Partner-Specific Affective Precision in Social Active Inference

📅 2026-09-21
📈 Citations: 0
Influential: 0
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🤖 AI Summary
研究通过引入伙伴特异性情感精度,解决多主体社交环境中模型可靠性差异问题,该方法调节策略选择时的信心,影响行为主要通过策略承诺而非直接改进伙伴状态推断。
📝 Abstract
In multi-agent social settings, model reliability varies across relationships. Beyond inferring what others will do, an agent must calibrate how confidently those inferences should guide policy selection for each relationship. An agent may maintain a well-validated model of one partner, a fragile model of another, and a model under revision for a third; collapsing these into a single confidence estimate loses information relevant to policy selection. We therefore formalize affective precision as a relationship-specific metacognitive estimate of confidence in the current partner model. Each partner's behavioral evidence updates a local confidence estimate that modulates policy precision during selection, regulating how strongly current beliefs are expressed in policy rather than changing the content of those beliefs. Simulations in a multi-partner graded trust game show that partner-local affective precision influences behavior primarily through policy commitment rather than direct improvement of partner-state inference. Because the mechanism tracks partner-response predictability rather than realized payoff, greater confidence produces sharper policy commitment without necessarily producing higher rewards. Under abrupt shifts in social behavior, confidence accumulated from previously reliable predictions can remain behaviorally active after the relationship changes, showing that confidence revision can lag behind social change. Finally, varying precision gain and priors produce distinct trust-calibration dynamics, showing how confidence accumulation and revision depend on model parameters. Together, these results show how relationship-specific affective precision can distinguish social prediction from social policy commitment.
Problem

Research questions and friction points this paper is trying to address.

model reliability
social settings
confidence estimate
policy selection
affective precision
Innovation

Methods, ideas, or system contributions that make the work stand out.

affective precision
social active inference
policy commitment
relationship-specific metacognitive estimate
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Harshil Shah
Harshil Shah
Unknown affiliation
A
Andrew Pashea
Division of the Social Sciences, University of Chicago, Chicago, IL, USA