Abstract
This chapter discusses one of the major debates in philosophy of science related to scientific knowledge, the debate between realists and anti-realists. Realists maintain that our best-confirmed scientific theories are true (or at least approximately true), but anti-realists think we should only accept that our best-confirmed scientific theories are useful in some sense without committing to their truth, approximate or otherwise. Some of the major arguments on both sides of this debate are evaluated in this chapter, though special attention is paid to the so-called “miracle argument” for scientific realism. Throughout the chapter a realist stance, which allows for genuine scientific knowledge, is defended. Ultimately, the chapter concludes that while anti-realist arguments are important and worth taking seriously, they do not pose an insurmountable threat to a realist conception of science. Such a conception holds that in the case of our best-confirmed theories the truth of those theories best explains their success, which gives us justification for believing that they are true.
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Notes
- 1.
Giere (2006) also construes anti-realism as a species of skepticism.
- 2.
The literature on scientific realism and anti-realism is vast. Our task would be made nigh impossible if we were to not only discuss realism and anti-realism in general, but also to discuss the myriad idiosyncratic formulations of these views. Additionally, there are many thought-provoking theories which fall somewhere in between realism and anti-realism such as “semirealism” (Chakravartty 1998, 2007), “scientific perspectivism” (Giere 1988, 2006), and “structural realism” (Ladyman 1998, 2011; Worrall 1989) among others. Providing a useful discussion of these theories would make this chapter unwieldy at best, and it is unnecessary for our current purpose.
- 3.
Roughly, things are thought to count as observable when it is possible for humans to observe them with our unaided senses. Anything that is not observable in this sense is considered unobservable. Interestingly, there is controversy concerning whether this distinction between observable and unobservable can actually be drawn in an unproblematic way. Churchland (1985) and Musgrave (1985) argue that this distinction is problematic. See Dicken and Lipton (2006) for insightful discussion of Musgrave’s argument. This is worth pointing out because if there are problems concerning how to draw this distinction, these are problems for anti-realist views (such as that of van Fraassen 1980) which claim we can know about the observable, but not the unobservable—problems that are not shared by scientific realists (Chakravartty 2014).
- 4.
Another way to understand this is that scientific realism is committed to the truth (at least for the most part) of the Real World Hypothesis defended in Chap. 11.
- 5.
See Psillos (1999) for a nice discussion of what it means for a theory to be ad hoc as well as for a discussion of what is required for a theory to be mature.
- 6.
- 7.
The use of entailment here, and in White’s own discussion, is to simplify matters. Of course, it is plausible that a theory, the truth of which makes some data highly probable without entailing that it, predicts or accommodates that data too.
- 8.
- 9.
For simplicity I will often drop the qualification “approximate truth” and speak just of the truth of our best scientific theories. However, the discussion should be understood in terms of our best scientific theories being true or approximately true.
- 10.
See McCain (2012) for further discussion of both of these objections.
- 11.
As we noted in Chap. 10, another significant criticism of IBE is that it is inconsistent with accepted theories of probabilistic reasoning such as Bayesianism. Although the literature on this line of criticism is quite interesting, we will not explore it here. For discussion of, and responses to, this criticism of IBE see Lipton (2004), McCain & Poston (2014), McGrew (2003), Okasha (2000), Psillos (1999), and Weisberg (2009).
- 12.
There are, of course, other ways of raising underdetermination problems for scientific realism. Nonetheless, the argument presented here represents the most common way of making this sort of objection. For an interesting variation on the original underdetermination argument see Stanford (2006), and for responses to Stanford see Chakravartty (2008) and Godfrey-Smith (2008).
- 13.
This is actually a simplified version of Laudan and Leplin’s argument. They argue for the additional claim that observing empirical consequences of a hypothesis does not always provide evidence for the hypothesis. I do not include this part of their discussion here for two reasons. First, there are problems with the argument that Laudan and Leplin provide in support of this additional claim. Second, this additional claim is not necessary for showing that the argument in support of UD is unsound. For our purposes this is all we need to concern ourselves with, so we do not need to explore Laudan and Leplin’s additional claim. For further discussion of Laudan and Leplin’s arguments and the relation of empirical equivalence to underdetermination see Kukla (1993, 1996), Leplin (1997), and Leplin and Laudan (1993).
- 14.
It should be noted that if the example presented by Laudan and Leplin actually demonstrates that two empirically equivalent theories can gain different degrees of evidential support from an observation, the example alone is sufficient to demonstrate that empirical equivalence does not entail underdetermination. This is a fact that Laudan and Leplin recognize, however, they present the rest of their argument to drive home their point. It is worth following their lead and presenting the entire argument instead of just this example because understanding the full argument helps to better illuminate the nature of the relation between empirical equivalence and underdetermination.
- 15.
- 16.
This is also sometimes referred to as the “Pessimistic Meta-Induction”.
- 17.
- 18.
Another strategy for responding to the PI which has been put forward in recent years is to argue that the PI is actually a fallacious argument. Lange (2002) argues that the PI commits what he calls the “turn over fallacy”. Lewis (2001) argues that the PI commits the fallacy of false positives. Saatsi (2005) argues that both Lange and Lewis are mistaken—the PI does not commit either fallacy. Mizrahi (2013) argues that Saatsi is mistaken because the PI can be understood in three different ways, and on each understanding it is fallacious. We will not explore the merits of this strategy for responding to the PI here.
- 19.
See Psillos (1999) for an excellent discussion of why many of the particular past scientific theories that Laudan refers to in pressing the PI are not actually successful.
- 20.
The idea that the elements responsible for past scientific theories being empirically successful are retained in our current best theories is another point that is often claimed in response to the PI. This approach allows that past scientific theories were successful, but it casts doubt on the claim that past scientific theories were totally false. What this approach seeks to do is show that the parts of past scientific theories responsible for their empirical successes are the true parts which have been retained in our current best scientific theories. If this is correct, then the PI fails to provide a reason to doubt that empirical success is a reliable guide to the truth of a scientific theory. For this sort of approach see Kitcher (1993), Psillos (1999), and Worrall (1989).
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McCain, K. (2016). Anti-realism About Science. In: The Nature of Scientific Knowledge. Springer Undergraduate Texts in Philosophy. Springer, Cham. https://doi.org/10.1007/978-3-319-33405-9_14
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