Aside from the main areas identified in the other pages on my website, I conduct research in the following areas:
- (S,s) Policies
- State Dependent Stochastic Choice and Rational Inattention
- The Bio-Behavioral Complex
- Emotional Economics, Behavioral Medicine, and Social Discounting
- Social Learning
- Shared Equity Mortgages and U.S. Housing Finance Policy
- Imperfect Competition and 64%-Majority Rule
- Indivisibilities and Graph Theory
(S, s) and State Dependence
“Economic Theory and the World of Practice: A Celebration of the (S,s) Model” [JEP 2010 with John Leahy] provides a broad introduction to research on microeconomic indivisibilities.
“The Variability of Aggregate Demand with (S,s) Inventory Policies,” [Econometrica 1985] introduced an approach to aggregating (S,s) policies.
“Menu Costs and the Neutrality of Money” [QJE 1985 w. Daniel Spulber] introduced state dependent pricing to the macroeconomics literature, showing the difference with time dependent pricing.
State-Dependent Pricing and the Dynamics of Money and Output [w. John Leahy, QJE 1991] and “Aggregation and Optimization With State Dependent Pricing,” [w. Leahy, Econometrica, 1997] further analyzed distinctions between time and state dependence
“Equilibrium in a Durables Goods Market with Lumpy Adjustment Costs,” [w. Leahy, JET, 2006] is well named.
“Monetary Policy as a Process of Search” [w. Leahy, AER, 1996] models difficulties in learning the state of the economy when investors have fixed costs.
Thinking about externalities associated with fixed costs of adjustment led John’s and my research focus on social learning.
Emotions and Policy
“Psychological Expected Utility Theory and Anticipatory Feelings” (with John Leahy, QJE 1997) models anticipatory emotions (e.g fear) as prizes. The model can be used to analyze avoidance of medical information:
“AIDS Policy and Psychology,” w. Kfir Eliaz, RAND 2003.
“The Supply of Information by a Concerned Expert” w. Leahy, EJ, 2004.
Non-behavioral data is needed to understand information avoidance. John and I established also the need for data enrichment in dynamic policy settings:
“The Social Discount Rate” w. Leahy, JPE 2004.
The limitations of standard behavioral data launched my path to data engineering.
Imperfect Competition and 64%-Majority Rule
My research in this area is all joint with Barry Nalebuff. The following three papers introduce a series of somewhat surprising technical results. In essence, we show that previously noted problems with voting systems and with models of price competition may be mitigated if individual differences are “not too great” in a sense that we make precise. The mean voter results offer the chance of progress in political models in which there is more than one dimension of difference. The imperfect competition results are used in applied research on differentiated product markets in industrial organization and in marketing.
On 64%-Majority Rule, with Barry Nalebuff, Econometrica, 1988, 787-814
Aggregation and Social Choice, with Barry Nalebuff, Econometrica, 1991, 1-23
Aggregation and Imperfect Competition, with Barry Nalebuff, Econometrica, 1991, 25-59
In the paper “Competition Among Institutions” , Barry and I developed a framework for analyzing competition among institutions, taking account of feedback effects whereby the inhabitants of a neighborhood influence outcomes, and outcomes influence preferences among neighborhoods. We were not able to make definitive progress: answers to these interesting questions appear to be beyond our grasp.
Indivisibilities and Graph Theory
John Leahy and I have developed new machinery for analyzing markets for large indivisible goods, such as houses.
“A Graph Theoretic Approach to Markets for Indivisible Goods” and
“Comparative Statics in Markets for Indivisible Goods”.
These papers introduce “GA-structures” that we show to be ideally suited to comparative static analysis of markets with indivisibilities. In addition to having fascinating mathematical properties, they connect with a long-standing economic tradition, in particular the “rent gradient” models of Ricardo. We will apply the new methods of analysis in due course.
Social Learning
“Sectoral Shocks, Learning, and Aggregate Fluctuations,” with John Leahy, Review of Economic Studies, 777-794, 1993.
Business as Usual, Market Crashes, and Wisdom After the Fact, with John Leahy, American Economic Review, 548-565, 1994.
“Miracle on Sixth Avenue,” with John Leahy, Economic Journal, 60-74, 1998.
“Mass Layoffs and Unemployment,” with John Leahy, Journal of Monetary Economics, vol. 46, 121-142, 2000.
“Social Learning and Selective Attention” with John Leahy and Filip Matejka
State-Dependent Stochastic Choice Data
By definition, private information is hard to observe. Data engineers dream up revealing data sets. One such is “State Dependent Stochastic Choice” data records the impact of the underlying state of the world on patterns of choice.
A Testable Theory of Imperfect Perception (with Daniel Martin) Economic Journal 2015 uses this data set to characterize Bayesian expected utility maximization
Rational Inattention, Revealed Preference, and Costly Information Processing (with Mark Dean), uses it to characterize rational inattention
Social Learning and Selective Attention (with John Leahy and Filip Matejka) uses it to identify individual preferences from market data.
A second engineered data set is “Choice process” data, which records provisional choices in the period before a decision is finalized.
In “Search, Choice and Revealed Preference”, Mark Dean characterize sequential search and reservation utility stopping rules.
In “Search and Satisficing,” Mark, Daniel Martin and I gather data on the choice process. In our experiments most subjects search sequentially and stop search when a “satisficing” level of reservation utility is realized.
The Bio-Behavioral Complex
The “bio-behavioral complex” is the dynamic interplay between biology, behavior and the environment. A paper on this has just been published in “Big Data”.
Using Big Data to Understand the Human Condition: The Kavli HUMAN Project
In Smoking, Genes, and Health, new evidence is provided on the impact of genetic factors on measured smoking (small) and on health (large). The implications for measurement are profound.
As Director of the Scientific Agenda for the Kavli HUMAN Project, I have the privilege of working with a wonderful group of advisors on KHP White Papers four of which have also been published in Big Data.
How Genetic and Other Biological Factors Interact with Smoking Decisions
Real time assessment of wellness and disease in daily life
Dopamine, Reward Prediction Error, and Economics provides an axiomatic model of dopaminergic function based on neurological data.
Measuring Beliefs and Rewards: A Neuroeconomic Approach tests the theory, with broadly positive results.
Continued research in this area will contribute to our understanding of how beliefs and preferences are formed, how they evolve, and how they play out in the act of choice.
A broad rationale for using the axiomatic methods of decision theory to bridge the gap between economic theory and non-standard “psychological” data was sketched out in Economic Theory and Psychological Data: Bridging the Divide published in the Foundations of Positive and Normative Economics.