Models of Language Evolution - GitHub Pages · Models of Language Evolution Evolutionary game...
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Models of Language EvolutionEvolutionary game theory & signaling games
Michael Franke
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Topics for today
1 (flavors of) game theory
2 signaling games (& conversion into symmetric form)
3 Nash equilibrium (in symmetric games)
4 evolutionary stability
5 meaning of signals
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Game Theory Signaling games Population Games
Game Theory
Signaling games
Population Games
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Game Theory Signaling games Population Games
(Rational) Choice Theory
Decision Theory: a single agent’s solitary decision
Game Theory: multiple agents’ interactive decision making
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Game Theory Signaling games Population Games
Game Theory
• abstract mathematical tools for modeling and analyzing multi-agent interaction• since 1940: classical game theory (von Neumann and Morgenstern)
• perfectly rational agents ::: Nash equilibrium• initially promised to be a unifying formal foundation for all social sciences• Nobel laureates: Nash, Harsanyi & Selten (1994), Aumann & Schelling (2006)
• since 1970: evolutionary game theory (Maynard-Smith, Prize)• boundedly-rational agents ::: evolutionary stability & replicator dynamics• first applications in biology, later also elsewhere (linguistics, philosophy)
• since 1990: behavioral game theory (Selten, Camerer)• studies interactive decision making in the lab
• since 1990: epistemic game theory (Harsanyi, Aumann)• studies which (rational) beliefs of agents support which solution concepts
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Game Theory Signaling games Population Games
Games vs. Behavior
Game: abstract model of a recurring interactive decision situation• think: a model of the environment
Strategies: all possible ways of playing the game• think: a full contingency plan or a (biological) predisposition for how to act in every
possible situation in the game
Solution: subset of “good strategies” for a given game• think: strategies that are in equilibrium, rational, evolutionarily stable, the outcome of
some underlying agent-based optimization process etc.
Solution concept: a general mapping from any game to its specific solution• examples: Nash equilibrium, evolutionary stability, rationalizability etc.
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Game Theory Signaling games Population Games
Kinds of Games
uncertainty choice points
simultaneous in sequence
no strategic/static dynamic/sequentialwith complete info
yes Bayesian dynamic/sequentialwith incomplete info
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Game Theory Signaling games Population Games
Game Theory
Signaling games
Population Games
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Game Theory Signaling games Population Games
t ∈ T
?
sender knows state, but receiver does not
t ∈ Tm ∈ M
sender sends a signal
a ∈ A
receiver chooses act
State-Act Payoff Matrixa1 a2 . . .
t1 1,1 0,0t2 1,0 0,1...
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(Lewis, 1969)
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Game Theory Signaling games Population Games
Signaling game
A signaling game is a tuple
〈{S, R} , T, Pr, M, A, US, UR〉with:
{S, R} set of players
T set of states
Pr prior beliefs: Pr ∈ ∆(T)
M set of messages
A set of receiver actions
US,R utility functions:T×M×A→ R .
Talk is cheap iff for all t, m, m′, a andX ∈ {S, R}:
UX(t, m, a) = UX(t, m′, a) .
Otherwise we speak of costly signaling.
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model of the context/environment/world
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Game Theory Signaling games Population Games
Example (2-2-2 Lewis game)2 states, 2 messages, 2 acts
Pr(t) a1 a2
t1 p 1, 1 0, 0
t2 1− p 0, 0 1, 1
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Game Theory Signaling games Population Games
Example (Alarm calls)
NS S
R R
R R
〈1, 1〉 〈0, 0〉
〈1, 1〉 〈0, 0〉
〈0, 0〉 〈1, 1〉
〈0, 0〉 〈1, 1〉
pt1
1− pt2
m2
m1
m2
m1
a1 a2 a1 a2
a1 a2 a1 a2
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Game Theory Signaling games Population Games
Strategies
Pure
s ∈ MT r ∈ AM fixed contingency plan
Mixed
s̃ ∈ ∆(MT) r̃ ∈ ∆(AM) uncertainty about plan
Behavioral
σ ∈ (∆(M))T ρ ∈ (∆(A))M probabilistic plan
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Game Theory Signaling games Population Games
Pure sender strategies in the 2-2-2 Lewis game
“mamb”:ma
mb
t1
t2
“mbma”:ma
mb
t1
t2
“mama”:
ma
mb
t1
t2
“mbmb”:ma
mb
t1
t2
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Game Theory Signaling games Population Games
Pure receiver strategies in the 2-2-2 Lewis game
“aaab”:a1
a2
ma
mb
“abaa”:a1
a2
ma
mb
“aaaa”:
a1
a2
ma
mb
“abab”:a1
a2
ma
mb
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Game Theory Signaling games Population Games
All pairs of sender-receiver pure strategies for the 2-2-2 Lewis game
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Game Theory Signaling games Population Games
Game Theory
Signaling games
Population Games
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Game Theory Signaling games Population Games
(One-Population) Symmetric Game
A (one-population) symmetric game is a pair 〈A, U〉, where:• A is a set of acts, and• U : A×A→ R is a utility function (matrix).
Example (Prisoner’s dilemma)
U =
( ac ad
ac 2 0
ad 3 1
)Example (Hawk & Dove)
U =
( ah ad
ah 1 7
ad 2 3
)
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Game Theory Signaling games Population Games
Mixed strategies in symmetric games
A mixed strategy in a symmetric game is a probability distribution σ ∈ ∆(A).
Utility of mixed strategies defined as usual:
U(σ, σ′) = ∑a,a′∈A
σ(a)× σ(a′)×U(a, a′)
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Game Theory Signaling games Population Games
Nash Equilibrium in Symmetric Games
A mixed strategy σ ∈ ∆(A) is a symmetric Nash equilibrium iff for all other possiblestrategies σ′:
U(σ, σ) ≥ U(σ′, σ) .
It is strict if the inequality is strict for all σ′ 6= σ.
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Game Theory Signaling games Population Games
Examples
Prisoner’s Dilemma
U =
(2 0
3 1
)symmetric ne: 〈0, 1〉
Hawk & Dove
U =
(1 7
2 3
)symmetric ne: 〈.8, .2〉
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Game Theory Signaling games Population Games
Symmetrizing asymmetric gamesExample: signaling game
• big population of agents• every agent might be sender or receiver• an agent’s strategy is a pair 〈s, r〉 of pure sender and receiver strategies• utilities are defined as the average of sender and receiver role:
U(〈s, r〉 ,⟨s′, r′
⟩) = 1/2(US(s, r′) + UR(s′, r)))
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Game Theory Signaling games Population Games
Example (Symmetrized 2-2-2 Lewis game)s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16
s1 〈m1, m1, a1, a1〉 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5s2 〈m1, m1, a1, a2〉 .5 .5 .5 .5 .75 .75 .75 .75 .25 .25 .25 .25 .5 .5 .5 .5s3 〈m1, m1, a2, a1〉 .5 .5 .5 .5 .25 .25 .25 .25 .75 .75 .75 .75 .5 .5 .5 .5s4 〈m1, m1, a2, a2〉 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5s5 〈m1, m2, a1, a1〉 .5 .75 .25 .5 .5 .75 .25 .5 .5 .75 .25 .5 .5 .75 .25 .5s6 〈m1, m2, a1, a2〉 .5 .75 .25 .5 .75 1 .5 .75 .25 .5 0 .25 .5 .75 .25 .5s7 〈m1, m2, a2, a1〉 .5 .75 .25 .5 .25 .5 0 .25 .75 1 .5 .75 .5 .75 .25 .5s8 〈m1, m2, a2, a2〉 .5 .75 .25 .5 .5 .75 .25 .5 .5 .75 .25 .5 .5 .75 .25 .5s9 〈m2, m1, a1, a1〉 .5 .25 .75 .5 .5 .25 .75 .5 .5 .25 .75 .5 .5 .25 .75 .5
s10 〈m2, m1, a1, a2〉 .5 .25 .75 .5 .75 .5 1 .75 .25 0 .5 .25 .5 .25 .75 .5s11 〈m2, m1, a2, a1〉 .5 .25 .75 .5 .25 0 .5 .25 .75 .5 1 .75 .5 .25 .75 .5s12 〈m2, m1, a2, a2〉 .5 .25 .75 .5 .5 .25 .75 .5 .5 .25 .75 .5 .5 .25 .75 .5s13 〈m2, m2, a1, a1〉 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5s14 〈m2, m2, a1, a2〉 .5 .5 .5 .5 .75 .75 .75 .75 .25 .25 .25 .5 .5 .5 .5 .5s15 〈m2, m2, a2, a1〉 .5 .5 .5 .5 .25 .25 .25 .25 .75 .75 .75 .75 .5 .5 .5 .5s16 〈m2, m2, a2, a2〉 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5
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non-strict symmetric ne, strict symmetric ne
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Game Theory Signaling games Population Games
All pairs of sender-receiver pure strategies for the 2-2-2 Lewis game
13
9
5
1
14
10
6
2
15
11
7
3
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12
8
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Reading for Next Class
Brian Skyrms (2010) “Information” Chapter 3 of “Signals” OUP.
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References
Lewis, David (1969). Convention. A Philosophical Study. Cambridge, MA: HarvardUniversity Press.