Effects of Group Practice of the Transcendental Meditation Program on Preventing Violent Crime in Washington,D.C.: Results of the National Demonstration Project,June--July 1993

This paper reports the results of a prospective experiment in which a group of approximately 4,000 participants in the Transcendental Meditation and TM-Sidhi programs of Maharishi Mahesh Yogi assembled in Washington, D.C., from June 7 to July 30, 1993. It was hypothesized that levels of violent crime in the District of Columbia would fall substantially during the Demonstration Project, as a result of the group's effect of increasing coherence and reducing stress in the collective consciousness of the District. A 27-member Project Review Board comprising independent scientists and leading citizens approved the research protocol and monitored the research process. Weekly crime data was derived from database records provided by the District of Columbia Metropolitan Police Department (DCMPD), which are used in the FBI Uniform Crime Reports. Statistical analysis considered the effect of weather variables, daylight, historical crime trends and annual patterns in the District of Columbia, as well as trends in neighboring cities. Consistent with previous research, levels of homicides, rapes and assaults (HRA crimes) correlated with average weekly temperature. Robberies approximately followed an annually recurring cycle. Time series analysis of 1993 data, controlling for temperature, showed that HRA crimes dropped significantly during the Demonstration Project, corresponding with increases in the size of the group; the maximum decrease was 23.3% (p < 2 × 10–9) [24.6% using a longer baseline, with 1988--1993 data (p < 3 × 10–5)], coincident with the peak number of participants in the group during the final week of the assembly. When the same period in each of the five previous years was examined, no significant decreases in HRA crimes were found. Robberies did not decrease significantly. However, a model that jointly estimated the effect of the Demonstration Project on both HRA crimes and robberies showed a significant reduction in violent crimes overall of 15.6% (p = 0.0008). Further analysis showed that the effect of the coherence-creating group on reducing HRA crimes could not be accounted for by additional police staffing. The time series analysis for HRA crimes gave results that are highly robust to alternative model specifications, and showed that the effect of the group size was cumulative and persisted after the Demonstration Project ended. Also, calculation of the steady state gain based on the time series model predicted that a permanent group of 4,000 coherence-creating experts in the District would have a long-term effect of reducing HRA crimes by 48%.     

THE EFFECT OF RETAIL PROMOTIONAL ACTIVITIES ON SALES*

This article reports the formulation and results of a multiple equation econometric model to relate retail sales by brand and package size to retail promotional variables for a branded, frequently purchased grocery product. Primary emphasis is placed on the formulation of the model as opposed to the results obtained from its use.     

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.     

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.     

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.     

A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces

Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likelihood) using Markov Chain Monte Carlo (MCMC) simulation. This paper integrates the essential ideas of DE and MCMC, resulting in Differential Evolution Markov Chain (DE-MC). DE-MC is a population MCMC algorithm, in which multiple chains are run in parallel. DE-MC solves an important problem in MCMC, namely that of choosing an appropriate scale and orientation for the jumping distribution. In DE-MC the jumps are simply a fixed multiple of the differences of two random parameter vectors that are currently in the population. The selection process of DE-MC works via the usual Metropolis ratio which defines the probability with which a proposal is accepted. In tests with known uncertainty distributions, the efficiency of DE-MC with respect to random walk Metropolis with optimal multivariate Normal jumps ranged from 68% for small population sizes to 100% for large population sizes and even to 500% for the 97.5% point of a variable from a 50-dimensional Student distribution. Two Bayesian examples illustrate the potential of DE-MC in practice. DE-MC is shown to facilitate multidimensional updates in a multi-chain “Metropolis-within-Gibbs” sampling approach. The advantage of DE-MC over conventional MCMC are simplicity, speed of calculation and convergence, even for nearly collinear parameters and multimodal densities.