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The Authority Expectancy Effect in Multi-Party Conflict

arXiv 2608.08026 ↗First posted 8 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

In multi-party competitive settings, across experiments on resource allocation, fault attribution, and dispute mediation, we test whether social authority (SA) cues such as occupational status and institutional documentation behave as if scalar weights were added to one side of a judgment. We reject this scalar-weight null on both of its predictions: cue effects are not independent in allocation decisions, where the same pair of cues interacts in opposite directions across models, and evidentiary cues do not exert a fixed directional influence in multi-turn disputes, where identical documentation draws protection to one party or extends it to both depending on which party holds it. The cues also govern whether a judgment is issued at all: removing an occupational label while retaining documentary evidence drives four of six models into refusal, yet the same label sharply reduces refusal when documentation is present, so the gate is subject to the same interaction as the judgment. We formalize this pattern as the Authority Expectancy Effect (AEE), comprising evidential reinterpretation, whereby identical content yields different judgments depending on which party bears the SA signal, and textitdirection sensitivity, whereby the same evidentiary cue favours different parties, or one party versus both, depending on the holder's authority position.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 8 Aug 2026.

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11 of 28 references.

References · 11

  1. 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 354
  2. 2024Humans or LLMs as the Judge? A Study on Judgement Biasescited by 314
  3. 2023Benchmarking Cognitive Biases in Large Language Models as Evaluatorscited by 147
  4. 2023Generative Language Models Exhibit Social Identity Biasescited by 107
  5. 2025Assessing Judging Bias in Large Reasoning Models: An Empirical Studycited by 30
  6. 2023The Moral Machine Experiment on Large Language Modelscited by 24
  7. 2025Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluationcited by 23
  8. 2024Measuring the Inconsistency of Large Language Models in Preferential Rankingcited by 18
  9. 2026Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Modelscited by 7
  10. 2026A Mechanistic View of Authority Hierarchy in LLM Sycophancycited by 6
  11. 2026The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisionscited by 2

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