiftly – The agent is quick to forgive – Susceptible to noise – Backwards induction issue • Imagine gene for when to start defecting • Alternatives – Forgiving TFT, TF2T, Pavlov, walk-away • Under what conditions does TFT beat it’s opponent? 13 ISCI 330 Lecture 18 Simple Iterated PD Model TFT ALLD ALLD TFT TFT TFT TFT D ALLD TF TFT ALLD ALL T FT T TFT TFT LLD TFT A TFT A LLD ALLD ALLD TFT ALLD D T ALLD ALL TF pick pairs offspring opponent’s behaviour at random replace C D contributes b contributes 0 parents w0 + b – c w0 – c C actor’s sacrifices c 4 0 TFT ALLD TFT behaviour w0 + b w0 D ALLD FT ALLD T sacrifices 0 5 1 offspring TFT proportional to cumulative payoff play i times 14 ISCI 330 Lecture 18 Numerical Simulations of Iterated PD varying Q, i, and b (c = 1) 0.1 ∆Q 0 Q = 0.2; i = 2 Q = 0.15; i = 2 Q = 0.15; i = 4 Q = 0.15; i = 15 Q = 0.1; i = 2 -0.1 1 3 5 7 9 11 13 15 b 15 ISCI 330 Lecture 18 – Fletcher & Zwick, 2006. The American Naturalist Simple Iterated PD Model ALL D D ALLD LDALLD D ALL ALL ALL AL D D ALLD AL ALLD ALLD ALL LD ALLD D ALLD ALLD LL ALL A TFT A D LLD ALLD ALLD ALLD ALLD D LD ALLD ALL AL pick pairs offspring opponent’s behaviour at random replace C D contributes b contributes 0 parents w0 + b – c w0 – c C A actor’s sacrifices c 4 0 ALLD LLD ALLD behaviour w0 + b w0 D ALLD FT ALLD T sacrifices 0 5 1 offspring ALLD proportional to cumulative payoff play i times 16 ISCI 330 Lecture 18 Simple Iterated PD Model TF TFT TFT T TFT TF TFT TFT T TFT TF TFT TFT TFT T FT T TFT TF FT TF FT T T T TFT T TFT TFT TFT ALLD T TFT TFT TF pick pairs offspring opponent’s behaviour at random replace C D contributes b contributes 0 parents w0 + b – c w0 – c C actor’s sacrifices c 4 0 TFT ALLD TFT behaviour w0 + b w0 D TFT FT TFT T sacrifices 0 5 1 offspring TFT proportional to cumulative payoff play i times 17 ISCI 330 Lecture 18 Evolutionary Stable Strategy (ESS) • A strategy which if adopted by a population cannot be invaded by any competing alternative strategy • How does this compare to a Nash Equilibrium? – Here assume almost all players play the same strategy (call it S) – S is a Nash Eq. if u(S, S) ≥ u(S', S) for any S' – S is an ESS if •u(S, S) > u(S', S) for any S' (strict Nash) •Or u(S, S) = u(S', S) AND u(S, S’) > u(S', S') – If S is an ESS, then it is a Nash Eq. – If S is a strict Nash Eq. (given a population of S), then it is an ESS 18 ISCI 330 Lecture 18 ESS and Hawk-Dove (Chicken) • Is Hawk an ESS? Is Dove and ESS? • Is there a mixture of playing Hawk and Dove that is an ESS? – Find it assuming original game • Note that we can think of this in 2 ways – Agents playing a mixed strategy • Monomorphic solution where an allele for this mixture has fixed in the population – A mixed population of strategies at this ratio opponent’s behaviour • Polymorphic stable equilibrium Dove Hawk • Learning vs. Evolving Dove actor’s 5 0 behaviour Hawk 10 -5 19 ISCI 330 Lecture 18 ESS and the PD • Is C an ESS? Is D and ESS? • Is there a mixture of playing C and D that is an ESS? • What if we break the assumption of random interactions? • Is TFT an ESS? Is ALLD and ESS? – Does it depend on iterations in iPD? – Find game (payoff matrix) for TFT vs. ALLD if i = 3 – What do we call this game? • Can think of TFT in an iPD in two ways: – Conditional behaviour causes C opponent’s behaviour behaviours of others to be more C D contributes b contributes 0 assorted with TFT than with ALLD w0 + b – c w0 – c C – In iPD, TFT and ALLD actor’s sacrifices c 4 0 behaviour w0 + b w0 change the game to Assurance D sacrifices 0 5 1 20 ISCI 330 Lecture 18 N-Player Prisoner’s Dilemma (Tragedy of the Commons) fitness to a defector, ws b average fitness, wav c fitness to a cooperator, wa fitness per individual 0 00.51.0 q, fraction of cooperators in a group • ai' = ai [1 + wa(qi)] si' = si [1 + ws(qi)] • wa(qi) = bqi – c + w0 ws(qi) = bqi + w0 21 ISCI 330 Lecture 18 – Fletcher & Zwick, 2007. Journal of Theoretical Biology Simpson’s Paradox Group 1 Group 2 Total Non-altruists Altruists Time • Altruists become a smaller portion of each group • But altruists become a larger portion of the whole 22 ISCI 330 Lecture 18.
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