AI Aversion In Retailer–manufacturer Bargaining: Contracts And Pricing

Abstract

Despite artificial intelligence (AI) algorithms often outperforming human predictions, decision-makers frequently discount or override forecasts generated by AI systems. This behavioral tendency, often referred to as AI aversion, has important implications for wholesale price setting and contract negotiations between a retailer and a manufacturer. In this study, we examine two prevalent contract forms—a wholesale price contract and a two-part tariff contract—and use the Nash bargaining solution to investigate how a retailer's aversion to algorithm-generated forecasts affects wholesale prices and expected profits. Our analysis shows that the retailer prefers the wholesale price contract, whereas the manufacturer prefers the two-part tariff contract. Moreover, the effect of AI aversion depends on the discrepancy between the retailer's own demand estimate and the AI forecast. When the retailer's own estimate exceeds the AI forecast, stronger AI aversion raises the bargained wholesale price and can increase both parties' expected profits. When the retailer's own estimate is lower than the AI forecast, stronger AI aversion reduces expected profits for both parties. Numerical experiments further illustrate these results. By explicitly modeling AI aversion as the retailer's behavioral discounting of an algorithm-generated forecast, this study provides new insight into trust, bargaining, and contract design in AI-assisted supply chains.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

National Natural Science Foundation of China, Grant 72471124

Keywords and Phrases

AI aversion; Contract design; Nash bargaining; Pricing; Supply chain optimization

International Standard Serial Number (ISSN)

1866-1505; 1109-2858

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Publication Date

01 Dec 2026

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