In the context of statistics and machine learning, it refers to the interplay between a model's complexity, its predictive accuracy, and its ability to generalize to new, unseen data that was not part of the training process.
Published in Chapter:
Beyond the Odds: Framing and Taming Base-Rate Neglect in Organizational and Consumer Decision-Making
Copyright: © 2024
|Pages: 23
DOI: 10.4018/979-8-3693-1766-2.ch001
Abstract
This chapter offers an exploration of base-rate neglect in decision-making, focusing on its implications across various domains, including marketing, consumer behavior, and strategic management. The chapter unfolds in four key dimensions: first, it revisits and discusses base-rate neglect within a novel framework. Second, it provides theoretical perspectives elucidating how base-rate neglect operates in decision-making, incorporating theoretical stances from the heuristics and biases program, evolutionary paradigms, and construal level theory. Third, the chapter examines recent developments in the literature to identify mitigating factors that influence this bias. Lastly, it proposes managerially relevant remedies for decision-makers to counteract base-rate neglect, offering insights into the application of bias-mitigating strategies. In light of an interdisciplinary theoretical lens, this chapter contributes to a deeper understanding of base-rate neglect, offering theoretical insights and practical strategies for decision-makers at both the organizational and consumer levels.