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Philosophers of science have a long tradition of making a connection between explanation and understanding, but only lately have they started to give the latter notion a substantial role in their theories. In this chapter I will employ a well-known scientific research heuristic of studying how something works by focusing on circumstances in which it does not work. Rather than trying to describe what scientific understanding would ideally look like, I will try to learn something about it by looking at mundane cases where understanding is partly illusory. The main thesis of this paper will be the following: scientists are prone to the illusion of depth of understanding (IDU), and as a consequence, they sometimes overestimate the detail, coherence, and depth of their understanding. I will start my argument by presenting an analysis of the notion of understanding and its relation to a sense of understanding. In order to make plausible the claim that these are often disconnected, I will describe an interesting series of psychological experiments by Frank Keil and co-authors. These experiments suggest that ordinary people routinely overestimate the depth of their understanding. In Section 3, I will argue that we should take seriously the possibility that scientific cognition is also affected by IDU. Section 4 will spell out some possible causes of explanatory illusions in science. In the final section, I will discuss how scientific explanatory practices could be improved and how the philosophy of science might be able to contribute to this process.

In this paper, I will discuss the idea of contrastive explanandum. I will restrict my discussion to singular causal explanation, but the basic ideas and the arguments have a broader application. They are relevant also to other kinds of explanations. In the first section I will first present the intuitive idea of contrastive questions, and then elaborate it by discussing typical criteria for the choice of a contrast. I also suggest a novel way to see the difference between scientific and everyday explanatory questions. In the second section I will discuss the major criticisms presented against contrastive theories of explanation in order to further clarify my position. I argue that all explananda can be analyzed as contrastive and that this is a fruitful approach in understanding explanatory questions. I also argue that the contrastive thesis should be understood as a claim about what an explanation can explain, not as a thesis about what the explainee has in her mind. Finally, I defend the thesis that a contrastive explanandum can be reduced to a non-contrastive explanandum against the arguments presented by Dennis Temple and John W. Carroll.

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation.

The article discusses agent-based simulation as a tool of sociological understanding. Based on an inferential account of understanding, it argues that computer simulations increase our explanatory understanding both by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. However, our ability to understand simulations limits our ability to understand real world phenomena through them. Thomas Schelling’s checkerboard model of ethnic segregation is used to demonstrate the important role played by abstract how-possibly models in the process of building a mechanistic understanding of social phenomena. ©2014 by The Massachusetts Institute of Technology.

In this paper we argue that, despite its influence, critical realism is not the most promising version of scientific realism for economics. The main problem with critical realism is its hermetic insulation from the mainstream of the philosophy of science. We argue that this intellectual isolation is unfortunate, as it has meant that critical realism has missed many opportunities to develop its central concepts, such as causal mechanism, emergence, and explanation. At the same time, we argue, critical realists have missed some crucial aspects of the intellectual strategy of modern economics. Our point is not to defend mainstream economics, rather it is to show that a better understanding of modeling as a scientific research strategy opens up the possibility of a more penetrating analysis of its possible shortcomings.

Social mechanisms and mechanism-based explanation have attracted considerable attention in the social sciences and the philosophy of science during the past two decades. The idea of mechanistic explanation has proved to be a useful tool for criticizing existing research practices and meta-theoretical views on the nature of the social-scientific enterprise. Many definitions of social mechanisms have been articulated, and have been used to support a wide variety of methodological and theoretical claims. It is impossible to cover all of these in one chapter, so I will merely highlight some of the most prominent and philosophically interesting ideas.

This paper provides an inferentialist account of modelKbased understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models.

This paper reviews controversies about methodological individualism and aims to sort out various debates ,motivations, and claims involved in the debate. It suggests some reasons why it is do difficult to be precise about MI and describes the dialectics of the debate. The long and unresolved history of MI is mostly explained by the ambiguity of more basic notions involved in its for- mulation. Thus, people have very different views about the content of MI. Also the emphasis on individuals might be partly misleading, as the real issue for many individualists seems to be agency rather than individuality as such.

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, detail, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues in the first place. We accomplish this by using the contrastive-counterfactual approach to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. We also show how our account explains intuitions linking explanation to unification or to the exhibition of a mechanism.

This volume is a product of the international research project Theory of Explanation,which was funded by the Joint Committee for Nordic Research Councils for the Humanities and the Social Sciences (NOS-HS). The project started in 2001 and operated for a period of three years by organizing a number of workshops on scientific explanation in Norway, Iceland, Sweden and Finland. The workshops included presentations by people involved in the project and by invited guests. Both groups are represented in this volume, which brings together some of the papers presented in these meetings. The central theme of the research project was scientific explanation, but it was approached from many different angles. This plurality of approaches is also visible in the present volume. The authors share a joint interest in explanation, but not the same theoretical or methodological assumptions. As a whole, this volume shows that, although the theory of explanation has been a major industry within philosophy of science, there are still both conceptual problems to be solved and fresh philosophical ideas to explore. The papers in this volume have been divided into two broad groups. Part 1 consists of papers dealing with general issues in the theory of explanation, while the papers in Part 2 focus on some more specific problems.