If there is one characteristic of today's environmental problems is its complexity. We are now faced with the challenge of solving problems where time delays, feedback loops, non-linearities and system interconnectedness make prediction particularly difficult. In this realm small events can have big effects, causes are multiple and separated in time, problems transcend interdisciplinary arenas. Additionally, we, with our beliefs and perceptions about nature, are part of the problem to be solved influencing the way in which we identify and conceptualize reality, and drive inference from it. These characteristics make environmental problems intrinsically uncertain, difficult to predict and to manage.
When dealing with these types of problems, computer models play a central role. They constitute general and flexible tools that can help dealing with complex and uncertain phenomena. In this session we investigate what complexity and uncertainty mean for models and the way we approach modeling. We aim at revising the assumptions upon which models are being built and used, claiming that the modeling activity needs to shift its goals of predictions to a new role of communication, exploration and understanding. Particularly our focus is on models and methods that allow a pluralistic representation of reality, embracing different types of uncertainties and ambiguities, and that advance the modeling activity by combining with other methods of knowledge integration.
The workshop is organized as part of TIAS (The Integrated Assessment Society).
Click here to go to the associated Session 1.