Long papers
Main text limited to nine (9) content pages, including all figures and tables; references, appendices, and checklist excluded from page limit.
Machine Learning/CORE Unranked
EconML NeurIPS2026 2026
Workshop on Economics for Machine Learning at NeurIPS 2026
EconML@NeurIPS'26 is a workshop that brings together researchers from machine learning, economics, and game theory to explore how economic principles can improve machine learning systems and how ML ecosystems give rise to new economic phenomena. The workshop focuses on two themes: using economic tools to enhance learning, alignment, and evaluation, and understanding economic dynamics when multiple AI models interact in shared environments.
Key deadlines
Verified September 9, 2026Abstract registration
August 30, 2026
AoE
Full paper
August 30, 2026
AoE
Workshop timeline
Submission and decisions
Abstract registrationKey deadline
August 30, 2026 · AoE
Full paperKey deadline
August 30, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Main text limited to four (4) content pages, including all figures and tables; references, appendices, and checklist excluded from page limit.
Research areas in scope
01
Economics in Training, Alignment, and Evaluation
Preference aggregation for alignment and its limitationsPricing of data, training, and inferenceSocial choice and auction mechanisms for steering alignmentStrategic behavior in model evaluation, and the design of incentive-aware evaluationStrategic classificationMechanisms for eliciting high-quality data and feedbackDiscrete choice and behavioral modeling in learning pipelinesAI decision making and bias in economic contextsAlgorithmic collective actionFormal abstractions of AI rationality and bias in economic contextsNew formal models of incentive misalignment and information gaps around AI systems
02
Ecosystems with Many Interacting Models
Competition between AI service providersAI supply chains and their dynamicsAlgorithmic collusion among learning systemsAlgorithmic monoculture and model multiplicityMarket concentration among AI service providersMulti-agent learning dynamics in economic environmentsPricing and evaluation of many interacting agentsFeedback loops and performative prediction effectsEcosystem-level incentive designNew formal models of emerging economic phenomena around AI systems
Policies worth checking twice
- Submissions must be anonymized for double-blind review using \usepackage[dblblindworkshop]{neurips_2026} and \workshoptitle{Economics for Machine Learning}.
- Submissions must use the NeurIPS 2026 style file and include the NeurIPS paper checklist.
- Reviewers are not required to read beyond the main text; submissions must be self-contained.
- Papers under review or recently accepted at other venues may be submitted, provided they do not violate dual-submission policies of those venues.
- Dual submissions within NeurIPS 2026 (e.g., to multiple workshops or concurrently to this workshop and the main track) are discouraged.
- Extended abstracts of papers under review at other conferences/journals are allowed if permitted by the original venue.
- Current students, postdocs, or hosts of organizers are not allowed to submit papers.
- Organizers will not review submissions from individuals within the same organization.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026