Endogenous Networks in Random Population Games
In the last years, many contributions have been exploring population learning in economies where myopic agents play bilateral games and are allowed to repeatedly choose their pure strategies in the game and, possibly, their opponents in the game. These models explore bilateral stage-games reflecting very simple strategic situations (e.g. coordination). Moreover, they assume that payoffs are common knowledge and all agents play the same game against the others. Therefore, population learning acts on smooth landscapes where individual payoffs are relatively stable across strategy configurations. In this paper, we address a preliminary investigation of dynamic population games with endogenous networks over ‘rugged’ landscapes, where agents face a strong uncertainty about expected payoffs from bilateral interactions. We propose a simple model where individual payoffs from playing a binary action against everyone else (conditional to any possible combination of actions performed by the others) are distributed as a i.i.d. U[0,1] r.v.. We call this setting a ‘random population game’ and we study population adaptation over time when agents can update both actions and partners using deterministic, myopic, best reply rules. We assume that agents evaluate payoffs associated to networks where an agent is not linked with everyone else by using simple rules (i.e. statistics such as MIN, MAX, MEAN, etc.) computed on the distributions of payoffs associated to all possible action combinations performed by agents outside the interaction set. We investigate the long-run properties of the system by means of computer simulations. We show that both the LR behavior of the system (e.g. convergence to steady-states) and its short-run dynamic properties are strongly affected by: (i) the payoff rule employed; (ii) whether players are change-adverse or not. We find that if agents use the MEAN rule, then, irrespective of the change-aversion regime, the system displays multiplicity of steady-states. Populations always climb local optima by first using AU/NU together and then NU only. Climbing occurs through successful adaptation and generates LR positive correlation between number of links and average payoffs. With MIN or MAX rules, the LR behavior of the system is instead affected by whether players are change-adverse. If they are, and employ the MIN rule, then the network converges to a steady-state where all agents are (almost) fully connected but strategies are not, so that average payoffs oscillate. If agents employ the MAX rule then the system displays many steady-states (in both networks and actions) characterized by few links and different levels of average payoff. Finally, if agents are change-lovers, then the population can explore a larger portion of the landscape. Therefore, with agents using the MIN rule, the network will quickly approach to the complete one, but from then on exploration on strategies and networks will go on forever. If they employ the MAX rule, then the system will reach a unique payoff optimum. All populations converge to the same payoff distribution but neutral NU will continue forever (without affecting realized payoffs).
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- Goyal, Sanjeev & Janssen, Maarten C. W., 1997. "Non-Exclusive Conventions and Social Coordination," Journal of Economic Theory, Elsevier, vol. 77(1), pages 34-57, November.
- L. Blume, 2010.
"The Statistical Mechanics of Strategic Interaction,"
Levine's Working Paper Archive
488, David K. Levine.
- Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
- George Mailath & Larry Samuelson & Avner Shaked, 1994.
"Evolution and Endogenous Interactions,"
Game Theory and Information
- H. Peyton Young, 1996. "The Economics of Convention," Journal of Economic Perspectives, American Economic Association, vol. 10(2), pages 105-122, Spring.
- Fernando Vega Redondo & Ventakamaran Bhaskar, 1996.
"Migration and the evolution of conventions,"
Working Papers. Serie AD
1996-23, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Kandori, Michihiro & Mailath, George J & Rob, Rafael, 1993.
"Learning, Mutation, and Long Run Equilibria in Games,"
Econometric Society, vol. 61(1), pages 29-56, January.
- Kandori, M. & Mailath, G.J., 1991. "Learning, Mutation, And Long Run Equilibria In Games," Papers 71, Princeton, Woodrow Wilson School - John M. Olin Program.
- M. Kandori & G. Mailath & R. Rob, 1999. "Learning, Mutation and Long Run Equilibria in Games," Levine's Working Paper Archive 500, David K. Levine.
- Giorgio Fagiolo, 2001.
"Coordination, Local Interactions and Endogenous Neighborhood Formation,"
LEM Papers Series
2001/15, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Giorgio Fagiolo, 2002. "Coordination, Local Interactions, and Endogenous Neighborhood Formation," Computing in Economics and Finance 2002 98, Society for Computational Economics.
- Jackson, Matthew O. & Watts, Alison, 2002.
"The Evolution of Social and Economic Networks,"
Journal of Economic Theory,
Elsevier, vol. 106(2), pages 265-295, October.
- Stanley, E.A. & Ashlock, Daniel & Tesfatsion, Leigh, 1994. "Iterated Prisoner's Dilemma with Choice and Refusal of Partners," Staff General Research Papers 11180, Iowa State University, Department of Economics.
- Page, Scott E, 1997. "On Incentives and Updating in Agent Based Models," Computational Economics, Society for Computational Economics, vol. 10(1), pages 67-87, February.
- Glen Ellison, 2010.
"Learning, Local Interaction, and Coordination,"
Levine's Working Paper Archive
391, David K. Levine.
- Joerg Oechssler, 1994.
"Decentralization and the Coordination Problem,"
Game Theory and Information
- Edward Droste & Robert P. Gilles & Cathleen Johnson, 2000. "Evolution of Conventions in Endogenous Social Networks," Econometric Society World Congress 2000 Contributed Papers 0594, Econometric Society.
- Blume,L.E. & Durlauf,S.N., 2000. "The interactions-based approach to socioeconomic behavior," Working papers 1, Wisconsin Madison - Social Systems.
- Alexander F. Tieman & Harold Houba & Gerard van der Laan, 1998. "Cooperation in a Multi-Dimensional Local Interaction Model," Game Theory and Information 9803002, EconWPA.
- Watts, Alison, 2001. "A Dynamic Model of Network Formation," Games and Economic Behavior, Elsevier, vol. 34(2), pages 331-341, February.
- Brock,W.A. & Durlauf,S.N., 2000.
"Discrete choice with social interactions,"
7, Wisconsin Madison - Social Systems.
- Dieckmann, Tone, 1999. "The evolution of conventions with mobile players," Journal of Economic Behavior & Organization, Elsevier, vol. 38(1), pages 93-111, January.
- Hirshlifer, David & Rassmusen, Eric, 1989. "Cooperation in a repeated prisoners' dilemma with ostracism," Journal of Economic Behavior & Organization, Elsevier, vol. 12(1), pages 87-106, August.
- Alan Kirman, 1997. "The economy as an evolving network," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 339-353.
- Nobuyuki Hanaki & Alexander Peterhansl, 2002. "Viability of Cooperation in Evolving Interaction Structures," Computing in Economics and Finance 2002 120, Society for Computational Economics.
- Ashlock, Daniel & Smucker, Mark D. & Stanley, E. Ann & Tesfatsion, Leigh S., 1996.
"Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma,"
Staff General Research Papers
1687, Iowa State University, Department of Economics.
- Dan Ashlock & Mark D. Smucker & E. Ann Stanley & Leigh Tesfatsion, 1995. "Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma," Game Theory and Information 9501002, EconWPA, revised 20 Jan 1995.
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