Less is more: Restructuring decisions to improve agent search

Abstract

In many settings and for various reasons, people fail to make optimal decisions. These factors also influence the agents people design to act on their behalf in such virtual environments as eCommerce and distributed operating systems, so that the agents also act sub-optimally despite their greater computational capabilities. In some decision-making situations it is theoretically possible to supply the optimal strategy to people or their agents, but this optimal strategy may be non-intuitive, and providing a convincing explanation of optimality may be complex. This paper explores an alternative approach to improving the performance of a decision-maker in such settings: the data on choices is manipulated to guide searchers to a strategy that is closer to optimal. This approach was tested for sequential search, which is a classical sequential decision-making problem with broad areas of applicability (e.g., product search, partnership search). The paper introduces three heuristics for manipulating choices, including one for settings in which repeated interaction or access to a decision-maker's past history is available. The heuristics were evaluated on a large population of computer agents, each of which embodies a search strategy programmed by a different person. Extensive tests on thousands of search settings demonstrate the promise of the problem-restructuring approach: despite a minor degradation in performance for a small portion of the population, the overall and average individual performance improve substantially. The heuristic that adapts based on a decision-maker's history achieved the best results.