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AbstractThis paper is an excerpt from The Electronic Oracle: Computer Models and Social Decisions (Chichester: John Wiley & Sons, 1985), by the late Donella Meadows and Jennifer Robinson. The book investigates the practice and impact of systems analysis and computer modeling, particularly as applied to social policy. The authors explore the nature of models, the biases and hidden assumptions of different modeling methods, the pragmatics of the modeling process, and the impact of modeling on the real world. These issues are approached through detailed case studies of nine models designed to address issues of economic development, resources and the environment. The models used methods including econometrics, linear programming, input/output analysis, and system dynamics. Though the models were "identified as 'better than average'" by the authors and by "other modelers, clients, and sponsors," Meadows and Robinson (p. 104) found "mismatches of methods with purposes, sloppy documentation, absurd assumptions buried in overcomplex structures, conclusions that do not even follow from model output, and project management strategies that destroy the possibility of influencing actual policy." The record in the world of business modeling is at least as dismal. The excerpt presented here focuses on implementation. The authors explore why so many modeling projects fail and present guidelines for improvement. The methods developed in the book to expose hidden assumptions, to make undiscussable values and biases discussable, remain central to anyone seeking to have an impact on the real world. The book both anticipated and shaped important developments, including the focus on 'modeling for learning,' group model building, and the systems thinking and organizational learning movements. Yet despite welcome advances in hardware, software, and modeling methods since the book was written, we have not yet realized the authors' vision of a world in which modelers are not only scientific and rigorous, but also "compassionate, humble, open‐minded, responsible, self‐insightful, and committed." John Sterman Copyright © 1985 International Institute for Applied Systems Analysis. Reprinted by permission.

This study sought to examine the kinds of mindsets that the participating teachers demonstrated in their teaching and the impact of teachers' mindsets on their ways of teaching mathematics and science. Seven teachers who were teaching science or mathematics in high-needs schools in the urban context of the Bronx, New York were the participants of the study, with interview data and analysis provided for three participants representing the low, medium, and high mindset groupings. Qualitative research method was used to analyze the data. The data were collected through three different questionnaires, lesson observations, and interviews. Data analysis revealed that six participating teachers demonstrated a growth mindset with different degrees and one teacher seemed to have a fixed mindset. Teachers with a growth mindset adopted instructional practices that are more aligned with those that are recommended in the growth mindset literature.