People often fail to make “rational” decisions. Economic agents are subject to multiple biases that affect the way they perceive events, act upon them, and learn from experience. Most of these anomalies are remarkably persistent and are widely documented in real world and laboratory environments by behavioural data.
To cite just a few, individuals have systematically biased beliefs about:
- the prospects of their entrepreneurial endeavours,
- the amount they need to save for retirement, and
- the size of the mortgages they can afford.1
These behaviours cannot be ignored since they may have disastrous consequences for the economy – as we have recently witnessed during the subprime mortgage crisis. Knowing what type of mistakes and biases are prevalent is an important starting point. The main challenge, however, is to understand why they emerge, so that they can be predicted and, possibly, avoided.
A grey cloud over economics
Economics has always relied on a careful modelling of decision-makers. They are described by utility functions that represent their goals, and they interact at (Nash) equilibrium. Nevertheless, the discrepancies between theoretical predictions and observed behaviour have haunted the field for many decades.
To cope with this mismatch, behavioural economists have developed new theories of decision-making that are a better fit for the behavioural data than traditional models. The methodology consists in building models to demonstrate the relationship between a cause (such as a preference for a particular object) and a behavioural anomaly. This line of research formulates possible explanations for behavioural data, but it is nevertheless subject to shortcomings. Often the cause is not observable, and there is no evidence of the relationship provided by the model. Most notably, the freedom provided by the introspection method leads to a model selection problem. Also, the cause of the behavioural anomaly may simply lie elsewhere.
Introducing neuroeconomic theory
Neuroeconomics offers a solution through an additional set of data obtained via a series of measurements of brain activity at the time of decisions. Experimental neuroeconomics can be seen as a subfield of experimental economics, where behavioural data is enriched with brain data. Neuroeconomic theory proposes to build brain-based models capable of predicting observed behaviour.
Experimental neuroeconomics is controversial. While some consider it to be an irrelevant body of research, there are those who claim it is essential (see Camerer, et al. 2005, Gul and Pesenderfor 2008). In fairness, the field is probably too young to tell. Surprisingly, the discussion has been centred on empirical issues regarding the collection method, amount, cost, and quality of brain data – the broad implications have not received as much attention. Indeed, the new set of data provided by experimental neuroeconomics will shed light on the causes of behaviour (and therefore of the behavioural anomalies) and help build new theories capable of explaining and predicting decisions, a long-term goal of economics. Neuroeconomic theory offers to do precisely this. So far, research in that direction has been very limited and its impact has been largely ignored.
The objective of neuroeconomic theory is to build models based on evidence from the brain sciences, such as experimental neuroeconomics, but also other fields in neuroscience and neurobiology. Measurement of brain activity provides information about the underlying mechanisms used by the brain during choice processes. In particular, it shows which brain regions are activated when a decision is made and how these regions interact with each other. This knowledge can then be used to build a model that represents this particular mechanism. Contrary to behavioural economics, the model does not rely on introspection or plausible assumptions but rather on an existing and documented biological property of the brain.
Advantage 1: A guideline for constraints
The methodology used in neuroeconomic theory has two advantages. Primarily, evidence from the brain sciences provides precise guidelines for the constraints that should be imposed on decision-making processes. This can help uncover the "true" motivations for the "wrong" choices and improve the predictive power of the theory. Behavioural theories that account for biases in judgment build on specific models of preferences over beliefs or non-Bayesian updating processes. Rather than guessing a cause for biases, neuroeconomic theory builds a model based on the existing physiological properties underlying learning and belief formation. In principle, this can help pinpoint biological foundations for anomalous choices. For example, research in neurobiology demonstrates that the brain cannot encode all the information contained in a signal. A decision is triggered when “enough” information supporting one alternative is obtained, and the brain uses a variety of biological mechanisms to filter information in a constrained optimal way. In a recent paper we show that these properties of the brain result in a behavioural tendency to confirm initial priors (Brocas and Carrillo 2009). Behavioural data reports precisely that individuals stick too often to first impressions. These confirmatory biases may all emerge from the same set of physiological information processing constraints. Further work in that direction may help uncover the causes of other biases and determine whether they are all related to the same physiological limitations.
Advantage 2: Uncovering “exogenous” preferences
The second advantage is that by explicitly modelling physiological properties, it is possible to provide foundations for some elements of preferences traditionally considered exogenous, such as risk aversion, ambiguity aversion, or time-preference rates. Choices involving risk, uncertainty, or time delays may require complex trade-offs. Measures of brain activity allow us to determine if the evaluation process is centralised or if different brain systems compete to influence the final decision. Neuroeconomic theory proposes to model the actual brain organisation, determine the behaviour that emerges from it, and evaluate which theory fits best.
One example is “discounting”. The standard neoclassical theory derives time-preference rates from a set of axioms on the preferences of individuals. A nice property of these axioms is that discounting must be represented by a time-consistent function. To account for the observed tendency of individuals to procrastinate, behavioural economists have modified this function by introducing a parameter of time-inconsistency whereas decision theorists have modified the original axioms. In both cases, the motivation for the new theories is a behavioural observation that cannot be reconciled with the original theory. Instead, our recent research uses neurobiological evidence to model inter-temporal choices as the result of a conflict between two brain systems, one interested in immediate gratification and one that can form a mental representation of future rewards. Using this approach we are able to derive from first principles three properties of dynamic choices commonly observed in the data: positive discount rate, decreasing impatience, and heterogeneity of discount rates across activities (Brocas and Carrillo 2008). A similar methodology can be applied to rationalise other observed characteristics of preferences.
Conclusion: The individual as an organisation
Neuroeconomic theory will soon play a crucial role in the building of new reliable theories capable of explaining and predicting individual behaviour and strategic choices. The main message is that the individual is not one coherent body. The brain is a multi-system entity (with conflicting objectives, restricted information, etc.) and therefore the decision-maker must be modelled as an organisation. We conclude with an analogy. Before the so-called modern theory of the firm, organisations were modelled as individual players characterised by an input-output production function. The systematic study of interactions between agents and decision processes within organisations (acknowledging informational asymmetries, incentive problems, restricted communications channels, hierarchical structures, etc.) led to novel economic insights. Applying a similar methodology to study individual decision-making is, in our view, the most fruitful way to understand the bounds of rationality.
1 See e.g. the series "Anomalies" in the Journal of Economic Perspectives 1987-91 and 1995-2001. We will not discuss here whether these behaviors are 'irrational' or simply result from preferences different from those that are usually assumed.
Brocas, Isabelle and Juan D Carrillo (2008) "The Brain as a Hierarchical Organisation" American Economic Review 98(4); 1312-46.
Brocas, Isabelle and Juan D Carrillo (2009) "From Perception to Action: an Economic Model of Brain Processes" mimeo, USC.
Camerer, Colin, George Loewenstein, and Drazen Prelec (2005), "Neuroeconomics: How Neuroscience Can Inform Economics", Journal of Economic Literature, 43: 9-64
Gul Faruk and Wolfgang Pesenderfor (2008), "The Case for Mindless Economics" in: The Foundations of Positive and Normative Economics, by Andrew Caplin and Andrew Schotter (eds.). Oxford U. Press