Welcome
- I work on Artificial Intelligence and its applications within investing, policy, science, and decision-making.
- Within A.I., much of my work is related to machine learning, natural language processing, and agent-based simulation modeling.
- 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 Brooklyn Investment Group.
- Twitter: @johnjnay.
Publications on machine learning
applied to decision-making / institutional design:
- Generalizability: Machine learning and humans-in-the-loop for decision systems
- Predicting human cooperation with machine learning
- Automatically estimating models of decision-making with machine learning
Publications on machine learning
applied to law and policy:
- Natural language processing and machine learning for law & policy
- Predicting law-making with machine learning
- Legal informatics
- Big data law
- Natural language processing of presidential documents
- Gov2Vec: Machine learning 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 machine learning
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.
My 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