Decision Making & Negotiations

Actions by individuals and households to reduce carbon-based energy consumption have the potential to change the picture of U.S. energy consumption and carbon dioxide emissions in the near term. To tap this potential, however, energy policies and programs need to replace outmoded assumptions about what drives human behavior; they must integrate insights from the behavioral and social sciences with those from engineering and economics. This integrated approach has thus far only occasionally been implemented.

We provide insights into earnings quality from a survey of 169 CFOs of public companies and in-depth interviews of 12 CFOs and two standard setters.

We present a method that dynamically designs elicitation questions for estimating preferences, focusing on the parameters of cumulative prospect theory and time discounting models. Typically these parameters are elicited by presenting decision makers with a series of choices between alternatives, gambles or delayed payments. The method dynamically (i.e., adaptively) designs such choices to optimize the information provided by each choice, while leveraging the distribution of the parameters across decision makers (heterogeneity) and capturing response error.

The SEC is currently considering a rulemaking petition requesting that the Commission shorten the ten-day window, established by Section 13(d) of the Williams Act, within which investors must publicly disclose purchases of a 5% or greater stake in public companies. In this Article, we provide the first systematic empirical evidence on these disclosures and find that several of the petition's factual premises are not consistent with the evidence.

It is standard in economics to assume that assets are normal goods and demand is downward sloping in price. This view has its theoretical foundation in the classic single period model of Arrow with one risky asset and one risk free asset, where both are assumed to be held long, and preferences exhibit decreasing absolute risk aversion and increasing relative risk aversion.

The diagnosticity of feelings in judgment depends not only on their representativeness and relevance, but also on people's trust in their feelings in general. Trust in feelings is the degree to which individuals believe that their feelings generally point toward the "right" direction in judgments and decisions.

We consider the problem of producing lower bounds on the optimal cost-to-go function of a Markov decision problem. We present two approaches to this problem: one based on the methodology of approximate linear programming (ALP) and another based on the so-called martingale duality approach. We show that these two approaches are intimately connected. Exploring this connection leads us to the problem of finding "optimal" martingale penalties within the martingale duality approach which we dub the pathwise optimization (PO) problem.