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Ontology, epistemology, and teleology for modeling and simulation : philosophical foundations for intelligent M&S applications
Tolk A., Springer Publishing Company, Incorporated, New York, NY, 2013. 392 pp.  Type: Book (978-3-642311-39-0)
Date Reviewed: Aug 28 2013

Modeling and simulation are commonplace in the scientific investigation of laboratory experiments and natural phenomena, economic systems, and engineering projects. They are used to explain what is being observed and to make predictions about future measurements and observations. Often, they are the only means of studying systems that cannot be constructed or prepared on a laboratory scale, such as the internal processes taking place in the sun, the trajectories of hurricanes, or policy decisions that will affect national economies. Modeling and simulation are not merely tools in the service of other disciplines. They have an integrity that puts them on a level equivalent with the earlier and more traditional disciplines in science, engineering, and economics, as well as with the two general components of any science: theory and experiments. In this view, modeling and simulation can lay claim to having a philosophy of their own, similar to that of the philosophy of science.

“Ontology,” “epistemology,” and “teleology” are venerable terms from classical Greek philosophy. Ontology is the study of what exists. In the context of modeling and simulation, ontology answers the questions, “What do we model?” and “How do we model?” Ontological questions exist on more than one level: in the content area in which modeling and simulation take place, and at the deeper level of the modeling and simulation activity as a more encompassing meta-issue. In the content areas, we have ontologies in the plural, as well as the ontology of modeling and simulation in itself.

“Epistemology” is the study of how we come to know. Particular issues include confidence that the model and simulation can reveal trustworthy information and predictions, and confidence that their results can lead to explanations that may support or challenge hypotheses or theories. Are the results reproducible? Most modern models and simulations are highly complex systems. Computational complexity always represents a potential limitation on understanding any algorithm, including those in the simulation.

“Teleology,” in this context, is the study of the means and ends to which we apply knowledge. Is the model applied in a manner consistent with the intent of its designers? Is the model used to generate knowledge or to encapsulate it? A central issue is trust that the model has an internal structure that is transparent enough to be understood and capable of being critically compared to alternative models and simulations.

These issues are addressed in depth in the book’s 17 chapters and epilogue. Andreas Tolk, the editor, begins the book with a synopsis of each chapter. The table of contents follows. It is impractical to discuss each chapter individually. The following chapters were particularly interesting to me.

The first five chapters emphasize the philosophy of modeling and simulation. The first chapter, “Truth, Trust, and Turing--Constraints for Modeling and Simulation,” by Tolk, is required reading. It lays out the philosophical issues and their overlap with the science and technology of modeling and simulation. The second chapter, “Guidelines for Developing Ontological Architectures in Modeling and Simulation,” by Partridge et al., expands on the first chapter by critically evaluating the need for an ontology of modeling and simulation, which is necessary for an epistemology. The third chapter, “Ontologies in Modeling and Simulation: An Epistemological Perspective,” by Hofmann, analyzes the ontology of modeling and simulation and ontologies of the content areas, and advocates an epistemological approach for designing ontologies to capture knowledge. Chapter 4, “Ontological Implications of Modeling and Simulation in Postmodernity,” by Heath and Jackson, emphasizes computational representations. Many modern simulations employ animations to present the results. The use of animation changes the expectations of the viewer and disguises any potential limitations and constraints in the model. The rhetoric of visualization also figures in chapter 9, “Philosophical and Theoretic Underpinnings of Simulation Visualization Rhetoric and Their Practical Implications,” by Collins and Ball. In chapter 5, “Models as Partial Explanations,” Weirich analyzes the utility of models and simulations to explain phenomena, despite errors in assumptions and restrictions in implementation. To what extent can their results be useful despite their shortcomings?

Philosophical issues become central in chapter 8, “Philosophical Aspects of Modeling and Simulation,” by Ören and Yilmaz, where the emphasis is on reproducibility, credibility, and ethics. In chapter 11, “Toward Replicability-Aware Modeling and Simulation: Changing the Conduct of M&S in the Information Age,” these same authors continue their discussion with a practical recommendation to create electronic portfolios of simulations. Just as experiments and field observations can be used to create and modify theories, Diallo et al. argue in chapter 10, “Modeling and Simulation as a Theory Building Paradigm,” that modeling and simulation can be used in theoretical investigations in a similar manner. In chapter 13, “On the Value of a Taxonomy in Modeling,” Smith observes that modeling and simulation lack the organizational schemes given by the chemist’s periodic table or the biologist’s Linnaean taxonomy, and argues that the lack emphasizes the artistic approach in the practice of modeling and simulation.

Although philosophical issues are central to this book, the clarity and style of writing make them accessible to readers coming from the sciences, engineering, or economics. Designing models and implementing simulations are often just done in order to solve some problem in a content area, without much deeper reflection beyond the practical difficulties of solving the problem. This book encourages metacognition, that is, standing back from the problem and from yourself to reflect on what you are working on, the extent to which you can know and understand what you are doing, and the ways in which you will use the results. This is a book worth reading and rereading.

Reviewer:  Anthony J. Duben Review #: CR141504 (1311-0990)
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