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The actions of intelligent agents, such as chatbots, recommender systems, andvirtual assistants are typically not fully transparent to the user.Consequently, using such an agent involves the user exposing themselves to therisk that the agent may act in a way opposed to the user's goals. It is oftenargued that people use trust as a cognitive shortcut to reduce the complexityof such interactions. Here we formalise this by using the methods ofevolutionary game theory to study the viability of trust-based strategies inrepeated games. These are reciprocal strategies that cooperate as long as theother player is observed to be cooperating. Unlike classic reciprocalstrategies, once mutual cooperation has been observed for a threshold number ofrounds they stop checking their co-player's behaviour every round, and insteadonly check with some probability. By doing so, they reduce the opportunity costof verifying whether the action of their co-player was actually cooperative. Wedemonstrate that these trust-based strategies can outcompete strategies thatare always conditional, such as Tit-for-Tat, when the opportunity cost isnon-negligible. We argue that this cost is likely to be greater when theinteraction is between people and intelligent agents, because of the reducedtransparency of the agent. Consequently, we expect people to use trust-basedstrategies more frequently in interactions with intelligent agents. Our resultsprovide new, important insights into the design of mechanisms for facilitatinginteractions between humans and intelligent agents, where trust is an essentialfactor.

When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games