The past fifteen years or so have witnessed considerable progress in our understanding of how the human brain works. One of the objectives of the fast-growing field of neuroscience is to deepen our knowledge of how the brain perceives and interacts with the external world. Advances in this direction have been made possible by progress in brain imaging techniques and by clinical data obtained from patients with localized brain lesions. A relatively new field within neuroscience is neuroeconomics, which focuses on individual decision making and aims to systematically classify and map the brain activity that correlates with decision-making that pertains to economic choices. Neuroeconomic studies rely heavily on functional magnetic resonance imaging (fMRI), which measures the haemodynamic response (that is, changes in the blood flow) related to neural activity in the brain.

Understanding more about how the brain functions should help us understand economic behaviour. But some would have us believe that it has done this already, and that insights from neuroscience have already provided insights in economics that we would not otherwise have. Much of this is just academic marketing hype, and to get down to substantive issues we need to identify that fluff for what it is. After we clear away the distractions, what is left? The answer is that a lot is left, but it is still all potential. That is not a bad thing, or a reason to stop the effort, but it does point to the need for a serious reconsideration of what neuroeconomics is and what passes for explanation in this literature. I argue that neuroeconomics can be a valuable field, but not the way it is being developed and “sold” now. The same is true more generally of behavioural economics, which shares many of the methodological flaws of neuroeconomics.

Neuroeconomics is the newest of the economic sciences with a focus on how the embodied human brain interacts with its institutional and social environment to make economic decisions. This paper presents an overview of neuroeconomics methods and reviews a number of results in this emerging field of study.

The goal of neuroeconomics is a mathematical theory of how the brain implements decisions, that is tied to behaviour. This theory is likely to show some decisions for which rational-choice theory is a good approximation (particularly for evolutionarily sculpted or highly learned choices), to provide a deeper level of distinction among competing behavioural alternatives, and to provide empirical inspiration for economics to incorporate more nuanced ideas about endogeneity of preferences, individual difference, emotions, endogeneous regulation of states, and so forth. I also address some concerns about rhetoric and practical epistemology. Neuroscience articles are necessarily speculative and the science has proceeded rapidly because of that rhetorical convention. Single-study papers are encouraged and are necessarily limited in what can be inferred, so the sturdiest cumulation of results, and the best guide forward, comes in review journals which compile results and suggest themes. The potential of neuroeconomics is in combining the clearest experimental paradigms and statistical methods in economics, with the unprecedented capacity to measure a range of neural and cognitive activity that economists like Edgeworth, Fisher and Ramsey daydreamed about but did not have.

We argue that neuroeconomics should be a mechanistic science. We defend this view as preferable both to a revolutionary perspective, according to which classical economics is eliminated in favour of neuroeconomics, and to a classical economic perspective, according to which economics is insulated from facts about psychology and neuroscience. We argue that, like other mechanistic sciences, neuroeconomics will earn its keep to the extent that it either reconfigures how economists think about decision-making or how neuroscientists think about brain mechanisms underlying behaviour. We discuss some ways that the search for mechanisms can bring about such top-down and bottom-up revision, and we consider some examples from the recent neuroeconomics literature of how varieties of progress of this sort might be achieved.

Neuroscience can contribute to economics by inspiring new models, helping to distinguish models that have similar implications for readily available data, and guiding interpretations of decision-making processes by policy-makers. However, there is an additional less straightforward role for it to play: augmenting, along with survey data and other non-revealed-preference sources, assessments of well-being. The need for such augmentation lies in the slightly bizarre stance taken by modern economic theory, namely that economics is concerned only with choices and not with welfare per se. It is shown that this is neither historical nor at all necessary, even within the standard paradigm. Although neuroscience is by no means a panacea for determining true utility, which ultimately remains a subjective concept, it provides a uniquely useful complementary dataset.

We briefly describe ways in which neuroeconomics has made contributions to its contributing disciplines, especially neuroscience, and a specific way in which it could make future contributions to both. The contributions of a scientific research programme can be categorized in terms of (1) description and classification of phenomena, (2) the discovery of causal relationships among those phenomena, and (3) the development of tools to facilitate (1) and (2). We consider ways in which neuroeconomics has advanced neuroscience and economics along each line. Then, focusing on electrophysiological methods, we consider a puzzle within neuroeconomics whose solution we believe could facilitate contributions to both neuroscience and economics, in line with category (2). This puzzle concerns how the brain assigns reward values to otherwise incomparable stimuli. According to the common currency hypothesis, dopamine release is a component of a neural mechanism that solves comparability problems. We review two versions of the common currency hypothesis, one proposed by Read Montague and colleagues, the other by William Newsome and colleagues, and fit these hypotheses into considerations of rational choice.

The following is a set of reading notes on, and questions for, the Neuroeconomics enterprise. My reading of neuroscience evidence seems to be at odds with basic conceptions routinely assumed in the Neuroeconomics literature. I also summarize methodological concerns regarding design, implementation, and statistical evaluation of Neuroeconomics experiments.

If neuroscience is to contribute to economics, it will do so by the way of psychology. Neural data can and do lead to better psychological theories, and psychological insights can and do lead to better economic models. Hence, neuroscience can in principle contribute to economics. Whether it actually will do so is an empirical question and the jury is still out. Economics currently faces theoretical and empirical challenges analogous to those faced by physics at the turn of the twentieth century and ultimately addressed by quantum theory. If “quantum economics” will emerge in the coming decades, it may well be founded on such concepts as cognitive processes and brain activity.

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success.

I distinguish between two styles of research that are both called “neuroeconomics”. Neurocellular economics (NE) uses the modelling techniques and mathematics of economics – constrained maximization and equilibrium analysis – to model relatively encapsulated functional parts of brains. This approach rests upon the fact that brains are, like markets, massively distributed information-processing networks over which executive systems can exert only limited and imperfect governance. Harrison's (2008) deepest criticisms of neuroeconomics do not apply to NE. However, the more famous style of neuroeconomics is behavioural economics in the scanner. This is often motivated by complaints about conventional economics frequently heard from behavioural economists. It attempts to use neuroimaging data to justify arguments for replacing standard aspects of microeconomic theory by facts and conjectures about human psychology. Harrison's grounds for unease about neuroeconomics apply to most BES, or at least to its explicit methodology. This methodology is naively reductionist and illegitimately assumes that economics should not do what all successful science does, namely, model abstract aspects of its target phenomena instead of would-be complete and fully ecologically situated facsimiles of them.

Neuroeconomics is examined critically using data on the response times of subjects who were asked to express their preferences in the context of the Allais Paradox. Different patterns of choice are found among the fast and slow responders. This suggests that we try to identify types of economic agents by the time they take to make their choices. Nevertheless, it is argued that it is far from clear if and how neuroeconomics will change economics.

Neuroeconomics focuses on brain imaging studies mapping neural responses to choice behaviour. Economic theory is concerned with choice behaviour but it is silent on neural activities. We present a game theoretic model in which players are endowed with an additional structure – a simple “nervous system” – and interact repeatedly in changing games. The nervous system constrains information processing functions and behavioural functions. By reinterpreting results from evolutionary game theory (Germano 2007), we suggest that nervous systems can develop to “function well” in exogenously changing strategic environments. We present an example indicating that an analogous conclusion fails if players can influence endogenously their environment.

In this short note I speculate about the various ways in which the study of neurological aspects of decision making could be fruitful for economic modelling.

Neuroeconomics illustrates our deepening descent into the details of individual cognition. This descent is guided by the implicit assumption that “individual human” is the important “agent” of neoclassical economics. I argue here that this assumption is neither obviously correct, nor of primary importance to human economies. In particular I suggest that the main genius of the human species lies with its ability to distribute cognition across individuals, and to incrementally accumulate physical and social cognitive artifacts that largely obviate the innate biological limitations of individuals. If this is largely why our economies grow, then we should be much more interested in distributed cognition in human groups, and correspondingly less interested in individual cognition. We should also be much more interested in the cultural accumulation of cognitive artefacts: computational devices and media, social structures and economic institutions.

Nobody in this debate questions the point that neuroeconomics remains full of potential, and little else as yet. If so, that really is progress of sorts. I was getting afraid that we would have to open nominations for the Captain Ahab Award for obsessive work on the promotion of neuroeconomics.