caa a22 4500 605514577 CHVBK 20210128100705.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10458-014-9247-3 doi (NATIONALLICENCE)springer-10.1007/s10458-014-9247-3 Problem restructuring for better decision making in recurring decision situations [Elektronische Daten] [Avshalom Elmalech, David Sarne, Barbara Grosz] 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. The Author(s), 2014 Decision Making nationallicence Sequentially Dependent Decisions nationallicence Platform Design nationallicence Experimentation nationallicence Elmalech Avshalom Computer Science Department, Bar-Ilan University, 52900, Ramat-Gan, Israel aut Sarne David Computer Science Department, Bar-Ilan University, 52900, Ramat-Gan, Israel aut Grosz Barbara School of Engineering and Applied Science, Harvard University, 02138, Cambridge, MA, USA aut Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/1(2015-01-01), 1-39 1387-2532 29:1<1 2015 29 10458 https://doi.org/10.1007/s10458-014-9247-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10458-014-9247-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Elmalech Avshalom Computer Science Department, Bar-Ilan University, 52900, Ramat-Gan, Israel aut NATIONALLICENCE 700 1- Sarne David Computer Science Department, Bar-Ilan University, 52900, Ramat-Gan, Israel aut NATIONALLICENCE 700 1- Grosz Barbara School of Engineering and Applied Science, Harvard University, 02138, Cambridge, MA, USA aut NATIONALLICENCE 773 0- Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/1(2015-01-01), 1-39 1387-2532 29:1<1 2015 29 10458