An ontology of decision models

Area of Science:

• Cognitive Science
• Decision Science
• Computational Psychology

Background:

• Decision models are crucial for studying choice behavior but lack consensus across fields.
• Different research programs often use isolated models, limiting generalization of insights.

Purpose of the Study:

• To create a unified representational structure for decision models.
• To analyze and synthesize prominent models of risky and intertemporal choice.
• To quantify relationships between diverse decision models.

Main Methods:

• Large-scale computational analysis using landscaping techniques.
• Development of an ontology for decision models.
• Inclusion of 89 prominent models of risky and intertemporal choice.

Main Results:

• Generated a representational structure (ontology) for decision models.
• Interpreted the ontology in terms of model spaces, clusters, hierarchies, and graphs.
• Quantified properties and relationships of individual decision models.

Conclusions:

• Decades of quantitative research on human choice behavior can be synthesized within a single framework.
• The developed ontology provides a novel way to understand and compare decision models.
• Facilitates generalization of insights across different decision modeling approaches.