A LABORATORY STUDY OF INDIVIDUAL SEARCH PATTERNS IN A DECISION-MAKING SITUATION

This study investigates the effects of time and probability constraints on the search process in a decision-making situation. The setting was a behavioral laboratory in which subjects were asked to choose one art print from a selection of 260 prints. The prints were in sets of five for convenience in showing. In the nonconstraint condition, subjects were able to set their own pace in viewing prints and could obtain any print in the whole set that was desired. Under the constraint condition, the time and probability constraints were both operative. Under the time constraint, viewing additional sets required increasing amounts of time. Under the probability constraint, there was a constantly reducing probability—down to a minimum of ten percent—of obtaining an earlier print as more prints were viewed. In comparison to unconstrained subjects, constrained subjects viewed fewer prints and did more rechecking in total while the search was in process.     

ON ESTIMATING PARAMETERS FOR BETA DISTRIBUTIONS

The purpose of this paper is to comment on and give historical perspective to two methdologies for estimating parameters of beta distributions. Fielitz and Myers [3] [4] developed and advocated a methodology using the method of moments, while Romesburg [20] advocated a methodology usingthe method of maximum likelihood. However, what Fielitz and Myers presented as new research and suggested as an area needing further study is ground already trampled. The authors have prepared a graph to underline the superiority of the maximum likelihood method in fitting beta distributions.     

SENSITIVITY ANALYSIS WITH INTERACTION EFFECTS

The purpose of this paper is to extend the sensitivity analysis methodology as it applies to linear programming. The methodology developed herein is concerned with those changes in a technological coefficient that cause simultaneous changes in the relevant cost coefficients of the objective function. By proceeding from the definitions of feasibility and optimality, the authors develop formulas for determining bounds on the extent to which a technological coefficient, which has a predetermined effect on the corresponding cost coefficient, can vary without changing the optimal set of activities. After the formulas are developed, their use is demonstrated on a product-mix problem. This technique is of interest because changes in the rate at which an input is utilized can be expected to change the cost per unit of a resulting product. This interdependence should be recognized whenever a sensitivity analysis is performed on a technological coefficient of either a cost minimization or a contribution margin maximization problem.     

EXPLORING MARKET PRICING STRATEGIES VIA DYNAMIC PROGRAMMING

Price determinants as well as strategies can be studies by use of simulation, particularly if cost and price relationships can be related to market activity [1] [9] [11]. But, through the use of dynamic programming, given the market conditions, one can extend the analysis to include an optimal strategy. This paper describes a dynamic programming approach to studying price strategy. A model is developed to show that in a market characterized by cost/volume and price/volume relationships, profitability can be extended beyond that resulting from a dominant market strategy to an optimal maximizing strategy. Extension of the model is suggested for studying (a) sensitivity of a strategy (solution) to price level and cost changes, (b) optimal timing of withdrawal, and (c) present value analysis.     

How Latinos Are Transforming the United States: Research, Theory, and Policy

The United States is entering a new era, a period marked by some important demographic changes in the composition of the population, most especially significant increases in the Latino and Latino immigrant segments of society. These population shifts require corresponding interpersonal, organizational, and structural changes. The present issue bridges research and theory across disciplines and includes studies incorporating a variety of methodologies to examine these important areas. These articles begin to fill some of the voids where a systematic and robust corpus of knowledge is lacking. The contributions address topics ranging from issues of identity and interpersonal relations to pressing matters of educational significance to general approaches to navigating the cultural transitions that mark fluid transnational adaptations. Finally, each contribution delineates the policy implications resulting from the processes and literatures that are examined.     

Dynamic reasoning and time pressure: Transition from analytical operations to experiential responses

Based upon the Decision Field Theory (Busemeyer and Townsend 1993), we tested a model of dynamic reasoning to predict the effect of time pressure on analytical and experiential processing during decision-making. Forty-six participants were required to make investment decisions under four levels of time pressure. In each decision, participants were presented with experiential cues which were either congruent or incongruent with the analytical information. The congruent/incongruent conditions allowed us to examine how many decisions were based upon the experiential versus the analytical information, and to see if this was affected by the varying degrees of time pressure. As expected, the overall accuracy was reduced with greater time pressure and accuracy was higher when the experiential and analytical cues were congruent than when they were incongruent. Of great interest was the data showing that under high time pressure participants used more experiential cues than at other time pressures. We suggest that the dynamic reasoning paradigm has some future potential for predicting the effects of experiential biases in general, and specifically under time pressure.     

Convergence of Heavy-tailed Monte Carlo Markov Chain Algorithms

Abstract.  In this paper, we use recent results of Jarner & Roberts ( Ann. Appl. Probab., 12, 2002, 224) to show polynomial convergence rates of Monte Carlo Markov Chain algorithms with polynomial target distributions, in particular random-walk Metropolis algorithms, Langevin algorithms and independence samplers. We also use similar methodology to consider polynomial convergence of the Gibbs sampler on a constrained state space. The main result for the random-walk Metropolis algorithm is that heavy-tailed proposal distributions lead to higher rates of convergence and thus to qualitatively better algorithms as measured, for instance, by the existence of central limit theorems for higher moments. Thus, the paper gives for the first time a theoretical justification for the common belief that heavy-tailed proposal distributions improve convergence in the context of random-walk Metropolis algorithms. Similar results are shown to hold for Langevin algorithms and the independence sampler, while results for the mixing of Gibbs samplers on uniform distributions on constrained spaces are rather different in character.     

The Zen of physician initiatives

Many physicians today feel ravaged by the brutal speed with which change has been occurring. They see the beliefs and practices of a lifetime being abandoned and replaced by the flavor of the month, management du jour. But if you are willing and able to take the brave step of approaching your physicians without an agenda, meeting with them to listen to their concerns, and can also avoid hanging a lightning-rod label on every bright new idea that comes out of the sessions, you'll be on the way to effective new management. This new style is a "Zen" approach (but don't give it that label) that lets real collaboration come into the place that is supposed to be all about healing--the health care organization. From "never call it anything" to "stay with them until they get it," ideas on how to be a Zen manager are presented, with the ultimate goal of truly partnering with physicians and infusing them with a desire to move beyond the frustration and disenchantment they are feeling.     

A queuing-theory-based approach to evaluate the efficiency of a network of automated stations and of a communication system

The Regional Meteorological Service for the Emilia-Romagna Region manages a network of automatic weather stations equipped with electronic sensors suitable for measuring meteorological parameters. The automatic stations consist of electronic instruments, which are subject to failures at more or less frequent intervals. A summary of their performance is necessary. In this paper, we compare the results of the summary, such as the contiguous absence or simultaneous inactivity of different stations, with theoretical simulations in order to evaluate the nature and recurrence of the failures. A single- and multi-server queue simulation model was also used to evaluate the performance of the data transmission system, so as to optimize the communications system.