A burgeoning literature in economics argues that bounded cognition can explain many observed empirical deviations from rationality. Consistent with this hypothesis, we show that individuals with greater cognitive ability behave more closely in accordance with economic decision theory. However, even the most cognitively skilled individuals display significant biases. In two laboratory studies, one conducted with Harvard undergraduates and one with Chilean high school students, we find that individuals with greater cognitive ability are more patient over short-term trade-offs and less risk-averse over small-stakes gambles. In both studies, mathematical ability seems to be more predictive of normative decision-making than verbal ability. In the sample of Chilean students, achievement in elementary school is strongly predictive of decisions made at the end of secondary school. Drawing on the National Longitudinal Survey of Youth, we show that, even after controlling carefully for labor income, more cognitively skilled individuals are more likely to participate in financial markets, are more knowledgeable about their pension plans, accumulate more assets, and are more likely to have tax-deferred savings. These findings persist when we use sibling relationships to identify models using within-family

Tranel, Joseph Wang), to skeptics for forcing us to think harder and write more clearly about the enterprise, and to many neuroscientists (especially John Allman, Paul Glimcher, John O’Doherty and Read Montague) for tutoring and advice over the last few Neuroeconomics seeks to ground microeconomic theory in details about how the brain works (see Zak, 2004; Camerer, Loewenstein and Prelec, 2005; Chorvat and McCabe, 2005; and Sanfey et al, 2006). Neuroeconomics is a subfield of behavioral economics (behavioral economics uses empirical evidence of limits on computation,

Some individuals borrow extensively on their credit cards. This paper tests whether present-biased preferences, that is a disproportionate preference for immediate consumption, can explain differences in credit card borrowing. In a field study, we elicit individual time preferences through incentivized choice experiments, and match resulting time preference measures to individual credit reports and annual tax returns. The results show that individuals with present-biased time preferences have significantly higher amounts of credit card debt, even after controlling for disposable income, credit constraints and other socio-demographic characteristics. Present-biased individuals appear to be naive, charging their cards too much given their long-run preferences.

We examine the robustness of the fourfold pattern of risk attitudes under two elicitation procedures. We find that individuals are, on average, risk-seeking over low-probability gains and high-probability losses and risk-averse over high-probability gains and low-probability losses when we elicit prices for the gambles. However, a choice-based elicitation procedure, where participants choose between a gamble and its expected value, yields individual decisions that are indistinguishable from random choice. Sensitivity to elicitation procedure holds between and within participants, and remains when participants are allowed to review and change decisions. The price elicitation procedure is more complex; this finding may be further evidence that an increase in cognitive load exacerbates behavioural anomalies. Individual decisions over risky outcomes often deviate from that predicted by expected utility theory, and alternative models have been proposed to explain behaviour better. 1 Perhaps the most accepted alternative is cumulative prospect theory (CPT) by Tversky and Kahneman (1992). 2 Two central assumptions in CPT are that individuals are riskaverse over gains and risk-seeking over losses, and that they tend to overweight lowprobability events while underweighting the likelihood of high-probability ones.

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Subjective and affective elements are well-known to influence human decision making. This dissertation presents a theoretical and empirical framework on how human decision makers’ subjective experience and affective prediction influence their choice behavior under uncertainty, frames and emotions. The framework extends and integrates existing theories of prospect theory (PT) and reinforcement learning (RL), drawing on a growing literature illuminating the role of affect in decision making and the neural underpinnings of human decision behavior. The proposed Affective-Cognitive (AC) model extends Prospect Theory (PT)-based subjective value functions to model human experienced-utility and predictedutility functions. The AC model assumes that the shapes (or parameters) of these subjective value functions dynamically vary with the decision maker’s affective states in sequential decision making. Human decision-making experiments were conducted to infer how people adjust the parameters (i.e., shape and reference point) of their experienced-utility and predicted-utility functions in sequential decision-making situations involving incidental affective states (e.g., anger, fear, economic fear) and task-related confidence. I constructed

Affective decision-making is a strategic model of choice under risk and uncertainty where we posit two cognitive processes — the "rational " and the "emotional" process. Observed choice is the result of equilibirum in this intrapersonal game. As an example, we present applications of affective decision-making in insurance markets, where the risk perceptions of consumers are endogenous. We then derive the axiomatic foundation of affective decision making, and show that, although beliefs are endogenous, not every pattern of behavior is possible under affective decision making.

In this paper, we ask whether variation in preference anomalies is related to variation in cognitive ability. Evidence from a new laboratory study of Chilean high school students shows that small-stakes risk aversion and short-run discounting are less common among those with higher standardized test scores, although anomalies persist even among the highest-scoring individuals. The relationship with test scores does not appear to result from differences in parental education or wealth. A laboratory experiment shows that reducing cognitive resources using a “cognitive load ” manipulation tends to exacerbate small-stakes risk aversion, with similar but statistically weaker effects on short-run impatience. Explicit reasoning about choice seems to reduce the prevalence of these anomalies, especially among the less skilled. Survey evidence suggests that the role of cognitive ability may extend to adult behaviors that are related to small-stakes risk preference and short-run time preference.

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which modelbased and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and modelbased predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decisionmaking.