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In 1974 the results of widely publicized police experiment, the Kansas City Preventive Patrol Experiment (KCPPE), were released. The experiment generated considerable dialogue among police practitioners and researchers alike, who debated such issues as the policy implications of the research findings and the methods employed for obtaining those findings. This author was one of several who wrote critiques of the KCPPE. In a recent issue of Evaluation Review, an article critiquing my critique was published. This note represents my response to that article.

AbstractWe reflect on our past 7 years of collaboration to develop systems models of U.S. higher education and scientific workforce development. Based on three recent modeling examples, we offer a methodological proposition that many traditional operations research (OR) models can be improved by including feedback processes, as is commonly done in system dynamics (SD) modeling. Such models, even if simple and approximate, can be powerful, insightful, easy to communicate, and effective. While these modeling examples may not follow conventional SD or OR modeling, they benefit from and contribute to both schools of modeling. We argue that to build such synergy modeling teams should be willing to create models building on the strengths of each school of modeling.Copyright © 2018 System Dynamics Society

The last decade has seen considerable concern regarding a shortage of science, technology, engineering, and mathematics (STEM) workers to meet the demands of the labor market. At the same time, many experts have presented evidence of a STEM worker surplus. A comprehensive literature review, in conjunction with employment statistics, newspaper articles, and our own interviews with company recruiters, reveals a significant heterogeneity in the STEM labor market: the academic sector is generally oversupplied, while the government sector and private industry have shortages in specific areas.

PurposeThe purpose of this paper is to study and review some major impacts of the disasters during the past 105 years and develop a new theoretical classification of disasters.Design/methodology/approachA detailed study of disasters in the world during the period (1900‐2005) has been obtained from the recent published sources. In that period more than 40 lessons have been reported based on statistical data analysis of disasters. Furthermore, a two‐dimensional probability density function is developed to categorize the different types of disasters. This paper studies and reviews some major impacts of disasters during the past 105 years and summarizes some major lessons for the future. Furthermore, a new scaling system is presented to determine the actual damage of disasters to human life.FindingsThere is no doubt that the impacts of future disasters will not be the same as previous ones but lessons from the past can be very helpful for improving one's knowledge about disasters and providing better response programs for local and international organizations. A new scaling system will also be a useful guide for the development and evaluation of national and international disaster planning, mitigation, and hazard reduction efforts.Originality/valueImportant lessons have been reported based on statistical data analysis of disasters. Moreover, a new classification of disasters is presented to relate the vulnerability factors of a society to the magnitude of the natural disasters.

The act of evaluation requires an expenditure of resources. A simple decision tree model, borrowed from operations research, is used to provide a conceptual framework for considering whether or not to commit such resources in the case of an automobile purchase. Once the evaluation is carried out, the problem of evaluating the evaluation as a vehicle for producing useful information to decisionmakers is addressed. Evaluation inputs, processes, & outcomes are defined & discussed within the context of comprehensive evaluation of evaluations. 3 Tables, 2 Figures. Modified HA.