Exploring Scientific Method: Evidence, Explanation, and Unification in Science (8-9 May 2017)
Idea & Motivation
Philosophical investigation into scientific method is a well-established area within the philosophy of science. The aim of this workshop is to advance our understanding of three central topics within this investigation: the role that evidence plays in science; the structure of scientific explanations; and the role of unification in science. To this end, the following questions will be addressed. Concerning evidence: In what sense is it better to gather evidence from various sources? What does it mean for evidence to be reliable? Can biased research furnish good evidence? As regards explanation: How should we characterise causal explanation? Is causal explanation possible in quantum mechanics? How are various different forms of explanation in science related? Can certain of them be fruitfully thought of as species of a single genus? Finally, unification: What is the best way to characterise the inferential structure of certain types of unifying explanation? How do unifying hypotheses guide theory construction?
- Lorenzo Casini (Geneva/MCMP)
- Michael Cuffaro (University of Western Ontario/MCMP)
- Molly Kao (Université de Montréal)
- Reuben Stern (LMU Munich/MCMP)
Day 1 (08 May 2017)
|10:00 - 11:00||Michael Cuffaro: Algorithmic How-Possibly Explanation|
|11:15 - 12:15||Laura Felline: A role for Mechanistic Explanation in Physics: the Measurement Problem|
|12:15 - 14:00||Lunch|
|14:00 - 15:00||Barbara Osimani: Reliability and Replication: Statistics Meets Formal Epistemology|
|15:15 - 16:15||Alex Reutlinger: What is Epistemically Wrong With Biased Research?|
|16:30 - 17:30||Lorenzo Casini and Radin Dardashti: How Minimal Models Explain -- Within and Outside Physics|
Day 2 (09 May 2017)
|10:00 - 11:00||Ben Eva and Reuben Stern: Asymmetric Explanatory Power|
|11:15 - 12:15||Andreas Hüttemann: Causation in an Explanatory Context|
|12:15 - 14:00||Lunch|
|14:00 - 15:00||Juergen Landes: Variety of Evidence|
|15:15 - 16:15||Molly Kao: Unification as a Strategy for Pursuit|
|16:30 - 17:30||Branden Fitelson: Confirmation, Causation, and Simpson’s Paradox|
"Minimal" models purport to explain why systems, which may be highly different at the micro level, nonetheless display the same macro-level behaviour. Batterman and Rice (2014) have recently argued that explanation by minimal models rests on proving the "universality" of the systems’ macro-behaviour. While drawing inspiration from Batterman’s (2001) renormalization account of explanation in physics, Batterman and Rice's proposal is explicitly intended to apply to minimal models both within and outside physics. In this talk, we critically discuss the scope of the proposal by looking at examples of minimal models from physics and economics.top
In a recent paper I argued that questions regarding certain comparative characteristics of computer algorithms should be thought of as how-possibly, rather than how-actually, questions. Call an answer to such a question an 'algorithmic how-possibly explanation'. In this talk I will describe the essential features of algorithmic how-possibly explanation, the kinds of investigation in which it is most likely to be useful, and I will situate it within the broader landscape of concepts of explanation. I will argue that algorithmic how-possibly explanation shares features with both the mechanistic conception of explanation and with structural explanation, but that it is distinct from both.top
In recent work, we use structural equation models to propose and defend a framework for assessing the explanatory power of causal explanations. In this paper, we consider the prospects of generalizing our framework so that it applies not only to causal explanations, but also to grounding explanations. Our hope is that such a framework can be used to measure the explanatory power of any explanation that is backed exclusively by asymmetric dependency relations.top
In the last two decades the mechanistic account of explanation experienced a growing success in the philosophy of special sciences. Notwithstanding such a success, in the domain of physics not only it seems that law-based accounts of explanation (e.g. the unificationist account) are still in good shape, but they are widely assumed to be sufficient to account for explanation in physics, leaving no space for mechanistic explanation. Contra such a conclusion, in this section I argue that we have very good reasons to think that the genuine explanation and understanding of some physical phenomena require a mechanistic strategy. In order to defend such a thesis I analyse a time-honoured problem in quantum theory: the measurement problem. As I will argue, mechanistic explanation can account for the desiderata of a suitable solution to the measurement problem, while law-based theories of explanation fail in this respect.
In this talk, I will compare and contrast several confirmation-theoretic and causal "rationalizing explanations" of why Simpson's Paradox may reasonably seem paradoxical (even though it is, in fact, a rather common actual statistical phenomenon). This discussion will include a new, purely confirmation-theoretic "rationalizing explanation" of the apparent paradoxicality of Simpson's Paradox, which trades on a distinction between conjunctive vs suppositional support (a distinction which is closely related to other distinctions that are often blurred in other contexts, e.g., "import-export"-style reasoning in the context of indicative conditionals).
In this paper I will present a process theory of causation in terms of quasi-inertial processes. Causation takes place when quasi-inertial processes are interfered with. For this account to be informative both ‘quasi-inertial process’ and ‘interference’ need to be spelled out in more detail. In order to solve some longstanding problems in the causation literature such as the pre-emption problem it is essential to individuate the quasi-inertial processes appropriately. I will argue that this individuation has to take place relative to explanatory contexts.top
I provide a heuristic conception of the feature of unification in the context of developing scientific theories. I argue that the value of a unifying hypothesis is not necessarily that of its ability to explain phenomena, nor must it be that it is more likely to be true. Instead, unifying hypotheses can be valuable because they guide experimental research in different domains in such a way that the results from those experiments contribute to the determination of the actual contents of a theory under pursuit. I support this characterization by appealing to the early development of quantum theory.top
The Variety of Evidence Thesis is taken to state that varied evidence speaking in favor of a hypothesis confirms it more strongly than less varied evidence, ceteris paribus. This epistemological thesis enjoys widespread intuitive support. Its evidential character makes it highly amenable to a Bayesian analysis. I here give such an analysis. I thus put forward Bayesian models of inquiry in which I explicate the notion of varied evidence. Subsequently, I show that this explication of the notion of varied evidence entails that a Variety of Evidence Thesis holds in all these models. The Variety of Evidence Thesis emerges strengthened.top
The talk investigates the notion of reliability as a central dimension of evidence in classical statistics and compares this to the analysis provided in the formal epistemology framework (especially Bayesian epistemology); in particular, two notions of reliability are identified and their distinctive roles in interaction with consistency of replications is investigated in the two settings. Also, the talk presents implications of these considerations for modelling “dependence of observations” and “independent replications” in different research contexts and scientific ecosystems.top
Biased research occurs frequently in the sciences. It is a major challenge for present-day philosophy of science to improve our understanding of biases in science. I will address on the following question: what precisely is epistemically defective (that is, unjustified or irrational) about biased research? In light of a specific example of a preference bias, I defend the claim that biased research is epistemically defective because biased research fails to provide evidence for the hypothesis to be tested, contrary to the assertions of the scientists carrying out biased research projects. I support this claim by drawing on major accounts of evidence and confirmation.top
Room A 181
If you need help finding the venue or a particalur room, you might consider using the LMU's roomfinder, a mobile web app that lets you display all of the 22.000 rooms at the 83 locations of the LMU in Munich.