Coping with uncertainties-examples of modeling approaches at IIASA

Abstract

Uncertainty is a pervasive characteristic of all research addressed at the International Institute for Applied Systems Analysis (IIASA) which is at the core of this Special Issue. The role of science in better coping with uncertainty is twofold. First, to describe uncertainties as comprehensively and well as possible, both quantitatively and qualitatively; second, to develop methods that can lead to improved decision-making under uncertainty. Here increasingly the concept of “optimal” is replaced by one of “robust” decisions, i.e. decisions that make sense vis à vis multiple uncertainties. This paper illustrates selective examples from IIASA research that contribute to the twin objectives of a better description of uncertainty and improved decision-making under uncertainty, drawing from research in the fields of technology dynamics, climate change policy, as well as catastrophic risk management and portfolio analysis. The conclusions emphasize the need for a basic research strategy aimed at elucidating uncertainties in parameters as well as in alternative model representations, and in developing improved models for robust decision-making. Models of robust decision making emphasize risk hedging spatially and through a portfolio of policies and technology options.