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Contemporary scholars describe mathematical modeling as a transformation of a real-world problem to a mathematical problem and back again. This paper treats a critical issue in the modeling process: how modelers determine if the transformation from the real world to mathematics was carried out well. I present an empirically derived typology of validating activities explaining how validating functions to ensure a mathematical model will yield a reasonably accurate prediction. The typology arose from analysis of four engineering undergraduates’ production of 276 instances of validating. The nuances of validating suggest that creating and maintaining relationships between reality and mathematics is more complex than a transformation and that we should afford a more prominent role to validation in the modeling process. [ABSTRACT FROM AUTHOR]

Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of data quality management in information systems within an enterprise. Motivated by legislative mandates such as the Sarbanes-Oxley Act of 2002 on the reliability and integrity of the data and the enterprise systems from which the data are produced, we propose a process-based modeling framework to assess the impact of data errors in the business process information flow and the resulting data quality metrics. This framework is then integrated with a business control framework in which the placement and effectiveness of control procedures alter the propagation of errors and, ultimately, the quality of the data in the business process. This integrated framework enables mathematical formulations of managerial problems that lead to effective data quality control strategies. We develop a two-stage multiple-choice knapsack model as a special case, and we illustrate the model and analysis through a revenue realization process. [ABSTRACT FROM AUTHOR]

This paper demonstrates the estimation and simulation of discharge and sediment concentration for two river basins in the USA and India. The first-order Sugeno fuzzy inference system was utilised to model the stage, discharge and sediment concentration relationship. A subtractive clustering algorithm, along with a least-squares estimation, was used to generate the fuzzy rules that describe the relationship between input and output data of stage, discharge and sediment concentration, which change over time. The fuzzy rules were tuned by a back-propagation algorithm. The results are illustrated using simulation and virtual reality. A comparison was made between the estimates provided by the neuro-fuzzy model and a multi-linear regression model. Different statistical criteria were used to evaluate the performance of both models in estimating discharge and sediment concentration. Comparison of the results reveals that, in general, the neuro-fuzzy model gives better estimates than the multi-linear regression model in terms of root mean square and sum of squares errors. Furthermore, compared with the multi-linear regression model, the neuro-fuzzy model yields statistical properties of estimates that are closer to actual historical data. [ABSTRACT FROM AUTHOR]