Unification, T-theoreticity, and Testing: The Case of Fitness in Natural Selection (original) (raw)
Related papers
Explanatory unification and natural selection explanations
The debate between the dynamical and the statistical interpretations of natural selection is centred on the question of whether all explanations that employ the concepts of natural selection and drift are reducible to causal explanations. The proponents of the statistical interpretation answer negatively, but insist on the fact that selection/drift arguments are explanatory. However, they remain unclear on where the explanatory power comes from. The proponents of the dynamical interpretation answer positively and try to reduce selection/drift arguments to some of the most prominent accounts of causal explanation. In turn, they face the criticism raised by statisticalists that current accounts of causation have to be violated in some of their core conditions or otherwise used in a very loose manner in order to account for selection/drift explanations. We propose a reconciliation of both interpretations by conveying evolutionary explanations within the unificationist model of scientific explanation. Therefore, we argue that the explanatory power in natural selection arguments is a result of successful unification of individual-and population-level facts. A short case study based on research on sympatric speciation will be presented as an example of how population-and individual-level facts are
The Coherence of Evolutionary Theory with Its Neighboring Theories
Evolutionary theory coheres with its neighboring theories, such as the theory of plate tectonics, molecular biology, electromagnetic theory, and the germ theory of disease. These neighboring theories were previously unconceived, but they were later conceived, and then they cohered with evolutionary theory. Since evolutionary theory has been strengthened by its several neighboring theories that were previously unconceived, it will be strengthened by infinitely many hitherto unconceived neighboring theories. This argument for evolutionary theory echoes the problem of unconceived alternatives. Ironically, however, the former recommends that we take the realist attitude toward evolutionary theory, while the latter recommends that we take the antirealist attitude toward it.
On the foundations of the theory of evolution
2010
Abstract In this paper we suggest an alternative to standard neodarwinian evolution theory. The problem is that Darwinism, which sees evolution as a consequence of random variation and natural selection is based on a materialistic-ie matter-based-view of science, while matter in itself is considered to be a very complex notion in modern physics.
2009
We have argued elsewhere that natural selection is not a cause of evolution, and that a resolution-of-forces (or vector addition) model does not provide us with a proper understanding of how natural selection combines with other evolutionary influences. These propositions have come in for criticism recently, and here we clarify and defend them. We do so within the broad framework of our own'hierarchical realization model'of how evolutionary influences combine.
The Causal Structure of Evolutionary Theory
One contentious debate in the philosophy of biology is that between the statisticalists and causalists. The former understand core evolutionary concepts like fitness and selection to be mere statistical summaries of underlying causal processes. In this view, evolutionary changes cannot be causally explained by selection or fitness. The causalist side, on the other hand, holds that populations can change in response to selection—one can cite fitness differences or driftability in causal explanations of evolutionary change. But on the causal side, it is often not clear how, precisely, one should understand these causes. Thus, much more could be said about what sort of causes fitness and driftability are. In this paper, I borrow Dretske’s distinction between structuring and triggering causes and suggest that fitness and driftability are structuring causes of evolution.
In addition to theorizing about the role and value of mechanisms in scientific explanation or the causal structure of the world, there is a fundamental task of getting straight what a 'mechanism' is in the first place. Broadly, this paper is about the challenge of application: the challenge of aligning one's philosophical account of a scientific concept with the manner in which that concept is actually used in scientific practice. This paper considers a case study of the challenge of application as it pertains to the concept of a mechanism: the debate about whether natural selection is a mechanism. By making clear what is and is not at stake in this debate, this paper considers various strategies for dealing with the challenge of application and makes a case for definitional pluralism about mechanism concepts.
The Structure of Scientific Evolution
The Behavior Analyst, 2013
Science is the construction and testing of systems that bind symbols to sensations according to rules. Material implication is the primary rule, providing the structure of definition, elaboration, delimitation, prediction, explanation, and control. The goal of science is not to secure truth—a binary function on accuracy—but rather to increase the information about data communicated by theory. This process is symmetric and thus entails an increase in the information about theory communicated by data. Important components in this communication are the elevation of data to the status of facts, the descent of models under the guidance of theory, and their close alignment through the evolving retroductive process. The information mutual to theory and data may be measured as the reduction in the entropy, or complexity, of the field of data given the model. It may also be measured as the reduction in the entropy of the field of models given the data. This symmetry explains the important status of parsimony—how thoroughly the data exploit what the model can say—alongside accuracy—how thoroughly the model represents what can be said about the data. Mutual information is increased by increasing model accuracy and parsimony, and by enlarging and refining the data field under purview.