A Theoretical Examination of Practical Game Playing: Lookahead Search

  • Vahab Mirrokni
  • Nithum Thain
  • Adrian Vetta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7615)


Lookahead search is perhaps the most natural and widely used game playing strategy. Given the practical importance of the method, the aim of this paper is to provide a theoretical performance examination of lookahead search in a wide variety of applications. To determine a strategy play using lookahead search, each agent predicts multiple levels of possible re-actions to her move (via the use of a search tree), and then chooses the play that optimizes her future payoff accounting for these re-actions. There are several choices of optimization function the agents can choose, where the most appropriate choice of function will depend on the specifics of the actual game - we illustrate this in our examples. Furthermore, the type of search tree chosen by computationally-constrained agent can vary. We focus on the case where agents can evaluate only a bounded number, k, of moves into the future. That is, we use depth k search trees and call this approach k-lookahead search. We apply our method in five well-known settings: industrial organization (Cournot’s model); AdWord auctions; congestion games; valid-utility games and basic-utility games; cost-sharing network design games. We consider two questions. First, what is the expected social quality of outcome when agents apply lookahead search? Second, what interactive behaviours can be exhibited when players use lookahead search? We demonstrate how the answer depends on the game played.


game theory market games valid utility games cournot stackelberg adwords network design bounded rationality 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vahab Mirrokni
    • 1
  • Nithum Thain
    • 2
  • Adrian Vetta
    • 3
  1. 1.Google ResearchNew YorkUSA
  2. 2.Department of Mathematics and StatisticsMcGill UniversityUSA
  3. 3.Department of Mathematics and Statistics and School of Computer ScienceMcGill UniversityUSA

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