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Performing prosociality in public presents a paradox: only by doing so can people demonstrate their virtue and also influence others through their example, yet observers may derogate actors’ behavior as mere “virtue signaling.” Here we investigate the role of observability of actors’ behavior as one reason that people engage in such “virtue discounting.” Further, we investigate observers’ motivational inferences as a mechanism of this effect, using the comparison of generosity and fairness as a case study among virtues. Across 14 studies (7 preregistered, total N = 9,360), we show that public actors are perceived as less virtuous than private actors, and that this effect is stronger for generosity compared to fairness (i.e., differential virtue discounting). Exploratory factor analysis suggests that three types of motives—principled, reputation-signaling, and norm-signaling—affect virtue discounting. Using structural equation modeling, we show that observability’s effect on actors’ trait virtue ratings is largely explained by inferences that actors have less principled motivations. Further, we leverage experimental evidence to provide stronger causal evidence of these effects. We discuss theoretical and practical implications of our findings, as well as future directions for research on the social perception of virtue.

 

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration.

 

Successfully navigating the social world requires reasoning about both high-level strategic goals, such as whether to cooperate or compete, as well as the low-level actions needed to achieve those goals. While previous work in experimental game theory has examined the former and work on multi-agent systems has examined the later, there has been little work investigating behavior in environments that require simultaneous planning and inference across both levels. We develop a hierarchical model of social agency that infers the intentions of other agents, strategically decides whether to cooperate or compete with them, and then executes either a cooperative or competitive planning program. Learning occurs across both high-level strategic decisions and low-level actions leading to the emergence of social norms. We test predictions of this model in multi-agent behavioral experiments using rich video-game like environments. By grounding strategic behavior in a formal model of planning, we develop abstract notions of both cooperation and competition and shed light on the computational nature of joint intentionality.