Leveraging Fee-Based, Imperfect Advisors in Human-Agent Games of Trust

This paper explores whether the addition of costly, imperfect, and exploitable advisors to Berg’s investment game enhances or detracts from investor performance in both one-shot and multi-round interactions. We then leverage our findings to develop an automated investor agent that performs as well as or better than humans in these games. To gather this data, we extended Berg’s game and conducted a series of experiments using Amazon’s Mechanical Turk to determine how humans behave in these potentially adversarial conditions. Our results indicate that, in games of short duration, advisors do not stimulate positive behavior and are not useful in providing actionable advice. In long-term interactions, however, advisors do stimulate positive behavior with significantly increased investments and returns. By modeling human behavior across several hundred participants, we were then able to develop agent strategies that maximized return on investment and performed as well as or significantly better than humans. In one-shot games, we identified an ideal investment value that, on average, resulted in positive returns as long as advisor exploitation was not allowed. For the multi-round games, our agents relied on the corrective presence of advisors to stimulate positive returns on maximum in-