Models in Decision‐Making Under Risk and Uncertainty

This paper systematically compares dominant frameworks for modeling decision‐making under risk and uncertainty, evaluating their theoretical trade‐offs and practical relevance for economic research. We establish key criteria for model selection—including predictive accuracy, descriptive realism, computational tractability, and ecological validity—to guide researchers in matching frameworks to specific contexts. While classical axiomatic models provide normative benchmarks, our analysis highlights the need for context‐sensitive models. We propose the following three research frontiers: (1) integrating behavioral axioms with machine learning architectures, (2) neuroeconomic validation of decision‐theoretic assumptions, and (3) dynamic models for evolving uncertainty landscapes. The survey provides a structured framework for advancing decision theory while maintaining methodological pluralism in behavioral economics.