基于词典选择的先考虑后选择模型下的价格竞争
Price Competition Under Platform-Mediated Search: A Consider-Then-Choose Framework
AI总结：
针对电商平台排序筛选影响消费决策与定价的问题，研究带词典选择的先考虑后选择（CLC）模型下的价格竞争，刻画均衡结构，提出梯度优势条件以实现高效均衡计算，并验证理论结果的鲁棒性。
摘要：
现代电商平台提供的排序与筛选功能显著影响消费者的购买决策，以及平台上竞争卖家设定的最终价格。受这一实际情况驱动，我们研究了一种灵活选择模型下的价格竞争：带词典选择的先考虑后选择模型（Consider-then-Choose with Lexicographic Choice, CLC）。在该模型中，消费者首先基于（i）自身支付意愿和（ii）商品非价格属性的任意一组标准形成卖家考虑集；随后根据词典排序选择排名最高的商品，即在更重要属性上表现更优的商品排名更高。我们对由此产生的价格竞争博弈的均衡给出了结构刻画，并推导出一个具有经济学解释性的条件，我们称之为梯度优势，在该条件下可高效计算均衡。对于这类CLC模型子类，我们证明了基于分布式梯度的定价动态会收敛到均衡集。大量数值实验表明，在梯度优势不成立的情况下，我们的理论发现仍具有鲁棒性。
英文摘要：
We study the problem of predicting price equilibria on e-commerce platforms where sellers compete across multiple attributes (e.g., price, average rating, delivery speed). In these settings, a platform's design choices --- such as its display ranking, filtering tools, and promotional badges --- critically shape customer search and purchase behavior, which in turn determine sellers' equilibrium pricing strategies. Our goal is to develop a tractable framework that allows a platform to anticipate the market impact of its design interventions. We consider a behavioral model --- Consider-then-Choose with Lexicographic Choice (CLC) --- specifically tailored to platform-mediated search. We establish that any local Nash equilibrium admits a sequential-move characterization; this yields a tractable procedure for computation under an interpretable sufficient condition, which we term gradient dominance. We further prove that under gradient dominance, simple, decentralized gradient-based algorithms converge to an equilibrium, providing platforms with a method for simulating market outcomes. Finally, we use our framework to study how platform design affects market outcomes. Our framework applies to any platform in which sellers compete on multiple attributes and customer choice is guided by the platform's interface. In these environments, sellers' pricing strategies must be understood not in isolation, but as a response to the platform's design. Our work provides platform operators with a rigorous toolbox to efficiently evaluate how changes to interface design, information disclosure, and ranking policies can affect competitive outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
