He says despite 390 years and usefulness of Prospect theory, its applications have been limited so far (mostly in finance and insurance). However, things are looking better with some researchers looking at applications in other fields:
Prospect theory, first described in a 1979 paper by Daniel Kahneman and Amos Tversky, is widely viewed as the best available description of how people evaluate risk in experimental settings. While the theory contains many remarkable insights, economists have found it challenging to apply these insights, and it is only recently that there has been real progress in doing so. In this paper, after first reviewing prospect theory and the difficulties inherent in applying it, I discuss some of this recent work. While it is too early to declare this research effort an unqualified success, the rapid progress of the last decade makes me optimistic that at least some of the insights of prospect theory will eventually find a permanent and significant place in mainstream economic analysis.
Apart from PT’s applications, paper also explains basics of PT. It has four elements:
This formulation illustrates the four elements of prospect theory: 1) reference dependence, 2) loss aversion, 3) diminishing sensitivity, and 4) probability weighting.
First, in prospect theory, people derive utility from gains and losses, measured relative to some reference point, rather than from absolute levels of wealth…we are more attuned to changes in attributes such as brightness, loudness, and temperature than we are to their absolute magnitudes.
Second, the value function captures “loss aversion,” the idea that people are much more sensitive to losses – even small losses — than to gains of the same magnitude.
Informally, loss aversion is generated by making the value function steeper in the region of losses than in the region of gains.
Third, as shown in Figure 1, the value function is concave in the region of gains but convex in the region of losses. This element of prospect theory is known as diminishing sensitivity because it implies that, while replacing a $100 gain (or loss) by a $200 gain (or loss) has a significant utility impact, replacing a $1000 gain (or loss) by a
$1100 gain (or loss) has a smaller impact.
In cumulative prospect theory, the weighting function is applied to cumulative probabilities – for example, to the probability of gaining at least $100, or of losing $50 or
more. For the purposes of understanding the applications I describe later, the main thing the reader needs to know about probability weighting is that it leads the individual to overweight the tails of any distribution – in other words, to overweight unlikely extreme outcomes.
Hmmm.. The paper then discusses applications around these four central aspects of PT.
Why applications of PT have been limited?
I noted earlier that the reason that developing applications of prospect theory in economics is taking a long time is because it is not always obvious how, exactly, to apply
it. The central idea in prospect theory is that people derive utility from “gains” and “losses” measured relative to a reference point. But, in any given context, it is often unclear how to define precisely what a gain or loss is, not least because Kahneman and Tversky offered relatively little guidance on how the reference point is determined.
An example from finance may help to make this difficulty more concrete. Suppose that we want to predict what kind of portfolio an investor with prospect theory
preferences will hold. Right away, we need to specify the “gains” and “losses” the investor is thinking about. Are they gains and losses in overall wealth, in the value of
total stock market holdings, or in the value of specific stocks? If the investor’s focus is on gains and losses in the value of his stock market holdings, does a “gain” in the stock
market simply mean that the return on the stock market was positive? Or does it mean that the stock market return exceeded the risk-free rate, or the return the investor expected to earn? And is the investor thinking about annual gains and losses, or about monthly or even weekly fluctuations?
Some researchers have been scared off by the lack of a clear answer to these questions. Other researchers, however, have grasped the challenge of trying to understand
how people conceptualize gains and losses in different contexts. The best way to tackle this question — and the main approach researchers are taking — is to derive the
predictions of prospect theory under a variety of plausible definitions of gains and losses, and to then test these predictions, both in the laboratory and in the field. Through this process, we are gradually developing better theories of how people construe these gains and losses.
Why applications of PT fit in finance and insurance?
Prospect theory is, first and foremost, a model of decision-making under risk. As such, the most obvious places to look for applications are areas such as finance and
insurance where attitudes to risk play a central role. I therefore start by discussing efforts to integrate prospect theory into these two fields and then turn to other areas of
Finance is the field of economics where prospect theory has been most actively applied. The research in this area applies prospect theory in three main contexts: 1) the
cross-section of average returns, where the goal is to understand why some financial assets have higher average returns than others; 2) the aggregate stock market; and 3) the trading of financial assets over time. I take each of these in turn.
…Insurance is another area of economics where attitudes to risk play a central role. As such, it, too, is a promising place to look for applications of prospect theory. The most important consumer insurance markets are those for property and casualty insurance, mortality insurance — the main products here are life insurance and annuities — and health insurance. Thus far, prospect theory has been used to shed light on the first two of these three markets.
What are the other areas? PT is being used to help explain things like Consumption, Endowment effect, labor supply, betting markets etc.
In the end. It is unlikely that PT can replace traditional eco. However, it can help u understand our wprld better:
Even prospect theory’s most ardent fan would concede that economic analysis based on this theory is unlikely to replace the analysis that we currently present in our
introductory textbooks. It makes sense to teach students the fundamental concepts of economics using a traditional utility function, not least because this is simpler than using prospect theory. Indeed, while Mankiw’s best-selling undergraduate economics textbook devotes part of a chapter to behavioral economics, it makes no specific mention of prospect theory anywhere in its 900 pages. However, as prospect theory becomes more established in economics, a reasonable vision for future textbooks is that, once they complete the traditional coverage of some topic – of consumer behavior, say, or of consumption-savings decisions, industrial organization, or labor supply – they will follow this with a section or chapter that asks: Can we make more sense of the data using models that are based on psychologically more realistic assumptions? I expect prospect theory to figure prominently in some of these, as yet unwritten, chapters.
Superb stuff. A very useful primer on PT. Should be made a part of reading list on behavioral economics…