- AI research; and AI applications within investing, law, and science. Ph.D. dissertation was on “A Machine Learning Approach to Modeling Dynamic Decision-Making in Strategic Interactions and Prediction Markets.”
- Twitter: @johnjnay.
- Fellow at the Stanford Center for Legal Informatics, operated by Stanford Law School and the Stanford Computer Science Department, working on A Legal Informatics Approach to AI Safety.
- Co-founder & CEO of Brooklyn Artificial Intelligence Research and its quantitative investment firm, Brooklyn Investment Group.
Publications related to AI safety, and AI applications in law/policy:
- Law Informs Code: A legal informatics approach to aligning AI with humans
- Aligning artificial intelligence with humans through public policy
- Generalizability: Machine learning and humans-in-the-loop
- Predicting law-making with machine learning
- Natural language processing and machine learning on legal data
- Predicting human cooperation with machine learning
- Automatically estimating models of human decision-making with machine learning
- Legal informatics
- Big data law
- Natural language processing and machine learning for presidential documents
- Gov2Vec: Machine learning based distributed representations of government institutions
- Hierarchical Bayesian modeling of urban water policy
- Deriving the market’s political predictions with policy impact indices constructed with machine learning
Publications related to AI & climate change / markets / impact investing:
- Climate-contingent finance
- Environmental impact bonds: A common framework and looking ahead
- Computational simulation modeling of trader behavior in climate prediction markets
- Machine-learning approach to forecasting remotely sensed vegetation health
- Application of machine learning to prediction of vegetation health
- Decision-support computational models for climate change adaptation
- Empirically-grounded simulation model of impact of climate forecasts on agricultural income
- Agent-based simulation model of the influence of climate forecast accuracy on farmer behavior
- Participatory simulation modeling of urban flooding for decision support
- Modeling agricultural response to drought in the U.S.
- Qualitative and quantitative analysis of drought, risk, and politics
Coverage of some research in Science: Artificial intelligence can predict which congressional bills will pass: Machine learning meets the political machine.
My (older) open-source
- datafsm: Estimating Decision-making Models with Machine Learning
- forecastVeg: Forecasting Vegetation Health with Machine Learning
- predMarket: A Climate Prediction Market Simulation Model
- sa: Sensitivity Analysis for Complex Computational Models
- eat: Empirical Agent Training Software for Data-Driven Modeling
- agp: Agent Gentic Programming Software for Data-Driven Modeling