A community-maintained collection of resources for economists applying microeconomic theory, game theory, econometrics, industrial organization, and behavioral economics to the development of AI systems: post-training, alignment, safety, mechanism design for AI, and multi-agent systems.
Contributions are welcome. If you know of a resource that should be listed here, please open an issue or submit a pull request.
This guide grew out of Kireyev and Maura-Rivero (2026), "From Microeconomics to AI Research: A Guide for Economists" (arXiv link forthcoming).
- Ouyang et al. (2022) -- InstructGPT. The foundational RLHF paper.
- Rafailov et al. (2023) -- DPO. Collapses reward modeling and policy optimization into a single objective.
- Christiano et al. (2017) -- Deep RL from Human Preferences. The original deep RLHF paper; foundational for understanding how human preferences train policies.
- Bai et al. (2022) -- Constitutional AI. How to use AI-generated feedback (RLAIF) instead of or in addition to human feedback.
- Lambert et al. (2024) -- Tulu 3. Introduces reinforcement learning from verifiable rewards (RLVR) as a post-training method using deterministic reward signals.
- DeepSeek-AI (2025) -- DeepSeek-R1. Demonstrates RLVR at scale, producing strong reasoning capabilities.
- Vaswani et al. (2017) -- Attention Is All You Need. The transformer architecture.
- UK AI Safety Institute (2025) -- Economic Theory and Game Theory for AI Alignment. Research agenda with concrete open problems for economists.
- Conitzer et al. (2024) -- Social choice should guide AI alignment. Maps RLHF to social choice theory.
- Dafoe et al. (2021) -- Cooperative AI. The case for game theory in multi-agent AI.
- Igami (2020) -- AI as structural estimation. Translates RL into structural econometric language, making the conceptual bridge between dynamic programming in IO and RL explicit.
- Rahwan, Parkes et al. (2019) -- Machine Behaviour. Argues for studying AI systems as behavioural agents using tools from the social sciences.
- Athey & Luca (2019) -- Economists (and Economics) in Tech Companies. How economists came to play a central role in tech; context for the next frontier in AI labs.
- Breiman (2001) -- Statistical Modeling: The Two Cultures. Foundational essay framing the data-modeling vs. algorithmic-modeling divide that runs between econometrics and ML.
- Sutton (2019) -- The Bitter Lesson. Influential ML manifesto: general methods that leverage compute outperform hand-engineered structure.
- Varian (2014) -- Big Data: New Tricks for Econometrics. First major JEP piece introducing the ML toolkit to economists.
- Mullainathan & Spiess (2017) -- Machine Learning: An Applied Econometric Approach. Reframes the divide as prediction vs. estimation; the sharpest introduction for economists.
- Athey & Imbens (2019) -- Machine Learning Methods That Economists Should Know About. The standard bridge survey.
- Kaplan et al. (2020) -- Scaling Laws for Neural Language Models. Original power-law forms in parameters, data, and compute.
- Hoffmann et al. (2022) -- Training Compute-Optimal LLMs (Chinchilla). Compute-optimal allocation between parameters and data.
- Snell et al. (2024) -- Scaling LLM Test-Time Compute. Test-time compute can substitute for parameters (ICLR 2025).
- Gao, Schulman & Hilton (2023) -- Scaling Laws for Reward Model Overoptimization. The cleanest scaling result for RLHF specifically (ICML 2023).
| Resource | Topic | Link |
|---|---|---|
| Andrew Ng's Machine Learning | Supervised learning, regularization, evaluation | Coursera |
| MIT 6.S191: Introduction to Deep Learning | Neural architectures, training dynamics, updated annually | introtodeeplearning.com |
| DeepMind RL Course (UCL) | Reinforcement learning foundations | YouTube |
| Andrej Karpathy: Neural Networks Zero to Hero | Transformers, attention, tokenization, full training pipeline | YouTube |
Hugging Face Transformers + trl |
Standard library for working with pretrained LLMs; trl provides RLHF/DPO fine-tuning pipelines |
huggingface.co |
| Sutton & Barto: Reinforcement Learning: An Introduction | The standard RL textbook, freely available online | incompleteideas.net |
| LeetCode | Algorithmic interview preparation (non-negotiable for industry roles) | leetcode.com |
| Community | Description | Link |
|---|---|---|
| Cooperative AI Foundation | Research and events on game theory and multi-agent AI. YouTube channel connects economic theory to AI. | cooperativeai.com |
| UK AI Safety Institute | Research agenda on economic theory and game theory for AI alignment, with concrete open problems for economists. | Alignment Project |
| ACM EC community | The annual ACM Conference on Economics and Computation. Natural home for economists publishing on AI topics. | sigecom.org |
| SC4AI (Social Choice for AI) | Workshop series on how social choice can help with AI alignment. | SC4AI |
| MDAIS (Mechanism Design for AI Safety) | Reading group and seminar on mechanism design, social choice, and game theory applied to AI alignment. Roughly monthly meetings. | Alignment Forum |
| CHAI (Center for Human-Compatible AI) | Stuart Russell's center at UC Berkeley. Frames alignment in decision-theoretic and principal-agent terms; natural fit for economists. | humancompatible.ai |
| Alignment Forum | Central online hub for alignment research discussion and debate. | alignmentforum.org |
| BATES (Bay Area Tech Economics Seminars) | Seminar series at Stanford bringing together economists from academia and industry working on the technology economy, including AI. Free and open. | Stanford Data Science |
| CEPR AI Research Policy Network | Led by Anton Korinek (UVA/Brookings). Webinar series and annual conference. Broad scope, but covers mechanism design, alignment, and compute governance alongside other AI economics topics. | CEPR AI Network |
| GovAI researcher network | The Centre for the Governance of AI at Oxford maintains an active researcher network and runs workshops relevant to compute governance and AI cooperation. | governance.ai |
| Program | Description | Link |
|---|---|---|
| EEML (Eastern European Machine Learning Summer School) | Intensive exposure to cutting-edge ML research with strong networking opportunities. | eeml.eu |
| M2L (Mediterranean Machine Learning Summer School) | Similar format with focus on deep learning and applications. | m2lschool.org |
| Extensive list of ML summer schools | Community-maintained directory. | awesome-mlss |
For economists interested in the intersection of economic theory and AI safety, particularly mechanism design, social choice, and principal-agent problems applied to alignment.
| Program | Description | Link |
|---|---|---|
| BlueDot Impact | Online course providing foundational knowledge in AI safety principles. Good starting point. | bluedot.org |
| MATS (ML Alignment & Theory Scholars) | Competitive fellowship pairing researchers with mentors at leading alignment labs. | matsprogram.org |
| PrincInt (Principles of Intelligence) | Summer fellowship for researchers applying insights from social science and biology to AI safety. Formerly PIBBSS. | princint.ai |
| PIVOTAL | Fellowship program for AI safety research. | pivotal-research.org |
| Constellation | Astra Fellowship supporting independent AI safety research. | constellation.org |
| LASR (London AI Safety Research Labs) | 13-week research program in London. Small teams produce an AI safety paper with expert supervision. | lasrlabs.org |
| Anthropic Fellowship | Fellowship at Anthropic for safety-focused research. | anthropic.com |
| ARENA (AI Research Experience for Academics and Newcomers) | Structured program for researchers transitioning into AI. | arena.education |
| GovAI Summer Fellowship | Summer fellowship at the Centre for the Governance of AI, Oxford. Relevant for economists working on compute governance and mechanism design for AI regulation. | governance.ai |
| AI Safety Camp | Collaborative research program where small teams work on focused AI safety projects over several months. Multiple cohorts per year. | aisafety.camp |
| SPAR (Supervised Program for Alignment Research) | Semester-long, virtual, part-time mentored alignment research, run under the Kairos AI Project. Lower commitment than MATS or PrincInt. | sparai.org |
| CAIRF (Cooperative AI Research Fellowship) | 3-month in-person fellowship in Cape Town. Tracks in multi-agent safety, cooperative AI, and gradual disempowerment. | cai-research-fellowship.com |
Found a dead link, a new fellowship, or a resource that should be here? Please open an issue or submit a pull request. See CONTRIBUTING.md for guidelines.