Andrew Caplin’s research while affiliated with New York University and other places

This chapter introduces cognitive legal studies and cognitive economic methods for measuring and minimizing decision-making mistakes using cognitive nudges . It outlines a case study that shows how adding an index to complex case files improved the quality of justice in Mexican labor arbitration courts in cases of unfair dismissal. It also indicates the need for cognitively-informed mandated disclosure regulations. It stresses how cognitively demanding is the Duty to Understand legal doctrine for complex online privacy disclosures and other sign-in-wrap contracts. It introduces experimental methods to test the effects of mandated disclosure regulations in reducing decision-making mistakes based on Bloom's educational taxonomy .

This chapter introduces cognitive economics. It explains its inter-disciplinary nature and the fundamental role of utility functions, subjective beliefs, costs of learning , and Blackwell experiments . It outlines the need for economic data engineering to separate out beliefs from preferences and thereby to scientifically study decision-making mistakes . It also discusses distinctions and connections between cognitive economics and behavioral economics .

This chapter highlights important next steps in cognitive economic research in relation to the cognitive revolution. Particular topics discussed are design of human- AI interactions, ensuring that workers and students are as well informed as possible about the impact of AI on their future earnings, and how to test students in the era of Large Language Models. It also stresses business and policy applications of cognitive economic research in relation to financial planning, cognitive decline, and online consumer protection.

This chapter presents a history of cognitive economics framed in terms of Walt Rostow’s stages of economic growth. It highlights the important early contribution of Paul Samuelson, who introduced revealed preference theory, as well as that of Henry Block and Jacob Marschak, who were the first to propose measure¬ment of new basic observations to separate preferences from beliefs. It outlines ongoing research on rational inattention, bounded rationality, efficient coding, salience-based attention, imperfect memory, resource rationality, and cognitive hierarchies. It discusses important new forms of measurement that are advancing the research frontier in cognitive economics, including economic surveys , process measures , and state dependent stochastic choice data. It outlines the broader convergence of interest between psychologists and economists on the importance of cognitive constraints.

This chapter introduces research on how the rise of AI will impact wages as the cognitive revolution unfolds. It sketches recent research suggesting that AI helps those of lower skill to close the productivity and earnings gap with those of higher skill . It introduces new results suggesting that to so benefit requires those of lower skill to have beliefs that are well-calibrated to reality. It outlines the importance of long-run career skills, such as the ability to search effectively for new jobs. It also outlines research showing that one can impact students’ choice of education and career by transmitting more accurate information.

Chapter 4 introduces cognitive labor economics . Inspired by the industrial revolution, standard production functions model the transformation of physical factors of production into physical outputs. With the growing importance of higher level decision-making skills, we need to model inputs as cognitive and outputs as improvements in decision quality. The chapter discusses how to amend human capital theory to capture the open-ended tasks that are increasingly important to earnings. The chapter also introduces recent research measuring and modeling economic decision-making skills based on the ability to apply the principle of comparative advantage. It introduces evidence that these skills help explain earnings from employment, and outlines ongoing research to track these effects in the Danish population registries.

This chapter introduces cognitive capital and human-AI interactions . The upcoming cognitive revolution, like its industrial precursor, is defined by a radical change in technology. The machines of today introduce intangible cognitive capital that substitutes for human mental effort. The chapter lays out a three stage human-AI decision-making pipeline . This starts with human labeling of cases, continues with minimization of the AI loss function, and involves a final stage of human-AI collaboration. The chapter introduces outlines a field study in which elicited beliefs improve labeling for cancer detection. It introduces recent findings showing that standard engineering methods of defining loss functions can fail in their own terms. As a result, there is a bilateral alignment problem that requires not only that the AI is trained in human preferences, but that humans understand the AI's costs and constraints.

This chapter proposes development of an Accelerator for Cognitive Economics to speed to lower these barriers. It invites readers to join in and to speed development of cognitive economics. It invites readers to complete a survey to indicate areas of particular interest. It also invites participation in a series of meet-ups, both virtual and personal. This includes meetings of interdisciplinary teams of researchers with grant officers, policy makers, business leaders, community leaders, and other interested parties.