A cross-game equilibrium framework for learning new product ideas from consumer preferences

Learning new product ideas (NPIs) from consumer preferences is a fundamental yet challenging task in market-driven new product development (NPD). Existing studies typically treat the generation of NPIs as a static mathematical aggregation problem, overlooking the fact that it inherently involves strategic interactions among consumers competing over limited product definition resources. Consequently, these methods rarely yield stable and fair equilibrium solutions. This paper introduces an innovative cross-game equilibrium (CGE) framework, which reconceptualizes NPI generation as a decentralized strategic negotiation process. The framework models consumers as rational agents whose preferences dynamically evolve under peer-induced compatibility constraints. No single consumer preference dominates the product design; instead, preferences mutually constrain and negotiate, converging to a stable state where no individual has an incentive to deviate. The resulting equilibrium solution balances individual rationality with collective compatibility, fundamentally avoiding the averaging bias typical of traditional aggregation methods. An empirical study in the automotive industry shows that the CGE framework generates NPIs with significantly higher structural consistency and market representativeness compared with benchmark methods, offering firms a more robust and practical tool for market-driven NPD decisions.