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Autonomous agents and human cultures in the trust–revenge game

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Abstract

Autonomous agents developed by experts are embedded with the capability to interact well with people from different cultures. When designing expert agents intended to interact with autonomous agents developed by non-game theory agents (NGTE), it is beneficial to obtain insights on the behavior of these NGTE agents. Is the behavior of these NGTE agents similar to human behavior from different cultures? This is an important question as such a quality would allow an expert agent interacting with NGTE agents to model them using the same methods that are used to model humans from different cultures. To study this point, we evaluated NGTE agents behavior using a game called the Trust–Revenge game, which is known in social science for capturing different human tendencies. The Trust–Revenge game has a unique subgame-perfect equilibrium strategy profile, however, very rarely do people follow it. We compared the behavior of autonomous agents to the actions of several human demographic groups—one of which is similar to the designers of the autonomous agents. We claim that autonomous agents are similar to human players from various cultures. This enables the use of approaches, developed for handling cultural diversity among humans, to be applied for interaction with NGTE agents. This paper also analyzes additional aspects of autonomous agents behavior and whether composing autonomous agents affects human behavior.

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Notes

  1. All human subjects played all five settings as Player A exactly once in random order, and played five more games as player B. Unfortunately, due to matching difficulties, we could not ensure that all human subjects played each of the five settings as player B.

  2. We also considered a different criterion which tests whether the difference between the group \(A\) and \(\cup \mathcal{B}\) are statistically significant. However, such a criterion would be sensible to the size of the data since, theoretically, any two groups which have different averages would differ statistically significantly if enough data is gathered. Futhermore, we do not claim that group \(A\)’s distribution or average is similar to that of \(\cup \mathcal{B}\), but simply that it falls among the diversity of the groups in \(\mathcal{B}\). We therefore did not test this method on our data.

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Acknowledgments

We thank Shira Abuhatzera for her help. A very preliminary version of this paper appears in Azaria et al. [52].

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Authors and Affiliations

  1. Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA

    Amos Azaria

  2. Department of Industrial Engineering, Lev Academic Center, Jerusalem, Israel

    Ariella Richardson & Avi Rosenfeld

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  1. Amos Azaria
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  2. Ariella Richardson
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  3. Avi Rosenfeld
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Correspondence to Amos Azaria.

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Azaria, A., Richardson, A. & Rosenfeld, A. Autonomous agents and human cultures in the trust–revenge game. Auton Agent Multi-Agent Syst 30, 486–505 (2016). https://doi.org/10.1007/s10458-015-9297-1

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