It's 832 Chapter 10 Values In Computational Models Revalued

Its 832 Chapter 10values In Computational Models Revaluedinformation

Its 832 Chapter 10values In Computational Models Revaluedinformation 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 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 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

Paper For Above instruction

The role of values in computational models has garnered increasing attention in recent years, particularly as these models are used to inform public decision-making processes in complex and controversial issues. Chapter 10 of the course material explores this intersection between values, bias, and the effectiveness of computational models within a broader socio-technical framework, emphasizing that models are not neutral or value-free but are inherently influenced by underlying assumptions, design choices, and the data used.

To understand the significance of these influences, it is essential first to recognize that models are constructed within social contexts and often reflect the biases—whether intentional or unintentional—of their creators. The debate on whether models are biased, and if so, whether this bias is a reflection of technological determinism or social construction, constitutes a central theme in the discussion. Technological determinism posits that technology develops independently of societal influences, often assuming neutrality, whereas social construction argues that technology is shaped by social values, power dynamics, and intentional design choices. The truth lies somewhere in between, with models often embodying biases from their conception, leading to questions about their neutrality and objectivity (Bijker et al., 2012).

This perception of bias has critical implications for public decision-making. Policy processes increasingly rely on complex models for predicting outcomes, such as flood risks, urban congestion, livestock disease outbreaks, and air pollution—in these cases, the data, models, and decision processes all carry embedded values that influence results. The chapter emphasizes that understanding where bias originates—from data, model design, or decision-making processes—is vital for evaluating model reliability and legitimacy (Kuhn, 2008). For example, in the case studies analyzed—ranging from morphological predictions in Belgium and the Netherlands to congestion charging in London—the trustworthiness of data and model assumptions strongly affected the credibility of the results.

Methodologically, the study involved selecting six case studies characterized by complex, controversial issues and designing models specifically tailored to examine policy relevance. These case studies highlighted the importance of model design and the need for clarity regarding the values embedded at each stage. Outcomes from analysis revealed that models with higher data trustworthiness yielded more reliable predictions, but all models exhibited margins of error due to the inherent uncertainties in complex systems (Lee, 2014). Furthermore, the decision-making process's structure—whether authority was clearly assigned or distributed—affected the potential for conflict and the legitimacy of outcomes.

In sum, the chapter concludes that the effectiveness of computational models in policy relies heavily on transparent acknowledgment of underlying biases and values. Model builders and users must recognize that models do not merely serve as neutral tools but are influenced by embedded assumptions about social, economic, and environmental factors. To improve model reliability and to foster better policy outcomes, there must be a deliberate effort to scrutinize and disclose these values at each stage—from data collection through model design, to decision-making contexts (O’Neil, 2016). Only with such an understanding can policymakers appropriately interpret model results and avoid the pitfalls of implicit bias.

References

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• Kuhn, T. S. (2008). The Structure of Scientific Revolutions. University of Chicago Press.
• Lee, K. (2014). Complexity and Uncertainty in Flood Risk Modeling. Environmental Modelling & Software, 55, 192-201.
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