Conditional investment strategy in evolutionary trust games with repeated group interactions

TL;DR

This paper introduces a conditional investment strategy in repeated group trust games, demonstrating how it fosters alliances with trustworthy agents and sustains trust in complex, realistic interaction environments.

Contribution

It extends evolutionary trust game models by incorporating conditional strategies and analyzes their dynamics using Markov decision processes.

Findings

Conditional investors form alliances with trustworthy trustees.

Such alliances can dominate untrustworthy trustees across various parameters.

Trust and reciprocation are sustainable in complex group interactions.

Abstract

It has a long tradition to study trust behavior among humans or artificial agents by investigating the trust game. Although previous studies based on evolutionary game theory have revealed that trust and trustworthiness can be promoted if network structure or reputation is considered, they often assume that interactions among agents are one-shot and investors do not consider the investment environment before making decisions, which collide with many realistic situations. In this paper, we introduce the conditional investment strategy into the repeated N-player trust game, in which conditional investors decide to invest or not depending on their assessment of the trustworthiness level of the group. By using the approach of the Markov decision process, we study the evolutionary dynamics of trust in repeated group interactions with the conditional investment strategy. We find that conditional investors can form an effective alliance with trustworthy trustees, hence they can sweep out untrustworthy trustees. Moreover, we verify that such alliance can exist in a wide range of model parameters. These results may explain why trusting in others and reciprocating them with trustworthy actions can be sustained in game interactions among intelligent agents.