AGENT-BASED MODELING OF THE TRAGEDY OF THE COMMONS by G üven Demirel

1. AGENT-BASED MODELING OF THE TRAGEDY OF THE COMMONS by Güven Demirel

2. OUTLINE • Common Pool Resources and Tragedy of Commons • Prescriptive vs. Descriptive Models • System Dynamics and Agent-Based Modeling • Model Overview • Initial Simulation Results • Future Work

3. Common Pool Resources • CPRs are common pools that have to be used by multiple extractors and the resource is depleted by the usage. • Examples to CPRs: fisheries, groundwaters, forestry and global atmosphere. • Tragedy of Commons: cooperation dilemma among common pool users. The tension between local and global (social) optimum. • Applying the game theory to the field, it is proposed that all the individuals try to maximize their own benefits and the long-term social benefit is lost due to resource depletion.

4. Tragedy of Commons • Ostrom: The real life strategies developed by human agents are mixed strategies, neither pure Nash nor perfect cooperative strategies. • Ostrom develops a theory of collective action, claiming that the only driving force is not utility maximization, alternative motives such as reciprocity, reputation and trust affect the way people behave in common pool resource problem domains.

5. Prescriptive vs. Descriptive • Prescriptive Models: • Aim to design communication protocols and reasoning of rational agents about protocols in the tragedy of commons context in such a way that would solve the dilemma. • Design strategies for agents that take the behaviors of other agents into consideration in the scope of a protocol. • Saha & Sen (2003) and Durfee (1999).

6. Descriptive Models • Descriptive Models: • Aim to develop a theory about how the human beings reason and act in a tragedy of commons setting. • “In what kind of environments with what type of agents does cooperation emerge, to what extent?” • The model proposed in the scope of this project is also a descriptive model that aims to understand why the tragedy of commons emerge and when the cooperation can evolve.

7. Descriptive Models • Examples: • Approaches based on some theoretical framework (e.g. rational actor paradigm (RAP), game theoretical approach (GT)). • Data Oriented Agent-Based Models: “Starting from observation and extracting regularities of behaviour.” Human reasoning is based on simple heuristics, therefore agent-based representation of these heuristics and the reasoning of agents on these heuristics are valid.

8. Descriptive Models • Theory Oriented Agent-Based Models: Combines concepts and theories from different social sciences and develop an interdisciplinary approach. • System Dynamics is such an interdisciplinary modeling methodology. • In the broadest sense, System Dynamics(SD) is a modeling paradigm that seeks to describe the causal relations among the variables in the system in a feedback loop structure. An increase in one factor may further cause an increase (positive feedback loop) or a decrease (negative feedback loop) in its value as the time passes. • Beliefs: state variables, the theory says the decision rules that formulate how beliefs change.

9. Model Overview • Using the feedback structure of SD model developed by Castillo & Saysel. • Problem domain: fisheries in a lake. • Two types of agents: • Observer: serves as the central agent and coordinates the flow of agent actions. • Fishermen: 5 fishermen simulated. • Fishermen give extraction decisions over 8 effort units. • According to the individual and total extraction efforts, return is gained.

10. Model Overview • Individual_Payoff = 60 * individual_extraction_effort - 2.5 * individual_extraction_effort ^2 + 20 * 5 * 8 - 20 * total_extraction_effort • As individual effort increases individual payoff increases; on the other hand as total extraction effort increases individual payoff decreases (tragedy of commons) • Social Optimum: full cooperation, everyone extracts min(1) units. • Individual Optimum: other agents extract min(1), I extract max(8) units. (Freeriding)

11. Model Overview to go while [time <= 20] [ ask fishermen[without-interruption[update_belief]] ask fishermen[without-interruption[give_extraction_decision]] set total_extraction_effort lput ( sum values-from fishermen [individual_extraction_effort] ) total_extraction_effort set time ( time + 1 ) ] end

12. Model Overview to update_belief let temp individual_perceived_to_desired_payoff_ratio set individual_trust_to_group (individual_trust_to_group + ( group_reputation - individual_trust_to_group) / 2 ) reciprocating_behavior set excluding_me_perceived_extraction_effort ( excluding_me_perceived_extraction_effort + ( ( last total_extraction_effort -individual_extraction_effort ) - excluding_me_perceived_extraction_effort) / 2 ) set excluding_me_effort_ratio (excluding_me_perceived_extraction_effort / excluding_me_normal_total_effort) freeriding_behavior set individual_perceived_payoff (individual_perceived_payoff + (individual_payoff - individual_perceived_payoff ) / 2 ) set individual_perceived_to_desired_payoff_ratio (individual_perceived_payoff / individual_desired_payoff) profit_maximizing_behavior set individual_to_group_effort_ratio (individual_extraction_effort / (last total_extraction_effort)) set individual_effort_payoff_ratio ( individual_to_group_effort_ratio / temp ) set individual_awareness_ratio (individual_awareness / maximum_awareness) effect_on_awareness_building set individual_awareness (individual_awareness + individual_effect_on_awareness_building * individual_effort_payoff_ratio) set individual_awareness_ratio (individual_awareness / maximum_awareness) awareness_behavior end

13. Model Overview • “Update_belief” procedure represents the reasoning of fishermen. • The beliefs about • the group: individual_trust_to_group (trustworthiness of the group), excluding_me_perceived_extraction_effort (expected extraction of the group) • individual payoff: perceived_individual_payoff (expected individual payoff) • the dilemma: individual_awareness (awareness level about the dilemma). • Each of these beliefs has an effect on extraction level. • Therefore the extraction effort is affected from reciprocity, freeriding, awareness and profit maximization attitudes. • “Give_extraction_decision” is the procedure where the extraction decision is given by the fishermen. The formulation is as follows:

14. Model Overview • individual_extraction_effort=((normal_effort*individual_temptation_to_freeride_effect_on_extraction*individual_profit_maximizing_ effect_on_extraction*individual_willingness_to_cooperate)/ individual_awareness_effect_on_extraction). • “The more trustworthy the group -agent believes-, the less extraction effort it makes”. (willingness to cooperate) • “The fewer resources the group uses –the agent believes-, the more it extracts”. (temptation to free ride) • “The more aware about the tragedy of commons dilemma the agent is, the less it extracts”. (effect on awareness) • “The less the agent gains, the more it extracts”. (profit maximization) • The relative strengths of these factors depend on the levels of the stated variables, and also on the personal characteristics of the agents.

15. Simulation Results -1- Case 1:

16. Simulation Results -1-

17. Simulation Results -2- Case 2:

18. Simulation Results -2-

19. Simulation Results -3- • Case 3:

20. Simulation Results -3-

21. Further Research • Model completion and validation in Repast. • Effect of Local Information: • Full Extraction Information • Full Extraction Information Until T • Effect of Communication • Effect of Government Intervention

22. References • Arthur, B. (1994). “Inductive Reasoning and Bounded Rationality”. AEA Proceedings, p. 406-411. • Bousquet, F. et. al. (2001). “Agent-Based Modelling, Game Theory And Natural Resource Management Issues”, Journal of Artificial Societies and Social Simulation, vol. 4, no. 2. • Castillo, D., and Saysel, A.K. (2005). “Simulation of Common Pool Resource Field Experiments: A Behavioral Model of Collective Action”, Ecological Economics, 55. • Deadman, P. J., Schlager, E., and Gimblett, R. (2000) “Simulating Common Pool Resource Management Experiments with Adaptive Agents Employing Alternate Communication Routines”, Journal of Artificial Societies and Social Simulation, vol. 3, no. 2. • Durfee, E. (1999). "Practically coordinating." AI Magazine 1999, p. 99-116. • Ostrom, E. (1998). “A Behavioral Approach to the Rational Choice Theory of Collective Action”, American Political Science Review, 92(1). • Ostrom, E., Gardner, G., and Walker, J. (2002) “Rules, Games, & Common-Pool Resources”, The University of Michigan Press. • Ostrom, E., and Walker, J. (2003). “Trust and Reciprocity”, Russell Sage Foundation. • Pahl-Wostl, C., and Ebenhöh, E. (2004) “An Adaptive Toolbox Model: A Pluralistic Modelling Approach For Human Behaviour Based On Observation”, Journal of Artificial Societies and Social Simulation, vol. 7, no. 1. • Pepper, J.W., and Smuts, B.B. (2000). “The Evolution of cooperation in an Ecological Context: An Agent-Based Model”, in “Dynamics in Human and Primate Societies”, edited by Kohler, T. A., and Gumerman, G. J. Oxford University Press. • Saha, S., and Sen. S. (2003). “Local Decision Procedures for Avoiding the Tragedy of Commons”, Distributed Computing, IWDC 2003.

23. thanks… THANKS…