Why is the Marginal Utility of losses deminishing in Prospect Theory? This is Kahneman's value-plot on prospect theory:



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 dollar for sure or a 50% chance to lose 200 dollar I would definately 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 dollar I might regret losing 100 dollar very much, but I will regret losing a second 100 dollar 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
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}
&amp;\text{gains}&amp;\text{losses}\\\hline
\text{low probability}&amp;\text{risk loving}&amp;\text{risk averse}\\
\text{high probability}&amp;\text{risk averse}&amp;\text{risk loving}
\end{array}

See Kahneman and Tversky (1992) for more detail.