Prospect Theory Wakker Pdf Free: How to Apply the Latest Insights from Decision Theory to Real-World

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).

Prospect Theory Wakker Pdf Free

Download Zip

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. 2ff7e9595c