Summary
Population learning in dynamic economies with endogenous network formation has been traditionally studied in basic settings where agents face quite simple and predictable strategic situations (e.g. coordination). In this paper, we begin instead to explore economies where the overall payoff landscape is very complicated (rugged). We propose a model where the payoff of any agent changes in an unpredictable way as soon as any small variation in the strategy configuration within her network occurs. We study population learning when agents: (i) are allowed to periodically adjust both the strategy they play in the game and their interaction network; (ii) employ some simple criteria (e.g. statistics such as MIN, MAX, MEAN, etc.) to myopically form expectations about their payoff under alternative strategy and network configurations. Computer simulations show that: (i) allowing for endogenous networks implies higher average payoff as compared to ”static” networks; (ii) populations learn by employing network updating as a ”global learning” device, while strategy updating is used to perform ”fine tuning”; (iii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s); (iv) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.
Thanks to Hans Hamman, Koen Frenken, Dan Levinthal and Nick Vriend. L. M. and M. V. gratefully acknowledge financial support from the EC-FP5 project NORMEC (SERD-2000-00316).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
E. Droste, R.P. Gilles, and C. Johnson. Evolution of conventions in endogenous social networks. Unpublished Manuscript, CentER, Tilburg University, The Netherlands, 2000.
G. Fagiolo. Spatial interactions in dynamic decentralized economies: a review. In P. Cohendet, P. Llerena, H. Stahn, and G. Umbhauer, editors, The Economics of Networks. Interaction and Behaviours. Berlin — Heidelberg, Springer Verlag, 1998.
G. Fagiolo. Endogenous neighborhood formation in a local coordination model with negative network externalities. Journal of Economic Dynamics and Control, 2004. Forthcoming.
G. Fagiolo, L. Marengo, and M. Valente. Endogenous networks in random population games. LEM Working Paper 2003/03, Sant'Anna School of Advanced Studies, Pisa, Italy, 2003.
K. Fischer and J. Hertz. Spin Glasses. Cambridge, Ma, Cambridge University Press, 1991.
S. Goyal and F. Vega-Redondo. Learning, network formation and coordination. Unpublished Manuscript, Erasmus University, Rotterdam, 2001.
M. Jackson. The stability and efficiency of social and economic networks. In B. Dutta and M. Jackson, editors, Networks and Groups: Models of Strategic Formation. Springer-Verlag, Forthcoming, 2003.
M. Jackson and A. Watts. On the formation of interaction networks in social coordination games. Unpublished Manuscript, Division of Humanities and Social Sciences, Caltech, Pasadena, CA and Vanderbilt University, Nashville, TN, 2000.
S.A. Kauffman. The origins of order. Oxford, Oxford University Press, 1993.
A.P. Kirman. The economy as an evolving network. Journal of Evolutionary Economics, 7:339–353, 1997.
M. Taylor. The Possibility of Cooperation. Cambridge, MA, Cambridge University Press, 1987.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fagiolo, G., Marengo, L., Valente, M. (2005). Population Learning in Random Games with Endogenous Network Formation. In: Lux, T., Samanidou, E., Reitz, S. (eds) Nonlinear Dynamics and Heterogeneous Interacting Agents. Lecture Notes in Economics and Mathematical Systems, vol 550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27296-8_11
Download citation
DOI: https://doi.org/10.1007/3-540-27296-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22237-8
Online ISBN: 978-3-540-27296-0
eBook Packages: Business and EconomicsEconomics and Finance (R0)