We analyse the decision of an agent with time-inconsistent preferences to consume a good that exerts an externality on future welfare. The extent of the externality is initially unknown, but may be learned via a costless sampling procedure. We show that when the agent cannot commit to future consumption and learning decisions, incomplete learning may occur on a Markov perfect equilibrium path of the resulting intra-personal game. In such a case, each agent's incarnation stops learning for some values of the posterior distribution of beliefs and acts under self-restricted information. This conduct is interpreted as strategic ignorance. All equilibria featuring this property strictly Pareto dominate the complete learning equilibrium for any posterior distribution of beliefs.

Based on recent neuroscience evidence, we model the brain as a dual-system organization subject to three conflicts: asymmetric information, temporal horizon, and incentive salience. Under the first and second conflicts, we show that the uninformed system imposes a positive link between consumption and labor at every period. Furthermore, decreasing impatience endogenously emerges as a consequence of these two conflicts. Under the first and third conflicts, it becomes optimal to set a consumption cap. Finally, we discuss the behavioral implications of these rules for choice bracketing and expense tracking, and for consumption over the life cycle.

Economics has traditionally relied on revealed preferences (and, occasionally, on verbal reports) to understand the desires of people. Another source of information has been developed in recent years: the direct observation of choice processes. This mechanism, possible thanks to the improvements in the designs and techniques to measure brain activity, is explored in the bur geoning field of experimental neuroeconom ics (see Paul W. Glimcher and Aldo Rustichini (2004) and Colin Camerer, George Loewenstein, and Drazen Prelec (2005) for recent surveys). In this article, we argue that the evidence on brain activity can also be used to build theo retical models that help us understand choices and predict behaviors. We label this research “Neuroeconomic Theory.” In Section I, we describe the procedure, discuss some advan tages over traditional methodologies, and estab lish some facts that motivate our approach. In Section II, we illustrate the methodology with two brain-based models of decision making. I. What is “Neuroeconomic Theory”? Neuroeconomic theory is an interdisciplin ary line of investigation that combines research from neuroscience, neurobiology, and econom ics. Experimental neuroscience and neurobiology provide detailed evidence of the functionality, interconnectivity, and physiological constraints of the brain systems involved in decision making. Microeconomic theory supplies the toolkit to build simple optimization models that incorporate these network interactions and well-defined con straints into the mechanisms of choice.

We develop a novel graphical paradigm of a strict-dominance-solvable game to study the developmental trajectory of steps of reasoning between 8 years old and adulthood. Most participants play the equilibrium action either always or only when they have a dominant strategy. Although age is a determinant of equilibrium choice, some very young participants display an innate ability to play at equilibrium. Finally, the proportion of equilibrium play increases significantly until fifth grade and stabilizes afterward, suggesting that the contribution of age to equilibrium play vanishes early in life.

We conduct a controlled laboratory experiment in the spirit of Merton (1971), in which subjects dynamically choose their portfolio allocation between a risk-free and risky asset. Using the optimal allocation of an investor with hyperbolic absolute risk aversion (HARA) utility, we fit the experimental choices to characterize the risk profile of our participants. Despite substantial heterogeneity, decreasing absolute risk aversion and increasing relative risk aversion are the predominant types. We also find some evidence of increased risk taking after a gain. Finally, the session level risk attitudes show a different profile than the individual descriptions of risk attitudes.