Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Authors: Daniel Jarrett, Alihan Hüyük, Mihaela Van Der Schaar
ICML 2021 | Venue PDF | 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. |