8Cf. (Ruben, 1992, 160). Since I am only concerned with scientific explanation in the natural sciences, I take this claim to be unproblematic.
9Note that here I focus solely on explanation of empirical systems. In Section 6.2.1 I discuss further how to approach explanation of non-empirical systems.
10Alisa Bokulich (Bokulich, 2010) presents an interesting work on the explanatory power of fictions.
However, her account requires minimal degrees of representation from the model. I believe that she would agree that an entirely false model such as the Ptolemaic model has no explanatory value whatsoever.
11For Belousov-Zhabotinsky simulations, see (Field and Noyes, 1974). For the simulations to be stiff, cf. (Krohs, 2008, 281).
12An interesting work on the exploratory power of computer simulations can be found in (García and Velasco, 2013).
13The notion of ‘structure’ here is broadly construed. Ontic theories take it as the causal rela-tionships that bring the phenomenon about and that can be tracked back. Unificationists, on the other hand, take it as patterns of behavior common to many phenomena.
14The justification condition only requires that the belief that qualifies as knowledge has the property of being justified. This rules out the possibility of arguing that an agent must engage in the activity of justifying the belief. See also (Moser, 2002; Steup and Sosa, 2005).
15See Gettier’s second counterexample to the conception of knowledge as justified true belief in (Gettier, 1963).
16See also, for instance (Goldman, 1988, 2009).
17Cf. (Goldman, 1999, 28).
18Goldman refers to his position as ‘Historical Reliabilism,’ for he emphasizes a process of belief-generation from a historical perspective.
19I have selected these two examples to show that a reliable process can be purely cognitive, as in areasoning process; or external to our mind, as the example of a tree outside my window shows.
20Under this reliabilist account, neither skepticism in the form of the evil genius as presented by Descartes (Descartes, 1996 (1641), nor ‘epistemic luck’ (Radford, 1966) are sound arguments.
21See also (Gillies, 2000).
22See, for instance, the works of (Kvanvig, 2003, 2009; Zagzebski, 2000).
23The notion of ‘good representation’ stems from the working definition ongoodness of representa-tionas discussed in Section 1.2.2.1. The general idea is that what is represented in the simulation model does not differ significantly from the way the world is (or at least how it is understood by the sciences). This notion must be complemented with my discussion on implementing scientific models as computer simulations in Chapter 2 and Chapter 3.
24This point has been discussed in Section 3.3.2 and Section 3.3.3.
25There has been some work done where it is possible to claim for an agent to have understanding while lacking knowledge of the relevant propositions; for instance, (Elgin, 1996, 2004, 2009) and (Riggs, 2002).
26I analyze the character of this naturalist viewpoint in more depth in Section 5.2. For now, we can take a ‘true belief’ as representations that ‘match nature.’ As for the notion of ‘matching nature,’
it is enough to say that scientific practice can provide justifying arguments for concluding that the actual world is not likely to be significantly different from an ideal world that is represented in models and theories. Cf. (Kitcher, 1989, 453).
27Cf. (Elgin, 2007, 39).
28Of a similar opinion are Henk De Regt and Dennis Dieks (De Regt and Dieks, 2005, 151).
29Cf. (Elgin, 2009, 324).
30Let me note here that I am not interested in acts of explaining, but rather in the explanation as aproduct. Hence, there will be no differences between talking of ‘theories, models, or computer simulations explaining something’ and ‘an agent using a theory, model, or computer simulation for explaining.’ In both cases the emphasis is meant to be on the logic of explanation, not on the agent doing the explanation.
31This example is brought up by Michael Scriven (Scriven, 1970) when objecting to Hempel’s account. In (Hempel, 1965, 348), Hempel answered that such examples are not of concern for scientific explanation.
32Note that here I made no difference between explanation of particulars (e.g., puerperal fever) and explanation of laws (i.e., the second law of thermodynamics). These explanations represent two different problems for the philosopher of science, and must be treated accordingly. In this work I only focus on explanation of particulars.
33See (Hempel, 1965; Hempel and Oppenheim, 1948).
34For thestatistical relevance model, see (Salmon et al., 1971). For theontic approach, see (Salmon, 1984). For the pragmatic approach, see (van Fraassen, 1980). Finally, for the unificationist ap-proach, see (Friedman, 1974; Kitcher, 1981).
35Also known as explanatory realism (Kim, 1994, 273).
36In this work I do not make differences between ‘an explanation yields understanding of a phe-nomenon’ and ‘wegain understanding by explaining a phenomenon.’ Both sentences refer to the same epistemic product of an agent gaining insight into the way the world is.
37It is worth restating here that I do consider computer simulations as methodologically and epistemically novel.
38For each explanatory relationship see, for instance (Salmon, 1984; Woodward, 2003), (Hempel, 1965; Kitcher, 1989), (van Fraassen, 1980), Craver (2006); Machamer (2004) and (Pincock, 2010) respectively.
39(Machamer, 2004).
40Cf. (Krohs, 2008).
41Also known as Covering Law Model.
42Cf. (Hempel, 1965, 336).
43At the time of Pascal and Périer, the only available model of explanation was Aristotle’s. Un-fortunately, this phenomenon cannot be explained by his account because, as Aristotle believed, the existence of a vacuum in nature was absurd (horror vacui) (Aristotle, 1936).
44Cf. (Hempel, 1965, 413).
45Cf. (De Regt and Dieks, 2005, 140-141).
46See (Suppe, 1977).
47Cf. (Hempel, 1945, 7).
48Cf. (Kitcher, 1989, 423).
49Cf. (Hempel, 1965, 101)
50See my discussion on this issue on Section 5.2.
51Cf. (Kitcher, 1993, 150).
52Cf. (Steiner, 1978, 19).
53Cf. (Steiner, 1989, 453).
54The examples are found in (Batterman, 2002, 37) and (Batterman, 2002, 46) respectively.
55See (Pincock, 2011, 4).
56(Pincock, 2011).
57(Bueno and Colyvan, 2006).
58Cf. (Bokulich, 2008, Chapter 6)
59Cf. (Craver, 2006, 361). See also (Machamer, 2004).
60Cf. (Craver, 2006, 361).
61Cf. (Bokulich, 2011, 34).
62Cf. (Elgin and Sober, 2002, 446).
63Bokulich renames it the causal model explanation, but here I keep the original hypothetico-structureal expalantion as coined by McMullin (McMullin, 1978).
64Cf. (McMullin, 1978, 139).
65Cf. (Bokulich, 2011, 43, footnote 16).
66Cf. (Woodward, 2003, 11).
67Cf. (Woodward, 2003, 187ff) for the complete details of the example.
68Cf. (Woodward, 2003, 191).
Chapter 5
Scientific explanation by computer simulations
5.1 Introduction
The initial motivation of this dissertation was to defend the epistemic power of computer simulations. I claimed that such a defense could be achieved by showing how computer simulations explain simulated phenomena. In previous chapters I diligently put the pieces together. Let me now briefly revisit some of the results obtained and indicate what role they play in this chapter.
Let us begin with the shift proposed in Chapter 2 consisting in analyzing com-puter simulations from the viewpoint of the philosophy of comcom-puter science. This shift allowed the introduction of three units of analysis, namely, the specification, the algorithm, and the computer process. All three concepts proved to be central for the construction of a working conceptualization of computer simulation, as en-trenched in Section 3.3.1.1. Such a working conceptualization, together with the discussion on verification, validation (see Section 3.3.2), and errors in computers (Section 3.3.3) are at the core of the process of explaining in computer simulations.
Equally important was to discuss current trends in the philosophical literature on scientific explanation, as carried out in Chapter 4. As a result, several theoretic conclusions were reached. Among the most prominent was that the metaphysics of explanation compel us to set two conditions for explaining in computer simulations, namely, thegoodness of representation of the simulation model, and the correctness of computability of it (see Section 4.2.1). Also central was the distinction between internalist and externalist explanatory accounts, which reduced the number of the-ories of explanation to analyze to the former accounts; and the metaphysical and ontological questions which are at the core of any theory of explanation (see Section
4.2.2). As I have argued in Chapter 4, the unificationist account of explanation as elaborated by Kitcher is the most suitable theory for computer simulations. This chapter, then, has the responsibility to expatiate on the reasons why I hold such a view. It must also show how a computer simulation works as an explanatory de-vice, and in what sense it yields understanding of the simulated phenomena (Kim’s metaphysical and epistemic questions respectively).
Following these indications, this chapter is organized as follows: in Section 5.2 I address the diverse commitments of the unificationist. Traditionally, there are many dissensions on the idea of a unified world to the point that the unificationist (in all its variants) has fallen into disgrace. It is important, therefore, to restitute their good name. In this first section, I specify the kind of unificationist underlying Kitcher’s theory (i.e., themodest unificationist). The following two sub-sections are a detailed discussion on how the modest unificationist answers Kim’s metaphysical and epistemic questions. Section 5.3 addresses at face value computer simulations as an explanatory device. As anticipated, its explanatory power flourishes within the conceptual framework provided by the unificationist. The aim of this last section, then, is to answer Kim’s epistemic and metaphysical questions within the context of explanation by computer simulations.