6/13/2023 0 Comments The anchoring heuristic![]() ![]() Such heuristics typically produce correct judgments, but they may also lead to systematic errors. In 1974, Tversky and Kahneman published a seminal study of judgment under uncertainty, pointing out that people rely on a limited number of heuristics to carry out complex tasks. This view offered a cognitive alternative to explain human error, while not assuming irrationality in decisions. The core idea behind these studies is that judgments made under uncertainty are frequently based on a limited number of simplifying heuristics, rather than more formal extensive processing. In the 1960s and 1970s, a series of articles by Amos Tversky and Daniel Kahneman revolutionized the academic research of human judgment. ![]() ![]() Due to these limitations, individuals use practical rules, or heuristics, in decision making. Instead, they attempt to do their best, given the limitations to which they are subject. Simon argues that decision makers are limited in their ability to process information and, consequently, do not take entirely rational actions. A more realistic perspective is bounded rationality. This condition permits the attainment of maximum utility in choices, and any limitation in individual processing capacity is ignored or presumably avoided. Rational models assume that individuals have access to complete information in their decisions. Research has shown that individual decision making behavior systematically deviates from normative guidelines based on the presupposition of rationality. This approach considers human behavior to be complex, and its understanding should also take psychological aspects such as intuition and emotion into account. As behavioral decision theory, neuroeconomics also refuses to accept that decisions are led solely by rational thinking. Under this denomination, studies have been conducted on financial decisions of several natures (investment choices purchase, sale and exchange of goods and others). The link between these two fields in the decision-making process led to the development of what is called neuroeconomics. Behavioral finance studies are based on these principles, structured from an interdisciplinary analysis between economics and psychology. Behavioral decision theory has occupied itself mainly with the study, explanation and interpretation of the discrepancies between predictions derived from normative models and real judgments and decisions. Since the 1950s, studies on judgment and decision-making have considered normative models to be important research tools. Over the years, several approaches to the decision-making process have been developed. In this context, accounting and auditing research concerning information processing in individual decision making has focused on understanding, evaluating and improving decision and judgment as applied to these areas’ contexts. Research indicates that decisions are made based on limited or incomplete information, and individuals frequently fail to realize which information is relevant this leads them to erroneous reasoning (). The decision-making process is constantly present in people’s lives. ![]() Therefore, as outlined by, accounting theory seeks to explain and predict the practice of accounting, without a rigorous need for solely approaching future phenomena in fact, in many cases, it specifically targets existing, if unobserved, behaviors of the decision process. In this sense, the use of tools meant to monitor the quality of information and decision-making mechanisms has been soaring in important accounting studies seeking to create models that would explain reality and the empirical verification of observed phenomena. The current Brazilian panorama reflects recent international changes, with the search for greater disclosure, accountability and ethical corporate behavior becoming a constant. Introduction Much of the work pertaining to positive accounting in Brazil and overseas focuses on aspect related to the impact of a certain set of variables on a specific behavior that provides decision makers with information. ![]()
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