Uncertainty management in integrated assessment modeling: towards a pluralistic approach

Area of Science:

• Environmental Science
• Social Science
• Policy Analysis

Background:

• Integrated Assessment (IA) is an interdisciplinary research field addressing complex societal issues.
• Modeling is the primary methodology within IA, with a rich history and diverse approaches.
• Current IA modeling faces significant challenges, particularly in managing uncertainty.

Purpose of the Study:

• To describe the state-of-the-art in Integrated Assessment modeling.
• To outline sources and types of uncertainty in IA.
• To evaluate current uncertainty management practices and propose improvements.

Main Methods:

• Literature review of IA modeling history, features, and classes.
• Analysis of strengths, weaknesses, dilemmas, and challenges in IA modeling.
• Evaluation of current uncertainty management techniques in IA.

Main Results:

• IA modeling encompasses various approaches with inherent strengths and weaknesses.
• Uncertainty in IA stems from diverse sources and requires careful management.
• Existing methods for uncertainty management are insufficient, necessitating complementary approaches.

Conclusions:

• IA modeling is crucial for tackling complex societal problems.
• Pluralistic uncertainty management is essential for robust IA.
• Exploring new IA concepts can improve the incorporation of multiple perspectives in models.