# Inverse Decision Modeling:Learning Interpretable Representations of Behavior

## Details

Title : Inverse Decision Modeling:Learning Interpretable Representations of Behavior Author(s): Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

## Summary

• Provides a unifying perspective on inverse decision modeling: a general framework for learning parameterized representations of sequential decision-making behavior. This is done by first introducing a forward problem $$F$$ which is normative and subsumes a lot of control problems, meanwhile the inverse problem $$G$$ which is descriptive and subsumes work on imitation and reward learning. The paper provides a formalism on behaviour, the planner and inverse planner both of whom have normative and descriptive parameters, the inverse problem, and other relevant concepts like behaviour project, the inverse decision model.
• Descriptive models are those that capture observable decision-making behavior as-is (e.g. an imitator policy in behavioral cloning), and by “normative” models, are those that specify optimal de- cision-making behavior (e.g. with respect to some utility function).
• Observed behaviour is looked from a bounded rationality perspective.
• Inverse Bounded Rational Control is introduced as a concrete example of the framework.

## Thoughts

Created: 2022-03-13 Sun 21:45

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