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Why Experiments Matter (with Adrian Currie)
We argue that experiments play a distinctive and privileged role in science. They do so in virtue of two properties. Experiments are controlled investigations of specimens. A ‘specimen’ is a token of the type of phenomenon under investigation. Experimental scientists isolate and study representative samples of the systems that interest them. ‘Control’ is the scientific capacity to conduct repeated, finely varied manipulations of experimental systems. Experiments matter because the combination of these two properties allows scientists to produce rich targeted information about their investigative targets. Recently, experimentation’s privilege has come under pressure from two directions. First, the experiment/theory distinction has been blurred via examination of the experiment-like roles played by theoretical devices like models and simulations. Second, the experiment/observation distinction is blurred by investigation of naturally occurring cases which have experimental properties: ‘natural experiments’. Here, scientists (more or less) directly observe specimen of their investigative targets, making the relevance between their observations and their investigative goals (hypothesis testing, data-gathering, identifying phenomena, exploring systems of interest, etc…) relatively straightforward. However, we argue that the combination of control and specimen grants experiments epistemic privilege: it allows scientists to generate remarkably targeted, rich information about the system of interest. Models can be controlled, but are surrogates rather than specimen. Naturally occurring events can be specimen, but are not controlled. Experiments, then, are special.
We argue that experiments play a distinctive and privileged role in science. They do so in virtue of two properties. Experiments are controlled investigations of specimens. A ‘specimen’ is a token of the type of phenomenon under investigation. Experimental scientists isolate and study representative samples of the systems that interest them. ‘Control’ is the scientific capacity to conduct repeated, finely varied manipulations of experimental systems. Experiments matter because the combination of these two properties allows scientists to produce rich targeted information about their investigative targets. Recently, experimentation’s privilege has come under pressure from two directions. First, the experiment/theory distinction has been blurred via examination of the experiment-like roles played by theoretical devices like models and simulations. Second, the experiment/observation distinction is blurred by investigation of naturally occurring cases which have experimental properties: ‘natural experiments’. Here, scientists (more or less) directly observe specimen of their investigative targets, making the relevance between their observations and their investigative goals (hypothesis testing, data-gathering, identifying phenomena, exploring systems of interest, etc…) relatively straightforward. However, we argue that the combination of control and specimen grants experiments epistemic privilege: it allows scientists to generate remarkably targeted, rich information about the system of interest. Models can be controlled, but are surrogates rather than specimen. Naturally occurring events can be specimen, but are not controlled. Experiments, then, are special.
Against the Ontic Conception of Explanation
This paper targets an important assumption underlying much philosophical discussion of explanation, namely that scientific explanation is solely a matter of citing person-independent causal facts. This idea, first formulated by Wesley Salmon in the late 1970s, is often termed 'the ontic conception of explanation'. I suggest that the best (most compelling) version of the ontic conception is to be found in David Lewis' paper "Causal Explanation". I then argue against it. I discuss a variety of cases showing that the way in which casual facts are represented plays an important role in explanatory judgments. In closing I suggest a two-dimensional view, according to which explanatory power is a matter of (1) citing causal facts (2) in the right way.
Evolutionary Modeling and Political Stability
The role of stability in evolutionary modelling -- especially work on cultural evolution -- is connected to its role in political philosophy. This is a seen as one important place where evolutionary results may bear on normative political thinking. This connection, it is argued, does not leap illegitimately over the is/ought gap.
Idealization and Abstraction: Refining the Distinction
Idealization and abstraction are central concepts in the philosophy of science and in science itself. Despite (or perhaps because) of this, there is no commonly agreed-upon understanding of their content and significance. This hampers communication and progress in the field, and has led some authors down blind allies. Here, I will try to correct this situation by offering an account of idealization and abstraction, emphasizing the similarities and differences between them. The account relies, and is intended as an improvement, on an existing view due to Martin Thomson-Jones (2005) and Peter Godfrey-Smith (2000). On this line of thought, abstraction involves the omission of detail, whereas idealization consists in a deliberate mismatch between a description (or a model) and the world. I refine their distinction in several ways and discuss some central implications of setting it out this way.
Mechanisms, Causal Order and the Uniqueness of Biology
Philosophical thinking about mechanisms arose mainly out of reflection on the practice of certain parts of the life sciences such as genetics, cell biology and neurophysiology. Was this a coincidence? Is there some special connection between biology and mechanistic explanation? Is all of biology mechanistic, or only some parts of it? And if so: which, and why?
This paper offers a framework for thinking about these questions. I begin by distinguishing two basic sorts of mechanisms: orderly versus disorderly. The distinction concerns whether the mechanism exhibits an internal division of labor, i.e. differentiation among parts and integration of their activities. I then suggest that whereas disorderly mechanisms occur in a variety of areas, orderly mechanisms are more central to the biological world. I discuss a number of reasons for this, as well as for assessing (in a rough and ready way) how much order we may expect to find in biology. These pertain primarily to features that enhance evolvability, such as modularity and generative entrenchment.
Philosophical thinking about mechanisms arose mainly out of reflection on the practice of certain parts of the life sciences such as genetics, cell biology and neurophysiology. Was this a coincidence? Is there some special connection between biology and mechanistic explanation? Is all of biology mechanistic, or only some parts of it? And if so: which, and why?
This paper offers a framework for thinking about these questions. I begin by distinguishing two basic sorts of mechanisms: orderly versus disorderly. The distinction concerns whether the mechanism exhibits an internal division of labor, i.e. differentiation among parts and integration of their activities. I then suggest that whereas disorderly mechanisms occur in a variety of areas, orderly mechanisms are more central to the biological world. I discuss a number of reasons for this, as well as for assessing (in a rough and ready way) how much order we may expect to find in biology. These pertain primarily to features that enhance evolvability, such as modularity and generative entrenchment.
Evolutionary Debunking Arguments Meet Evolutionary Science [with Yair Levy]
We examine the empirical premises of recent evolutionary debunking arguments and find them wanting: the empirical evidence suggests that our moral sense has not evolved by natural selection, or if it has, then not in the form that advocates of EDAs require for their arguments to go through.
We examine the empirical premises of recent evolutionary debunking arguments and find them wanting: the empirical evidence suggests that our moral sense has not evolved by natural selection, or if it has, then not in the form that advocates of EDAs require for their arguments to go through.
Towards Mechanism 2.0 [with William Bechtel]
The power of traditional mechanistic explanations stems, in large part, from the fact that they appeal to distinct and well-demarcated components contained within a spatiotemporally defined context. In such explanations, parts tend to have stable structures and locations and the typically dynamics involve single scale, spatially and/or temporally. In a mechanistic explanation, parts are assigned distinctive roles, which they perform in virtue of their intrinsic structure and relations with other parts. Mechanistic description and explanation has tended to look at mechanisms as if they operate largely independently of their surroundings (but for inputs and outputs). Temporally, it is often assumed that all operations occur on a commensurate time-scale. Increasingly, researchers have found these assumptions to be problematic. It is common in present-day biology, especially, to find explanations that appeal not to individual components, but to aggregates; and causal structures that operate not on one scale but multiple scales at once. We look at a range of cases in this vein and argue that they motivate an expansion and a refinement of the mechanistic approach.
The power of traditional mechanistic explanations stems, in large part, from the fact that they appeal to distinct and well-demarcated components contained within a spatiotemporally defined context. In such explanations, parts tend to have stable structures and locations and the typically dynamics involve single scale, spatially and/or temporally. In a mechanistic explanation, parts are assigned distinctive roles, which they perform in virtue of their intrinsic structure and relations with other parts. Mechanistic description and explanation has tended to look at mechanisms as if they operate largely independently of their surroundings (but for inputs and outputs). Temporally, it is often assumed that all operations occur on a commensurate time-scale. Increasingly, researchers have found these assumptions to be problematic. It is common in present-day biology, especially, to find explanations that appeal not to individual components, but to aggregates; and causal structures that operate not on one scale but multiple scales at once. We look at a range of cases in this vein and argue that they motivate an expansion and a refinement of the mechanistic approach.
Metaphor and Scientific Explanation
Abstract Under construction
Abstract Under construction
