Pretrial Bargaining in the Face of a Random Court Decision: Evidence from Laboratory Games

If negotiation over ownership of an asset is unsuccessful, agents go to court to determine possession. Experiments examine how the presence of a stochastic court decision affects pretrial bargaining behavior. Two players have private information over the value of an asset, owned by one player. If there is no acceptable trade price, a random court decision assigns ownership. The impact of a second stage court decision on bargaining outcomes and the efficiency of trades is measured. Courts reduce the total earnings of players and the frequency of efficient trades. Relative earnings and bargaining behavior depend on which agent proposes the trade price.     

“Positive Feelings”: Group Support for Children of HIV-Infected Mothers

This paper describes a support group for children, ages 9–13, whose mothers are living with the Human Immunodeficiency Virus (HIV). Beginning with identification of the need for, and purpose of the group, the paper describes the group recruitment, screening and intake process; group composition; group content and process; barriers to service and issues specific to facilitation of the group. The group provided support to children by increasing knowledge of HIV and Acquired Immunodeficiency Syndrome (AIDS) issues, providing a forum for expression of feelings and concerns regarding their parent's HIV status, improving communication skills, identifying and expanding coping strategies, and facilitating peer support for normalization and validation of fears and concerns.     

Merger as a Strategic Response to Government Contracting Pressures

This article describes the planning, decision-making, implementation, and evaluation phases of a recent, successful merger of five small social service agencies in Grand Rapids, Michigan. The merger was undertaken as a conscious strategy to enable the newly formed Arbor Circle Corporation to cope more effectively with environmental uncertainty, especially in regard to government contracting. The successes and the struggles encountered in the merger are instructive for other nonprofits that may be contemplating this kind of organizational restructuring as well as those interested in the impact of contracting on the government-nonprofit relationship.     

Simple and effective number-of-bins circumference selectors for a histogram

Two very effective data-based procedures which are simple and fast to compute are proposed for selecting the number of bins in a histogram. The idea is to choose the number of bins that minimizes the circumference (or a bootstrap estimate of the expected circumference) of the frequency histogram. Contrary to most rules derived in the literature, our method is therefore not dependent on precise asymptotic analyses. It is shown by means of an extensive Monte-Carlo study that our selectors perform well in comparison with recently suggested selectors in the literature, for a wide range of density functions and sample sizes. The behaviour of one of the proposed rules is also illustrated on real data sets.     

Evaluation of Embedded Intelligent Real‐time Systems*

Over the past two decades, questions have surfaced about the effectiveness and contribution of intelligent systems to decision makers in a variety of settings. This paper focuses on the evaluation challenges associated with intelligent real‐time software systems that are embedded in larger host systems. With the proliferation of such systems in operational settings such as aerospace, medical, manufacturing, and transportation systems, increased attention to evaluations of such systems, and to resulting software safety, is warranted. This paper describes one such evaluation and proposes a set of evaluation criteria for embedded intelligent real‐time systems (EIRTS). Implications of the evaluation and the evaluation criteria are discussed.     

Discrete–Time Approximations of the Holmström–Milgrom Brownian–Motion Model of Intertemporal Incentive Provision

This paper studies the relation between discrete–time and continuous–time principal–agent models. We derive the continuous–time model as a limit of discrete–time models with ever shorter periods and show that optimal incentive schemes in the discrete–time models approximate the optimal incentive scheme in the continuous model, which is linear in accounts. Under the additional assumption that the principal observes only cumulative total profits at the end and the agent can destroy profits unnoticed, an incentive scheme that is linear in total profits is shown to be approximately optimal in the discrete–time model when the length of the period is small.     

Is There a Curse of Dimensionality for Contraction Fixed Points in the Worst Case?

This paper analyzes the complexity of the contraction fixed point problem: compute an ε‐approximation to the fixed point V*Γ(V*) of a contraction mapping Γ that maps a Banach space B_d of continuous functions of d variables into itself. We focus on quasi linear contractions where Γ is a nonlinear functional of a finite number of conditional expectation operators. This class includes contractive Fredholm integral equations that arise in asset pricing applications and the contractive Bellman equation from dynamic programming. In the absence of further restrictions on the domain of Γ, the quasi linear fixed point problem is subject to the curse of dimensionality, i.e., in the worst case the minimal number of function evaluations and arithmetic operations required to compute an ε‐approximation to a fixed point V*∈B_d increases exponentially in d. We show that the curse of dimensionality disappears if the domain of Γ has additional special structure. We identify a particular type of special structure for which the problem is strongly tractable even in the worst case, i.e., the number of function evaluations and arithmetic operations needed to compute an ε‐approximation of V* is bounded by Cε^−p where C and p are constants independent of d. We present examples of economic problems that have this type of special structure including a class of rational expectations asset pricing problems for which the optimal exponent p1 is nearly achieved.     

Individual differences in numerical representations of risk in health decision making: A fuzzy-trace theory approach

Fuzzy-trace theory predicts that decisionmakers process numerical information about risk at multiple levels in parallel: the simplest level, nominal (categorical some-none) gist, and at more fine-grained levels, involving relative comparison (ordinal less-more gist) and exact quantities (verbatim representations). However, little is known about how individual differences in these numerical representations relate to judgments and decisions, especially involving health tradeoffs and relative risks. To investigate these differences, we administered measures of categorical and ordinal gist representations of number, objective numeracy, and intelligence in two studies (Ns = 978 and 956). In both studies, categorical and ordinal gist representations of number predicted risk judgments and decisions beyond objective numeracy and intelligence. Participants with higher scores in categorical gist were more likely to choose options to avoid cancer recurrence risks; those who were higher in ordinal gist of numbers were more likely to discriminate relative risk of skin cancer; and those with higher scores in objective numeracy were more likely to choose options that were numerically superior overall in terms of relative risk of skin cancer and of genetic risks of breast cancer (e.g., lower numerical probability of cancer). Results support parallel-processing models that assume multiple representations of numerical information about risk, which vary in precision, and illustrate how individual differences in numerical representations are relevant to tradeoffs and risk comparisons in health decisions. These representations cannot be reduced to one another and explain psychological variations in risk processing that go beyond low versus high levels of objective numeracy.     

Getting your supervisors and managers on the right team

They do a great job. So we promote them. Now they supervise people who don't do the job as well as they did. We give them a big raise. But because they're exempt they make less than their leads. They have the toughest job in the company—managing people. So we grant them an entire day of training to prepare. We ask them to mentor, coach, and have close relationships with their direct reports—all 132 of them. Then when the stock dips, we blame “middle management” and lay a bunch of them off.