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..
Agent Incentives: A Causal Perspective
Authors: Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg11487-11495
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a framework for analysing agent incentives using causal influence diagrams. We establish that a well-known criterion for value of information is complete. We propose a new graphical criterion for value of control, establishing its soundness and completeness. We also introduce two new concepts for incentive analysis: response incentives indicate which changes in the environment affect an optimal decision, while instrumental control incentives establish whether an agent can influence its utility via a variable X. For both new concepts, we provide sound and complete graphical criteria. |
| Researcher Affiliation | Collaboration | Tom Everitt,*1* Ryan Carey,*2 Eric D. Langlois,*1,3,4 Pedro A. Ortega,1 Shane Legg1 1Deep Mind, 2University of Oxford, 3University of Toronto, 4Vector Institute EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'An open-source Python implementation of CIDs has recently been developed5 (Fox et al. 2021). 5https://github.com/causalincentives/pycid'. This refers to a general implementation of CIDs, not explicitly the specific code for the theoretical methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup involving specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for reproducibility of experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameter details. |