Online research in philosophy

This introduction to the volume begins with a manifesto that puts forward two theses: first, that the sciences are the best place to turn in order to understand causality; second, that scientifically-informed philosophical investigation can bring something to the sciences too. Next, the chapter goes through the various parts of the volume, drawing out relevant background and themes of the chapters in those parts. Finally, the chapter discusses the progeny of the papers and identifies some next steps for research into causality in the sciences.

This cutting edge collection of new and previously published articles by philosophers and social scientists addresses just what it means to invoke causal ...

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory of causality that unifies its mechanistic and probabilistic aspects. We argue that the epistemic theory of causality provides the required unification.

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms, or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences—pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory of causality that unifies its mechanistic and probabilistic aspects. We argue that the epistemic theory of causality provides the required unification.

Evolutionary psychology and human sociobiology often reject the mere possibility of symbolic causality. Conversely, theories in which symbolic causality plays a central role tend to be both anti-nativist and anti-evolutionary. This article sketches how these apparent scientific rivals can be reconciled in the study of disgust. First, we argue that there are no good philosophical or evolutionary reasons to assume that symbolic causality is impossible. Then, we examine to what extent symbolic causality can be part of the theoretical toolbox of the evolutionary social sciences. This examination leads to the conclusion that it is possible to make evolutionary sense of Mary Douglas’s theory of disgust, and that her view of symbolic causality can and should inform evolutionary theories of (sociocultural) disgust.

The book tackles these questions as well as others concerning the use of causality in the sciences.

After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.

Reinforcement schemes are a class of non-Markovian stochastic processes. Their non-Markovian nature allows them to model some kind of memory of the past. One subclass of such models are those in which the past is exponentially discounted or forgotten. Often, models in this subclass have the property of becoming trapped with probability 1 in some degenerate state. While previous work has concentrated on such limit results, we concentrate here on a contrary effect, namely that the time to become trapped may increase exponentially in 1/x as the discount rate, 1− x, approaches 1. As a result, the time to become trapped may easily exceed the lifetime of the simulation or of the physical data being modeled. In such a case, the quasi-stationary behavior is more germane. We apply our results to a model of social network formation based on ternary (three-person) interactions with uniform positive reinforcement.

One of the cardinal assumptions about the nature of grammar is that it is a formal system, meaning that the operations and symbols in the grammar should have a precise meaning, so that one can tell precisely how it functions, and whether a given structure is in fact created by the grammar. The issue of how much information is available to the grammar, viewed as a computational device that computes structures, is called the issue of computational complexity. The computational powers of various grammars, and the capacity of recognition devices to characterize as licit or not the structures that they generate, has been the province of mathematical linguistics, but has also occasionally been felt to have implications for empirical syntactic theory. One central question that has raised its head over the years is the question of whether or not grammar ( which is now referred to as CHL, for Computation of Human Language (Chomsky (1995)) is Markovian, an issue first raised in Chomsky (1957). For a computational device to be Markovian, it can only make reference to the current state that the device is in, when deciding what the next state of the device can be; it cannot, for example, make reference to alternative states, earlier states, future states, or , as a consequence of its being a formal system, factors outside of the computational device.

In this paper we present the syntax and semantics of a temporal action language named Alan, which was designed to model interactive multimedia presentations where the Markov property does not always hold. In general, Alan allows the specification of systems where the future state of the world depends not only on the current state, but also on the past states of the world. To the best of our knowledge, Alan is the first action language which incorporates causality with temporal formulas. In the process of defining the effect of actions we define the closure with respect to a path rather than to a state, and show that the non-Markovian model is an extension of the traditional Markovian model. Finally, we establish relationship between theories of Alan and logic programs.