TY - JOUR

T1 - Decision theory with resource-bounded agents

AU - Halpern, Joseph Y.

AU - Pass, Rafael

AU - Seeman, Lior

PY - 2014/4

Y1 - 2014/4

N2 - 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.

AB - 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.

KW - Bounded rationality

KW - Cost of computation

KW - Decision theory

UR - http://www.scopus.com/inward/record.url?scp=84899526408&partnerID=8YFLogxK

U2 - 10.1111/tops.12088

DO - 10.1111/tops.12088

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

C2 - 24764140

AN - SCOPUS:84899526408

SN - 1756-8757

VL - 6

SP - 245

EP - 257

JO - Topics in Cognitive Science

JF - Topics in Cognitive Science

IS - 2

ER -