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What is Bayesian Reasoning

Overcoming Cognitive Biases in Strategic Management and Decision Making
Bayesian reasoning (i.e., Bayesian inference) is a probabilistic approach to decision-making that incorporates prior knowledge and adjusts beliefs based on new evidence. It is a method of rational inference based on Bayesian statistics, providing a framework for updating probabilities as information evolves.
Published in Chapter:
Beyond the Odds: Framing and Taming Base-Rate Neglect in Organizational and Consumer Decision-Making
Caner Cesmeci (Istanbul Beykent University, Turkey)
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.
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More Results
Computers and Artificial Intelligence in Future Education
The Bayes’ theorem calculates the conditional probability P(A/B) of an event A to happen when the event B has already happened, with the help of the inverse in time conditional probability P(B/A), the prior probability P(A) and the posterior probability P(B). Since the changes of the value of P(A) result to different values of P(A/B), Bayesian Reasoning defines a multi-valued logic treating the existing due to the imprecision of the values of P(A) uncertainty in a way analogous to fuzzy logic. Therefore, Bayesian Reasoning could be considered as an interface between bivalent and fuzzy logic. Recent researches acknowledge the important role of Bayesian reasoning to everyday life and AI applications and for the whole science in general.
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Business Processes, Dynamic Contexts, Learning
Stochastic reasoning based on conditional probabilities.
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