assignment

ITS 832

Chapter 10

Values in Computational Models Revalued Information Technology in a Global Economy

Introduction

• Technology perceptions

• Technology and public decision making

• Methodology

• Case studies

• Analysis

• Summary and conclusions

Technology Perceptions

• Debate on underlying assumptions of models • Are models biased?

• Is technology biased? • Are model builders biased? • Are model users biased?

• Technological determinism • Technology is not neutral of value-free

• Social construction of technology • Technology is designed with bias, or values

• Technological instrumentalism • Technology is neutral and value-free

Technology and Public Decision Making

• Policy making involves complex systems

• Model bias must be understood to evaluate results

• Bias, or value can be categorized • Values of the data

• Values of the model

• Values of the decision-making process

Methodology

• Select six case studies

• Carry out secondary analysis of results

• Identify cases with three basic characteristics • New model designed for case

• Relate to policy issues with the natural or built world

• Highly complex and controversial issues

Case Studies

• Morphological Predictions in the Westerschele (Belgium and the Netherlands)

• Morphological Predictions in the Unterlbe (Germany) • Flood-Risk Prediction (Germany and the Netherlands) • Determining the Implementation of Congestion Charging

in London (UK)

• Predicting and Containing the Outbreak of Livestock Diseases (Germany)

• Predicting Particular Matter Concentrations (the Netherlands)

Analysis

• Analyzing empirical data resulted in several findings

• Values in data • Cases 1-4 exhibited higher trustworthiness of data

• Margin of error high in all cases

• Values in the model • Similar to values in data findings

• Values in the decision-making process • Clear lines of authority in cases 1, 4, and 5

• Lack of clear authority (cases 2, 3, and 6) leads to conflict

Summary and Conclusions

• Model effectiveness is impacted by bias

• Values can originate from multiple sources

• Data

• Model design

• Model use

• Outcome validity requires a clear understanding of values put forth by model use