Problem restructuring for better decision making in recurring decision situations

Avshalom Elmalech, David Sarne, Barbara J. Grosz

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


This paper proposes the use of restructuring information about choices to improve the performance of computer agents on recurring sequentially dependent decisions. The intended situations of use for the restructuring methods it defines are website platforms such as electronic marketplaces in which agents typically engage in sequentially dependent decisions. With the proposed methods, such platforms can improve agents’ experience, thus attracting more customers to their sites. In sequentially-dependent-decisions settings, decisions made at one time may affect decisions made later; hence, the best choice at any point depends not only on the options at that point, but also on future conditions and the decisions made in them. This “problem restructuring” approach was tested on sequential economic search, which is a common type of recurring sequentially dependent decision-making problem that arises in a broad range of areas. The paper introduces four heuristics for restructuring the choices that are available to decision makers in economic search applications. Three of these heuristics are based on characteristics of the choices, not of the decision maker. The fourth heuristic requires information about a decision-makers prior decision-making, which it uses to classify the decision-maker. The classification type is used to choose the best of the three other heuristics. The heuristics were extensively tested on a large number of agents designed by different people with skills similar to those of a typical agent developer. The results demonstrate that the problem-restructuring approach is a promising one for improving the performance of agents on sequentially dependent decisions. Although there was a minor degradation in performance for a small portion of the agents, the overall and average individual performance improved substantially. Complementary experimentation with people demonstrated that the methods carry over, to some extent, also to human decision makers. Interestingly, the heuristic that adapts based on a decision-maker’s history achieved the best results for computer agents, but not for people.

Original languageEnglish
Pages (from-to)1-39
Number of pages39
JournalAutonomous Agents and Multi-Agent Systems
Issue number1
StatePublished - Jan 2014

Bibliographical note

Publisher Copyright:
© 2014, The Author(s).


Preliminary results of this research appear in a conference paper []. This research was partially supported by ISF grant 1083/13 and IIS-0705406 from the U.S. National Science Foundation. We are grateful to Moti Geva for his help with developing the agent-based experimental infrastructure and the proxy program.

FundersFunder number
Israel Science Foundation
National Science Foundation
National Science Foundation
Israel Science Foundation1083/13, IIS-0705406


    • Decision Making
    • Experimentation
    • Platform Design
    • Sequentially Dependent Decisions


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