AI RFI Responses, October 26, 2018 _____________________________________________________________ Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan RFI Responses DISCLAIMER: The RFI public responses received and posted do not represent the views and/or opinions of the U.S. Government, National Science and Technology Council (NSTC) Select Committee on Artificial Intelligence (AI), NSTC Subcommittee on Machine Learning and AI, NSTC Subcommittee on Networking and Information Technology Research and Development (NITRD), NITRD National Coordination Office, and/or any other Federal agencies and/or government entities. We bear no responsibility for the accuracy, legality or content of all external links included in this document. A Rapid Learning System for Economics By Lloyd S. Etheredge I. Overview To speed the growth of AI methods and secure economic and national security benefits, I forward an idea that you might be able to use. I suggest that your initiative build a national capacity for rapid learning economics. The project will design, pay for, and develop new methods to analyze expanded R&D data systems. It will be designed to discover the behavioral variables and mechanisms that affect economic performance and that are missing from current data systems. Competitive, multi-year, renewable grants will create Centers for Rapid Learning Economics to engage stakeholders and address different dimensions of the problem. Although the Centers will conduct their own research, their first goal will be to design, purchase (or create) the new R&D data systems that include a wide range of potential causal variables nominated by behavioral science advisers and stakeholders. These data systems will be curated, placed in the public domain, and will be available online (with analysis tools and supercomputer capacity) for software development and fast discovery research 1 by all stakeholders. Centers also will be crossroads for research ideas, new discoveries, and strategy discussions: they will have funds for lecture series and conferences that will be videocast (e.g., NIH’s www.videocast.nih.gov) to national and global audiences to accelerate the creative process.1 2 The strategy of the new system will build upon NIH’s fast discovery strategy for genetics-based biomedical research that has been transforming thinking about cancer and other diseases. Achieving a similar design to their “Everything 1 For-profit companies and coalitions of institutions will be eligible to apply for Center grants. The Centers may build partnerships and develop further financial support from stakeholders. 2 Concerning the design of Centers and a national system: The Administration’s commitment to free inquiry is useful to state. (Otherwise, there may be alert players who may seek to politicize this learning system.) As a further safeguard, there should be a clear expectation that the entire system will provide Honest Broker inclusion and evaluation of the full range of perceptions and ideas in the political process. Although it will not censor or bias the process, a national advisory process can add lines of investigation and funds to assure balance. 2 Included” R&D data systems is an open-ended challenge for the behavioral sciences. However, there already are growing data resources that Centers can purchase and include quickly. (For example, rather than rely upon the aggregate quarterly data of national income accounting, Centers might use Mastercard International’s daily time series to study the causal pathways of recessions and recoveries.) 3 - Another attractive new database to include is the global investment by Google to digitize all news in 100 languages, with reliable translation and online analysis tools, and sophisticated psychological software for the analysis of emotions and events (www.gdeltproject.org). Daily historical data now are available from January 1, 1979. Psychological variables such as confidence, mistrust, anger, and fear in mass publics can be more reliably measured and explored at early stages of the new research. 3 AI methods analogous to AlphaGo Zero might build useful reclassifications of data and improve current variables crudely defined by accountants and the tax code into optimum data systems for behavioral prediction. (For example: households with young and growing children, may think about consumption and investment differently from other consumers.) 3 II. Two Priorities As first projects, I recommend two priorities for rapid learning (and multi- disciplinary) economic science. The first project will a.) Create early warnings for recessions (and improved standby options for prevention and early, precision treatment) in the US and (later) all major economies. This priority is merited by the current absence, from government data systems in major economies, of the variables that cause and reliably forecast turning points (recessions, crises, and recoveries). Statistically, the US and other major economies are overdue for the next downturn. However, countercyclical fiscal and monetary policy options since 2008 are dangerously limited by the steep rise of national deficits, debt, and future interest charges and the already- near-zero interest rates. Unless a new R&D learning system can work quickly, there may be unnecessary economic hardship and greater political instability ahead when recessions occur without swift, new, and precise remedies. The second project will b.) Improve economic science for all countries so that the intelligence community can make more reliable forecasts of political stress and national security challenges. Behavioral science has found that in advanced countries, and even more so in UDCs, prolonged economic hardship, and especially high levels of youth unemployment, predict to political instability – 4 including recruitment to terrorism, ethnic conflict, the rise of demagogues and violent oppression, civil and gang violence, and – now – growing and urgent immigration for safety and opportunity. 4 The domestic and national security problems that any US Administration faces are made worse by the poor economic performance since the unforeseen 2008 crisis and the failure of traditional policy tools to work as well in a changing world Both priorities will require AI methods beyond the first-generation Big Data analysis tools used by NIH. Olivier Blanchard, a Chief Economist at the IMF after 2008, said of traditional policy tools "How reliable are these tools? They work, but they don't work great. People and institutions find a way around them." Thus, AI modeling will need to move beyond earlier fixed coefficient models to discover evolving strategies and allow for conflicting interests and learning in a global system. By the same logic, one of Barack Obama's first instructions to Leon Panetta and the intelligence community was that he did not want to be blind-sided by another 2008: today, this will require AI methods that can reverse 4 AI methods may (like AlphaGo Zero) evolve new paradigms and pathways for targeted investments that support faster economic growth and political stability. 5 engineer fast-trade computer algorithms and forecast risks of unforeseen interactions and remedies. III. Supporting Analysis Six appended documents support the case for this national initiative: 1. Attachment A. The Congressional Budget Office Forecasting Record: 2017 Update compares (p. 16, Box 2) two-year GDP forecasts of government models and the (about 50) leading Blue Chip private sector and academic forecasting models from 1976 through 2014. Forecasting is a highly competitive business that uses the government data system. For decades, the same data have been reworked and, as Box 2 illustrates, the models track one another closely. There is not much to be improved by remaining within these datasets. - There is widespread professional agreement about error rates and several kinds of suspected missing variables. CBO reports (pp. 10-14) the professional agreement that “turning points” (recessions and recoveries) are caused by variables that are not yet in the US government’s data system. Also, any new, consequential, and fundamental changes in the world are missing and obscured by forecasting and data analysis based on the linear regression analysis of quarterly time series data. 6 2.) Attachment B. “Proposal: A Rapid Learning System for G-20 Macroeconomics: From Greenspan to Shiller and Big Data” (Draft, 2014) reviews a universe of new psychological and cultural metrics recently recommended by Alan Greenspan. Their absence often produces unproductive disagreement with liberal economists, in part because the absence of needed metrics allows unresolved arguments about interpretation. (This 2014 draft proposal was far beyond the budget that NSF felt able to provide and would have taken too long without a high- level commitment.) It is possible that libertarian thinktanks will extend Greenspan’s thinking to propose a universe of metrics. 3.) Attachment C. “The Capitalist’s Dilemma, Whoever Wins on Tuesday” (2012) by Harvard’s Clayton Christensen illustrates the potential learning and transformation of forecasting and monetary policy by greater inclusion of psychological observations of the real world. Monetary policy tools making Interest rate adjustments might have worked with CEO’s like Henry Ford and Thomas Edison whose scientific and business focus was industrial innovation. However today’s CEO’s may come from finance or marketing and, thus, pursue 7 maximum profit (or short-term