The diverse aims of science

Potochnik, Angela

doi:10.1016/j.shpsa.2015.05.008

Cited by 59 publications

(30 citation statements)

References 24 publications

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“…In applied contexts, the hypotheses investigated serve to inform policy decisions and need thus provide the information needed by society in order to properly inform decision‐making. This requires that idealizations in model construction account for social aims and values . For instance, since considerations of justice count in decision‐making, we need information about the spatial and temporal distribution of effects, rather than about aggregated effects .…”

Section: Adequacy For Purposementioning

confidence: 99%

Building confidence in climate model projections: an analysis of inferences from fit

Baumberger

Knutti

Hadorn

2017

WIREs Climate Change

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Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of 'model fit'; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. © 2017 The Authors.WIREs Climate Change published by Wiley Periodicals, Inc. How to cite this article:WIREs Clim Change 2017Change , 8:e454. doi: 10.1002 INTRODUCTIONT here is now broad scientific consensus that the Earth's climate has changed significantly over the last century, that much of the observed large-scale warming can be attributed to greenhouse gas emissions associated with human activities, and that these trends will continue in the near future. 1 Less clear is how exactly the climate will change in the more distant future on the global and on regional scales if greenhouse gas emissions increase, stabilize or decrease in particular ways. Climate predictions that are conditional on forcing scenarios are called climate projections. They tell us how one or more climate characteristics would evolve if greenhouse gas concentrations and other external forcings were to follow specified pathways in the future. Projections are currently a major focus of climate research, which is undertaken (and funded) in part with the aim of providing input to policy decisions for mitigation and adaptation to anthropogenic climate change. To project climate change, climate scientists inevitably have to rely on complex numerical climate models. But to what extent can we trust model-based climate projections?Climate model projections cannot be directly evaluated. Most of them have forcing scenarios...

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Section: Adequacy For Purposementioning

confidence: 99%

Building confidence in climate model projections: an analysis of inferences from fit

Baumberger

Knutti

Hadorn

2017

WIREs Climate Change

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“…In fact, palaeosciences are left with only physical things and no consciousness or life that is preserved in the traces or fossils and seem to be more physicalism (meaning all the realities supervene on physical reality) in a strict sense. Without invoking any theory and only relating to the present day phenomena with each other, palaeosciences will not serve any purpose in understanding (Potochnik, 2015). I mean just describing the fossils and other traces of the past and its relationship among themselves, as positivist or empiricist does, will not even tell about the physical history and even less about the histories of life (biological) and consciousness (anthropological and sociological).…”

Section: Metaphysics For the Palaeosciencesmentioning

confidence: 99%

Philosophies for Palaeosciences – A Review

Morthekai¹

2018

Proceedings of the Indian National Science Academy

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The philosophy of historical sciences focusing on palaeontology, earth sciences, geology, ecology and evolutionary biology has been given much interest in the recent past. In this article, the focus is on the philosophies that are relevant to palaeosciences that only deal with the temporally disconnected past. Palaeosciences, an aggregate of sciences that deal with the past, are shown to be slightly different than the historical sciences. The questions that are attempted to be answered by reviewing the research literature are, 1) what are the appropriate metaphysical views and metaphysical guidelines for palaeosciences? 2) can a palaeoscientist follow multiple methodologies to understand the past and what are the philosophical foundations for that approach? and 3) are the palaeosciences, science? It is found that scientific realism seems to be the appropriate metaphysics for palaeosciences. Uniformity of process and simplicity (Ockham's razor) are shown to be necessary but not sufficient metaphysical guidelines for palaeosciences. Catastrophism and complexity may be invoked in explaining a palaeoscientific hypothesis on the availability of sufficient evidence. The disunity of sciences, epistemological pluralism, and methodological pluralism are shown to be beneficial philosophical views and strategies to do research in palaeosciences. The palaeosciences are science not on the basis of naïve falsifiability (Popper) but on sophisticated falsifiability (Lakatos) and family resemblance approach (Wittgenstein). Explained are the palaeoscientific methods such as 1) smoking gun with common cause explanation and 2) consilience when there is plenty of trace evidence recognized in the field (lucky circumstance) and, methodological omnivorous (Currie; unlucky circumstances). It is also shown that the misuse of palaeosciences can be minimized within Lakatosian methodological framework, and by the active participation of civil society in palaeoscientific research activities. A case study, on the color of the dinosaurs, is given to explain the salient features of palaeosciences as mentioned above.

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“…A model which is too detailed will obscure and obfuscate the general principles or patterns we seek to identify, while a model which is too abstract in its characterization of the system will be too course-grained to tell us about the workings of the particular mechanisms we seek (for elaboration, see : Levins 1966;Jackson & Pettit 1992;Matthewson & Weisberg 2009;Batterman 2001Batterman , 2002Potochnik 2010Potochnik , 2015. Thus in this case, the model must make a trade-off between explanatory goals (3) and (4).…”

Section: Explanatory Goals and Trade-offsmentioning

confidence: 99%

“…As a result, the WDS model satisfies explanatory goal (2) and (3). This has led some, like Angela Potochnik (2015) and Collin Rice (2015), to conclude that optimality models like the WDS model provide a good scientific explanation for why a given trait occurs in a population (i.e. by identifying what is locally optimal for the organism).…”

Section: Optimality Modelsmentioning

confidence: 99%

Why one model is never enough: a defense of explanatory holism

Hochstein

2017

Biol Philos

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Abstract:Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation (e.g. generating understanding, identifying mechanisms, making predictions, identifying high-level patterns, allowing us to control and manipulate phenomena). Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model's ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of these goals are in fact necessary for explanation, and as such which types of models can and cannot provide explanations (e.g. dynamical models, optimality models, topological models, etc). Explanatory monists argue that one goal will be explanatory in all contexts, while explanatory pluralists argue that the goal will vary based on pragmatic considerations. In this paper, I argue that such debates are misguided, and that both monists and pluralists are incorrect. Instead of any goal being given explanatory priority over others in a given context, the different goals are all deeply dependent on one another for their explanatory power. Any model that sacrifices some explanatory goals to attain others will always necessarily undermine its own explanatory power in the process. And so when forced to choose between individual scientific models, there can be no explanatory victors. Given that no model can satisfy all the goals typically associated with explanation, no one model in isolation can provide a good scientific explanation. Instead we must appeal to collections of models. Collections of models provide an explanation when they satisfy the web of interconnected goals that justify the explanatory power of one another.What kind of information must a scientific model convey in order to provide a good scientific explanation? This question has been at the heart of many recent debates within philosophy of science (see, for example: Batterman 2002;Craver 2006;Potochnik 2007Potochnik , 2010Weber 2008;Huneman 2010;Kaplan & Craver 2011;Lange 2013;Chirimuuta 2014;Rice 2015;Povich 2016). Traditionally, the explanatory power of a theory or model in science has been thought to relate to its ability to help us satisfy certain kinds of scientific goals. While there are disagreements regarding which goals in particular ought to be considered essential for explanation, a list of frequently defended scientific goals include:(1) Successfully conveying understanding about the target phenomenon, or making it intelligible, to an audience or inquirer (Achinstein 1983;Braverman et al. 2012Waskan et al, 2014.(2) Determining when a given phenomenon is expected to occur, and under what conditions (Hempel & Oppenheim 1948;Hempel 1965;Chemero & Silberstein 2008;Rice 2015). (5) Providing information sufficient to control, manipulate, and reproduce the target phenomenon (...

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The diverse aims of science

Cited by 59 publications

References 24 publications

Building confidence in climate model projections: an analysis of inferences from fit

Building confidence in climate model projections: an analysis of inferences from fit

Philosophies for Palaeosciences – A Review

Why one model is never enough: a defense of explanatory holism

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