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This is Kahneman's value-plot on prospect theory:

enter image description here

QUESTION: Why is the Marginal Utility of losses deminishing?

CONTEXT:

  • I fully understand that the Marginal Utility of gains deminishes: 100 dollar gains has less utility to me when I am rich than when I am poor.

  • But to me it is different with losses: If I can choose to lose 100 dollars for sure or a 50% chance to lose 200 dollars I would definitely not go for the bet! Why would I risk losing even more? After all, like most people, I am risk-averse. My reference point would be that I already lost 100 euro's.

  • Furthermore: If all I own should be 200 dollars, I might regret losing 100 dollars very much, but I will regret losing a second 100 dollars even more because then I would be left with nothing and I can't buy any food.

  • In other words: I guess my reference point is always zero and compared to that I will always be risk-averse... but isn't that what most people do or should do?

UPDATE: This article shows that the Value-curve can also be shaped differently, for example under time pressure: https://www.sciencedirect.com/science/article/pii/S0749597815000722

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The value function used in Kahneman's prospect theory (which your plot shows) is supposed to capture empirically observed behavior of people's attitudes towards gains/losses as well as to risks in those domains.

In the domain for gains, people are usually risk averse. However, in the domain for losses, people do tend to take larger risks. A nice illustration is the observation that when a gambler is losing money (relative to the amount he came to the casino with), he tends to wager on higher stakes (larger risks), perhaps in an attempt to "win back" the losses.

So the convex curvature in the domain of losses is meant to capture this type of behavior.

In fact, prospect theory's treatment of people's risk attitude is even more nuanced than the above. In addition to the S-shaped value function you showed, it also has a component that allows for non-linear probability weighting. Together, the value function and non-linear probability weighting generate the following four-fold classification of risk attitudes: \begin{array}{c|cc} &\text{gains}&\text{losses}\\\hline \text{low probability}&\text{risk loving}&\text{risk averse}\\ \text{high probability}&\text{risk averse}&\text{risk loving} \end{array}

See Kahneman and Tversky (1992) for more detail.

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  • $\begingroup$ Doesn't your table imply that the shape of the value function depends on the quantity of the probabilities involved? $\endgroup$
    – GambitSquared
    Commented Mar 1, 2018 at 0:08
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    $\begingroup$ @GambitSquared: Yes, it does. As I said, the value function and non-linear probability weighting generate the four-fold classification of risk attitudes. Prospect theory consists of both of these components. What you show in the question is only about the value function, which is only part of PT. $\endgroup$
    – Herr K.
    Commented Mar 1, 2018 at 0:12
  • $\begingroup$ Furthermore the fact that this plot is supposed to be empirical doesn't connect it to my intuition and personal preference (see examples in my question). I just don't understand why someone would take such a bet. We aren't all addicted to gambling, are we? $\endgroup$
    – GambitSquared
    Commented Mar 1, 2018 at 0:16
  • $\begingroup$ Would it be correct that the shape of the value function represents the high-probability case? What would the shape of the value function be in the low-probability case? $\endgroup$
    – GambitSquared
    Commented Mar 1, 2018 at 0:19
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    $\begingroup$ @GambitSquared: The gambler's example in my answer is well-documented. I'm not making it up. Also, the paper linked at the end reports experimental evidence that a convex curvature in the domain of losses is justified. Regarding your last two questions: it may appear the way you're suggesting, but when the S-shaped value function and non-linear probability weighting are put together, it's hard to separate the effects of individual components, since the combo is highly non-linear. $\endgroup$
    – Herr K.
    Commented Mar 1, 2018 at 0:27

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