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<p>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.</p>

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AI aversion in retailer–manufacturer bargaining: contracts and pricing

  • 期刊论文

  • 发表日期：

    2026年7月25日

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  • Jian Liu,
  • Ruifeng Sun,
  • Sudan Chen,
  • Bo Mao,
  • Donald C. Wunsch II

摘要

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.