Convergence and Scientific Realism

The debate between scientific realism and anti-realism remains at a stalemate, making reconciliation seem hopeless. Yet, important work remains: exploring a common ground, even if only to uncover deeper points of disagreement and, ideally, to benefit both sides of the debate. I propose such a common ground. Specifically, many anti-realists, such as instrumentalists, have yet to seriously engage with Sober's call to justify their preferred version of Ockham's razor through a positive account. Meanwhile, realists face a similar challenge: providing a (...) non-circular explanation of how their version of Ockham's razor connects to truth. The common ground I propose addresses these challenges for both sides; the key is to leverage the idea that everyone values some truths and to draw on insights from scientific fields that study scientific inference—namely, statistics and machine learning. This common ground also isolates a distinctively epistemic root of the irreconcilability in the realism debate. (shrink)

This article reviews and develops an epistemological tradition in the philosophy of science, known as convergentism, which holds that inference methods should be assessed based on their ability to converge to the truth across a range of possible scenarios. Emphasis is placed on its historical origins in the work of C. S. Peirce and its recent developments in formal epistemology and data science (including statistics and machine learning). Comparisons are made with three other traditions: (1) explanationism, which holds that theory (...) choice should be guided by a theory's overall balance of explanatory virtues, such as simplicity and fit with data; (2) instrumentalism, which maintains that scientific inference should be driven by the goal of obtaining useful models rather than true theories; and (3) Bayesianism, which shifts the focus from all-or-nothing beliefs to degrees of belief. (shrink)

Scientific realists with traditional semantic inclinations are often pressed to explain away the distinguished series of referential failures that seem to plague our best past science. As recent debates make it particularly vivid, a central challenge is to find a reliable and principled way to assess referential success at the time a theory is still a live concern. In this paper, I argue that this is best done in the case of physics by examining whether the putative referent of a (...) term is specifiable within the limited domain delineated by the range of parameters over which the theory at stake is empirically accurate. I first implement this selective principle into a general account of reference, building on Stathis Psillos's works. Then, I show that this account offers a remarkably reliable basis to assess referential success before theory change in the case of effective theories. Finally, I briefly show that this account still works well with other physical examples and explain how it helps us to handle problematic cases in the history of physical sciences. (shrink)

Although scientific realism is the default position in the life sciences, philosophical accounts of realism are geared towards physics and run into trouble when applied to fields such as biology or neuroscience. In this paper, I formulate a new robustness-based version of entity realism, and show that it provides a plausible account of realism for the life sciences that is also continuous with scientific practice. It is based on the idea that if there are several independent ways of measuring, detecting (...) or deriving something, then we are justified in believing that it is real. I also consider several possible objections to robustness-based entity realism, discuss its relationship to ontic structural realism, and show how it has the potential to provide a novel response to the pessimistic induction argument. (shrink)

If scientists embrace scientific realism, they can use a scientific theory to explain and predict observables and unobservables. If, however, they embrace scientific antirealism, they cannot use a scientific theory to explain observables and unobservables, and cannot use a scientific theory to predict unobservables. Given that explanation and prediction are means to make scientific progress, scientists can make more scientific progress, if they embrace scientific realism than if they embrace scientific antirealism.

First, I identify a methodological thesis associated with scientific realism. This has different variants, but each concerns the reliability of scientific methods in connection with acquiring, or approaching, truth or approximate truth. Second, I show how this thesis bears on what scientists should do when considering new theories that significantly contradict older theories. Third, I explore how vulnerable scientific realism is to a reductio ad absurdum as a result. Finally, I consider which variants of the methodological thesis are the most (...) defensible in light of the earlier findings. (shrink)

Does science move toward truths? Are present scientific theories (approximately) true? Should we invoke truths to explain the success of science? Do our cognitive faculties track truths? Some philosophers say yes, while others say no, to these questions. Interestingly, both groups use the same scientific theory, viz., evolutionary theory, to defend their positions. I argue that it begs the question for the former group to do so because their positive answers imply that evolutionary theory is warranted, whereas it is self-defeating (...) for the latter group to do so because their negative answers imply that evolutionary theory is unwarranted. (shrink)

The pessimistic induction is built upon the uniformity principle that the future resembles the past. In daily scientific activities, however, scientists sometimes rely on what I call the disuniformity principle that the future differs from the past. They do not give up their research projects despite the repeated failures. They believe that they will succeed although they failed repeatedly, and as a result they achieve what they intended to achieve. Given that the disuniformity principle is useful in certain cases in (...) science, we might reasonably use it to infer that present theories are true unlike past theories. Hence, pessimists have the burden to show that our prediction about the fate of present theories is more likely to be true if we use the uniformity principle than if we use the disuniformity principle. (shrink)

Robustness is often presented as a guideline for distinguishing the true or real from mere appearances or artifacts. Most of recent discussions of robustness have focused on the kind of derivational robustness analysis introduced by Levins, while the related but distinct idea of robustness as multiple accessibility, defended by Wimsatt, has received less attention. In this paper, I argue that the latter kind of robustness, when properly understood, can provide justification for ontological commitments. The idea is that we are justified (...) in believing that things studied by science are real insofar as we have robust evidence for them. I develop and analyze this idea in detail, and based on concrete examples show that it plays an important role in science. Finally, I demonstrate how robustness can be used to clarify the debate on scientific realism and to formulate new arguments. (shrink)

I review prominent historical arguments against scientific realism to indicate how they display a systematic overshooting in the conclusions drawn from the historical evidence. The root of the overshooting can be located in some critical, undue presuppositions regarding realism. I will highlight these presuppositions in connection with both Laudan’s ‘Old induction’ and Stanford’s New induction, and then delineate a minimal realist view that does without the problematic presuppositions.

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in (...) order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

We presuppose a position of scientific realism to the effect (i) that the world exists and (ii) that through the working out of ever more sophisticated theories our scientific picture of reality will approximate ever more closely to the world as it really is. Against this background consider, now, the following question: 1. Do the empirical theories with the help of which we seek to approximate a good or true picture of reality rest on any non-empirical presuppositions? One can answer (...) this question with either a 'yes' or a 'no'. 'No' is the preferred answer of most contemporary methodologists -- Murray Rothbard is one distinguished counterexample to this trend -- who maintain that empirical theories are completely free of non-empirical ('a priori') admixtures and who see science as a matter of the gathering of pure 'data' obtained through simple observation. From such data scientific propositions are then supposed to be somehow capable of being established. (shrink)

Scientific Realists argue that it would be a miracle if scientific theories were getting more predictive without getting closer to the truth; so they must be getting closer to the truth. Van Fraassen, Laudan et al. argue that owing to the underdetermination of theory by data (UDT) for all we know, it is a miracle, a fluke. So we should not believe in even the approximate truth of theories. I argue that there is a test for who is right: suppose (...) we are at the limit of inquiry. Suppose that we then have all the logically possible theories that are adequate to all the actual data. If they all resembled in their theoretical claims, since one of them must be true, all of them would then resemble it, whichever it is. We would thus be justified in saying they all approximated the truth in the degree to which they co-resembled. If they don't all co-resemble, the SRs are wrong; more predictive theories are not necessarily closer to the theoretical truth. Prior to the limit, if, in spite of our best efforts to the contrary, all the theories we can make adequate to current data tend to co-resemble, we have inductive warrant for thinking more predictive theories are closer to the truth. If they don't resemble, we have inductive warrant for thinking that more predictive theories are not necessarily closer to the truth. (shrink)