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…

Paper title: “My Identity as a White Female”

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 Dilemma Player 2 CooperateDefect Player 1 CooperateR 1 = 6 R 2 = 6 S 1 = 3 T 2 = 8 DefectT 1 = 8 S 2 = 3 P 1 = 4 P 2 = 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  Important Examples: All Cooperate GRIM Trigger PAVLOV(Win-stay, lose shift) Tit-for-Tat

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)= Fitness i /Fitness all 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

Baseline: No Punishment

Hobbes: Punishment p=1.0

Mean Fitness Increases With Punishment Probability

Gene Frequency: All Regimes

Strategy Frequency: All Regimes

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

Strategy Frequency: Cooperative Regimes

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