Decision Making & Negotiations

We investigate determinants of slave family discounts in the New Orleans slave market. We find large price discounts for families unrelated to scale effects, childcare costs, legal restrictions, or transport costs. We posit that because family members voluntarily cared for each other, sellers sometimes found it advantageous to keep families together (when families included needy or dependent members). Evidence from ship manifests carrying slaves for sale in New Orleans provides direct evidence for selectivity bias in explaining slave family discounts.

Media firms compete in two connected markets. They face rivalry for the sale of content to consumers, and at the same time, they compete for advertisers seeking access to the attention of these consumers. We explore the implications of such two-sided competition on the actions and source of profits of media firms. One main conclusion we reach is that media firms may charge higher content prices in a duopoly than in a monopoly.

Many new products (e.g., PDA phones) share features with multiple categories, but are also significantly different from each of these categories. When consumers encounter such a product, they may create a new subcategory (e.g., smart phones) to accommodate it. In such situations, consumers must decide where to position the new subcategory. We develop the Category Activation Model (CAM) to predict where within a category structure consumers are likely to position a subcategory that they have created to accommodate a new product that could potentially belong to multiple product categories.

Product and waste take-back is becoming more regulated by countries to protect the environment. Such regulation puts an economic burden on firms, while creating fairness concerns and potentially even missing its primary target: environmental benefits. This research discusses the economic and environmental impacts of extended producer responsibility type of legislation and identifies efficiency conditions.

This paper provides an optimal filtering methodology in discretely observed continuous-time jump-diffusion models. Although the filtering problem has received little attention, it is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter estimation. Our approach combines time-discretization schemes with Monte Carlo methods. It is quite general, applying in nonlinear and multivariate jump-diffusion models and models with nonanalytic observation equations.

A criticism of mechanism design theory is that the optimal mechanism designed for one environment can produce drastically different actions, outcomes, and payoffs in a second, even slightly different, environment. In this sense, the theoretically optimal mechanisms usually studied are not "robust." To study robust mechanisms while maintaining an expected utility maximization approach, we study a multiagent model in which the mechanism must be designed before the environment is as well understood as is usually assumed.

We investigate the effect of discrete sampling and asset price jumps on fair variance and volatility swap strikes. Fair discrete volatility strikes and fair discrete variance strikes are derived in different models of the underlying evolution of the asset price: the Black-Scholes model, the Heston stochastic volatility model, the Merton jump-diffusion model and the Bates and Scott stochastic volatility and jump model.