A dynamic dual process model of risky decision making.

Area of Science:

• Cognitive Psychology
• Decision Science
• Computational Modeling

Background:

• Dual process theories are common in judgment and decision-making research.
• Existing dual process theories are often verbal and lack formal mathematical or computational models.
• Previous formal models have not been quantitatively fit to experimental data or addressed the timing of reasoning systems.

Purpose of the Study:

• To present a dynamic dual process model framework for risky decision-making.
• To provide an account for the timing and interaction of dual reasoning systems.
• To explain both choice and response-time data in decision-making.

Main Methods:

• Development of a dynamic dual process model framework.
• Exploration of different assumptions on preference construction and system interaction (simultaneous vs. serial).
• Quantitative comparison of dual process models against single process models using experimental data.

Main Results:

• One dual process model significantly outperformed other models.
• The proposed framework successfully accounted for both choice and response-time data.
• The model's predictions regarding timing and time pressure were outlined.

Conclusions:

• A dynamic dual process model framework can successfully formalize and test theories of judgment and decision-making.
• The timing and interaction of reasoning systems are crucial for understanding risky choices.
• Formalized, data-driven dual process models offer superior explanatory power compared to verbal theories or single process models.