Abstract
Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM—an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision—theory, experiments, and applications).
Highlights
There is a long tradition of studying decision making (DM) from normative, descriptive, and prescriptive views
We explore the theoretical origins of behavioral decision making (BDM) in an interdisciplinary prescriptive through a critical review of literature
Our paper is concentrated on the theoretical pieces that we deem as the origins of modern BDM
Summary
There is a long tradition of studying decision making (DM) from normative, descriptive, and prescriptive views. Economists study DM under strong assumptions, such as the well-axiomatized von Neumann and Morgenstern expected utility theory (von Neumann and Morgenstern 1944, vNM hereafter). Once these assumptions are satisfied, people’s preferences can be represented by quantitative and measurable values of utility functions, which rationally tell people what decisions to make. These strong assumptions, such as (absolute) rationality and self-interests, seem too ideal to describe the reality of people’s daily DM. As emphasized by Chai and Ngai (2020), descriptive and normative theories should not be distinguished by the interpretation of the models but by their mathematical forms of models
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