Autonomous agents and human cultures in the trust–revenge game

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.

Social decisions and fairness change when people’s interests are represented by autonomous agents

“…On the one hand, in negotiation -which can be interpreted as a more complex version of the ultimatum game [4,53] -we showed that participants were also likely to be more demanding with unfair counterparts when interacting via agents than when engaging with them directly. On the other hand, in the trust-revenge game, Azaria, Richardson, and Rosenfeld [83] report no difference in trusting and a slight increase in revenge behavior when programming agents, when compared to direct interaction with others. Though the revenge portion of this task may seem similar to an ultimatum game, it is important to note that here it reflects a breach in trust, whereas in the ultimatum game there is no initial allocation of trust.…”

Facilitating cooperation in human-agent hybrid populations through autonomous agents

“…Conducting human experiments is crucial for testing predictions from theoretical models and gaining insights into various aspects, including psychological effects, emotions, and cultural differences. 20 , 26 Mechanisms such as communication sentiment, reward, and punishment in human-human interactions have provided clear evidence of prosocial behavior. 49 , 50 , 51 Empirical experiments not only test theoretical possibilities but also reveal what actually occurs.…”

“… 18 For instance, studying human-AI interaction within the general-sum environment and the trust-revenge game provides a comprehensive understanding of these domains. 19 , 20 As social interactions have become more hybrid, 16 , 21 involving humans and AAs, there lies an opportunity to gain new insights into how human cooperation is affected. 18 , 22 This work aims to examine the influence of AAs on human cooperative behavior when social dilemmas exist.…”

ChatGPT Usage and Limitations

“…The rational behavior in the trust game is for the trustee not to return any money to the investor, and thus, for the investor not to pass any money to the trustee [5,8]. However, in practice, human investors invest around half their money, and the trustees return more than they have received [21].…”