Pre-play signals that cost nothing are sometimes thought to be of no significance in interactions which are not games of pure common interest. We investigate the effect of pre-play signals in an evolutionary setting for Assurance, or Stag Hunt, games and for a Bargaining game. The evolutionary game with signals is found to have dramatically different dynamics from the same game without signals. Signals change stability properties of equilibria in the base game, create new polymorphic equilibria, and change the basins of attraction of equilibria in the base game. Signals carry information at equilibrium in the case of the new polymorphic equilibria, but transient information is the basis for large changes in the magnitude of basins of attraction of equilibria in the base game. These phenomena exemplify new and important differences between evolutionary game theory and game theory based on rational choice.

Almost all computational models of the mind and brain ignore details about neurotransmitters, hormones, and other molecules. The neglect of neurochemistry in cognitive science would be appropriate if the computational properties of brains relevant to explaining mental functioning were in fact electrical rather than chemical. But there is considerable evidence that chemical complexity really does matter to brain computation, including the role of proteins in intracellular computation, the operations of synapses and neurotransmitters, and the effects of neuromodulators such as hormones. Neurochemical computation has implications for understanding emotions, cognition, and artificial intelligence.

Logicism Lite counts number-theoretical laws as logical for the same sort of reason for which physical laws are counted as as empirical: because of the character of the data they are responsible to. In the case of number theory these are the data verifying or falsifying the simplest equations, which Logicism Lite counts as true or false depending on the logical validity or invalidity of first-order argument forms in which no numbertheoretical notation appears.

We distinguish dynamical and statistical interpretations of evolutionary theory. We argue that only the statistical interpretation preserves the presumed relation between natural selection and drift. On these grounds we claim that the dynamical conception of evolutionary theory as a theory of forces is mistaken. Selection and drift are not forces. Nor do selection and drift explanations appeal to the (sub-population-level) causes of population level change. Instead they explain by appeal to the statistical structure of populations. We briefly discuss the implications of the statistical interpretation of selection for various debates within the philosophy of biology—the ‘explananda of selection’ debate and the ‘units of selection’ debate.

Experimental research is commonly held up as the paradigm of “good” science. Although experiment plays many roles in science, its classical role is testing hypotheses in controlled laboratory settings. Historical science (which includes work in geology, biology, and astronomy, as well as paleontology and archaeology) is sometimes held to be inferior on the grounds that its hypothesis cannot be tested by controlled laboratory experiments. Using contemporary examples from diverse scientific disciplines, this paper explores differences in practice between historical and experimental research vis-à-vis the testing of hypotheses. It rejects the claim that historical research is epistemically inferior. For as I argue, scientists engage in two very different patterns of evidential reasoning and, although there is overlap, one pattern predominates in historical research and the other pattern predominates in classical experimental research. I show that these different patterns of reasoning are grounded in an objective and remarkably pervasive time asymmetry of nature.

We examine, from the partial structures perspective, two forms of applicability of mathematics: at the “bottom” level, the applicability of theoretical structures to the “appearances”, and at the “top” level, the applicability of mathematical to physical theories. We argue that, to accommodate these two forms of applicability, the partial structures approach needs to be extended to include a notion of “partial homomorphism”. As a case study, we present London's analysis of the superfluid behavior of liquid helium in terms of Bose-Einstein statistics. This involved both the introduction of group theory at the top level, and some modeling at the “phenomenological” level, and thus provides a nice example of the relationships we are interested in. We conclude with a discussion of the “autonomy” of London's model.