Model-Based Inferences in Modeling of Complex Systems
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Model-Based Inferences in Modeling of Complex Systems Miles MacLeod1
© The Author(s) 2018
Abstract Modelers are tackling ever more complex systems with the aid of computation. Model-based inferences can play a key role in their ability to handle complexity and produce reliable or informative models. We study here the role of model-based inference in the modern field of computational systems biology. We illustrate how these inferences operate and analyze the material and theoretical bases or conditions underlying their effectiveness. Our investigation reiterates the significance and centrality of model-based reasoning in day-to-day problem-solving practices, and the role that debugging processes of partial or incomplete models can play in scientific inference and scientific discovery, particularly with respect to complex systems. We present several deeper implications such an analysis has for philosophy of science regarding the role of models in scientific practice. Keywords Model-based reasoning · Model-based inference · Complexity · Systems biology · Computation
1 Introduction This paper explores the nature of scientific inference in discovery contexts. It aims to illustrate the fundamental importance of model-based inference in the construction of mathematical models of complex systems in the field of computational systems biology, emphasizing the central role partially complete and correct models play in solving such problems. This field aims to build detailed mathematical models of biological systems, particularly biochemical systems. Such systems are by nature complex. Systems biology represents to some extent trends in recent scientific practice towards applying relatively fast and available computer technology to complex systems. In certain types of modeling in systems biology, modelers rely heavily on the affordance of partial or incomplete models to draw inferences about their systems. Our primary goals are to (1) illustrate the structure and function of these inferences (their underlying heuristics and procedures) in systems biology—and describe in what sense they are model-based, and (2) analyze the basis or conditions underlying the ability to draw such inferences when handling complex systems.
Our analysis reiterates the significance and centrality of model-based reasoning in day-to-day mundane problemsolving practices, and the genuine role that otherwise humble engineering-type debugging processes of partial or incomplete models can play in scientific inference and scientific discovery (Wimsatt 2007). Importantly we show that the discovery processes we describe are strongly guided through various heuristics (for model manipulation) amounting to a set of procedures for discovery. In this regard we build on recent insights in discussion about scientific discovery, and show how even in complex cases there may well be an underlying logic guiding discovery rather than pure intuition alone (Ippoliti 2017, 2018a, b). Indeed the structure underlying discovery in these cases raise several implications for
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