Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Authors: Daniel Jarrett, Alihan Hüyük, Mihaela Van Der Schaar
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Illustrative Use Case So far, we have argued for a systematic, unifying perspective on inverse decision modeling ( IDM ) for behavior representation learning, and presented inverse bounded rational control ( IBRC ) as a concrete example of the formalism. Three aspects of this approach deserve empirical illustration: Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability Interpretability: IBRC gives a transparent parameterization of behavior that can be successfully learned from data. Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity Expressivity: IBRC more finely differentiates between imperfect behaviors, while standard reward learning cannot. Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability Applicability: IDM can be used in real-world settings, as an investigative device for understanding human decisions. |
| Researcher Affiliation | Academia | 1 Department of Applied Mathematics and Theoretical Physics, Uni- versity of Cambridge, UK; 2Department of Electrical Engineering, University of California, Los Angeles, USA. |
| Pseudocode | No | The paper provides mathematical formulations and theorems (e.g., Theorem 4 and 5), but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | For our real-world setting, we consider 6-monthly clinical data for 1,737 patients in the Alzheimer s Disease Neuroimaging Initiative [163] study (ADNI). |
| Dataset Splits | No | The paper mentions using 1,000 generated trajectories as a basis for learning and describes the configuration for ADNI, but it does not specify explicit training, validation, or test splits by percentage or sample count, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'inference is performed via MCMC' and 'Bayesian IRL', but it does not specify any software names with version numbers for libraries, frameworks, or specific tools used. |
| Experiment Setup | Yes | In DIAG, our normative specification (for υ) is that diagnostic tests cost 1, correct diagnoses award 10, incorrect 36, and γ =0.95. Accuracies are 70% in both directions (!env), and patients arrive in equal proportions ( env). ... In ADNI, the configuration is similar except each MRI costs 1, while 2.5 is awarded once beliefs reach >90% certainty in any direction; also, σ is centered at the IOHMM learned from the data. For simplicity, for , % we use uniform priors in both settings. |