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Preprint Aug 2026

The Authority Expectancy Effect in Multi-Party Conflict

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...

E. Lee · 0 citations
Preprint Aug 2026

Capability Is Not Propensity: Measuring Pressure-Robust Cooperative Behavior in Civic LLM Agents

This work argues that Cooperative AI evaluations should separate what models can do under benign instructions from what they tend to do under realistic civic pressure, and introduces DiffCoop-Civic, a 10-scenario pilot evaluation suite spanning preference understanding, evidence and persuasion, commitment design, asymm...

Neel Tushar Shah, Manglam Kartik, Akshat Karkar · 0 citations
#artificial intelligence Preprint Sep 2026

Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding

This work identifies a failure mode not addressed by a stronger model: when the context contains an assertion by a party with an incentive toward optimism - here the sales representative, a witness recorded in the CRM - the model treats the assertion as evidence and clears deals the company's own records deem unaccepta...

Rahul Balakavi · 0 citations
Open access Sep 2026

Cultural conformity reverses the effectiveness of trust-based management: an Ising-inspired model of the Care-Dare dilemma

An agent-based model inspired by the Ising framework is developed to formalize the Care-Dare dilemma in organizational trust dynamics: agents in a well-mixed population interact through trust-building “Care” actions and growth-demanding “Dare” actions, with behavioral tendencies updated via an action-feedback update ru...

Shosuke Inoue, Kaira Sekiguchi, Yukio Ohsawa · 0 citations
#explainable ai Book Open access Sep 2026

Exploring Trust Factors in Designing AI-Supported Performance Appraisal System

The findings suggest that trustworthy AI integration requires addressing pre-existing structural failures before introducing algorithmic decision support, and offer design recommendations for AI-supported appraisal systems, with implications for HCI research and organizational practice.

Andreas Harnindito, Akshat Sawarni · 0 citations

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