What Role, If Any, Should Neuroscience Play in Economics?
Colin Camerer
Introduction
In a 2007 article, Caltech neuroeconomist Colin Camerer makes his case for the role of neuroscience in economics.
In doing so, he lays out his answers to several important questions in the philosophy of economics: Is prediction possible in economics? Should prediction be an explicit goal? And what role, if any, should psychology and neuroscience play in economics?
Prediction Should Be A Goal of Microeconomics, and it is Best Achieved with Realistic Assumptions
“F53”
In an influential 1953 essay – which has come to be referred to simply as F53 - Milton Friedman argued that the metric by which to evaluate the success or failure of positive economic ideas is their predictive capacity:
Its [positive economics’] task is to provide a system of generalizations that can be used to make correct predictions about the consequences of any change in circumstances. Its performance is to be judged by the precision, scope, and conformity with experience of the predictions it yields.
[T]he only relevant test of the validity of a hypothesis is comparison of its predictions with experience. The hypothesis is rejected if its predictions are contradicted (“frequently” or more often than predictions from an alternative hypothesis); it is accepted if its predictions are not contradicted; great confidence is attached to it if it has survived many opportunities for contradiction. Factual evidence can never “prove” a hypothesis; it can only fail to disprove it, which is what we generally mean when we say, somewhat inexactly, that the hypothesis has been “confirmed” by experience.
Friedman, having asserted that the test of the success or failure of an economic theory is its capacity to make predictions, suggests that the realism of theories is irrelevant.
He asks us to consider the problem of trying to predict the shots taken by an expert billiards player. He points out that we can make accurate predictions about these shots by assuming that the player knows the complicated mathematical formulas that give the optimal directions of travel, can accurately estimate the angles describing the location of the balls merely by eyeing the table, can make lightning speed calculations using the mathematical formulas, and can make the balls travel in the direction suggested by the formulas. Of course, it is not actually possible for players to do all this. But, we can model the shots made by expert billiards players as if they can: for if their play does not correspond closely with the mathematically optimal strategy, then they cannot truly be professional billiards players.
Consider the problem of predicting the shots made by an expert billiard player. It seems not at all unreasonable that excellent predictions would be yielded by the hypothesis that the billiard player made his shots as if he knew the complicated mathematical formulas that would give the optimum directions of travel, could estimate accurately by eye the angles, etc., describing the location of the balls, could make lightning calculations from the formulas, and could then make the balls travel in the direction indicated by the formulas. Our confidence in this hypothesis is not based on the belief that billiard players, even expert ones, can or do go through the process described; it derives rather from the belief that, unless in some way or other they were capable of reaching essentially the same result, they would not in fact be expert billiard players.
Friedman suggests that this as-if approach is a valid one for economists to employ: why not model firms in an economy as if they are rational agents who possess perfect information and are seeking to maximize their expected returns.
It is only a short step from these examples to the economic hypothesis that under a wide range of circumstances individual firms behave as if they were seeking rationally to maximize their expected returns …. and had full knowledge of the data needed to succeed in this attempt; as if, that is, they knew the relevant cost and demand functions, calculated marginal cost and marginal revenue from all actions open to them, and pushed each line of action to the point at which the relevant marginal cost and marginal revenue were equal. Now, of course, businessmen do not actually and literally solve the system of simultaneous equations in terms of which the mathematical economist finds it convenient to express this hypothesis, any more than …. billiard players explicitly go through complicated mathematical calculations[.] …. [U]nless the behavior of businessmen in some way or other approximated behavior consistent with the maximization of returns, it seems unlikely that they would remain in business for long.
Camerer (2007)
Camerer (2007) argues – in line with the view articulated in F53 – that microeconomic theories should be judged by their ability to make predictions.
However, Camerer claims that Friedman’s view that this implies the realism of economic models does not matter is flawed.
The ignorance of psychology that Pareto explicitly advocated was cemented by Milton Friedman's (1953) development of 'positive economics'. Friedman, and the many economists influenced by his view, advocated two principles for judging theories which use assumptions A to make a formal prediction P.
1. Assumptions A should be judged by the accuracy of the predictions they mathematically imply.
2. Since false assumptions can yield accurate predictions, even if assumptions appear false their empirical weakness should be tolerated if they lead to accurate predictions P.
I wholeheartedly endorse the first principle (1), but not the corollary principle (2).
He argues that the goal of creating economic theory which yields accurate predictions is best accomplished by beginning with realistic assumptions.
And, for Camerer, this means using the tools of neuroscience to pull back the curtain on human decision-making.
Here is why: first, if assumptions A are false but lead to an accurate prediction, they presumably do so because of a hidden 'repair' condition R (that is, (not-A and R) -> P is a more complete theory at both ends than A -> P). Then the proper focus of progressive research should be specifying the repair assumption R and exploring implications, in conjunction with more accurate assumptions.
Second, the importance of making good predictions (1) is precisely the reason to explore alternative assumptions grounded in psychological and neuroscientific facts. We do this in behavioural economics because we hope that models based on accurate assumptions will make some interesting new predictions, and better predictions overall.
[T]echnology now allows us to open the black box of the mind and observe brain activity directly. These direct observations can only enhance the development of theories which are based on more accurate assumptions and make better predictions as a result.
Neuroscience Is Likely to Make Three Types of Contributions to Economics
Camerer (2007) argues that neuroeconomics is likely to:
(1) identify some neural mechanisms which, in certain situations, support human decision-making in line with the predictions of standard economics theory,
(2) identify other neural mechanisms which support ideas articulated by behavioral economics (e.g. present bias), and
(3) identify altogether new biological variables that are not currently considered by standard economics.
Neuroeconomics is likely to provide three types of evidence about economic behaviour. …. The three kinds of evidence are:
1. Evidence which shows mechanisms that implement rational choice (utility- maximisation and Bayesian integration of information), typically in tasks are highly-sculpted to make decisions that are useful for survival across species (vision, food, sex and danger).
2. Evidence which supports the kinds of variables and parameters introduced behavioural economics.
3. Evidence which suggests the influence of 'new' variables that are implicit, underweighted, or missing in rational-choice theory.
One finding is that simple kinds of economising for life-and-death decisions (food, sex and danger) do occur in the brain as rational theories assume. Another set of findings appears to support the neural basis of constructs posited in behavioural economics, such as a preference for immediacy and nonlinear weighting of small and large probabilities. A third direction shows how understanding neural circuitry permits predictions and causal experiments which show state-dependence of revealed preference - except that states are biological and neural variables.
“Black Box” Economics is not Useless, But Technology Now Allows Us to Do Better
Camerer does not outright dismiss the theories developed by treating the brain as a black box.
About Friedman’s “as-if” approach to economic research he writes:
As-if models based on dubious assumptions clearly work well in many respects, and always will[.] …. But tests of the predictions that follow from as-if rational choice have also established many empirical anomalies. Behavioural economics describes these regularities and suggests formal models to explain them (Camerer, 2007).
Thus, Camerer’s claim is that, while “as-if” economic models are not useless, they have generated cases in which observed human behavior deviates from what is predicted by the models, and these deviations can be explained by behavioral economics.
Moreover, he claims that technological developments and the progress of neuroscience mean we can now go beyond having to depend on such a black box approach.
The neuroeconomic theory of the individual replaces the (perennially useful) fiction of a utility-maximizing individual which has a single goal, with a more detailed account of how components of the individual - brain regions, cognitive control, and neural circuits – interact and communicate to determine individual behaviour.
The Implications for Macroeconomics are Still Unclear
Camerer suggests that it is not yet clear whether neuroscience will be useful for understanding macroeconomic phenomena.
It remains to be seen whether neural measurement will be useful for understanding macroeconomic phenomena like consumer confidence or stock market bubbles. However, many of these macro phenomena might spring from the interaction of many brains that are tightly linked through social networks and common responses to emotional and news shocks which can be reciprocal or contagious. If so, macro models could explore how the result of brain activity has a multiplier effect in the economy.
Written By: Aiden Singh Published: October 1, 2020
Sources
Colin Camerer. Neuroeconomics: Using Neuroscience to Make Economic Predictions. The Economic Journal, Vol. 117, No. 519, Conference Papers (Mar., 2007), pp. C26-C42
Milton Friedman. The Methodology of Positive Economics in Essays in Positive Economics. University of Chicago Press. 1953. pg. 3-43.