Presentation

Financial risks are a relatively recent phenomenon, evolutionary speaking. As such, the human brain may not have perfectly adapted protocol to deal with it. In a first stage, we would like to know how the brain perceives risks and how it generates decisions in very controlled, say "ecologically relevant" situations. We already know quite a bit about reward learning, and how simple but robust temporal difference modeling is hardwired in the human brain (and the brains of monkeys, rats and mice, for that matter). My team has brought another aspect to the forefront in this regard: risk perception and risk learning. These are necessary for any organism that is risk-sensitive (whether risk-averse of risk-loving) or even just attempts to learn to predict stochastic payoffs as accurately as possible.

We not only study risk-taking, but also exploration and related behaviors. And we pay particular attention to intentional risk (uncertainty created by a goal-directed opponent or even social structure -- like a financial market), because the brain does not treat this type of risk like non-intentional, "naturally occurring" risks. In many respects, the human brain is well adapted to recognize and react to such "social" risks, perhaps more than pure financial risks (which may explain why humans are so good at detecting insider traders).

We are also interested in studying how humans perceive causality, whether, why and when they think about "models" behind observed (uncertain) outcomes which, if only partially true, may help them in decision making. Particularly pressing is the question how humans actually manage to form models ("theorize") about their environment, because the complexity (of this environment) makes it basically impossible to consider all possibilities, and hence, somehow humans have to limit their attention to a few reasonable models.