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.
“Whenever there is a simple error that most laymen fall for, there is
always a slightly more sophisticated version of the same problem that
experts fall for.”
Amos Tversky
Top1. Introduction
The classical economic model often assumes perfect human rationality. In this model, individuals consistently act to maximize their utility as consumers and their profits as producers. Furthermore, it is assumed that they possess the ability to perform highly complicated calculations to evaluate all potential outcomes and opt for the most beneficial course of action accordingly (Camerer & Fehr, 2006). Nevertheless, this model has faced extensive criticism due to its limited capacity to comprehensively elucidate consumer behavior. In contrast to traditional economic models, empirical evidence and research from behavioral economics have shown that human decision-making is frequently affected by cognitive biases, emotions, and social factors that deviate from the assumption of perfect rationality (Kahneman, 2011; Thaler & Sunstein, 2008).
First and foremost, the concepts of human rationality, heuristics, and cognitive bias are crucial for understanding cognitive processes in decision-making (Kahneman, 2011). At this juncture, rationality can be defined as the ability to make logical, reasoned, and consistent decisions or judgments while adhering to established rules and standards (Stanovich & West, 2000). Heuristics, on the other hand, refer to cognitive shortcuts used to simplify decision-making processes and make quick judgments with minimal cognitive effort. Conversely, cognitive biases denote systematic deviations from rationality, frequently leading to systemic errors, suboptimal decisions, and judgmental evaluations (Kahneman et al., 1982; Tversky & Kahneman, 1974). These systematic deviations frequently occur when heuristics lead to errors or suboptimal judgments owing to their simplified nature (Tversky & Kahneman, 1974). While heuristics can be helpful in certain contexts (e.g., Gigerenzer & Brighton, 2009), they can also result in biases when they lead individuals to make decisions that do not align with objective criteria and/or statistical probabilities (Gilovich et al., 2002).
The impact of cognitive biases on decision-making has been extensively studied across various domains. For example, studies in the finance realm have highlighted the role of cognitive biases in suboptimal investment decisions (Barber & Odean, 2001). Likewise, in marketing, research has also addressed cognitive biases in consumer decisions (Cialdini, 2011; Griskevicius et al., 2009; Kahneman, 2011). A vast number of studies have highlighted how cognitive biases can lead to diagnostic errors and suboptimal treatment decisions (e.g., Croskerry, 2009; Graber et al., 2005); another cognitive bias such as negativity bias (Yan & Sengupta, 2013). Drawing upon the information processing model, Li and Sullivan (2020) suggest that managerial hubris results in a diminished level of strategic foresight due to biases in attending to information as well as encoding and processing information. In a similar vein, recurring cognitive biases were identified as having significant effects on managers’ decision-making processes while implementing a performance management system (PMS) (Hristov et al., 2022).
Key Terms in this Chapter
Bayesian Reasoning: 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.
Case Information: Case information (i.e., individuating information) involves specific details or instances pertinent to a particular target. In contrast to base-rate information, which deals with broader and more general statistical facts, case information focuses on individual examples and concrete details.
Psychological Distance: Psychological distance is a cognitive distinction between the self, here-and-now, and other entities, including persons, space, or times. The term is closely associated with construal level. An object, event, or action is mentally considered more abstract when it is more psychologically distant (in terms of time, space, social context, or hypotheticality). Similarly, an object, event, or action is construed in a more concrete manner when they are nearer in terms of psychological distance.
Evolutionary Paradigm: The evolutionary paradigm encompasses theories and perspectives that explore decision-making processes from an evolutionary standpoint. It investigates how cognitive mechanisms and biases may have evolved to enhance survival and reproduction, providing insights into the adaptive nature of decision-making (e.g., ecological rationality, domain-specificity).
Base-Rate Information: The term base rate refers to prior probabilities, which are the types of probabilities unconditional on featural evidence (i.e., likelihoods). Likewise, base-rate information refers to statistical information, including probabilities, that offer a general understanding of the frequency or prevalence of specific events within a given population. It serves as a foundational element in decision-making by providing context about the likelihood of occurrences.
Conservatism: Conservatism, when considered in opposition to base-rate neglect, signifies a decision-making tendency where individuals overutilize base-rate information while neglecting case information in their judgments.
Ecological Rationality: Ecological rationality involves decision-making strategies that align with the environmental or contextual demands of a specific situation. This paradigm emphasizes that heuristics and biases are not associated with irrationality; rather, they are environmentally optimized algorithms for organisms’ evolutionary adaptive success in certain circumstances.
Bias-Variance Tradeoff: 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.