Cognitive economics studies imperfect information and decision-making mistakes. A central scientific challenge is that these can’t be identified in standard choice data. Overcoming this challenge calls for data engineering, in which new data forms are introduced to separately identify preferences, beliefs, and other model constructs. I present applications to traditional areas of economic research, such as wealth accumulation, earnings, and consumer spending. I also present less traditional applications to assessment of decision-making skills, and to human–AI interactions. Methods apply both to individual and to collective decisions. I make the case for broader application of data engineering beyond cognitive economics. It allows symbiotic advances in modeling and measurement. It cuts across existing boundaries between disciplines and styles of research. (JEL C45, C80, D15, D80, D91, G50, J24)

This paper develops a general theory of the aggregate implications of (S, s) inventory policies. It is shown that (S, s) policies add to the variability of demand, with the variance of orders exceeding the variance of sales. Overall, the (S, s) theory contradicts the widely held notion that retail inventories act as a buffer, protecting manufacturers from fluctuating sales. In 1951, Arrow, Harris, and Marschak [3] introduced the (S, s) form of inventory policy. The policies are designed for retailers of finished goods, who face economies of scale when placing orders with their suppliers. To pursue an (S, s) inventory policy, the retailer establishes a lower stock point s, and an upper stock point S. No order is placed until inventories fall to s or below, whereupon they are restored to the maximum of S. A general proof of the optimality of these (S, s) inventory policies was provided by Scarf [13]. At the microeconomic level, the model has been extensively investigated. Formulae are available to compute optimal policies (e.g., Ehrhardt [6]), and these policies are xidely used in industry (e.g., Schwartz (ed.) [14]). In addition, the model has been extended to increasingly complex demand environments (e.g., Karlin and Fabens [11]). In contrast, little is known about the macroeconomic implications of (S, s) policies. Several recent papers have begun to correct this deficiency. Akerlof has suggested that pursuit of constant threshold money holding policies of the (S, s) variety might be responsible for the observed low short-run income elasticity of the demand for money (Akerlof [1] and Akerlof and Milbourne [2]). In the operations research literature, Ehrhardt, Schultz, and Wagner [7] analyzed the demand environment of a wholesaler supplying several retailers. They required that the distinct retailers have independent sales, ruling out the analysis of common factors in sales. Finally, simulation results of Blinder [4] suggested a role for the (S, s) model in understanding retail sector inventories. However the theoretical difficulties with the model remained unresolved. Blinder commented: If firms have a technology that makes the S, s rule optimal, aggregation across firms is inherently difficult. Indeed it is precisely this difficulty which has prevented the S, s model from being used in empirical work to date (Blinder [4, p. 459]). In this paper we present a general theory of the aggregate implications of (S, s) policies. Our central finding is that (S, s) policies add to the variability of demand, with the variance of orders exceeding the variance of sales. This result holds even in the presence of common factors in retail sales. In addition, a close connection

The neurotransmitter dopamine has been found to play a crucial role in choice, learning, and belief formation. The best-developed current theory of dopaminergic function is the “reward prediction error” hypothesis—that dopamine encodes the difference between the experienced and predicted “reward” of an event. We provide axiomatic foundations for this hypothesis to help bridge the current conceptual gap between neuroscience and economics. Continued research in this area of overlap between social and natural science promises to overhaul our understanding of how beliefs and preferences are formed, how they evolve, and how they play out in the act of choice.

Apparently mistaken decisions are ubiquitous. To what extent does this reflect irrationality, as opposed to a rational trade-off between the costs of information acquisition and the expected benefits of learning? We develop a revealed preference test that characterizes all patterns of choice “mistakes” consistent with a general model of optimal costly information acquisition and identify the extent to which information costs can be recovered from choice data. (JEL D11, D81, D83)

In reviewing the Challenger tragedy, Richard Feynman identified a flawed O-Ring as the proximate cause and NASA's flawed safety culture as a deeper cause. There has been no similar investigation of the mortgage mess, which has been baptized rather than understood. In part, this is due to committed ideological views in the public and press that eliminate the call for expert analysis and reform. Broader and deeper policy problems are identified and illustrated using NASA's past behavior and FHA's ongoing behavior. The Columbia tragedy sounds an ominous warning on the future stability of housing finance markets.

How and to what extent “neuroeconomic” data (broadly interpreted as data other than standard choice data) should be used in advancing economic theory is open to question. Several authors have attempted to make use of such nonstandard data to shed light on the process of economic decision making. John W. Payne, James R. Bettman, and Eric. J. Johnson (1993), Miguel Costa Gomes, Vincent P. Crawford, and Bruno Broseta (2001), and Xavier Gabaix et al. (2006) have used MouseLab software in order to determine the manner in which people use information. Joseph Wang, Michael Spezio, and Colin Camerer (2006) make use of eye-tracking data for the same purpose. More dramatically, researchers such as Paul William Glimcher, Joseph Kable, and Kenway Louie (2007) are using brain-scanning data in an attempt to constrain economic models of discounting and time preference. Camerer (forthcoming) presents an excellent review of economic research involving nonstandard data. In opposition to this trend, Faruk Gul and Wolfgang Pesendorfer (forthcoming) present a strong critique of the use of nonchoice data within economics. They put forward two specific arguments that users of “neuroeconomic” data must refute if their work is to be taken seriously. First—economic models were designed only to explain choices. Thus, nonchoice data can be used neither to confirm nor deny a particular economic model. Second, it is by and large true that economists are interested in choice behavior. Any two models will either make different predictions for choice, in which case they can be differentiated by standard choice data, or they will not, in which case an economist will not be interested in differentiating between them. Economic Insights from “Neuroeconomic” Data

Paul Glimcher (2003) and Colin Camerer, George Loewenstein, and Drazen Prelec (2005) make powerful cases in favor of neuroeconomic research. Yet in their equally powerful defense of standard “Mindless Economics,” Faruk Gul and Wolfgang Pesendorfer (forthcoming) point to the profound language gap between the two contributing disciplines. For example, for an economist, risk aversion captures preferences among wealth lotteries. From the neuroscientific viewpoint, it is a broader concept related to fear responses and the amygdala. Furthermore, as economic models make no predictions concerning brain activity, neurological data can neither support nor refute these models. Rather than looking to connect such distinct abstractions, Gul and Pesendorfer (forthcoming) argue for explicit separation: “The requirement that economic theories simultaneously account for economic data and brain imaging data places an unreasonable burden on economic theories.” We share the conviction of Glimcher (2003) and Camerer, Loewenstein, and Prelec (2005) concerning the potential value of neuroeconomics, yet we believe that the field will live up to its potential only if a common conceptual language can be agreed upon. Hence, we face the Gul and Pesendorfer challenge head on by developing theories that simultaneously account for behavioral and brain imaging data. The principal innovation lies in our use of the decision theorists’ standard axiomatic methodology in this highly nonstandard setting. This removes any linguistic confusion by defining concepts directly in terms of their empirical counterparts. It also allows us to pinpoint how to design experiments directed to the central tenets of the theory, rather than to particular parametrizations. If these experimental tests reveal the theory to be wanting, then knowing which axiom is The Neuroeconomic Theory of Learning

Monetary policy makers are uncertain about the state of the economy and learn from the economy's reaction to policy. Private agents, however, anticipate any systematic attempt to incorporate this information into future policy. We analyze this feedback in the context of a monetary authority's attempt to stimulate an economy in recession. We show that modest stimuli may prove ineffectual. If small reductions in interest rates are unlikely to promote a response, then they may be followed by further cuts. A vicious circle develops in which the expectation that the policy could fail leads investors to delay investment thereby promoting failure.