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Models in Decision‐Making Under Risk and Uncertainty

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Integrating behavioral axioms with machine learning enhances decision-making under uncertainty.
Classical models serve as normative benchmarks, showing limitations in real-world applications.
Assessment focuses on predictive accuracy and ecological validity to guide model selection.
Research must prioritize context sensitivity to address the complexities of evolving uncertainty landscapes.

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Systematic comparison of decision-making models highlights the importance of context in economic research.

Martin Höppner (2025) studied this question.

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