A new decision sciences for complex systems

Authored by RJ Lempert

Date Published: 2002-05-14

DOI: 10.1073/pnas.082081699

Sponsors: United States Department of Energy (DOE) United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Models of complex systems can capture much useful information but can be difficult to apply to real-world decision-making because the type of information they contain is often inconsistent with that required for traditional decision analysis. New approaches, which use inductive reasoning over large ensembles of computational experiments, now make possible systematic comparison of alternative policy options using models of complex systems. This article describes Computer-Assisted Reasoning, an approach to decision-making under conditions of deep uncertainty that is ideally suited to applying complex systems to policy analysis. The article demonstrates the approach on the policy problem of global climate change, with a particular focus on the role of technology policies in a robust, adaptive strategy for greenhouse gas abatement.

Tags

Agent-based modeling Complex adaptive systems computer-assisted reasoning deep uncertainty robust adaptive planning