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Autonomous Agents and Multi-Agent Systems

Erschienen in:

01.01.2015

Problem restructuring for better decision making in recurring decision situations

verfasst von: Avshalom Elmalech, David Sarne, Barbara J. Grosz

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 1/2015

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Abstract

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.

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The Yet2 marketplace operates as an online platform that allows “sellers” (industrial firms, entrepreneurial ventures, research universities and individual inventors) to post their inventions, while “buyers” can search the listed inventions [23].

These problems are known in other literature as directed search with full recall [103].

For example, it has been shown that people’s behavior tends to converge towards expected value maximization when repeatedly facing Allais type binary choice problems [8, 49]. This phenomena is also reflected in Samuelson’s Colleague’s decision to accept a series of 100 gambles versus his refusal to accept only one [79]. Much evidence has been given to a general phenomenon according to which most human participants accept risky gambles with positive expected values when the gambles will be played more than once but reject the corresponding single gamble [19, 48, 73, 102].

The proof of optimality given in [103] holds also for the case where values are defined based on a discrete probability function \(P_i(x)\), as in the example given above. In this case, the calculation of the reservation value \(r_i\) is given by \(c_{i}=\sum _{x \ge r_{i}}(x-r_{i})P_i(x)\).

The proof of optimality as given in [103] is four pages long and extensively uses mathematical manipulations.

For example, at each stage of the search disclosing to the searcher the opportunity that needs to be explored next according to optimal search strategy and terminate the process (e.g., by disclosing an empty set) when the value obtained so far is below the lowest reservation value of the remaining opportunities.

While most representing agents return a single performance output, some representing agents (i.e., the agent representing the class of searchers that are satisfied with a single selection, as described above) return a vector of possible outcomes.

The notation \(S\) is thus augmented to consider any set of searchers, rather than just the class-representing agents as before.

For a comparison between AMT and other recruitment methods see [68].

Of course the between-subject design raises the question of wiping out the individual heterogeneity in search behavior that can be quite large. Still, if we used a within-subject design we would have been affected by learning.

Since each participant in AMT has a unique ID, connected to a unique bank account, it is possible to block the same ID from participating more than once in a given experiment.

See Appendix for a detailed list of some of the more interesting strategies used.

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Titel: Problem restructuring for better decision making in recurring decision situations
verfasst von: Avshalom Elmalech
David Sarne
Barbara J. Grosz
Publikationsdatum: 01.01.2015
Verlag: Springer US
Erschienen in: Autonomous Agents and Multi-Agent Systems / Ausgabe 1/2015
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI: https://doi.org/10.1007/s10458-014-9247-3

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