Prospect Theory Wakker Pdf Free
Cherrie Patete
Prospect theory proposes a psychologically founded account of decision making under uncertainty, representing uncertainty preferences as a multifaceted concept. The major conceptual insights on which the theory is based can be summarized by reference dependence and likelihood dependence. Reference dependence entails that the utility people derive from a decision is defined over changes in outcomes rather than over absolute outcome levels.
Likelihood dependence captures the observation that people tend to distort probabilities nonlinearly when making a decision. One of the main psychological intuitions underlying both these departures from expected utility theory is the principle of decreasing sensitivity. In terms of utility, decreasing sensitivity implies that a given change in outcomes receives less weight the farther it falls from a reference outcome, resulting in concave utility for gains and convex utility for losses. In terms of probabilities, decreasing sensitivity entails that a given change in probabilities receives much more weight when it falls close to the probability endpoints of 0 and 1 than when it falls into intermediate probability ranges.
It is beyond the scope of this article to provide a comprehensive assessment of all relevant studies in political science. Instead, we start by conducting a review of a selection of prospect-theoretical applications or reviews that were published from 2004; onward, that is the last year in which comprehensive reviews of prospect-theoretical applications in political decision making appeared (Boettcher, 2004; McDermott, 2004). The articles were selected in four steps:
Step 4. Of these 247, we close-read those articles with prospect theory and/or loss aversion in the title and/or abstract. There were 21 articles that met this criterion (18 applications, 3 review articles).
When examining existing reviews of prospect theory and applications in the literature on political decision making with an eye to the notion of probability weighting, three things stand out: (1) many researchers in political science and international relations are aware of the centrality of probability weighting in prospect theory; (2) in prospect-theoretical applications, probability weighting is usually ignored (see Neilson, 2003, p. 171); and (3) this ignoring of probability weighting seems to occur more and more as the time passes since the first applications in political decision making in the early 1990s. Let us elaborate on these points some more.
Some studies do mention probability weighting, sometimes in a footnote, but do not include it in the analysis itself (e.g., Baekgaard, 2017; Elms, 2008; Haerem, Kuvaas, Bakken, & Karlsen, 2011). Elms (2008), for instance, concentrated on framing and loss aversion in her re-reading of existing studies in international political economy through a behavioral economics lens. Similarly, Haerem et al. (2011) discussed the fourfold pattern of risk attitudes but do not incorporate this in their analysis of whether military decision makers behave in line with prospect theory. Baekgaard (2017), moreover, conducted three experiments (one with Danish citizens and two with MTurkers from the United States) to examine whether prospect theory applies to public sector reforms. Baekgaard (2017) discussed probability weighting but formulates hypotheses only on risk aversion and the reflection effect.
2. The original formulation of prospect theory had some problems and limitations. By incorporating the idea of rank dependence (Quiggin, 1982), Tversky and Kahneman (1992) made the theory mathematically consistent and expanded its applicability from the case of risk (known probabilities) to uncertainty (unknown probabilities); see Wakker (2010) for details of the differences.
7. Some scholars in psychology have taken a different, albeit equally artificial, approach, whereby people are required to sample from unknown distributions to get to know the probabilities before making a decision (Hertwig & Ortmann, 2001). While initial claims suggested that data in this paradigm fundamentally contradicted prospect theory, other scholars quickly showed that the alleged differences were due to sampling issues that resulted in discrepancies between the true and perceived probabilities. Taking this into account, prospect theory indeed performed rather well in accounting for the observed decision patterns (Fox & Hadar, 2006). This example further underlines the importance of getting the subjective probabilities right.
12. To some extent, the different modeling approaches are interchangeable. Indeed, ignoring the loss outcomes beyond a certain point completely corresponds to adopting a utility function for losses that has no sensitivity for losses beyond that point. If one wants to adopt typical parameter values estimated in the prospect theory literature, however, such an approach would not work.
Introduction to the fundamental models involved in the classical optimization problems of operational research, from linear programming (simplex) to integer programming (branch-and-bound). The framework of the modern theory of decision under risk, from Von Neumann and Morgenstern's Expected Utility Theory to Kahneman and Tversky's Cumulative Prospect Theory. Finally, introduction to the theory of ordinary linear differential equations.
The syllabus of the module is structured into three parts, after a preliminary review of the fundamental notions and techniques of linear algebra and linear systems: (I) linear programming (simplex) and integer programming (branch-and-bound); (II) models of decision under risk, the framework of anticipated utility theory; (III) first and second order ordinary linear differential equations.
A final written exam, involving both theory and exercises, in which the student is assessed on his knowledge and understanding of the topics covered in the course. Exceptionally, the lecturer may request a supplementary oral exam.
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