Research

We study a public good game with N citizens and a Governor who allocates resources from a common fund. Citizens may voluntarily contribute or be compelled to do so if audited, in which case shirkers face a penalty. The Governor decides how much of the fund to devote to public good provision, with the remainder embezzled. Crucially, the Governor’s utility combines material payoffs from embezzlement with belief-dependent reputational concerns. We characterize the symmetric subgame perfect equilibria (SSPE) of the game. The model always admits at least one pure-strategy equilibrium, ranging from universal free-riding with complete embezzlement to full contribution with efficient provision. Symmetric mixed-strategy equilibria arise only within a limited region of the parameter space and may not be unique. Our analysis highlights the roles of penalties, audits, and reputational incentives in sustaining contribution and provision, thereby linking public good provision with the broader literature on corruption, embezzlement, and psychological game theory.

We generalize the model of Gallice and Monzon (2019) to incorporate a public goods game with groups, position uncertainty, and observational learning. Contributions are simultaneous within groups, but groups play sequentially based on their observation of an incomplete sample of past contributions. We show that full cooperation between and within groups is possible with self-interested players on a fixed horizon. Position uncertainty implies the existence of an equilibrium where groups of players conditionally cooperate in the hope of influencing further groups. Conditional cooperation implies that each group member is pivotal, so that efficient simultaneous provision within groups is an equilibrium.

Gallice and Monzón (2019) present a natural environment that sustains full co-operation in one-shot social dilemmas among a finite number of self-interested agents. They demonstrate that in a sequential public goods game, where agents lack knowledge of their position in the sequence but can observe some predecessors’ actions, full contribution emerges in equilibrium due to agents’ incentive to induce potential successors to follow suit. In this study, we aim to test the theoretical predictions of this model through an economic experiment. We conducted three treatments, varying the amount of information about past actions that a subject can observe, as well as their positional awareness. Through rigorous structural econometric analysis, we found that approximately 25% of the subjects behaved in line with the theoretical predictions. However, we also observed the presence of alternative behavioural types among the remaining subjects. The majority were classified as conditional co-operators, showing a willingness to cooperate based on others’ actions. Some subjects exhibited altruistic tendencies, while only a small minority engaged in free-riding behaviour.

Gallice and Monzón (2019) present a natural environment that sustains full cooperation in one-shot social dilemmas among a finite number of self-interested agents. They demonstrate that in a sequential public goods game, where agents lack knowledge of their position in the sequence but can observe some predecessors’ actions, full contribution emerges in equilibrium due to agents’ incentive to induce potential successors to follow suit. Furthermore, they show that this principle extends to a number of social dilemmas, with the prominent example that of the prisoner’s dilemma. In this study, we experimentally test the theoretical predictions of this model in a multi-player prisoner’s dilemma environment, where subjects are not aware of their position in the sequence and receive only partial information on past cooperating actions. We test the predictions of the model, and through rigorous structural econometric analysis, we test the descriptive capacity of the model against alternative behavioural strategies, such as conditional cooperation, altruistic play and free-riding behaviour. We find that the majority resorts to free-riding behaviour, around 30% is classified as Gallice and Monzón (2019) types, followed by those with social preference considerations and the unconditional altruists.

This paper investigates how natural language communication with an AI agent affects human cooperative behaviour in indefinitely repeated Prisoner’s Dilemma games. We conduct a laboratory experiment (n = 126) with two between-subjects treatments varying whether human participants chat with an AI chatbot (GPT-5.2) before every round or only before the first round of each supergame, and benchmark against human-human data from Dvorak and Fehrler (2024) (n = 108). We find four main results. First, cooperation against the AI is high and initially comparable to human-human levels, but unlike in the human-human setting, where cooperation converges to near-complete levels, cooperation against the AI plateaus and never reaches full cooperation. Second, repeated communication, which substantially increases cooperation in human-human interactions, has no detectable effect in the human-AI setting. Third, strategy estimation reveals that human-AI subjects favour Grim Trigger under pre-play communication and remain dispersed under repeated communication, whereas human-human subjects converge to Tit-for-Tat and unconditional cooperation respectively. Fourth, human-AI conversations contain more explicit strategy commitments but fewer emotional and social messages. These results suggest that humans cooperate with AI at high rates but do not develop the trust observed in human-human interactions. Cooperation in the human-AI setting is sustained through conditional rules rather than through the social bonds and mutual understanding that characterise human-human cooperation.