Models of Language Evolution Session 03 : Evolutionary Game Theory: Games & Stable Outcomes Michael Franke Seminar f¨ ur Sprachwissenschaft Eberhard Karls Universit¨ at T¨ ubingen
Classical GT Evolutionary GT Course Overview (tentative) date content 20 - 4 MoLE: Aims & Challenges 27 - 4 Evolutionary Game Theory 1 : Statics 04 - 5 Evolutionary Game Theory 2 : Macro-Dynamics 11 - 5 Evolutionary Game Theory 3 : Micro-Dynamics 18 - 5 Multi-Agent Systems: The A-Life Approach to LE 25 - 5 Communication, Cooperation & Relevance 01 - 6 Combinatoriality, Compositionality & Recursion 08 - 6 Evolution of Semantic Meaning & Pragmatic Strategies 15 - 6 P entecost — no class 22 - 6 work on student projects 29 - 6 work on student projects 06 - 7 work on student projects 13 - 7 presentations 20 - 7 presentations 2 / 20
Classical GT Evolutionary GT Today’s Session 1 (classical) game theory • static games • (strict) Nash equilibria • in pure strategies • in mixed strategies 2 games on populations • symmetric • asymmetric 3 evolutionarily stable states • in symmetric populations • in asymmetric populations • existence, uniqueness, some properties • relation to Nash equilibrium 3 / 20
Classical GT Evolutionary GT Game Theory • abstract mathematical tools for modeling and analyzing multi-agent interaction • since 1940 , classical game theory (von Neumann and Morgenstern) • (mostly) assumes perfectly rational agents • initially promised to be a unifying formal foundation for all social sciences • most central notion: Nash equilibrium • widely used tool in economics still • Nobel laureates: • Nash, Harsanyi & Selten ( 1994 ) • Aumann & Schelling ( 2006 ) • recently, connection to epistemic logic (Harsanyi, Aumann, Stalnaker) • recently, connection to linguistics (Lewis, Skyrms, J¨ ager, van Rooij) • since 1970 : evolutionary game theory (Maynard-Smith, Prize) • studies boundedly-rational agents • many applications in biology • most central notions: evolutionary stability & replicator dynamics • since 1990 , behavioral game theory (Selten, Camerer) • studies interactive decision making in the lab 4 / 20
Classical GT Evolutionary GT Static Game (Intuition) A static game is the specification of a situation in which two or more agents choose from a fixed set of options, together with an indication how preferable any possible outcome (combination of individual actions) is to each agent. Definition (Static Game (a.k.a. Strategic Game, Normal Form Game)) A static game is a triple � N , ( A ) i ∈ N , ( U ) i ∈ N � with: • N — set of players • A i — actions available to player i • U i : × j ∈ N A j → R — player i ’s utility function , with: • × j ∈ N A j — set of action profiles (outcomes). (Alternatively, we will occasionally consider the U i to be suitable matrices.) 5 / 20
Classical GT Evolutionary GT Example (Battle of the Sexes) • wife & husband want to go to the opera • N = { 1 , 2 } (wife ↔ 1 ) • there are two operas showing this night • A 1 = A 2 = � � a Bach , a Stravinsky • wife & husband cannot communicate where to go • they want to be together, � � � � 3 0 1 0 but: U 1 = U 2 = 0 1 0 3 • wife prefers Bach • husband Stravinsky • alternative notation in table: a Bach a Strav a Bach 3 , 1 0 , 0 a Strav 0 , 0 1 , 3 (player 1 is assumed to choose the row; her utils are listed first) 6 / 20
Classical GT Evolutionary GT Example (Prisoner’s Dilemma) • 2 prisoner’s held captive and charged with a crime • both can either confess ( a c ) or deny ( a d ) • they cannot communicate, and must make their choice independently • if both confess, they both go to prison for a short time only • if both deny, they both go to prison for a longer time • if one confesses and the other denies, the confesser goes to jail for a very long time, while the denier goes free a c a d � � � � 2 0 2 0 U 1 = U 2 = a c 2 , 2 0 , 3 3 1 3 1 a d 3 , 0 1 , 1 7 / 20
Classical GT Evolutionary GT Example (Hawks & Doves) • conflict over a food resource • hawks fight, doves share a hawk a dove a hawk 1 , 1 7 , 2 a dove 2 , 7 3 , 3 Example (Matching Pennies) • one’s victory is the other’s defeat a heads a tails a heads 1 , 0 0 , 1 a tails 0 , 1 1 , 0 8 / 20
Classical GT Evolutionary GT Example (Coordination) • perfectly aligned interests • but coordination problem a stay a go a stay 1 , 1 0 , 0 a go 0 , 0 1 , 1 Example (Anti-Coordination) • perfectly aligned interests • but coordination problem a stay a go a stay 0 , 0 1 , 1 a go 1 , 1 0 , 0 9 / 20
Classical GT Evolutionary GT Games vs. Strategies vs. Solutions • games: are models of a choice situation • strategies: model actual agent behavior • solution concepts: capture particular behavior: good, optimal, rational, stable (. . . ) Different (Classical) Solutions for Static Games Nash equilibrium: steady state Iterated Strict Dominance: step-by-step pruning of game Rationalizability: common belief in rationality (we only look at Nash equilibrium in this course) 10 / 20
Classical GT Evolutionary GT Nash Equilibrium (Intuition) A Nash equilibrium is an arrangement of strategies, one for each player, such that no player would benefit from unilateral deviation (i.e., no player would be better off doing something else if everybody else keeps doing the same thing). Definition (Nash Equilibrium (in Pure Strategies)) A Nash equilibrium in pure strategies is an action profile � a 1 , . . . , a i , . . . , a n � such that for all i ∈ N and a ′ i ∈ A i : a 1 , . . . , a ′ U i ( � a 1 , . . . , a i , . . . , a n � ) ≥ U i ( � � ) . i , . . . , a n It is strict if for all i ∈ N and a ′ i ∈ A i , a ′ i � = a i : a 1 , . . . , a ′ � � ) . U i ( � a 1 , . . . , a i , . . . , a n � ) > U i ( i , . . . , a n • not all games have pure ne s (e.g. Matching Pennies) • all games have ne s in mixed strategies 11 / 20
Classical GT Evolutionary GT Definition (Mixed Strategies) p ∈ ∆ k , where k = |A i | , and A mixed strategy for player i is a vector � � � k ∆ k = ( p 1 , p 2 , . . . , p k ) ∈ R k | p i ≥ 0 & ∑ p i = 1 . i = 1 ( ∆ k — the set of all probability vectors of size k alt.: the ( k − 1 ) unit simplex in R k ) • pure strategies are equivalently characterized as unit vectors: ( 0 , . . . , 0 , 1 , 0 , . . . , 0 ) • we can think of a mixed strategy either • as a deliberate randomization , or • as a probabilistic belief about opponent behavior, or • as a population aggregate 12 / 20
Classical GT Evolutionary GT Definition (Expected Utility) Fix a static 2 -player game. The expected utility for player i playing � p when j is playing � q is: EU i ( � p i , � q ) = � p i · U i � q . Reminder • matrix multiplication: if A is an m × o matrix, and B is a o × n matrix, then C = AB is an m × n matrix with: p ∑ C ij = A ik × B kj k = 1 a and � • dot product: if � b are n -place vectors, then: n a · � ∑ � b = a i × b i i = 1 13 / 20
Classical GT Evolutionary GT Definition (Best Response) Player i ’s best response to player j playing � q is any (mixed) strategy � p that maximizes player i ’s expected utility given � q : BR i ( � q ) = arg max p ∈ ∆ ( k ) EU i ( � p , � q ) . � (not playing a best response is irrational ) Definition (Nash Equilibrium (in Mixed Strategies)) The (mixed) strategy profile p = � � p 1 , � p 2 � is a (mixed) Nash equilibrium if for both players i : EU i ( � p j ) ≥ EU i ( � p j ) for all � x ∈ ∆ ( |A i | ) . p i , � x , � It is a (strict) Nash equilibrium if for both players i : EU i ( � p i , � p j ) > EU i ( � x , � p j ) for all � x ∈ ∆ ( |A i | ) ; � x � = � p i . 14 / 20
Classical GT Evolutionary GT Definition (Symmetric vs. Asymmetric Games) A 2 -player game is symmetric iff: • A 1 = A 2 , • U 1 = U T 2 , and (utilities as matrices) • players cannot distinguish their roles. Otherwise it is called asymmetric . Nash Equilibrium in Symmetric Games A mixed strategy � p is a symmetric Nash equilibrium iff for all other possible strategies � q : EU ( � p , � p ) ≥ EU ( � p , � q ) . It is strict if the inequality is strict for all � q � = � p . 15 / 20
Classical GT Evolutionary GT Mean-Field Population • (nearly) infinite populations for each distinguishable role • each population is entirely homogeneous • agents play pure strategies • each agent interacts purely at random with agents from: • the single population (symmetric games) • the other population (asymmetric games) • strategy update are rare 16 / 20
Classical GT Evolutionary GT Expected Utility in Mean-Field Populations (Symmetric) • let n i be the number of agents playing pure action a i • let n be the size of the population • population aggregate is a mixed strategy � p where: p i = n i n • if population is large enough, an agent who plays a i has expected utility as defined above: EU ( a i , � p ) = a i · U i � p (interpret a i as the i -th unit vector) Expected Utility in Mean-Field Populations (Asymmetric) • same, but with frequencies from respective other population 17 / 20
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