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Prospect Theory: For Risk and Ambiguity

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Prospect For Risk and Ambiguity provides the first comprehensive and accessible textbook treatment of the way decisions are made both when we have the statistical probabilities associated with uncertain future events (risk) and when we lack them (ambiguity). The book presents models, primarily prospect theory, that are both tractable and psychologically realistic. A method of presentation is chosen that makes the empirical meaning of each theoretical model completely transparent. Prospect theory has many applications in a wide variety of disciplines. The material in the book has been carefully organized to allow readers to select pathways through the book relevant to their own interests. With numerous exercises and worked examples, the book is ideally suited to the needs of students taking courses in decision theory in economics, mathematics, finance, psychology, management science, health, computer science, Bayesian statistics, and engineering.

518 pages, Paperback

First published May 1, 2010

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Peter P. Wakker

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Displaying 1 - 3 of 3 reviews
Profile Image for Chen Li.
3 reviews
April 2, 2013
You can find almost all you want to know about decision under risk and uncertainty, well organized and explained in this book.
Profile Image for Brok3n.
1,462 reviews113 followers
April 5, 2021
Dry but thorough

Early in Prospect Theory, Peter P. Wakker writes the following:

De Finetti's electrifying writing.

When I, as a mathematics student in 1978, expressed amazement about the claim made by my statistics teacher – a frequentist as I now know – that the probability of life on Mars could not be defined, and that it was even treated differently than the probability of a random repeatable event such as related to coin tosses, he referred me to Bruno de Finetti's work. (“There is a crazy Italian who thinks such things” were his exact words.) De Finetti's (1972) first chapter, written in a thought-provoking manner, opened up to me the technique of behavioral foundations, and the possibility of defining something as seemingly intangible as one's subjective degree of belief, in a tangible manner. De Finetti showed how we can read the minds (beliefs, i.e. subjective probabilities) of people. I felt electrified by his ideas, and decided that I wanted to work on them. I hope that the readers will also sense some of the magic of these ideas.


I am sorry to say that no one is likely to write paeans to Wakker's electrifying writing. It is not that Wakker lacks interesting ideas. He has definite thoughts on how behavioral models should be designed, which he presents. He mostly takes the attitude of one who studies the beast without judgment. That is, his goal is to describe and, as far as possible, understand how humans make decisions. He seldom speaks of how one SHOULD make decisions. The main exception is when he discusses whether a particular aspect of a decision model is "rational" Even then, one gets the impression that he does that not to make judgments, but to point out the practical implications of irrationality for modeling -- namely that the irrational parts of models tend to be volatile and hard to measure. In fact, as the quote about De Finetti illustrates, Wakker is deeply invested in the idea of measurement. For him, a model whose internals cannot be measured is a defective model.

This is a workmanlike textbook: dry but clear. I am not an economist -- I am a mathematician -- but this book felt very familiar to me. It reads like a graduate-level math textbook or monograph: axioms and theorems. Although the mathematics superficially appears fairly basic -- there is very little in the calculations that is not within reach of one who remembers high-school algebra (Wakker only rarely uses calculus), the math is actually quite advanced in the mode of reasoning and reliance on mathematically rigorous proofs.

Wakker makes it clear that it is not necessary to read the entire book. In fact, he goes to some effort to explain how to avoid reading the whole book. Appendix K shows in detail how to read parts of the book to learn only what you need to know. However, I read almost the whole book. That is, I read chapters 1-10 from beginning to end. I only skimmed chapters 11 and 12, because by then it had become apparent that the different theories presented were variations on e theme, and it was already fairly obvious how elements presented in chapters 1-10 could be mixed and matched to produce the theories discussed in 11 and 12.

Chapters 1-4 were a hard slog -- dull, and not terribly rewarding. Things began to get interesting in chapter 5, which presents the first (to me) genuinely original new idea: rank dependence. The hard work of chapters 1-4 then paid off. On that foundation the new ideas were fairly easily understood. Chapter 5 is the first of several later chapters that is less exclusively mathematical. It consists mostly of exposition and heuristics explaining and selling the new idea. Chapter 8, the first to introduce Prospect Theory proper, is another of these more explanatory chapters.

In summary, if you want a thorough, mathematically rigorous presentation of Prospect Theory, this is the book for you. If you want a more entertaining presentation of the ideas behind Prospect Theory, you're probably better off with Kahneman Daniel.
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