The Ambiguity Zone: When Randomized Social Preferences Induce Social Inference
Abstract
Randomized uncertain social preferences (RUSP) can produce reciprocity and reputation-like behavior in multi-agent reinforcement learning by randomizing social rewards and introducing structured uncertainty about others’ preferences, but the source of this pressure to understand other agents remains unclear. We propose that there exists a decision-critical region of preference space, the ambiguity zone, in which the best action depends on the partner’s latent type. The location of this zone can be computed before training from the value of social information. Our results support a two-gate account of social inference. The first gate is coverage: across payoff games and training distributions, partner sensitivity increases with the training mass placed on the ambiguity zone (condition-level Spearman , cluster permutation ), and the relation extends to a preregistered fourth game. The second gate is identifiability: a factorial experiment shows zero response outside the ambiguity zone at every information level, while partner-conditioned behavior appears inside the zone only when partner type becomes highly identifiable. A preregistered matched-marginal control reproduces this pattern while holding population cooperation fixed at 0.5. When both gates are open, agents use third-party observations to judge a partner they have never directly interacted with (); resetting the recurrent state removes the effect, localizing this cross-interaction judgment to memory. Finally, a compact RUSP-form bridge that restores randomized reward transformations, asymmetric preference uncertainty, and multi-agent shared-policy learning preserves the same gate ordering at smaller magnitude. Together, these results distinguish the roles of randomization and uncertainty in RUSP: randomization determines which regions of preference space receive training exposure, while identifiable social evidence determines whether that exposure creates pressure to understand other agents.
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