Neuroeconomics is an interdisciplinary field that seeks to explain human decision-making, the ability to process multiple alternatives and to follow through on a plan of action. It studies how economic behavior can shape our understanding of the brain, and how neuroscientific discoveries can guide models of economics.Center for Neuroeconomics Study at Duke University http://dibs.duke.edu/research/d-cides/research/neuroeconomics
It combines research from neuroscience, experimental and behavioral economics, with cognitive and social psychology. As research into decision-making behavior becomes increasingly computational, it has also incorporated new approaches from theoretical biology, computer science, and mathematics. Neuroeconomics studies decision-making by using a combination of tools from these fields so as to avoid the shortcomings that arise from a single-perspective approach. In mainstream economics, expected utility (EU) and the concept of rational agents are still being used. Neuroscience has the potential to reduce the reliance on this flawed assumption by inferring what emotions, habits, biases, heuristics and environmental factors contribute to individual, and societal preferences. Economists can thereby make more accurate predictions of human behavior in their models.
Behavioral economics was the first subfield to emerge to account for these anomalies by integrating social and cognitive factors in understanding economic decisions. Neuroeconomics adds another layer by using neuroscience and psychology to understand the root of decision-making. This involves researching what occurs within the brain when making economic decisions. The economic decisions researched can cover diverse circumstances such as buying a first home, voting in an election, choosing to marry a partner or go on a diet. Using tools from various fields, neuroeconomics works toward an integrated account of economic decision-making.
History
In 1989, Paul Glimcher joined the Center for Neural Science at NYU. Initial forays into neuroeconomic topics occurred in the late 1990s thanks, in part, to the rising prevalence of cognitive neuroscience research. Improvements in brain imaging technology suddenly allowed for crossover between behavioral and neurobiological enquiry. At the same time, critical tension was building between neoclassical and behavioral schools of economics seeking to produce superior predictive models of human behavior. Behavioral economists, in particular, sought to challenge neo-classicists by looking for alternative computational and psychological processes that validated their counter-findings of irrational choice. These converging trends set the stage for the sub-discipline of neuroeconomics to emerge, with varying and complementary motivations from each parent discipline.
Behavioral economists and cognitive psychologists looked towards functional brain imaging to experiment and develop their alternative theories of decision-making. While groups of physiologists and neuroscientists looked towards economics to develop their algorithmic models of neural hardware pertaining to choice. This split approach characterised the formation of neuroeconomics as an academic pursuit - not without criticism however. Numerous neurobiologists claimed that attempting to synchronise complex models of economics to real human and animal behavior would be futile. Neoclassical economists also argued that this merge would be unlikely to improve the predictive power of the existing revealed preference theory.
Despite the early criticisms, neuroeconomics grew rapidly from its inception in the late 1990s through to the 2000s. Leading many more scholars from father fields of economics, neuroscience and psychology to take notice of the possibilities of such interdisciplinary collaboration. Meetings between scholars and early researchers in neuroeconomics began to take place through the early 2000s. Important among them was a meeting that took place during 2002 at Princeton University. Organized by neuroscientist Jonathan Cohen and economist Christina Paxson, the Princeton meeting gained significant traction for the field and is often credited as the formative beginning of the present-day Society for Neuroeconomics.
The subsequent momentum continued throughout the decade of the 2000s in which research was steadily increasing and the number of publications containing the words "decision making" and "brain" rose impressively. A critical point in 2008 was reached when the first edition of Neuroeconomics: Decision Making and the Brain was published. This marked a watershed moment for the field as it accumulated the growing wealth of research into a widely accessible textbook. The success of this publication sharply increased the visibility of Neuroeconomics and helped affirm its place in economic teachings worldwide.
Major research areas The field of decision-making is largely concerned with the processes by which individuals make a single choice from among many options. These processes are generally assumed to proceed in a logical manner such that the decision itself is largely independent of context. Different options are first translated into a common currency, such as monetary value, and are then compared to one another and the option with the largest overall utility value is the one that should be chosen. While there has been support for this economic view of decision-making, there are also situations where the assumptions of optimal decision-making seem to be violated.
The field of neuroeconomics arose out of this controversy. By determining which brain areas are active in which types of decision processes, neuroeconomists hope to better understand the nature of what seem to be suboptimal and illogical decisions. While most of these scientists are using human subjects in this research, others are using animal models where studies can be more tightly controlled and the assumptions of the economic model can be tested directly.
For example, Padoa-Schioppa & Assad tracked the firing rates of individual neurons in the monkey orbitofrontal cortex while the animals chose between two kinds of juice. The firing rate of the neurons was directly correlated with the utility of the food items and did not differ when other types of food were offered. This suggests that, in accordance with the economic theory of decision-making, neurons are directly comparing some form of utility across different options and choosing the one with the highest value. Similarly, a common measure of prefrontal cortex dysfunction, the FrSBe, is correlated with multiple different measures of economic attitudes and behavior, supporting the idea that brain activation can display important aspects of the decision process.
Neuroeconomics studies the neurobiological along with the computational bases of decision-making. A framework of basic computations which may be applied to Neuroeconomics studies is proposed by A. Rangel, C. Camerer, and P. R. Montague. It divides the process of decision-making into five stages implemented by a subject. First, a representation of the problem is formed. This includes analysis of internal states, external states and potential course of action. Second, values are assigned to potential actions. Third, based on the valuations, one of the actions is selected. Fourth, the subject evaluates how desirable the outcome is. In the final stage, learning, includes updating all of the above processes in order to improve future decisions.
Decision-making under risk and ambiguity Most of our decisions are made under some form of uncertainty. Decision sciences such as psychology and economics usually define risk as the uncertainty about several possible outcomes when the probability of each is known. When the probabilities are unknown, uncertainty takes the form of ambiguity. Utility maximization, first proposed by Daniel Bernoulli in 1738, is used to explain decision-making under risk. The theory assumes that humans are rational and will assess options based on the expected utility they will gain from each.
Research and experience uncovered a wide range of expected utility anomalies and common patterns of behavior that are inconsistent with the principle of utility maximization – for example, the tendency to overweight small probabilities and underweight large ones. Daniel Kahneman and Amos Tversky proposed prospect theory to encompass these observations and offer an alternative model.
There seem to be multiple brain areas involved in dealing with situations of uncertainty. In tasks requiring individuals to make predictions when there is some degree of uncertainty about the outcome, there is an increase in activity in area BA8 of the frontomedian cortex as well as a more generalized increase in activity of the mesial prefrontal cortex and the frontoparietal cortex. The prefrontal cortex is generally involved in all reasoning and understanding, so these particular areas may be specifically involved in determining the best course of action when not all relevant information is available.
The Iowa Gambling Task developing in 1994, involved picking from 4 decks of cards where 2 decks were riskier, containing higher payoffs accompanied by much heftier penalties. Most individuals realise after a few rounds of picking cards that the less risky decks have higher payoffs in the long run due to the small losses, however individuals with damage to the ventromedial prefrontal cortex continue picking from the riskier decks. These results suggested the ventromedial prefrontal region of the brain in strongly associated with recognising the long term consequences of risky behavior as patients with damage to the region struggled to make decisions that prioritised the future over the potential for immediate gain.
In situations that involve known risk rather than ambiguity, the insular cortex seems to be highly active. For example, when subjects played a 'double or nothing' game in which they could either stop the game and keep accumulated winnings or take a risky option resulting in either a complete loss or doubling of winnings, activation of the right insula increased when individuals took the gamble. It is hypothesized that the main role of the insular cortex in risky decision-making is to simulate potential negative consequences of taking a gamble. Neuroscience has found the insular is activated when thinking about or experiencing something uncomfortable or painful.
In addition to the importance of specific brain areas to the decision process, there is also evidence that the neurotransmitter dopamine may transmit information about uncertainty throughout the cortex. Dopaminergic neurons are strongly involved in the reward process and become highly active after an unexpected reward occurs. In monkeys, the level of dopaminergic activity is highly correlated with the level of uncertainty such that the activity increases with uncertainty. Furthermore, rats with lesions to the nucleus accumbens, which is an important part of the dopamine reward pathway through the brain, are far more risk averse than normal rats. This suggests that dopamine may be an important mediator of risky behavior.