Strategic Adaptation Under Contextual Change: Insights from a Dyadic Negotiation Testbed for AI Coaching Technologies

TL;DR

This study introduces a controlled negotiation testbed to evaluate AI coaching systems' ability to adapt strategically to changing circumstances, revealing how adaptation impacts interaction dynamics and relational outcomes.

Contribution

It provides a novel, repeatable framework for assessing strategic adaptation in negotiation AI systems under controlled perturbations.

Findings

Perturbation reorganized interaction dynamics.

Distributive drift predicted worse relational experience.

Adaptation patterns were path dependent.

Abstract

Strategic adaptation -- the ability to adjust interaction behavior in response to changing constraints and leverage -- is a central goal of negotiation training and an emerging target for AI coaching systems. However, adaptation is difficult to evaluate because adaptation-relevant moments arise unpredictably in typical tasks. We study a reusable dyadic negotiation testbed that employs a controlled midstream change in one party's outside alternative as a repeatable perturbation to stress-test adaptation. In a six-round chat-based negotiation study (N=100), the perturbation reliably reorganized interaction dynamics: transitions between integrative (cooperative) and distributive (positional) behaviors declined, behavioral diversity narrowed, and interactions drifted toward more distributive tactics. Critically, this distributive drift predicted worse relational experience net of objective outcomes, and adaptation patterns were path dependent on prior behavior. These results establish a methodological bridge for evaluating and comparing AI coaching systems on strategic adaptation as a process and identify failure modes and design targets for adaptive interaction support.