Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

Area of Science:

• Decision Science
• Risk Management
• Behavioral Economics

Background:

• Decision biases frequently distort risk-cost-benefit analyses.
• These biases promote learning aversion, resulting in premature decisions under uncertainty.
• Biases like overconfidence and narrow framing lead to suboptimal learning and inaccurate risk assessments.

Purpose of the Study:

• To identify how decision biases contribute to learning aversion in risk management.
• To propose low-regret learning strategies to overcome these biases.
• To demonstrate the application of these strategies using air pollutant regulation as a case study.

Main Methods:

• Analysis of cognitive biases influencing risk perception and decision-making.
• Application of computational reinforcement learning models.
• Framework development for low-regret learning strategies.

Main Results:

• Decision biases systematically lead to underestimation of information value and suboptimal risk management.
• Low-regret learning strategies offer a method to balance exploration and exploitation, mitigating bias.
• The proposed framework can improve regulatory decisions for uncertain risks.

Conclusions:

• Cognitive biases create predictable regret in risk management policy.
• Low-regret learning strategies, informed by reinforcement learning, can enhance decision-making under uncertainty.
• Implementing these strategies is crucial for effective risk management and policy development.