Separating the Searches of Bounded Rational Decision-Makers

dc.contributor.authorHerrmann, Jeffrey
dc.date.accessioned2013-06-27T14:17:27Z
dc.date.available2013-06-27T14:17:27Z
dc.date.issued2013-06
dc.description.abstractIn practice, when faced with a complex optimization problem, human decision-makers often separate it into subproblems and then solve each subproblem instead of tackling the complete problem. This paper describes a study that simulated small teams of bounded rational decision-makers (“agents”) who try different approaches to solve optimization problems. In the “all-at-once” approaches, the agents collaborate to search the entire set of solutions in a sequential manner: each agent begins where the previous agent stopped. In other approaches, the agents separate the problem into subproblems, and each agent solves a different subproblem. Finally, in the hybrid approaches, the agents separate the problem but two agents will collaborate to solve one subproblem while another agent solves a different subproblem. In some cases, the subproblems are solved in parallel; in others, the subproblems are solved sequentially. The results show that the teams generally found better solutions when they separated the problem.en_US
dc.identifier.urihttp://hdl.handle.net/1903/13967
dc.language.isoen_USen_US
dc.relation.isAvailableAtInstitute for Systems Researchen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.relation.ispartofseriesTR_2013-13
dc.subjectdecision makingen_US
dc.subjectagentsen_US
dc.subjectteamsen_US
dc.subjectsearchen_US
dc.subjectdecompositionen_US
dc.subjectsimulationen_US
dc.titleSeparating the Searches of Bounded Rational Decision-Makersen_US
dc.typeTechnical Reporten_US

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