Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different sources of uncertainty (...) are combined to produce an estimate for a complex, unobservable quantity. These models have been carefully analyzed in recent biological work, which has determined the relationship between these sources of uncertainty, both quantitatively and qualitatively. I suggest here that this case shows us the limitations of univocal analyses of probability in evolution, as well as the simple dichotomy between “subjective” and “objective” probabilities, and I conclude by gesturing toward ways in which we might introduce more sophisticated interpretive taxonomies of probability as a path toward advancing debates on probability in the life sciences. (shrink)

A causal approach to biological information is outlined. There are two aspects to this approach: information as determining a choice between alternative objects and information as determining the construction of a single object. The first aspect has been developed in earlier work to yield a quantitative measure of biological information that can be used to analyze biological networks. This article explores the prospects for a measure based on the second aspect and suggests some applications for such a measure. These two (...) aspects are not suggested to exhaust all the facets of biological information. (shrink)

Drift is often characterized in statistical terms. Yet such a purely statistical characterization is ambiguous for it can accept multiple physical interpretations. Because of this ambiguity it is important to distinguish what sorts of processes can lead to this statistical phenomenon. After presenting a physical interpretation of drift originating from the most popular interpretation of fitness, namely the propensity interpretation, I propose a different one starting from an analysis of the concept of drift made by Godfrey-Smith. Further on, I show (...) how my interpretation relates to previous attempts to make sense of the notion of expected value in deterministic setups. The upshot of my analysis is a physical conception of drift that is compatible with both a deterministic and indeterministic world. (shrink)

Leslie Graves, Barbara Horan and Alex Rosenberg (1999) have argued that the process of evolution is really deterministic, so we should be instrumentalists about our probabilistic evolutionary theory. I criticize the consistency of their view. I argue that because they are realists towards multiple theories (quantum mechanics and macrophysics) their arguments against realism for another scientific theory fail. The main point of this paper is critical, but in order to set up this criticism I explore the ramifications of realism for (...) multiple theories. Finally, I offer a brief metaphysical justification for realism about multiple theories. This view justifies realism for evolutionary theory, which has been defended by Robert Brandon and Scott Carson (1996). (shrink)

Science is always presupposing some basic concepts that are held to be useful. These absolute presuppositions (Collingwood) are rarely debated and form the framework for what has been termed paradigm by Kuhn. Our currently accepted scientific model is predicated on a set of presuppositions that have difficulty accommodating holistic structures and relationships and are not geared towards incorporating non-local correlations. Since the theoretical models we hold also determine what we perceive and take as scientifically viable, it is important to look (...) for an alternative model that can deal with holistic relationships. One approach is to generalise algebraic quantum theory, which is an inherently holistic framework, into a generic model. Relaxing some restrictions and definitions from quantum theory proper yields an axiomatic framework that can be applied to any type of system. Most importantly, it keeps the core of the quantum theoretical formalism. It is capable of handling complementary observables, i.e. descriptors which are non-commuting, incompatible and yet collectively required to fully describe certain situations. It also predicts a generalised form of non-local correlations that in quantum theory are known as entanglement. This generalised version is not quantum entanglement but an analogue form of holistic, non-local connectedness of elements within systems, predicted to occur whenever elements within systems are described by observables which are complementary to the description of the whole system. While a considerable body of circumstantial evidence supports the plausibility of the model, we are not yet in a position to use it for clear cut predictions that could be experimentally falsified. The series of papers offered in this special issue are the beginning of what we hope will become a rich scientific debate. (shrink)

We argue that a fashionable interpretation of the theory of natural selection as a claim exclusively about populations is mistaken. The interpretation rests on adopting an analysis of fitness as a probabilistic propensity which cannot be substantiated, draws parallels with thermodynamics which are without foundations, and fails to do justice to the fundamental distinction between drift and selection. This distinction requires a notion of fitness as a pairwise comparison between individuals taken two at a time, and so vitiates the interpretation (...) of the theory as one about populations exclusively. (shrink)

Evolutionary theory (ET) is teeming with probabilities. Probabilities exist at all levels: the level of mutation, the level of microevolution, and the level of macroevolution. This uncontroversial claim raises a number of contentious issues. For example, is the evolutionary process (as opposed to the theory) indeterministic, or is it deterministic? Philosophers of biology have taken different sides on this issue. Millstein (1997) has argued that we are not currently able answer this question, and that even scientific realists ought to remain (...) agnostic concerning the determinism or indeterminism of evolutionary processes. If this argument is correct, it suggests that, whatever we take probabilities in ET to be, they must be consistent with either determinism or indeterminism. This raises some interesting philosophical questions: How should we understand the probabilities used in ET? In other words, what is meant by saying that a certain evolutionary change is more or less probable? Which interpretation of probability is the most appropriate for ET? I argue that the probabilities used in ET are objective in a realist sense, if not in an indeterministic sense. Furthermore, there are a number of interpretations of probability that are objective and would be consistent with ET under determinism or indeterminism. However, I argue that evolutionary probabilities are best understood as propensities of population-level kinds. (shrink)

The four case studies on chance in evolution provide a rich source for further philosophical analysis. Among the issues raised are the following: Are there different conceptions of chance at work, or is there a common underlying conception? How can a given concept of chance be distinguished from other chance concepts and from nonchance concepts? How can the occurrence of a given chance process be distinguished empirically from nonchance processes or other chance processes? What role does chance play in evolutionary (...) theory? I argue that in order to answer these questions, a careful distinction between process and outcome must be made; however, the purpose of this essay is not to answer these questions definitively, but rather to elaborate on them and to provide a starting point for further discussion. (shrink)

I examine different arguments that could be used to establish indeterminism of neurological processes. Even though scenarios where single events at the molecular level make the difference in the outcome of such processes are realistic, this falls short of establishing indeterminism, because it is not clear that these molecular events are subject to quantum mechanical uncertainty. Furthermore, attempts to argue for indeterminism autonomously (i.e., independently of quantum mechanics) fail, because both deterministic and indeterministic models can account for the empirically observed (...) behavior of ion channels. (shrink)

Discussions of “chance” and related concepts are found throughout philosophical work on evolutionary theory. By drawing attention to three very commonly-recognized distinctions, I separate four independent concepts falling under the broad heading of “chance”: randomness, epistemic unpredictability, causal indeterminism, and probabilistic causal processes. Far from a merely semantic distinction, however, it is demonstrated that conflation of these obviously distinct notions has an important bearing on debates at the core of evolutionary theory, particularly the debate over the interpretation of fitness, natural (...) selection, and genetic drift. (shrink)

I examine recent debates in the philosophy of biology over the determinism or indeterminism of the evolutionary process, focusing on two papers in particular: Glymour 2001 and Stamos 2001. I argue that neither of these papers succeeds in making the case for the indeterminism of the evolutionary process, and suggest that what is needed is a detailed analysis of the causal processes at every level from the quantum mechanical to the evolutionary.

The primary purpose of this paper is to argue that biologists should stop citing Karl Popper on what a genuinely scientific theory is. Various ways in which biologists cite Popper on this matter are surveyed, including the use of Popper to settle debates on methodology in phylogenetic systematics. It is then argued that the received view on Popper—namely, that a genuinely scientific theory is an empirically falsifiable one—is seriously mistaken, that Popper’s real view was that genuinely scientific theories have the (...) form of statements of laws of nature. It is then argued that biology arguably has no genuine laws of its own. In place of Popperian falsifiability, it is suggested that a cluster class epistemic values approach (which subsumes empirical falsifiability) is the best solution to the demarcation problem between genuine science and pseudo- or non-science. (shrink)

Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 4: Evolutionary Indeterminism.

Does the brain behave indeterministically? I argue that accounting for ion channels, key functional units in the brain, requires indeterministic models. These models are probabilistic, so the brain does behave indeterministically in a weak sense. I explore the implications of this point for a stronger sense of indeterminism. Ultimately I argue that it is not possible, either empirically or through philosophical argument, to show that the brain is indeterministic in that stronger sense.

To clarify and illuminate the place of probability in science Ellery Eells and James H. Fetzer have brought together some of the most distinguished philosophers ...

Contingency-theorists have gestured to a series of phenomena such as random mutations or rare Armageddon-like events as that which accounts for evolutionary contingency. These phenomena constitute a class, which may be aptly called the ‘sources of contingency’. In this paper, I offer a probabilistic conception of what it is to be a source of contingency and then examine two major candidates: chance variation and genetic drift, both of which have historically been taken to be ‘chancy’ in a number of different (...) senses. However,contrathe gesturing of contingency-theorists, chance variation and genetic drift are not always strong sources of contingency, as they can be non-chancy in at least one sense that opposes evolutionary contingency. The probabilistic conception offered herein allows for sources of contingency to appropriately vary in strength. To this end, I import Shannon’sinformation entropyas a statistical measure for systematically assessing the strength of a source of contingency, which is part and parcel of identifying sources of contingency. In brief, the higher the entropy, the greater the strength. This is also empirically significant because molecular, mutational, and replicative studies often contain sufficient frequency or probability data to allow for entropies to be calculated. In this way, contingency-theorists can evaluate the strength of a source of contingency in real-world cases. Moreover, the probabilistic conception also makes conceptual room for the converse of sources of contingency: ‘sources of directionality’, which ought to be recognised, as they can interact with genuine sources of contingency in undermining evolutionary contingency. (shrink)