scholarly journals Searching for Non-Causal Explanations in a Sea of Causes

2018 ◽  
Author(s):  
Alisa Bokulich
Keyword(s):  
Scientific Explanation ◽  
Causal Explanation ◽  
Causal Explanations ◽  
Explanatory Pluralism ◽  
Sand Ripples ◽  
Scientific Explanations ◽  
Model Based ◽  
Aeolian Geomorphology

In the spirit of explanatory pluralism, this chapter argues that causal and non-causal explanations of a phenomenon are compatible, each being useful for bringing out different sorts of insights. First the chapter reviews the author’s model-based account of scientific explanation, which can accommodate causal and non-causal explanations alike. Then it distills from the literature an important core conception of non-causal explanation. This non-causal form of model-based explanation is illustrated using the example of how Earth scientists in a subfield known as aeolian geomorphology are explaining the formation of regularly-spaced sand ripples. The chapter concludes that even when it comes to everyday “medium-sized dry goods” such as sand ripples, where there is a complete causal story to be told, one can find examples of non-causal scientific explanations.

Scientific Explanation

2015 ◽  
Author(s):  
Brad Skow
Keyword(s):  
Philosophy Of Science ◽  
Statistical Models ◽  
Scientific Explanation ◽  
Causal Explanation ◽  
Point Of View ◽  
Causal Explanations ◽  
Pragmatic Theory ◽  
Causal Theories

This chapter argues that the notion of explanation relevant to the philosophy of science is that of an answer to a why-question. From this point of view it surveys most of the historically important theories of explanation. Hempel’s deductive-nomological, and inductive-statistical, models of explanation required explanations to cite laws. Familiar counterexamples to these models suggested that laws are not needed, and instead that explanations should cite causes. One theory of causal explanation, David Lewis’s, is discussed in some detail. Many philosophers now reject causal theories of explanation because they think that there are non-causal explanations; some examples are reviewed. The role of probabilities and statistics in explanation, and their relation to causation, is also discussed. Another strategy for dealing with counterexamples to Hempel’s theory leads to unificationist theories of explanation. Kitcher's unificationist theory is presented, and a new argument against unificationist theories is offered. Also discussed in some detail are Van Fraassen’s pragmatic theory, and Streven’s and Woodward’s recent theories of causal explanation.

scholarly journals A deductive-nomological model for mathematical scientific explanation

2020 ◽  
Vol 24 (1) ◽  
pp. 1-27
Author(s):  
Eduardo Castro
Keyword(s):  
Hamiltonian Systems ◽  
North American ◽  
Scientific Explanation ◽  
Laws Of Nature ◽  
Mathematical Explanation ◽  
Causal Explanations ◽  
Scientific Explanations ◽  
The North

I propose a deductive-nomological model for mathematical scientific explanation. In this regard, I modify Hempel’s deductive-nomological model and test it against some of the following recent paradigmatic examples of the mathematical explanation of empirical facts: the seven bridges of Königsberg, the North American synchronized cicadas, and Hénon-Heiles Hamiltonian systems. I argue that mathematical scientific explanations that invoke laws of nature are qualitative explanations, and ordinary scientific explanations that employ mathematics are quantitative explanations. I analyse the repercussions of this deductivenomological model on causal explanations.

scholarly journals Lawful Humean explanations are not circular

Synthese ◽  
2021 ◽  
Author(s):  
Callum Duguid
Keyword(s):  
Crucial Role ◽  
Scientific Explanation ◽  
General Pattern ◽  
Central Feature ◽  
Scientific Explanations ◽  
Absurd Conclusion

AbstractA long-standing charge of circularity against regularity accounts of laws has recently seen a surge of renewed interest. The difficulty is that we appeal to laws to explain their worldly instances, but if these laws are descriptions of regularities in the instances then they are explained by those very instances. By the transitivity of explanation, we reach an absurd conclusion: instances of the laws explain themselves. While drawing a distinction between metaphysical and scientific explanations merely modifies the challenge rather than resolving it, I argue that it does point us towards an attractive solution. According to Humeanism, the most prominent form of the regularity view, laws capture information about important patterns in the phenomena. By invoking laws in scientific explanations, Humeans are showing how a given explanandum is subsumed into a more general pattern. Doing so both undermines a principle of transitivity that plays a crucial role in the circularity argument and draws out a central feature of the Humean approach to scientific explanation.

scholarly journals Using Connectivity to Explain Autism

2019 ◽  
Author(s):  
Jack Adamek ◽  
Yu Luo ◽  
Joshua Ewen
Keyword(s):  
Scientific Explanation ◽  
Causal Explanation ◽  
Autism Spectrum ◽  
Imaging Method ◽  
Imaging Data ◽  
Reverse Engineer ◽  
Physiological Processes ◽  
The Mind ◽  
The Brain

The chapters in this Handbook reveal the breadth of brilliant imaging and analysis techniques designed to fulfill the mandate of cognitive neuroscience: to understand how anatomical structures and physiological processes in the brain cause typical and atypical behavior. Yet merely producing data from the latest imaging method is insufficient to truly achieve this goal. We also need a mental toolbox that contains methods of inference that allow us to derive true scientific explanation from these data. Causal inference is not easy in the human brain, where we are limited primarily to observational data and our methods of experimental perturbation in the service of causal explanation are limited. As a case study, we reverse engineer one of the most influential accounts of a neuropsychiatric disorder that is derived from observational imaging data: the connectivity theories of autism spectrum disorder (ASD). We take readers through an approach of first considering all possible causal paths that are allowed by preliminary imaging-behavioral correlations. By progressively sharpening the specificity of the measures and brain/behavioral constructs, we iteratively chip away at this space of allowable causal paths, like the sculptor chipping away the excess marble to reveal the statue. To assist in this process, we consider how current imaging methods that are lumped together under the rubric of “connectivity” may actually offer a differentiated set of connectivity constructs that can more specifically relate notions of information transmission in the mind to the physiology of the brain.

scholarly journals Mechanism, process and the wider context of economic geography

2019 ◽  
Vol 9 (3) ◽  
pp. 273-278 ◽  
Author(s):  
Julie MacLeavy
Keyword(s):  
Reflective Practice ◽  
Economic Geography ◽  
Lived Experiences ◽  
Critical Realism ◽  
Causal Explanation ◽  
Transformative Change ◽  
Methodological Development ◽  
Causal Explanations ◽  
Critical Realist ◽  
Analytical Approaches

This commentary responds to Henry Wai-chung Yeung’s call to develop clearer causal explanations in geography through mechanism-based thinking. His suggested use of a critical realist framework to ground geographical research on economies is, on one level, appealing and may help to counteract taken-for-granted assumptions about socio-spatial conditions and the significance of economic structures for everyday lived experiences. However, the general lack of applied critical realist research means the distinction between ‘mechanism’ and ‘process’ is often difficult to define in analyses of specific empirical events or geographical episodes. Not only is there a need for methodological development but, I suggest, also for greater recognition of critical realism as a reflective practice. We need to consider the means by which scholars distinguish between contingent and necessary relations, identify structures and counterfactuals and infer how mechanisms work out in particular places. The critical realist goal of advancing transformative change through the provision of causal explanation relies upon inferences made on the basis of researcher experience. Hence, we need to recognise that research is always a political practice and be careful not to discount knowledge borne from other analytical approaches.

Scientific Explanation

Philosophy ◽  
2017 ◽  
Author(s):  
Joseph Pitt ◽  
Steven Mischler
Keyword(s):  
General Theory ◽  
Scientific Explanation ◽  
Causal Explanation ◽  
Mathematical Physics ◽  
Unified Theory ◽  
The Social ◽  
History Of ◽  
Carl Hempel ◽  
Lively Debate ◽  
The Individual

The modern search for an adequate general theory of explanation is an outgrowth of the logical positivist’s agenda: to lay the groundwork for a general unified theory of science. Carl Hempel and Paul Oppenheim’s “Studies in the Logic of Explanation” (Hempel and Oppenheim 1948, cited under the Deductive-Nomological Model of Explanation) was the first major attempt to put forth an account that met the positivist’s criteria. It initiated a lively debate that has continued up to the present. But as the attention of the philosophers of science became increasingly focused on the individual sciences, it quickly became clear that one general theory of explanation would not do since the particulars of the various sciences called for different accounts of what constituted an adequate explanation in physics and biology as well as chemistry, etc. This article attempts to capture the flavor of the debates and the nature of the shifting targets over the years. It does not profess to be complete, being largely restricted to work published in English, but it is a start. While the modern debates surrounding explanation can be said to begin with Hempel and Oppenheim, the history of philosophical accounts of explanation can be traced at least to Aristotle, whose metaphysics set the logical framework for explanations until Galileo urged that appeals to metaphysical categories be replaced by mathematics and measurement. For the most part, Galileo was not interested in appealing to causes or occult forces. The account of how things behaved was to be expressed in the language of mathematics. Descartes tried to capitalize on that insight with his resurrection of medieval discussions of causation relying on Aristotle’s framework framed in a mathematical physics, only to be countered by Newton, who introduced non-Aristotelian causal explanation grounded in mathematical physics. Finally John Stuart Mill begins the long march to contemporary accounts of causal explanation in both the physical and the social sciences, again relying on certain key assumptions about human nature. So the history of explanation is long and intertwined with a variety of metaphysical frameworks. The Positivists of the 20th century unsuccessfully eschewed metaphysics and sought to create an account of causal explanation that somehow aimed to stick strictly to the dictates of science, only to be thwarted by the metaphysical assumptions in the sciences themselves.

Actual Causality

2017 ◽  
Author(s):  
Joseph Y. Halpern
Keyword(s):  
Program Verification ◽  
Causal Explanation ◽  
Structural Equations ◽  
Language Usage ◽  
Causal Explanations ◽  
Actual Causation ◽  
Legal Case ◽  
The World ◽  
Legal Arguments ◽  
Definition Of

Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification.

Scientific explanation in US newspaper science stories

1995 ◽  
Vol 4 (2) ◽  
pp. 119-130 ◽  
Author(s):  
Marilee Long
Keyword(s):  
Content Analysis ◽  
Mass Media ◽  
Scientific Explanation ◽  
Scientific Concepts ◽  
Scientific Explanations ◽  
Science Stories ◽  
Science Information

Mass media are important sources of science information for many adults. However, this study, which reports a content analysis of science stories in 100 US newspapers, found that while 70 newspapers carried science stories, the majority of these stories contained little scientific explanation. Ten percent or less of content was comprised of elucidating (definitions of terms) and/or quasi-scientific explanations (explications of relationships among scientific concepts). The study also investigated the effect of production-based variables on scientific explanation. Stories in feature and science sections contained more explanation than did stories in news sections, perhaps indicating that science stories in feature and science sections have more of an explanatory mission. Additionally, the more time a writer had to compose a story, the more explanation in the story. This result suggests that writing explanations is cognitively demanding. Interestingly, longer stories did not contain significantly more scientific explanation than shorter stories.

scholarly journals Destigmatising Schizophrenia: An Investigation into the Effects of Different Causal Explanations upon Stigma

2021 ◽  
Author(s):  
◽  
Hannah Cunningham
Keyword(s):  
No Reduction ◽  
Causal Explanation ◽  
Mental Illnesses ◽  
Manipulation Check ◽  
Stigma Reduction ◽  
Future Directions ◽  
Causal Explanations ◽  
Significant Difference ◽  
Great Support ◽  
Designed Experiment

<p>While many people with mental illnesses are stigmatised, those with schizophrenia are the most severely stigmatised group (Crisp, Gelder, Rix, Meltzer, & Rowlands, 2000; Marie & Miles, 2008; Pescosolido et al., 1999). A vast body of psychology research has been devoted to investigating how education – particularly education about the causes of schizophrenia – can reduce this stigma that is attached to schizophrenia. While there is great support for the notion that education in general can reduce stigma (e.g. Costin & Kerr, 1962; Griffiths, Christensen, Jorm, Evans, & Groves, 2004; Ritterfeld & Jin, 2006), there is still disagreement regarding exactly which set of causal factors the general public should be educated about – biogenetic or psychosocial? Until now, only three previous studies (Lincoln, Arens, Berger, & Rief, 2008; Schlier, Schmick, & Lincoln, 2014; Walker & Read, 2002) have experimentally compared teaching a purely biogenetic causal explanation to teaching a purely psychosocial causal explanation. The results of this research appear to be somewhat contradictory leading to the need for another, more robustly designed experiment. In the present research, two experiments were conducted in which participants’ level of stigma was measured after they were given a biogenetic causal explanation of schizophrenia, a psychosocial explanation, or given no causal explanation. It was predicted that participants given a causal explanation would show reduced levels of stigma compared to participants given no causal information, and that there would be a significant difference in the stigma reduction effectiveness between types of causal explanation. Contrary to these expectations, the results of Experiment One showed no reduction in stigma when participants were given a causal explanation compared to no causal explanation, and revealed no significant differences in stigma reduction efficacy between the biogenetic and psychosocial causal explanations. Experiment Two utilised the same basic paradigm as Experiment One but with the addition of more convincing causal explanations and a manipulation check. The results of Experiment Two gave evidence that both a biogenetic and psychosocial causal explanation successfully reduces discrimination compared to giving no information on the causes of schizophrenia. In addition, a purely biogenetic causal explanation was also found to successfully reduce belief in other stereotypes compared to a psychosocial causal explanation or no causal explanation. Thus, I conclude that stigma can be effectively reduced by providing education about the causes of schizophrenia, and that a biogenetic causal explanation is a more effective stigma reduction tool as it reduces multiple types of stigma. Strengths, limitations, implications and future directions are discussed.</p>

scholarly journals Kognition og kultur: Boyer versus Wittgenstein

2005 ◽  
Author(s):  
Lars Albinus
Keyword(s):  
Cognitive Science ◽  
Ludwig Wittgenstein ◽  
Scientific Explanation ◽  
Social Reality ◽  
The Other ◽  
Human Language ◽  
Cognitive Capacities ◽  
Causal Explanations ◽  
The One ◽  
Psychological Activity

Cognitive science typically insists on procuring causal explanations for psychological activity on a pre-cultural level. In this article it is claimed that the price for doing so may be too high and that it escapes philosophical justification in the first place. A more specific criticism is directed against what thus seems to be an ignorant notion of culture in Religion Explained by Pascal Boyer. Drawing on Ludwig Wittgenstein and Meredith Williams, who is a lucid reader of his work, the psychological attempt to explain feelings and memories on the grounds of innate cognitive capacities is found to be profoundly misleading. The question is how to understand, on the one hand, human language and, on the other, the possible scope of scientific explanation. Arguing for an irreducible level of social reality, this article focuses on the limitations of cognitive science, while also bringing out the aporia caused by an epistemological trap of self-referentiality.

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