2021: The Hierarchy-of-Hypotheses Approach from a Philosophy of Science Perspective (original) (raw)
Related papers
The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective*
Philosophy of Science, 2010
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.
2013
This piece of work would never have emerged in its present form, were it not for the support of many. In particular, I want to thank my love Nel, for being there and complementing me; my daughter Ada for bringing her liveliness into our home: her look on the world was and is a constant inspiration; my supervisor Joke for allowing me to pursue my own path in these muddy waters; my co-supervisor Bert for his detailed and constructive remarks on every part of this thesis; the Centre for Logic and Philosophy of Science for giving me a four years grant; its current and former employees for their many useful comments and ideas over the years; my friends, they mean more to me than they realize; and finally, the world itself, for being such an intriguing and fascinating place to dwell in.
Cascade Versus Mechanism: The Diversity of Causal Structure in Science--Preprint
The British Journal for the Philosophy of Science, 2023
According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term "mechanism" gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept-it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the notion of mechanism and that this difference matters for our understanding of causation and explanation in science.
CONFIRMATION OF SCIENTIFIC HYPOTHESES AS RELATIONS
,” Journal for General Philosophy of Science, 2005
In spite of several attempts to explicate the relationship between a scientific hypothesis and evidence, the issue still cries for a satisfactory solution. Logical approaches to confirmation, such as the hypothetico-deductive method and the positive instance account of confirmation, are problematic because of their neglect of the semantic dimension of hypothesis confirmation. Probabilistic accounts of confirmation are no better than logical approaches in this regard. An outstanding probabilistic account of confirmation, the Bayesian approach, for instance, is found to be defective in that it treats evidence as a formal entity and this creates the problem of relevance of evidence to the hypothesis at issue, in addition to the difficulties arising from the subjective interpretation of probabilities. This essay purports to satisfy the need for a successful account of hypothesis confirmation by offering an original formulation based on the notion of instantiation of the relation urged by an hypothesis.
Hierarchies, Networks, and Causality: The Applied Evolutionary Epistemological Approach
Journal for General Philosophy of Science, 2021
Download available at https://rdcu.be/cl0vS. Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds.
Whewell's Theory of Hypothesis Testing and a Relational View of Evidence *
2000
The debate between William Whewell and John Stuart Mill is not only hard in the sense that both sides are difficult to understand, but the issue itself is unresolved. Whewell's idea of predictive tests is similar to the method of cross validation in statistics and machine learning, except that Whewell applies it in a hierarchical way at multiple levels. Or at least, that is how Whewell argues that hypothesis testing works in science. In contrast, the received view of theory testing is that the confirmation of rival hypotheses is measured by their degree of fit with the total evidence, provided that the rival hypotheses are equally simple. However, there is a growing realization that predictive tests are stronger in many ways. What this suggests is that the history of science could be used as a source of examples against which theories of learning may be tested. The purpose of this paper is to explain and highlight some of the features of Whewell's theory of hypothesis testin...