Intent Inference Produces the Retaliatory Cascades It Prevents

In noisy, repeated games, inferring partner intent can explain an observed defection as an execution error. We study how this ability affects learning and cooperation in Bayesian model-based learners using active inference (AIF) and approximate Bayes-adaptive planning. For symmetric $2\times 2$ games, we derive how execution noise changes payoff comparisons and the information available about latent intent, and formulate decision boundaries for Bayesian model-based planners. When contrasting these generative model architectures, intent-conditioned learners lose cooperation sharply in Stag Hunt and the iterated Prisoner's Dilemma (IPD). Against an unconditional cooperator in Stag Hunt, raising noise from $0.15$ to $0.20$ reduces mean late intended cooperation from $0.927$ to $0.201$, compared with $0.887$ to $0.695$ for the observation-conditioned learner, although cooperation remains the better one-round response to the known partner. AIF learning traces show that runs with different eventual outcomes can have similar beliefs about current partner intent but different predictions about whether mutual cooperation will continue. We hypothesise that defection helps preserve unfavourable predictions by reducing opportunities to test continued cooperation. Observation-conditioned clusters are well described by learned responses to executed outcomes; some intent-conditioned AIF runs instead alternate rapidly between C and D. Bayes-adaptive mutual cooperation is lower in IPD self-play than against Tit-for-Tat, and mainly gives way to asymmetric play in Chicken. Both intent-conditioned planners retain high intended agreement in Pure Coordination self-play. These findings motivate reporting per-run behaviour and intended and executed joint actions alongside mean cooperation. They also motivate research into how inferred intent could guide exploration toward poorly learned transitions and stabilise learning under execution noise.