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Top1. Introduction
Modeling and Simulation (M&S) has been used in various contexts; it has been considered to be a tool, a methodology, and a discipline. Pragmatically, the usefulness of insights obtained using M&S applications cannot be denied. However, the increasing use of M&S in numerous disciplines, such as political science, meteorology, oceanography, economics, and healthcare, among others, calls for finding what common challenges and advantages exist across these disciplines. We suggest that one way of looking at those commonalities is by looking at E/O/T/M foundations (premises and assumptions) of the use of M&S across disciplines.
From a high-level perspective, M&S uses models to represent a phenomenon of interest and simulates these models to gain insight or to predict. Gaining insight or prediction suggests that knowledge is generated from the modeling and/or simulation activity. This knowledge-generation activity has been under deliberation on questions such as: Can simulations generate knowledge? What kind of knowledge do simulations generate? Does simulation need its own epistemology? Although these are epistemological questions, they are not separate from ontological, methodological, and teleological issues regarding the modeler’s perspective, approach, and intent. Frigg and Reiss (2009), for instance, argue that despite simulation creating parallel worlds on more ideal conditions than the “real world”, this is not unique to simulation; ergo it does not warrant a new philosophy of science. Humphreys (2009), on the other hand, states that with the introduction of computational science new issues also have arisen within the discipline of philosophy of science, namely: epistemic opacity, semantics, temporal dynamics, and practice not principle. Epistemic opacity refers to cognitive agents’ limited access to knowledge; semantics refers to how simulations are applied to real system given the detachment of simulations from reality, how computer simulations are limited by syntax of computer code, and how semantics are subsumed under that syntax; temporal dynamics refers to the temporal representations of dynamic processes involved in simulation is an essential element for philosophy of science in terms of the speed of prediction, as opposed to deduction; and finally practice, not principle, refers to how computational methods have forced researchers to differentiate between what is practically applicable, and what can only stay as principle.
As it can be inferred, the gap between simulations and reality and how it is bridged in a manner that knowledge can be established has epistemological, ontological, methodological, and teleological implications. These implications include issues such as validation (Klein & Herskovitz, 2005), simulation model formulation and characterization (Lenhard, 2007), and ultimately, whether or not simulation generates and/or applies knowledge. In order to gain insight in these issues, we propose to look into how models and simulations can be characterized using E/O/T/M considerations. Turnitsa, Padilla, and Tolk (2010) have made an introduction into this proposition by overlapping E/O/T considerations with the semiotic triangle idea introduced by Ogden and Richards (1923). Their model is modified in this paper, as seen in Figure 1.
Figure 1. Semiotic triangle for M&S (Adapted from Turnitsa, Padilla and Tolk (2010))