What is optimal choice under uncertainty? This is a fundamental question in artificial intelligence, as well as in economics and psychology. The standard answer comes from expected utility theory but assumes an environment in which possible outcomes of actions, and their probabilities, are known. But what if this is not the case (as is commonly true in the real world)? When potential outcomes and their likelihoods have to be learned from experience (through sampling and exploration), depending on the exploration strategies being used, the representation of the outcome space may be systematically incomplete and the beliefs about event likelihoods may be systematically biased. By studying how humans deal with uncertainty – how we manage the information available to construct decision rules – and understanding the properties of the environments in which they are made, I expect to find effective and transparent rules of decision that help human-machine interaction in automated decision making. If successful, new ways of approaching automated decision making would be provided, with potential applications to improve decision making in industry and the public sector.
Experience-based decisions under uncertainty.
Cognitive models of judgment and decision making.
Bounded and environmental rationality.
BSc Economics at University of Almeria
MSc Finance at Carlos III University of Madrid
MSc Economics (Behavioral and Experimental Economics) at University of Granada
Dr Özgür Şimşek
Prof Ralph Hertwig (MPI, Berlin)