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Institutions and the Evolution of Collective Action

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.

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Institutions and the Evolution of Collective Action

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  1. Institutions and the Evolution of Collective Action Mark Lubell UC Davis

  2. Defining Collective Action • Collective-action problem: Individual decision-making leads to socially undesirable (Pareto-inefficient) outcomes • Cooperation: Adjusting behavior to minimize socially undesirable outcomes

  3. 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!

  4. From Global….

  5. To Local…

  6. To Local… Paper title: “My Identity as a White Female”

  7. Studying Collective Action Major Research Questions • Factors explaining cooperative behavior • 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

  8. Prisoner’s Dilemma Conditions: T>R>P>S; 2R>T+S Nash equilibrium: Both players defect

  9. 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>

  10. Structure of Simulation Generation 1: Randomly Select 40 Strategies Round Robin Tournament: Each strategy vs. itself and all others Next Generation: Survival of Fittest 1% Mutation Rate on Each Gene Proportional Fitness Reproduction: P(reproduction)= Fitnessi/Fitnessall Generation 5000 Generation 1

  11. 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

  12. Baseline: No Punishment

  13. Hobbes: Punishment p=1.0

  14. Mean Fitness Increases With Punishment Probability

  15. Gene Frequency: All Regimes

  16. Strategy Frequency: All Regimes

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

  18. Strategy Frequency: Cooperative Regimes

  19. Some Correlations

  20. 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

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