2013/08/17 by Joseph Y. Halpern, Rafael Pass, Halpern, Joseph Y. +3 · 4 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Algorithm #Artificial intelligence #Auction Theory and Applications #Automaton #Bounded function #Bounded rationality #Computation #Computer science #Decision problem #Decision theory #Finite-state machine #Game Theory and Applications #Game Theory and Voting Systems #Game theory #Mathematical economics #Mathematics #Model of computation #Theoretical computer science #Turing #Turing machine #cs.AI #cs.GT
paper · pdf · doi:10.48550/arxiv.1308.3780
published in arXiv (Cornell University) (Cornell University) · To appear, Topics in Cognitive Science
arxiv created 2013/08/17 · openalex publication_date 2013/08/17 · arxiv updated 2013/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
There have been two major lines of research aimed at capturing resource-bounded players in game theory. The first, initiated by Rubinstein, charges an agent for doing costly computation; the second, initiated by Neyman, does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We review recent work on applying both approaches in the context of decision theory. For the first approach, we take the objects of choice in a decision problem to be Turing machines, and charge players for the ``complexity'' of the Turing machine chosen (e.g., its running time). This approach can be used to explain well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on) and belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions) as the outcomes of quite rational decisions. For the second approach, we model people as finite automata, and provide a simple algorithm that, on a problem that captures a number of settings of interest, provably performs optimally as the number of states in the automaton increases.