Decision Making & Negotiations

The pricing of corporate debt is still a challenging and active research area in corporate finance. Starting with Merton (1974), many authors proposed a structural approach in which the value of the assets of the firm is modeled by a stochastic process, and all other variables are derived from this basic process. These structural models have become more complex over time in order to capture more realistic aspects of bankruptcy proceedings.

Following a successful idea generation exercise, a company might easily be left with hundreds of ideas, generated by experts, employees, or consumers. The next step is to screen these ideas, and identify those with the highest potential. In this paper we propose a practical approach to involving consumers in idea screening. Although the number of ideas may potentially be very large, it would be unreasonable to ask each consumer to evaluate more than a few ideas. This raises the challenge of efficiently selecting the ideas to be evaluated by each consumer.

Although the vast majority of real options models assume that investment opportunities are permanently available, many real-world investment opportunities are sporadic. Options to invest can suddenly become blacked-out, only to be followed by a re-opening in the future. For example, the option to develop real estate may open or close depending upon zoning and growth control decisions, competitive entry, or input (labor or material) availability. Similar factors influence complex R&D options. We term such randomly recurring investment opportunities as options on illiquid projects.

Several individual, social-setting, and choice-set factors have been shown to be related to satisfaction. This article argues that these factors operate through a set of choice goals. Using panel data on purchasers of consumer electronics, the authors examine how five goals (justifiability, confidence, anticipated regret, evaluation costs, and final negative affect) drive decision and consumption satisfaction, which in turn determine loyalty, product recommendations, and the amount and valence of word of mouth.

The article shows that two measures of the amount of private information in stock price — price nonsynchronicity and probability of informed trading (PIN) — have a strong positive effect on the sensitivity of corporate investment to stock price. Moreover, the effect is robust to the inclusion of controls for managerial information and for other information-related variables. The results suggest that firm managers learn from the private information in stock price about their own firms’ fundamentals and incorporate this information in the corporate investment decisions.

In this paper I explore the lessons learned from the redevelopment of Times Square at 42nd Street, where 13 acres of prime, if blighted, land was assembled by the customary method of condemnation. This experience vividly argues for a more efficient strategy, though in such large-scale redevelopment project where issues of overall control and the redefinition of land uses are often paramount, land readjustment schemes may be difficult to apply.

BACKGROUND: Delays for appointments are prevalent, resulting in patient dissatisfaction, higher costs, and possible adverse clinical consequences. A "just-in-time" approach to patient scheduling, called advanced access, has been effective in reducing delays in multiple clinical settings. Offering most patients appointments on the same day requires achieving an appropriate balance between supply of and demand for appointments, but no methods have been previously proposed to determine what this balance should be.

This paper develops a pure simulation-based approach for computing maximum likelihood estimates in latent state variable models using Markov Chain Monte Carlo methods (MCMC). Our MCMC algorithm simultaneously evaluates and optimizes the likelihood function without resorting to gradient methods. The approach relies on data augmentation, with insights similar to simulated annealing and evolutionary Monte Carlo algorithms. We prove a limit theorem in the degree of data augmentation and use this to provide standard errors and convergence diagnostics.

Our two-stage framework predicts that, during impression formation, individuals who hold ambivalent attitudes toward an issue are influenced by other sources regardless of their perceived reliability on the target issue. Less ambivalent individuals are presumed likely to check the reliability of the message's source before accepting it. Experiment 1 finds that highly ambivalent participants do not differentiate between a more versus less reliable source when forming impressions of a political candidate, whereas less ambivalent participants do.