evolutionary game theory i: well-mixed populations
DESCRIPTION
Evolutionary game theory I: Well-mixed populations. Collisional population dynamics. Traditional game theory. +T. +R. p D. 1. +R. +S. +. +S. +P. +T. +P. t. 0. Collisional population events. Collisional population events. R C. R R. R S. R D. R T. R P. C. +. +. C. D. C. - PowerPoint PPT PresentationTRANSCRIPT
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+R
+R +S
+T
+T
+S
+P
+P
Evolutionary game theory I: Well-mixed populations
1
Collisional population dynamics Traditional game theory
0
pD
1
t
+
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2
Collisional population events
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3
C
D
๐ถ+๐ท ๐[๐ ]
โ
๐ถ+2๐ท
๐ถ+๐ท ๐[๐ ]
โ
2๐ถ+๐ท2๐ถ ๐ [๐ ]
โ
3๐ถ
2๐ท ๐[๐ ]
โ
3๐ท
๐ถ ๐ 0โ
2๐ถ
๐ท ๐ 0โ
2๐ท
RC RR RS
RD RT RP
DC C+ +
Collisional population events
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๐๐ท๐๐ก
= ๐๐ท๐๐ ๐ท
๐๐ ๐ท
๐๐ก+ ๐๐ท๐ ๐ ๐
๐๐ ๐
๐๐ก+ ๐๐ท๐๐ ๐
๐๐ ๐
๐๐ก
๐๐ถ๐๐ก
= ๐๐ถ๐๐ ๐ถ
๐๐ ๐ถ
๐๐ก+ ๐๐ถ๐๐ ๐
๐๐ ๐
๐๐ก+ ๐๐ถ๐๐ ๐
๐๐ ๐
๐๐ก
4
Collisional population events
๐ถ+๐ท ๐[๐ ]
โ
๐ถ+2๐ท
๐ถ+๐ท ๐[๐ ]
โ
2๐ถ+๐ท2๐ถ ๐ [๐ ]
โ
3๐ถ
2๐ท ๐[๐ ]
โ
3๐ท
๐ถ ๐ 0โ
2๐ถ
๐ท ๐ 0โ
2๐ท
RC RR RS
RD RT RP
๐ 0๐ถ+1๐
[๐ ] [๐ถ ]๐ถ+1๐
[๐ ] [๐ท ]๐ถ+1
๐ 0๐ท+1๐
[๐ ] [๐ถ ]๐ท+1๐
[๐ ] [๐ท ]๐ท+1
๐๐ถ๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ ๐๐ท๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ท
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5
๐๐ถ๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ ๐๐ท๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ท
๐๐๐ก
๐๐ท=๐๐๐ก ( ๐ท
๐ถ+๐ท )=๐๐ท๐๐ก
(๐ถ+๐ท )โ๐ท ๐๐๐ก
(๐ถ+๐ท )
(๐ถ+๐ท )2
๐๐๐ท
๐๐ก=๐๐ถ๐๐ท [ (๐ โ๐ )๐๐ถ+(๐โ๐ )๐๐ท ]
๐๐๐ถ
๐๐ก+๐๐๐ท
๐๐ก=0STOP Check that total
probability is conserved
Evolutionary dynamics of demographics
๐๐ถ๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ ๐๐ท๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ท
ยฟ๐ถ๐๐ท๐๐ก
+๐ท๐๐ท๐๐ก
โ๐ท๐๐ถ๐๐ก
โ๐ท๐๐ท๐๐ก
(๐ถ+๐ท )2
ยฟ๐ถ ( ๐ 0+๐ ๐๐ถ+๐ ๐๐ท )๐ทโ๐ท ( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ
(๐ถ+๐ท )2
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6
Evolutionary dynamics of demographics
๐๐ถ๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ ๐๐ท๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ท๐๐๐ท
๐๐ก=๐๐ถ๐๐ท [ (๐ โ๐ )๐๐ถ+(๐โ๐ )๐๐ท ]
Consider the example T > R > P > S
๐๐๐ท
๐๐ก=๐๐ท (1โ๐๐ท ) [ (๐ โ๐ ) (1โ๐๐ท )+(๐โ๐ )๐๐ท ]
> 0
> 0
> 0
> 0> 0
0
pD
1.0
0.5
t4321
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7
Evolutionary dynamics of demographics
๐๐ถ๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ ๐๐ท๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ท๐๐๐ท
๐๐ก=๐๐ถ๐๐ท [ (๐ โ๐ )๐๐ถ+(๐โ๐ )๐๐ท ]
Consider the example T > R > P > S
๐๐๐ท
๐๐ก=๐๐ท (1โ๐๐ท ) [ (๐ โ๐ ) (1โ๐๐ท )+(๐โ๐ )๐๐ท ]
> 0
> 0
> 0
> 0> 0
0
pD
1.0
0.5
t4321
Stable
Unstable
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8
Evolutionary dynamics of demographics
๐๐ถ๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ถ ๐๐ท๐๐ก
=( ๐ 0+๐ ๐๐ถ+๐๐๐ท )๐ท
๐๐๐ท
๐๐ก=๐๐ถ๐๐ท [ (๐ โ๐ )๐๐ถ+(๐โ๐ )๐๐ท ]
Consider the example T > R > P > S
0
pD
1.0
0.5
t4321
Stable
Unstable
1. Enrichment in D because D is more fit than C (T > R and P > S)2. Loss of fitness of D (and of C) owing to enrichment in D (T > P and R > S)3. The fittest cells prevail, reducing their own fitness
Fitness of C Fitness of D
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+R
+R +S
+T
+T
+S
+P
+P
Evolutionary game theory I: Well-mixed populations
9
Collisional population dynamics Traditional game theory
0
pD
1
t
+
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?CD
10
Self-consistent quantity maximization
?
+?
+?
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?DCC
11
Self-consistent quantity maximization
C
D
?
? C D?
?
?
+?
+?
C+R
+R +S
+T
??
??
DC
+T
+S D D
+P
+P
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12
Self-consistent quantity maximization
C
D
?
? C D?
?
+R
+R +S
+T
+T+S
+P+P
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13
Self-consistent quantity maximization
C
D
+R+R +S
+T
+T+S
+P+P
C D Consider the example T > R > P > S
Individuals attempt to maximize payoff by adjusting strategy
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14
Self-consistent quantity maximization
C
D
+R+R +S
+T
+T+S
+P+P
C D Consider the example T > R > P > S
Individuals attempt to maximize payoff by adjusting strategy
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15
Self-consistent quantity maximization
C
D
+R+R +S
+T
+T+S
+P+P
C D Consider the example T > R > P > S
Individuals attempt to maximize payoff by adjusting strategy
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16
Self-consistent quantity maximization
C
D
+R+R +S
+T
+T+S
+P+P
C D Consider the example T > R > P > S
Individuals attempt to maximize payoff by adjusting strategy
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17
Self-consistent quantity maximization
C
D
+R+R +S
+T
+T+S
+P+P
C D Consider the example T > R > P > S
Individuals attempt to maximize payoff by adjusting strategy
D-vs.-D is a stable strategy pair in that neither agent can increase payoff through unilateral strategy change
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18
Self-consistent quantity maximization
C
D
+R+R +S
+T
+T+S
+P+P
C D Consider the example T > R > P > S
Individuals attempt to maximize payoff by adjusting strategy
D-vs.-D is a stable strategy pair in that neither agent can increase payoff through unilateral strategy change
Guided to solution D-vs.-D because T > R and P > S
Each individual obtains less-than-maximum payoff (P < T)owing to the other individualโs adoption of strategy D
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19
+R+R +S
+T
+T+S
+P+P
Consider example T > R > P > S
Agents try to maximize payoff
Solution := no agent can increase payoff through unilateral change of strategy. E.g., D-vs.-D (T > R and P > S).
Each agent obtains less-than-maximum payoff (P < T) owing to other agentโs adoption of strategy D
Rationality
Nash equilibrium
0
pD
1
t
Consider example T > R > P > S
T, R, P, and S are cell-replication coefficients associated with pairwise collisions
Stable homogeneous steady state, i.e. pD โ 1 because T > R and P > S.
Enriching in D reduces fitness of both cell types (because T > P and R > S)
Replicators with fitness
ESS
Evolutionary dynamics providing insight into a related game theory model
Game theory
Prisonerโs dilemma
Evolutionary game theory
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