Models in Decision‐Making Under Risk and Uncertainty

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This review evaluates frameworks for decision-making in economic research, suggesting practical advancements in behavioral economics.

This analysis reveals critical trade-offs between models for decision-making under risk and uncertainty.
Key criteria for model selection include predictive accuracy and ecological validity, guiding researchers in framework choice.
Assessment includes classical axiomatic models as benchmarks, emphasizing the need for context-sensitive approaches.
Future research directions involve integrating machine learning and validating decision-theoretic assumptions in evolving uncertainty landscapes.

Martin Höppner (2025) studied this question.

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View Full Paper

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Authors

This review evaluates frameworks for decision-making in economic research, suggesting practical advancements in behavioral economics.

This analysis reveals critical trade-offs between models for decision-making under risk and uncertainty.
Key criteria for model selection include predictive accuracy and ecological validity, guiding researchers in framework choice.
Assessment includes classical axiomatic models as benchmarks, emphasizing the need for context-sensitive approaches.
Future research directions involve integrating machine learning and validating decision-theoretic assumptions in evolving uncertainty landscapes.

Martin Höppner (2025) studied this question.

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