This paper defines in a formal and computational way the notion of ‘consilience’, a term introduced by Whewell in 1847 for the evaluation of scientific theories. Informally, as has been used to date, a model or theory is ‘consilient’ if it is predictive, explanatory and unifies the evide-nce. Centred in a constructive framework, where new terms can be intro-duced, we essay a formalisation of the idea of unification based on the avoidance of ‘sepa-ration’. However, it is soon manifest that this (...) classical approach is vulnerable to the introduction of fantastic concepts to unify disparate sub-theories. Our second approach is constructed by using a detailed evaluation of the relationship between the theory and the evid-ence by means of reinforcement propagation. With the use of reinfor-cement, fantastic concepts can be better detected and the role of con-silience for theory construction and revision can be specialised for dif-ferent inference mechanisms like explanatory induction, abduction, deduc-tion and analogy. (shrink)

This book is a sustained defense of the compatibility of the presence of idealizations in the sciences and scientific realism. So, the book is essentially a detailed response to the infamous arguments raised by Nancy Cartwright to the effect that idealization and scientific realism are incompatible.

The topic of the presence, legitimacy and epistemic worth of narrative explanations in different kinds of scientific discourse has already enjoyed several revivals within related discussions in contemporary philosophy of science. In fact, we have recently witnessed a more extensive, more unprejudiced and ambitious attention to narrative modes of making science. I think we need a systematic theoretical framework in order to categorize these different functions of narratives and understand their role in scientific explanatory and justificatory practice. My claim is (...) that some distinctions and analytic tools developed within the field of contemporary Argumentation Theory might be of help. (shrink)

Van Koppen and Mackor offer a scenario‐approach analysis of the case. They first explicate their approach, linking it to inference to the best explanation and theories of explanatory coherence. An important distinction in their analysis is between explaining known facts and predicting novel facts. They claim that their approach is cognitively feasible and stays close to descriptive theories of evidential reasoning. They want to keep it informal, so that legal professionals can apply it.

Robert Alexy's claim that law of necessity has a dual nature raises many interesting philosophical questions. In this article, I consider some of these questions, such as what the meaning of the correctness thesis is, whether Alexy's discourse theory supports this thesis, and whether the thesis is defensible; whether Alexy's argument from anarchy and civil war supports the claim that law of necessity has a real dimension; and what the implications are of the use of moral arguments, such as the (...) argument from injustice, for the status of Alexy's inquiry. (shrink)

The present paper first shows that the validity of deductive-nomological (D-N) explanations (systematizations) depends in general on the interpretation context of the predicates involved in the explanation. Therefore, no logical-semantical model can be adequate. This problem is solved by relativisation of the validity criteria on both the confirmation context and the definition context of the premisses. Based upon this, a logical-pragmatical model of D-N explanation is developed. Thereby, especially explanations of laws and global explanations are taken into consideration, since these (...) can be regarded as prototypes of scientific explanation. (shrink)

Abductive problems and their solutions are presented by means of justification logic. We introduce additional meta-constructions in order to generate and compare different solutions to the same abductive problem. Our approach has three advantages: (i) it makes structurally explicit the solution to an abductive problem (as it has a syntactic nature); (ii) it gives a precise meaning to the notion of evidence; (iii) it provides clear definitions and procedures for the comparison of solutions that can be adapted to different needs.

Scientific realism at least in large measure reflects the conviction that physics limns the true nature of reality; that it is the right metaphysical picture of things. This conviction is in turn a product of the failure of positivism's attempt to expunge metaphysics from the corpus of philosophically respectable activities. Since natural science is objective knowledge of the worldpar excellencepost-positivists have embraced it as the ontology which their predecessors had failed to make unnecessary. Scientific realism is metaphysics, shameless or unashamed.

Machine learning systems have shown great potential for performing or supporting inferential reasoning through analyzing large data sets, thereby potentially facilitating more informed decision-making. However, a hindrance to such use of ML systems is that the predictive models created through ML are often complex, opaque, and poorly understood, even if the programs “learning” the models are simple, transparent, and well understood. ML models become difficult to trust, since lay-people, specialists, and even researchers have difficulties gauging the reasonableness, correctness, and reliability (...) of the inferences performed. In this article, we argue that bridging this gap in the understanding of ML models and their reasonableness requires a focus on developing an improved methodology for their creation. This process has been likened to “alchemy” and criticized for involving a large degree of “black art,” owing to its reliance on poorly understood “best practices”. We soften this critique and argue that the seeming arbitrariness often is the result of a lack of explicit hypothesizing stemming from an empiricist and myopic focus on optimizing for predictive performance rather than from an occult or mystical process. We present some of the problems resulting from the excessive focus on optimizing generalization performance at the cost of hypothesizing about the selection of data and biases. We suggest embedding ML in a general logic of scientific discovery similar to the one presented by Charles Sanders Peirce, and present a recontextualized version of Peirce’s scientific hypothesis adjusted to ML. (shrink)

We investigated family members’ lived experience of Parkinson’s disease aiming to investigate opportunities for well-being. A lifeworld-led approach to healthcare was adopted. Interpretative phenomenological analysis was used to explore in-depth interviews with people living with PD and their partners. The analysis generated four themes: It’s more than just an illness revealed the existential challenge of diagnosis; Like a bird with a broken wing emphasizing the need to adapt to increasing immobility through embodied agency; Being together with PD exploring the kinship (...) within couples and belonging experienced through support groups; and Carpe diem! illuminated the significance of time and fractured future orientation created by diagnosis. Findings were interpreted using an existential-phenomenological theory of well-being. We highlighted how partners shared the impact of PD in their own ontological challenges. Further research with different types of families and in different situations is required to identify services required to facilitate the process of learning to live with PD. Care and support for the family unit needs to provide emotional support to manage threats to identity and agency alongside problem-solving for bodily changes. Adopting a lifeworld-led healthcare approach would increase opportunities for well-being within the PD illness journey. (shrink)

Nelson Goodman cast the ‘problem of induction’ as the task of articulating the principles and standards by which to distinguish valid from invalid inductive inferences. This paper explores some logical bounds on the ability of a rational reasoner to accomplish this task. By a simple argument, either an inductive inference method cannot admit its own fallibility, or there exists some non-inferable hypothesis whose non-inferability the method cannot infer (violating the principle of ‘negative introspection’). The paper discusses some implications of this (...) limited self-knowledge for the justifiability of inductive inferences, auto-epistemic logic, and the epistemic foundations of game theory. (shrink)