There are two competing interpretations of the modern synthesis theory of evolution: the dynamical (also know as ‘traditional’) and the statistical. The dynamical interpretation maintains that explanations offered under the auspices of the modern synthesis theory articulate the causes of evolution. It interprets selection and drift as causes of population change. The statistical interpretation holds that modern synthesis explanations merely cite the statistical structure of populations. This paper offers a defense of statisticalism. It argues that a change in trait frequencies in a population can be attributed only to selection or drift against the background of a particular statistical description of the population. The traditionalist supposition that selection and drift are description-independent causes of population change leads the dynamical interpretation into a dilemma: it must face a contradiction or accept the loss of explanatory power.

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom-up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding.

Time series of macroscopic quantities that are aggregates of microscopic quantities, with unknown one-many relations between macroscopic and microscopic states, are common in applied sciences, from economics to climate studies. When such time series of macroscopic quantities are claimed to be causal, the causal relations postulated are representable by a directed acyclic graph and associated probability distribution—sometimes called a dynamical Bayes net. Causal interpretations of such series imply claims that hypothetical manipulations of macroscopic variables have unambiguous effects on variables “downstream” in the graph, and such macroscopic variables may be predictably produced or altered even while particular microstates are not. This paper argues that such causal time series of macroscopic aggregates of microscopic processes are the appropriate model for mental causation.

A recent attempt to compute a (recursion-theoretic) noncomputable function using the quantum adiabatic algorithm is criticized and found wanting. Quantum algorithms may outperform classical algorithms in some cases, but so far they retain the classical (recursion-theoretic) notion of computability. A speculation is then offered as to where the putative power of quantum computers may come from.

Accounts of the concepts of function and dysfunction have not adequately explained what factors determine the line between low-normal function and dysfunction. I call the challenge of doing so the line-drawing problem. Previous approaches emphasize facts involving the action of natural selection (Wakefield 1992a, 1999a, 1999b) or the statistical distribution of levels of functioning in the current population (Boorse 1977, 1997). I point out limitations of these two approaches and present a solution to the line-drawing problem that builds on the second one.

This paper characterizes the increase in ‘scientific understanding’ that resulted from the Ingold Revolution in organic chemistry. By describing both the sorts of explanations facilitated by Ingold's Revolution and the sense in which organic chemistry was ‘unified’ by adopting these approaches to explanation, one can appreciate how this revolution led to a dramatic qualitative improvement in organic chemists’ understanding of the phenomena that they study. The explanatory unification responsible for this transformation in organic chemistry is contrasted with contemporary philosophical accounts of unification and its relationship to both scientific understanding and explanation.

Robots are being extensively used for the purpose of discovering and testing empirical hypotheses about biological sensorimotor mechanisms. We examine here methodological problems that have to be addressed in order to design and perform “good” experiments with these machine models. These problems notably concern the mapping of biological mechanism descriptions into robotic mechanism descriptions; the distinction between theoretically unconstrained “implementation details” and robotic features that carry a modeling weight; the role of preliminary calibration experiments; the monitoring of experimental environments for disturbing factors that affect both modeling features and theoretically unconstrained implementation details of robots. Various assumptions that are gradually introduced in the process of setting up and performing these robotic experiments become integral parts of the background hypotheses that are needed to bring experimental observations to bear on biological mechanism descriptions.