Welcome

• I work on artificial intelligence and its applications within law, investing, and science. Ph.D. dissertation was on “A Machine Learning Approach to Modeling Dynamic Decision-Making in Strategic Interactions and Prediction Markets.”
• Co-founder & CEO of Brooklyn Artificial Intelligence Research and its quantitative investment management firm, Brooklyn Investment Group.
• 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 Aligning Artificial Intelligence with Humans.
• Twitter: @johnjnay.

Publications related to AI alignment (with humans and society), and machine learning applied to law/policy:

• Aligning artificial intelligence with humans through public policy
• Generalizability: Machine learning and humans-in-the-loop
• Natural language processing and machine learning for law & policy
• Predicting law-making with machine learning
• 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
• The big shift: From monetary to fiscal policy

Publications on AI applied to 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 software packages:

• 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