cal engine.20 The most famous stochastic method is the Monte Carlo method,
which is particularly useful for simulating systems with many coupled degrees of freedom such as fluids, disordered materials, strongly coupled solids, and cellular structures, to mention a few.
1.3 Concluding remarks
This chapter had the sole purpose of addressing the question ‘what is a computer simulation?’ This is of course an important question, since it sets the grounds for much of what is discussed about computer simulations later in this book. For this reason, the first part of the chapter deals with some historical remarks about the many attempts to define computer simulations, whether offered by engineers, scien- tists or philosophers. In this context, I distinguished two kinds of definitions. Those that emphasize the computing power of computer simulations – called the problem- solving technique viewpoint – and those that take computer simulations to have, as a chief feature, the capacity to represent a given target system – called descrip- tion of patterns of behavior viewpoint. Although there are a handful of definitions where both viewpoints are combined, and arguably one that does not fit with our distinction, in general researchers across fields agree on conceptualizing computer simulations as one or the other viewpoint.
The second part of this chapter dealt with three different kinds of computer sim- ulations, as standardly found in the literature. These are, cellular automata, agent- based simulations, and equation-based simulations. As warned, this is neither an exhaustive taxonomy nor offers a unique classification. It could be relatively simple to show how an agent-based simulation could be interpreted as cellular automata (e.g., when focused on their nature as agents/cells), or as an equation-based sim- ulation (e.g., if the inner structure of an agent are equations). The key is to see which characteristic of the computer simulation is highlighted. Here, I offer some criteria for a sound characterization of each type. A final warning is issued, how- ever, regarding the methodology and epistemology tailored to each kind. It is not difficult to show that each kind of computer simulation entails specific and distinct methodological and epistemological concerns, and therefore they require a different treatment in their own way. In the reminder of this book, I focus my attention solely on the so-called equation-based simulations.
20The prefix ‘pseudo’ reflects the fact that these methods are based on an algorithm that produces
numbers on a recursive basis, eventually repeating the series of numbers produced. Pure random- ness in computers can never be achieved.
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Chapter 2
Units of analysis I: models and computer
simulations
Theories, models, experimental set-ups, prototypes: these are some of the typical units of analysis found in standard scientific and engineering work. Science and en- gineering are of course populated with other, equally decisive units of analysis that facilitate our description and knowledge of the world. These include hypotheses, conjectures, postulates, and a host of theoretical machinery. Computer simulations are the new acquisition in the scientific and engineering arena that count as novel units of analysis.1What are the constituents of computer simulations that comprise
such a new unit? What makes them different from other units of analysis? Answers to these questions are put forward here.
In the previous chapter, I turned our interests to computer simulations that im- plement equations as regularly found and used in the sciences and engineering. This chapter aims at making these general remarks more specific. To this end, the first section clarifies the notion of scientific and engineering model, as it is the basis of equation-based simulations. I also mentioned that their implementation is not di- rectly on the physical computer – recall that this was a fundamental assumption of the description of patterns of behavior viewpoint – but rather mediated by a proper methodology for computer simulations. The second part of this chapter presents in some detail how models are implemented as ‘simulation models.’ To this end, I present and discuss three main constituent parts of computer simulations, namely, specifications, algorithms, and computer processes. I will then present important social, technical, and philosophical problems emerging from this characterization. By doing this, I hope, we will be entrenching the status of computer simulations as units of analysis in their own right.
1Big Data – which I discuss in chapter 6 – and Machine Learning should be also included as novel
units of analysis in science and engineering.