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Explanatory Reasoning in the Sciences (The Second Jerusalem-MCMP Workshop in the Philosophy of Science)

Idea and Motivation

“How does explanatory reasoning in science work?” and “what is a scientific explanation?” are central questions in the philosophy of science. This workshop explores aspects of these central questions with respect to explanatory reasoning in physics and cognitive science. In particular, the contributions to this workshop address the following questions among others: Are there non-mechanistic explanatory strategies? Are there any computational explanations? Is there non-causal explanatory reasoning? What role do ‘abstraction’ and ‘scale-invariance’ play in scientific explanations? Is there a place for (different kinds of) IBE or best-explanation arguments in the metaphysics of physics? Is it possible to develop an epistemic theory of causation underpinning causal explanatory reasoning? What role does the phenomena/data distinction play in explanatory reasoning?



Day 1 (Thursday, 23 February 2017)

09:30 - 10:00 Gathering and Refreshments
10:00 - 10:10 Opening
10:10 - 11:10 Lotem Elber: "Different Explanations, Same Explanatory Virtue: How Non-Mechanistic Models Explain in Cognitive Science"
11:10 - 12:10 Dan Baras: "Why Do Certain States of Affairs Call for Explanation?"
12:10 - 14:00 Lunch Break
14:00 - 15:00 Patricia Palacios: "What Makes Scale Invariant Models Explanatory? The Case of Stock Market Crashes"
15:00 - 16:00 Alexander Reutlinger: "Expanding the Counterfactual Theory of Explanation"
16:00 - 16:20 Coffee Break
16:20 - 17:20 Nir Fresco: "The Indeterminacy of Computation"
19:00 Dinner

Day 2 (Friday, 24 February 2017)

10:00 - 11:00 Guy Hetzroni: "Best-Explanation Arguments in the Absolute\Relational Debate and the Reality of Potentials"
11:00 - 12:00 Reuben Stern: "Causation, Explanation, and Context"
12:00 - 12:20 Coffee Break
12:20 - 13:20 Pascal Ströing: "Phenomena, Evidences and Scientific Explanation"
Afternoon tour for participants


Lotem Elber (Department of Cognitive Sciences and Safra Center for Brain Research, The Hebrew University of Jerusalem): Different Explanations, Same Explanatory Virtue: How Non-Mechanistic Models Explain in Cognitive Science

A central question in philosophy of cognitive science is whether all explanations in cognitive science adhere to the same norms. According to one prominent view, explanations in cognitive science are predominantly mechanistic, i.e., they describe underlying mechanisms. Proponents of this view argue that various prima facie non-mechanistic explanations (e.g., functional and computational explanations) either turn out to be mechanistic or they turn out to be non-explanatory models. A central argument in support of this claim is that models are explanatory when they enable us to manipulate and control the environment, as mechanistic explanations enable us to do. This view has been heavily criticized on the grounds that it misses the different explanatory virtues of non-mechanistic models, such as computational, network, and dynamic models. In my talk I will argue that taking explanations to be about manipulation does no rule out non-mechanistic explanations. On the contrary, I will argue that manipulation should be understood less narrowly than it has been construed by proponents of the mechanistic position and that when a new notion of manipulation is adopted it becomes evident that computational, dynamic and other non-mechanistic models allow manipulation and therefore are explanatory. By following this line of argument a unified notion of explanation can be preserved that includes non-mechanistic

Nir Fresco (The Edelstein Center at The Hebrew University of Jerusalem and Ben Gurion University of the Negev): The Indeterminacy of Computation

Computational descriptions are pervasive in cognitive science, biology and even physics. But insofar as they are more than mere metaphors or analogies for the phenomena or systems concerned, an important question is ‘How does the dynamic of a physical system or phenomenon, at the relevant level of abstraction, uniquely determine the function it computes?’. This question reveals a challenge to computational descriptions as explanations. If a physical system/phenomenon computes more than one function “simultaneously”, what determines the relevant function computed to which a computational explanation can appeal? In this talk, I will formulate the indeterminacy of computation challenge and argue that the problem goes deeper than it

Guy Hetzroni (Department of History and Philosophy of Science, The Hebrew University of Jerusalem): Best-Explanation Arguments in the Absolute\Relational Debate and the Reality of Potentials

In the context of fundamental physics, ontological claims are commonly based on the explanatory role of formal and abstract concepts. Realism about the entities to which these concepts refer is thus based on the inference known as Inference to the Best Explanation (IBE). This paper focuses on a particular kind of best-explanation arguments, which are employed in the context of debates between the absolute and the relational approaches to physics. This divide is between two ontological stances that entail eminently different research programs towards major questions in contemporary physics. I will claim that the debate regarding the reality of electromagnetic potentials should be understood as part of the absolute\relational debate. To substantiate this claim, I will examine several ways of explaining the Aharonov-Bohm effect, indicating the differences between the ontological claims they infer using IBE. I will then compare these explanatory strategies with explanations employed in the famous controversy on the nature of space between relationists and substantivalists in Newtonian mechanics. The comparison reveals general features of absolute explanations vs. relational explanations and allows us to clarify the different workings of IBE in the two

Patricia Palacios (MCMP/LMU Munich): What Makes Scale Invariant Models Explanatory? The Case of Stock Market Crashes

We study the Johansen-Ledoit-Sornette (JLS) model of financial market crashes. On our view, the JLS model is a curious case from the perspective of the recent philosophy of science literature, as it is naturally construed as a “minimal model” in the sense of Batterman and Rice (Robert W. Batterman and Rice 2014) that nonetheless provides a causal explanation of market crashes, in the sense of Woodward’s interventionist account of causation (Woodward (2003)).

Dan Baras (Department of Philosophy, The Hebrew University of Jerusalem): Why Do Certain States of Affairs Call for Explanation?

Motivated by examples, many philosophers believe that there is a significant distinction between states of affairs that are striking and therefore call for explanation and states of affairs that are not. This idea underlies several influential debates in metaphysics, philosophy of mathematics, normative theory, philosophy of modality, and philosophy of science but is not fully elaborated or explored. This paper aims to address this lack of clear explanation first by clarifying the epistemological issue at hand. Then it introduces an initially attractive account for strikingness that is inspired by the work of Paul Horwich (1982) and adopted by a number of philosophers. The paper identifies two logically distinct accounts that have both been attributed to Horwich and then argues that, given a proper understanding of these accounts, they can withstand former criticisms. The final two sections present a new set of considerations against both Horwichian accounts that avoid the shortcomings of former critiques. It remains to be seen whether an adequate account of strikingness exists.

Alexander Reutlinger (MCMP/LMU Munich): Expanding the Counterfactual Theory of Explanation

In recent work, I have argued for a counterfactual theory of causal and non-causal explanations. The core idea of this theory is that causal and non-causal explanations are explanatory in virtue of exhibiting counterfactual dependencies between the explanandum and the explanans. In this talk, I will expand this approach by applying it to new examples of non-causal explanation in the

Reuben Stern (MCMP/LMU Munich): Causation, Explanation, and Context

In this talk, I hope to clarify the relation between causation and causal explanation. At first blush, it may seem reasonable to think that X causally explains Y if and only if X causes Y. But this view is not tenable if causal relevance is context-invariant (as realists typically think) while causal explanatory relevance varies with context (as some examples suggest). My aim in this talk is to use graphical causal models to develop a realist (context-invariant) analysis of causal relevance that is likewise a necessary but not sufficient condition for causal explanatory relevance. Then, I consider what other conditions must be satisfied in order for X to causally explain Y in some knowledge context K. The resulting account of causal explanation is ontic in the sense that X causally explains Y (in any context) only if X causes Y, but also inferentialist in the sense that whether X causally explains Y in K depends on whether, in K, one can justifiably infer Y from the fact that one intervenes to bring about X.

Pascal Ströing (MCMP/LMU Munich): Phenomena, Evidences and Scientific Explanation

Philosophical accounts about scientific explanation, explanatory strength or confirmation typically explicate or model these epistemic concepts as a relation between hypotheses and evidence. A discussion inaugurated by Bogen and Woodward (1988), brought life into a principal distinction between data and phenomena in science. Despite the fact that both threads of discussion -- explanation/confirmation on the one side and phenomena-data-distinction on the other side -- aim to describe perspectives on inferential relations between observations and theories, the basic concepts of the discussions cannot be transferred to the other thread in any straightforward way. In my talk, I want to present a clarification of the notions and conceptual relations between observations, data, evidences and phenomena with a particular view on the implications for the two threads of discussion. This helps to clarify the view on the two threads and provides the ground to make them more adaptable in a more general conceptual

Practical Information


Main University Building
Geschwister-Scholl-Platz 1
D-80539 München
Room M209

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The conference is supported by The Sydney M. Edelstein Center for History and Philosophy of Science, Technology and Medicine and the Alexander von Humboldt Foundation through an Alexander von Humboldt Professorship.