Summary:
Pricing decisions stand out as one of the most critical tasks a company faces, particularly in today's digital economy. As with other business decision-making problems, pricing unfolds in a highly competitive and uncertain environment. Traditional analyses in this area have heavily relied on game theory and its variants. However, an important drawback of these approaches is their reliance on common knowledge assumptions, which are hardly tenable in competitive business domains. This paper introduces an innovative personalized pricing framework designed to assist decision-makers in undertaking pricing decisions amidst competition, considering both buyer's and competitors' preferences. Our approach (i) establishes a coherent framework for modeling competition mitigating common knowledge assumptions; (ii) proposes a principled method to forecast competitors' pricing and customers' purchasing decisions, acknowledging major business uncertainties; and (iii) encourages structured thinking about the competitors' problems, thus enriching the solution process. To illustrate these properties, in addition to a general pricing template, we outline two specifications—one from the retail domain and a more intricate one from the pension fund domain.
Spanish layman's summary:
Este artículo presenta un marco de precios personalizado para tomadores de decisiones, abordando la competencia y la incertidumbre. Supera las limitaciones de la teoría de juegos al evitar suposiciones de conocimiento común, pronosticando precios de competidores y elecciones de clientes.
English layman's summary:
This paper presents a personalized pricing framework for decision-makers, addressing competition and uncertainty. It overcomes limitations of game theory by avoiding common knowledge assumptions, forecasting competitors' pricing and customers' choices, and enhancing structured thinking about competitors' strategies.
Keywords: pricing decisions; business competition; decision analysis; adversarial risk analysis; Bayesian methods
JCR Impact Factor and WoS quartile: 3,100 - Q2 (2023)
DOI reference:
https://doi.org/10.1111/itor.13545
In press: September 2024.
Citation:
D. García Rasines, R. Naveiro, D. Ríos Insua, S. Rodríguez-Santana, Personalized pricing decisions through adversarial risk analysis. International Transactions in Operational Research.
