Averting risk in the face of large losses: Bernoulli vs. Tversky and Kahneman

We experimentally question the assertion of Prospect Theory that people display risk attraction in choices involving high-probability losses. Indeed, our experimental participants tend to avoid fair risks for large (up to ? 90), high-probability (80%) losses. Our research hinges on a novel experimental method designed to alleviate the house-money bias that pervades experiments with real (not hypothetical) loses.Our results vindicate Daniel Bernoulli?s view that risk aversion is the dominant attitude,But, contrary to the Bernoulli-inspired canonical expected utility theory, we do find frequent risk attraction for small amounts of money at stake.In any event, we attempt neither to test expected utility versus nonexpected utility theories, nor to contribute to the important literature that estimates value and weighting functions. The question that we ask is more basic, namely: do people display risk aversion when facing large losses, or large gains? And, at the risk of oversimplifying, our answer is yes.

Cooperation and game theory, free software or the #15m movement: Two examples for universities

Closing talk of the Open Access Week 2011 at the UOC, by Josep Jover. Why do altruistic strategies beat selfish ones in the spheres of both free software and the #15m movement? The #15m movement, like software but unlike tangible goods, cannot be owned. It can be used (by joining it) by an indeterminate number of people without depriving anyone else of the chance to do the same. And that turns everything on its head: how universities manage information and what their mission is in this new society. In the immediate future, universities will be valued not for the information they harbour, which will always be richer and more extensive beyond their walls, but rather for their capacity to create critical masses, whether of knowledge research, skill-building, or networks of peers... universities must implement the new model or risk becoming obsolete.

The connection between distortion risk measures and ordered weighted averaging operators

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and finite random variables is presented. This connection offers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed.