Neuroeconomics: How Neuroscience Can Inform Economics

Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the "black box" of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems ("executive function") interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, "dual-process" models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are "rationally" neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in "theory of mind", correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information

Protesting too much: Self-deception and self-signaling

von Hippel and Trivers propose that self-deception has evolved to facilitate the deception of others. However, they ignore the subjective moral costs of deception and the crucial issue of credibility in self-deceptive speech. A self-signaling interpretation can account for the ritualistic quality of some self-deceptive affirmations, and for the often-noted gap between what self-deceivers say and what they truly believe

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data

Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori, i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method. Keywords: Imbalanced data; Binary classification; Multiclass classification; Bagging ensembles; Resampling; Posterior calibrationBurroughs Wellcome Fund (Grant 103811AI

Can Inform Economics

Who knows what I want to do? Who knows what anyone wants to do? How can you be sure about something like that? Isn’t it all a question of brain chemistry, signals going back and forth, electrical energy in the cortex? How do you know whether something is really what you want to do or just some kind of nerve impulse in the brain. Some minor little activity takes place somewhere in this unimportant place in one of the brain hemispheres and suddenly I want to go to Montana or I don’t want to go to Montana. (White Noise, Don DeLillo) 1

A New Method of Measuring Temporal Discounting: the Motivational Present Value of Future Rewards

In behaviors motivated by future concerns, people often appear myopic, e.g., saving insufficiently for retirement. We suggest that conventional measures of present value, sometimes used to predict such behavior, provide a poor measure of the present motivational value of future rewards. In four experiments we develop and examine a measure of the motivational present value of future rewards, demonstrating that the present value obtained by conventional measures overestimates their motivational present value. Additional results suggest this overestimation may reflect how people assess rewards using a monetary scale, and is not due to the presence of effort in our motivational present value measure

Neural Antecedents of the Endowment Effect

The “endowment effect” refers to the tendency to place greater value on items that one owns—an anomaly that violates the reference-independence assumption of rational choice theories. We investigated neural antecedents of the endowment effect in an event-related functional magnetic resonance imaging (fMRI) study. During scanning, 24 subjects considered six products paired with 18 different prices under buying, choosing, or selling conditions. Subjects showed greater nucleus accumbens (NAcc) activation for preferred products across buy and sell conditions combined, but greater mesial prefrontal cortex (MPFC) activation in response to low prices when buying versus selling. During selling, right insular activation for preferred products predicted individual differences in susceptibility to the endowment effect. These findings are consistent with a reference-dependent account in which ownership increases value by enhancing the salience of the possible loss of preferred products. Author Keyword