Institutions and the Evolution of Collective Action

Mark Lubell

UC Davis

Defining Collective Action

Collective-action problem: Individual decision-making leads to socially undesirable (Pareto-inefficient) outcomes

Cooperation: Adjusting behavior to minimize socially undesirable outcomes

Tragedy of the Commons

Garrett Hardin (1968):“Therein is the tragedy. Each man is locked into a system that compels

him to increase his herd without limit—in a world that is limited. Ruin is the destination towards which all men rush, each his own best interest in a society that believes in freedom of the commons.”

“Mutual coercion, mutually agreed upon”

Flip side of resource use: Maintenance of ecosystems/public goods

Collective action problems are ubiquitous!

From Global….

To Local…

To Local…

Paper title: “M

y Identity as a White F

emale”

Studying Collective Action

Major Research Questions

1. Factors explaining cooperative behavior

2. Role of institutions (e.g., punish defection, reward cooperation)

Theoretical Philosophy Game theory Evolutionary game theory Evolutionary simulations (This talk)

Empirical Field research (qualitative and quantitative) Experimental research

Prisoner’s DilemmaPlayer 2

Cooperate Defect

Player 1

Cooperate R1= 6

R2= 6

S1= 3

T2= 8

Defect T1= 8

S2= 3

P1= 4

P2= 4

Conditions: T>R>P>S; 2R>T+S

Nash equilibrium: Both players defect

Collective Action Agents

Five “gene” strategies; 32 possible Each gene determines behavior in current

round on basis of outcome in last round

<Nice (1st round), Reciprocal (CC), Sucker(CD), Forgive (DC), Protect (DD)>

Important Examples:All Cooperate <1,1,1,1,1>

GRIM Trigger <1,1,0,0,0>

PAVLOV(Win-stay, lose shift) <1,1,0,0,1>

Tit-for-Tat <1,1,0,1,0>

Structure of Simulation

Generation 1 Generation 5000

Generation 1: Randomly Select 40 Strategies

Round Robin Tournament: Each strategy vs. itself and all others

Proportional Fitness Reproduction:

P(reproduction)= Fitnessi/Fitnessall

Next Generation:

Survival of Fittest

1% Mutation Rate on Each Gene

A “Punishing” Experiment

Design Baseline 2-player repeated PD, with discount

rate= .9 Examine the effect of $2 punishment for defection,

with increasing probability ranging from [0,1] in .10 increments

10 runs of each experiment; 40 strategies, 5000 generations

Hypotheses Increasing levels of cooperation Increased population stability Shift in the population dynamics of cooperation

0

1

2

3

4

5

6

7

MeanFitness

0

5

10

15

20

25

30

35

40

45

1

29

0

57

9

86

8

11

57

14

46

17

35

20

24

23

13

26

02

28

91

31

80

34

69

37

58

40

47

43

36

46

25

49

14

grimpavlov

Bas

elin

e:

No

Pu

nis

hm

ent

Ho

bb

es:

Pu

nis

hm

ent

p=

1.0

0

1

2

3

4

5

6

7

MeanFitness

0

5

10

15

20

25

30

35

40

45

1

295

589

883

1177

1471

1765

2059

2353

2647

2941

3235

3529

3823

4117

4411

4705

4999

grimpavlov

4.4

4.6

4.8

5

5.2

5.4

5.6

5.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Punishment Probability

Mea

n F

itn

ess

Generation MeanFitness

Mean Fitness Increases With Punishment Probability

0

5

10

15

20

25

30

35

40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Punishment Probability

Av

era

ge

Ge

ne

Fre

qu

en

cy

Pe

r G

en

era

tio

nNice CC CD DC DD

Gene Frequency: All Regimes

Strategy Frequency: All Regimes

0

2

4

6

8

10

12

14

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Punishment Probability

Ave

rag

e S

trat

egy

Fre

qu

ency

GRIM

PAVLOV

SPAVLOV

Gene Frequency: Cooperative Regimes (Avg. Fitness>5.9)

0

5

10

15

20

25

30

35

40

45

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Punishment Probability

Gen

e F

req

uen

cy

Nice CC CD DC DD

Strategy Frequency: Cooperative Regimes

-1

4

9

14

19

24

29

34

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Punishment Probability

Ave

rag

e S

trat

egy

Fre

qu

ency

GRIM

PAVLOV

SPAVLOV

TFT

Some Correlations

Overall Fitness .21

Genes

Nice .11

CC .22

CD .10

DC .06

DD .24

Strategies

All Defect -.18

GRIM -.03

PAVLOV .10

Suspicious PAVLOV .10

TFT .04

Conclusions

Punishment institutions increase cooperation and stability, even in noisy environment

As punishment increase, basis of cooperation shifts towards PAVLOV

Institutions change population dynamics of cooperation, even if same behaviors observed

Must square with observed human behavior; e.g.; resistance to coercion, reduced effectiveness of reciprocity in coercive environments