Please email me if you want to look at a draft.
Why Experiments Matter (with Adrian Currie) [Under review]
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.
Must the Best Explanation be True? [Under review]
Should we hold that the best scientific explanation of a given phenomenon must be true, i.e. have a true explanans? I argue for a negative answer. More specifically, I’ll argue that there are no good arguments for a ‘yes’ answer, and given that many successful scientific explanations are idealized, hence untrue, we should accept that the best explanation need not be true. To this end, I look at three sorts of arguments: an argument from pragmatic norms; an argument connected with scientific realism; and the suggestion that a truth requirement is necessary as bulwark against relativism. None of these arguments, I suggest, succeed. In closing, I briefly sketch a view of explanation that does not assume a truth requirement and yet accords with the main motivations underlying the truth requirement.
Should we hold that the best scientific explanation of a given phenomenon must be true, i.e. have a true explanans? I argue for a negative answer. More specifically, I’ll argue that there are no good arguments for a ‘yes’ answer, and given that many successful scientific explanations are idealized, hence untrue, we should accept that the best explanation need not be true. To this end, I look at three sorts of arguments: an argument from pragmatic norms; an argument connected with scientific realism; and the suggestion that a truth requirement is necessary as bulwark against relativism. None of these arguments, I suggest, succeed. In closing, I briefly sketch a view of explanation that does not assume a truth requirement and yet accords with the main motivations underlying the truth requirement.
Evolutionary Modeling and Political Stability [Under review]
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.
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.
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 Yai Levy) [Under review]
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.
Discovering Mechanisms, 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
Completeness and Ontically Construed Mechanisms
Abstract Under construction
Abstract Under construction
