A Dynamic Level-k Model in Sequential Games

Teck Ho () and Xuanming Su ()
Additional contact information
Xuanming Su: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104

Management Science, 2013, vol. 59, issue 2, 452-469

Abstract: Backward induction is a widely accepted principle for predicting behavior in sequential games. In the classic example of the "centipede game," however, players frequently violate this principle. An alternative is a "dynamic level-k" model, where players choose a rule from a rule hierarchy. The rule hierarchy is iteratively defined such that the level-k rule is a best response to the level-(k-1) rule, and the level-[infinity] rule corresponds to backward induction. Players choose rules based on their best guesses of others' rules and use historical plays to improve their guesses. The model captures two systematic violations of backward induction in centipede games, limited induction and repetition unraveling. Because the dynamic level-k model always converges to backward induction over repetition, the former can be considered to be a tracing procedure for the latter. We also examine the generalizability of the dynamic level-k model by applying it to explain systematic violations of backward induction in sequential bargaining games. We show that the same model is capable of capturing these violations in two separate bargaining experiments. This paper was accepted by Peter Wakker, decision analysis.

Keywords: level-k models; learning; sequential games; backward induction; behavioral game theory (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (54)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.1120.1645 (application/pdf)

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:inm:ormnsc:v:59:y:2013:i:2:p:452-469

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().