Population Learning in a Model with Random Payoff Landscapes and Endogenous Networks

Giorgio Fagiolo (), Luigi Marengo () and Marco Valente

Computational Economics, 2005, vol. 24, issue 4, 383-408

Abstract: 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 start instead to explore economies where the payoff landscape is very complicated (rugged). We propose a model where the payoff to any agent changes in an unpredictable way as soon as any small variation in the strategy configuration within its network occurs. We study population learning where 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 in discriminating between options associated to the same expected payoff. Copyright Springer Science + Business Media, Inc. 2005

Keywords: adaptive expectations; dynamic population games; endogenous networks; fitness landscapes; population learning (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10614-005-6160-5 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:24:y:2005:i:4:p:383-408

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-005-6160-5

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().