arXiv subjects

Chin-wing Leung

Publications and source records attributed to Chin-wing Leung.

3 recordsLinked to original sources

arXiv2026

Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems

As the capabilities and adoption of Artificial Intelligence (AI) systems grow, trust in these AI systems is an increasingly urgent concern. Much research has focused on models of AI governance and has primarily examined incentives for safe development and effective regulation. Hence they typically represented users trust as a one-shot adoption choice rather than as a dynamic, evolving process shaped by repeated interactions. We instead model trust as the dynamic choice of reduced monitoring in a repeated, asymmetric interaction between users and AI developers, where checking developers' behaviour is costly. Using evolutionary game theory, we study how users' strategies of trust and developers' strategies of providing safe (compliant) or unsafe (non-compliant) AI co-evolve under different levels of monitoring cost and institutional regimes. We conduct the analysis on both imitation-based and learning-based perspectives, with the stochastic finite-population dynamics, the infinite-population replicator analysis and the reinforcement learning analysis. We find three robust long-run regimes: no adoption by users while developers provide unsafe AI, unsafe but widely adopted systems, and safe systems that are widely adopted. Only the last is desirable, and it arises when penalties for unsafe behaviour exceed the extra cost of safety and users can still afford to monitor at least occasionally. Our results formally support governance proposals that emphasise transparency, low-cost monitoring, and meaningful sanctions, and they show that neither regulation alone nor blind user trust is sufficient to prevent the drift towards unsafe or low-adoption outcomes.

cs.AI↗

arXiv2026

The Dynamics of Policy Gradient in Social Dilemmas with Partner Selection

In social dilemmas self-interested learning agents face the choice between the societal benefit of cooperation and the immediate reward of defection. Significant evidence exists on the benefits of assortment mechanisms such as partner selection for the emergence of cooperation, but this is largely available through agent-based simulations. In this paper, we provide an analytical solution to the problem, studying the policy-gradient dynamics in a multi-agent environment with partner selection. We show how partner selection changes the opponent distribution and hence the reward landscape, and prove this promotes cooperation under simple rules known from the literature. In particular, we find that population variance is a necessary condition for cooperation to emerge. Using a two-dimensional Wiener process, we extend the dynamics to capture the stochastic effects of partner selection and the resulting opponent distribution. We derive a sufficient condition for the population to be cooperation-promoting and prove the existence of a stationary distribution. Simulations confirm that the stochastic model accurately captures the policy-gradient dynamics and clarifies how the learning rate affects the emergence of cooperation.

cs.MA↗

arXiv2026

Convergence of Replicator Dynamics in the Repeated Prisoner's Dilemma with Restarts

We investigate a population of self-interested agents playing a repeated Prisoner's Dilemma under the trigger-restart mechanism. Under such a mechanism, agents play a sequence of symmetric games with their partner, and restart the interaction if their actions disagree. Our work focuses on the convergence of replicator dynamics in a well-mixed population of agents, where the emergence of cooperation is challenged by the individual incentive for exploitation. Formulating the corresponding parametrised normal-form game, with agents each adopting a length-m strategy sequence, we show that increasing the strategy length enables cooperation to emerge and stabilise. We provide exact convergence guarantees for restricted strategy lengths and, in the general payoff configuration, provide the necessary parametric conditions for the stability of cooperative strategies. By deriving an exact formula for the number of stable sequences, we find structural properties necessary for stability, as agents must learn to initially defect - the so-called "hazing period" - before cooperating indefinitely. Our analysis shows that, while optimal cooperative sequences exist, agents favour less-optimal sequences with a longer hazing period, which possess larger basins of attraction.

cs.GT↗