"Uncertainty is pervasive in all aspects of climate change. Although this is beyond dispute, the vast majority of research assessing climate ignores uncertainty, in large part because of the technical complexities involved. The present project aims at advancing substantially the way we conceptualize, model and frame the climate change policy making process, focusing on the central role of uncertainty.The first step is that of applying state of the art techniques from operation research (stochastic dynamic and approximate dynamic programming) to the realm of integrated assessment models (the conventional tool used to perform climate change analysis). These techniques enable us to capture a wide range of stochastic phenomena in the decision process. However, to really move forward the research edge one needs to shift the focus on to the way we, as individuals, perceive these uncertain phenomena.Indeed, the literature on decision making under uncertainty spans way beyond economics, statistics and operations research: Notably psychology and philosophy. These disciplines have had a major role in extending what we know about the process of decision making under uncertainty, and this project aims at reconciling this strand of literature with that on climate change policy design and assessment. The three main research questions are:1) What are key risk and uncertainty perception issues and “biases” when we face climate change and under what instances should they be included in normative analyses of climate change?2) How can we map these “alternative” representations of uncertainty and risk perception into integrated assessment models and how will these affect the normative predicaments of these models ?3) How can we communicate and frame uncertainty itself, as well as results of stochastic analyses, in a way that help us reducing those biases that have no normative role, but arise from our limited attentional and information processing capacity?"