Richard L. Peterson

Market Psychology Consulting and Stanford University



Review of  Decisions, Uncertainty, and the Brain:  The science of neuroeconomics


Neuroeconomics is a new academic discipline bridging the distance between neuroscientific research regarding human choice behavior and economic theory.  Neuroeconomics is the domain of economists, neuroscientists, psychologists, and psychiatrists who are attempting to understand the neural basis of judgment and decision making.  Experimental questions being explored within this field include: “How do emotions and memories affect judgment and decision making?   How do people develop trust and attachment (and how do these entities influence judgment)? How do people perceive uncertainty?  And how does risk alter human decision making?”  Experimental methodologies include neuroimaging, genetic profiling, psychopharmacological (and diet!) manipulations, psychophysiology (EMG, ERP, and EEG), and single neuron firing measurements.


Paul Glimcher’s book, Decisions, Uncertainty, and the Brain:  The science of neuroeconomics, is the only book yet published about the new field neuroeconomics.  While Glimcher’s laboratory studies the activity of individual neurons, other neuroeconomics researchers investigate genetic markers, collect neuroimaging and physiological data, and/or perform behavioral experiments.  Very few conferences have occurred at which researchers from the disparate disciplines encompassed within neuroeconomics could discuss their research.  The first academic conference entitled “neuroeconomics” was held at the University of Minnesota in October of 2002.  A prior conference on neurobehavioral economics was held in 1997 at Carnegie-Mellon University, and an early conference on neural economics was held at Princeton University in December 2000. 


Paul Glimcher is a neuroscientist at New York University.  His laboratory studies the activity of both single neurons and neuronal clusters in discrete brain regions of monkeys.  His research team has been steadily teasing apart the complex workings of the visual system.  Additionally, and more recently, they have begun exploring the process of probabilistic decision making.  They are attempting to localize the regions of the brain that govern the representation and computation of the expected utility of choice options in situations of uncertainty.


The book is organized into two parts.  The first section outlines the historical development of Western neuroscience from ancient Greece (Plato and Hippocrates) and enlightenment-era Europe (Descartes and Bacon) through the present (David Marr and behavioral ecology).  The second section describes both game theory and the experimental progress that Glimcher’s lab, and others like it, have made towards understanding how organisms compute probability and make choices in pursuit of rewards under conditions of uncertainty.


In the first half of the book, Glimcher opens with a discussion of ancient Greek representations of the mind, the body, and the soul.  He then moves into an examination of both Cartesian dualism and the development of probability theory in continental Europe.  Glimcher traces the medical theories of Galen into the enlightenment, and he explains how Galen’s division of the organism into the “material” and the “soul” governed the methods by which subsequent experimentation was performed.  The English neurologist Charles Sherrington, and the logic of reflexes, which holds that all neural activity (and, thus, human behavior) is a direct response to environmental stimuli, is critiqued extensively.  Glimcher argues that probability theory, in a synthesis with the reflex paradigm, is a closer approximation to the actual workings of the brain.


Glimcher next explains the limitations of the reflex paradigm with respect to Gödel’s theorem on the limits of determinate mathematics.  Gödel showed that no logical mathematical system could be both complete and consistent.  Behaviorists like Pavlov had been studying only those behaviors that were simple and predictable, and they ignored those behaviors that were outside that realm, leaving them to philosophers.  The introduction of neural computing incorporated the use of probabilistic problem-solving.  Yet neural networks themselves remained computational entities, unable to satisfy the hype surrounding the anticipated explosion in artificial intelligence applications. 


The author suggests that neuroscience research fundamentally changed its task-orientation as the works of David Marr were published in the late 1970s and early 1980s.  Marr suggested that in order to truly understand the relationship between brain and behavior, one must begin by defining the function, or computational goal, of a complete behavior.  This proposition changed the orientation of neuroscience further away from a reflex approach and more towards a functional approach.  At the end of part one, Glimcher outlines the historical development of expected utility theory and Bayes’ probability theorem. 


The theme of part two is the neuroscientific study of choice behavior.  The second half of the book argues that, “we should begin to employ probabilistically-based approaches to understand how the brain takes information from the outside world and uses that information in concert with stored representations of the structure of the world to achieve defined computational goals (p321).” While his lab studies the visual system, they became particularly interested in how organisms choose towards which visual stimuli to pay attention.  They designed probabilistic tasks for the study of choice behavior with food rewards given when successful choices were made.  Other studies have found that rewarding stimuli, whether food, juice, money, or attractive photographs, activate the same brain region (the ventral striatum).


Glimcher goes on to explain the origins of game theory and its applications within the experimental environment.  One of the most important and interesting findings from Glimcher’s research is the discovery of an encoding for the computation of Nash equilibrium in the left parietal cortex (the left, rear, top part of the brain).  Game theoretical concepts like Nash equilibrium (where organisms compete in a non-zero sum game environment) are dynamic processes that require continuous feedback about the choices of other players.  Nash equilibrium is a concept too complex for the organism to grasp (or compute) consciously, yet Glimcher’s laboratory discovered that distinct clusters of neurons code for the generation of behavior that conforms with the predictions of Nash equilibrium.  That is, when a reward looks to be unlikely, neurons fire that increase environmental scanning behavior by the organism.  These findings confirm one of the central insights of Von Neumann’s theory of games: that optimal interactions between competitors require that players adopt mixed strategies in which their play-by-play behavior is irreducibly uncertain, but over hundreds of plays their behavior appears lawfully probabilistic.


Besides this book, there are few publications attempting to bridge the technical and theoretical gaps between neuroscientists and economists.  There is an excellent and comprehensive working paper by Camerer, Loewenstein, and Prelec (2003): “Neuroeconomics: How neuroscience can inform economics.”  There are also several relevant neuroscience publications, particularly in the October 2002 issue of the journal Neuron, that are devoted to the study of motivation and choice.  Most representative in the popular press is the cover article of the October 2002 Money Magazine by Jason Zweig entitled, “Are You Wired for Wealth?”  Additional popular articles have appeared in the New York Times recently (Postrel, 2003).  A wide variety of research articles on neuroeconomics-related topics is appearing in the academic literature, and an upcoming special issue of the journal Games and Economic Behavior is devoted entirely to neuroeconomics.


One weakness of the book’s approach is the lack of references to affect (emotion), which is suggested to have an impact on human utility and probability estimations in (Loewenstein, Loewenstein, Weber, & Welch, 2000), (Slovic, Finucane, Peters, and MacGregor, 2002), and (Knutson and Peterson, 2003).  In fact, Glimcher states:  “Neuroeconomics provides a model of the architecture that links brain and behavior.  Mind, though it may very much exist, simply does not figure in that equation (p343).”  The author’s conclusions are limited to a logical, computational frame of reference – a form of computational behaviorism.  This style certainly has applications within limited experimental games, but it is doubtful that it will provide significant insight into the workings of complex dynamic systems, such as the financial markets, without incorporating symbolic psychological concepts (such as affect).


One of this book’s greatest gifts is to clearly explain how our thinking about our thinking (neuroscience) has evolved over the past 2000 years.  This book makes a significant contribution to the readers’ historical understanding of psychology, philosophy, mathematics, and game theory. 


This book is an excellent introduction to the field of neuroeconomics.  Glimcher’s exposition of the evolution of neuroscientific thought is fascinating and worth savoring.  However, there is little in this book that would directly appeal to financial practitioners.  Glimcher’s discussion of game theory is fascinating, but applications to real-world environments are not directly posited. 


The latter half of the book, while intellectually interesting, reads much like an expanded academic journal article.  Glimcher explains his lab’s experimental trials and tribulations in much of the second half, and this is important information for anyone interested in the methods and rigor of neuroscientific research, but is not useful for those looking to gain insight into the operations of the financial markets.


One fascinating conclusion that may be drawn from the author’s results is that the search for diversity in investment strategy discovery may be wired into our brains.  Perhaps there are only temporary, fleeting opportunities for positive alpha before competing profit seekers arbitrage them into oblivion.  The dilemma then becomes a question of whether or not to change to second order strategies to arbitrage the mistakes of the arbitrageurs or if we can determine how long our existing alphas will remain positive before reversing under the weight of competitive arbitrage.  These are questions that future neuroeconomics research may help us to answer.  The answers from neuroeconomics will be basic at first, simplified and crude representations of real behavior that may appear to have little, if any, application to the real-world chaos of the financial markets.





Camerer, C., Loewenstein, G., & Prelec, D. (2003).  Neuroeconomics:  How neuroscience can inform economics.  Downloaded from


Glimcher, P. (2003). Decisions, uncertainty, and the brain: The science of neuroeconomics. Cambridge, MA: MIT Press.


Kahneman, D. (2000). Experienced utility and objective happiness: A moment-based approach. In D. Kahneman & A. Tversky (Eds.), Choices, values, and frames (pp. 673-692). Cambridge, U. K.: Cambridge University Press.


Knutson, B., Nielsen, L., Larkin, G., & Carstensen, L. L. (2003a). Affect dynamics: Psychometric and physiological validation. Stanford University.


Knutson, B. & Peterson, R. (2003b).  Neurally reconstructing expected utility.  Submitted to Games and Economic Behavior.  Stanford University.


Loewenstein, C. K., Loewenstein, G. F., Weber, E. U., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 2, 267-286.


Postrel, V.  (2003).  “Looking inside the brains of the stingy.”  New York Times, February 23, 2003.


Slovic, P., Finucane, M., Peters, E., & MacGregor, D. (2002). The affect heuristic. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 397-420). New York: Cambridge University Press.


Zweig, J.  (2002) “Are you wired for wealth?”  Money Magazine.  October, 2002.