According to the no miracles argument, scientific realism provides the only satisfactory explanation of the predictive success of science. It is argued in the present article that a different explanatory strategy, based on the posit of limitations to the underdetermination of scientific theory building by the available empirical data, offers a more convincing understanding of scientific success.

Advocates of the "strong programme" in the sociology of knowledge have argued that, because scientific theories are "underdetermined" by data, sociological factors must be invoked to explain why scientists believe the theories they do. I examine this argument, and the responses to it by J.R. Brown (1989) and L. Laudan (1996). I distinguish between a number of different versions of the underdetermination thesis, some trivial, some substantive. I show that Brown's and Laudan's attempts to refute the sociologists' (...) argument fail. Nonetheless, the sociologists' argument falls to a different criticism, for the version of the underdetermination thesis that the argument requires, has not been shown to be true. (shrink)

The author critiques one of the arguments commonly used by opponents of scientific realism—namely, the thesis that scientific theories are underdetermined by empirical data.

The first part of the paper is a metatheoretical consideration of such philosophy of nature which allows for using scientific results in philosophical analyses. An epistemological 'judgment' of those results becomes a preliminary task of this discipline: this involves taking a position in the controversy between realistic and antirealistic accounts of science. It is shown that a philosopher of nature has to be a realist, if his task to build true ontology of reality is to be achieved. At the (...) same time he cannot be a realist ˗ a possibility that science itself is capable of deciding what beings really exist (a typical realistic claim is that scientific notions refer to something external and truly describe its characteristics) has to be denied, if the philosophy of nature is seen as a discipline investigating the natural world, while being epistemologically different from the natural sciences. A possibility of weakening this opposition is explored in the second part of the paper, where the idea of so-called "postulated ontology" of scientific theories is brought to the consideration. This idea appears in the context of a well-known thesis of the underdetermination of scientific theories by empirical data. It is argued in the paper, that the conviction of the existence of some kind of relation between a given theory and ontological ideas can be derived from this thesis, regardless of its particular form. Therefore, certain solutions to classical philosophical questions can be obtained, in principle, by careful inspection of scientific achievements. However, if the thesis of underdetermination holds, such philosophical solutions are not imposed by science itself. In order to arrive at some kind of ontology based on science, it seems necessary to accept certain philosophical presuppositions in the first place. This and the fact that scientific theories change in time show that although such a kind of ontology is possible, and perhaps desirable, it can never be ultimate. (shrink)

The underdetermination of scientific theory choice by evidence is a familiar but multifaceted concept in the philosophy of science. I answer two pressing questions about underdetermination: “What is underdetermination?” and “Why should we care about underdetermination?” To answer the first question, I provide a general definition of underdetermination, identify four forms of underdetermination, and discuss major criticisms of each form. To answer the second question, I then survey two common uses of (...) class='Hi'>underdetermination in broader arguments against scientific realism and in support of the use of values in scientific theory choice. I conclude that philosophers should also care about underdetermination because it impacts scientists in their practice. (shrink)

String theory promises to be able to provide us with a working theory of quantum gravity and a unified description of all fundamental forces. In string theory there are so called ‘dualities’; i.e. different theoretical formulations that are physically equivalent. In this article these dualities are investigated from a philosophical point of view. Semantic and epistemic questions relating to the problem of underdetermination of theories by data and the debate on realism concerning scientific theories (...) are discussed. Depending on ones views on semantic issues and realism different interpretations are possible of the dualities. (shrink)

String theory currently is the only viable candidate for a unified description of all known natural forces. This article tries to demonstrate that the fundamental structural and methodological differences that set string theory apart from other physical theories have important philosophical consequences. Focussing on implications for the realism debate in philosophy of science, it is argued that both poles of that debate face new problems in the context of string theory. On the one hand, the claim of (...) underdetermination of scientific theory by the available empirical data, which is a pivotal element of empiricism, loses much of its plausibility. On the other hand, the dissolution of any meaningful notion of an external ontological object destroys the basis for conventional versions of scientific realism. String theory seems to suggest an intermediate position akin to Structural Realism that is based on a newly emerging principle, to be called the principle of theoretical uniqueness. (shrink)

It is a familiar fact in the practice of science that the available observational evidence may not decide between rival hypotheses or theories. For instance, at the time of Copernicus it was widely held that his theory and the Ptolemaic theory did not differ in their predictions in regard to the available astronomical data. This situation can be illustrated by an analogy. Imagine a finite number of dots on a page of paper representing the available evidence. It (...) will always be possible to draw more than one curve connecting the points. How do we decide which curve or theory to adopt? A standard response is to look to some area where the theories make different predictions. We then seek new evidence, perhaps by conducting an experiment, to determine which theory's prediction is correct and opt tentatively for that one. If it is not feasible to gather this evidence, we can be agnostic for the moment on the question of which it is best to adopt. Or, more likely, we will look to factors other than tit with the data to assist us in resolving the matter (see pragmatic factors in theory acceptance). We will call this phenomena weak Underdetermination of theories, or WUT. (shrink)

This article investigates the implications of string theory for the conception of scientific theory confirmation. The classical understanding of theory confirmation is based on the assumption that scientific theory building is underdetermined by the available empirical data. Several arguments are presented, which suggest a devaluation of this ‘principle of scientific underdetermination’ in the context of string theory. An altered conception of scientific progress emerges that is not based on the (...) notion of theory succession. (shrink)

Philosophers of science are well aware that theories are underdetermined by data. But what about the data? Scientific data are selected and processed representations or pieces of nature. What is useless context and what is valuable specimen, as well as how specimens are processed for study, are not obvious or predetermined givens. Instead, they are decisions made by scientists and other research workers, such as technicians, that produce different outcomes for the data. Vertebrate fossils provide (...) a revealing case of this data-processing, because they are embedded in rock that often matches the fossils’ color and texture, requiring an expert eye to judge where the fossil/context interface is. Fossil preparators then permanently define this interface by chiseling away the material they identify as rock. As a result, fossil specimens can emerge in multiple possible forms depending on the preparator’s judgment, skill, and chosen tools. A prepared fossil then is not yet data but potential data, following Leonelli’s relational framework in which data are defined as evidence that scientists have used to support a proposed theory. This paper draws on ethnographic evidence to assess how scientists overcome this underdetermination of specimens, as potential data, in addition to the underdetermination of theories and of data, to successfully construct specimen-based knowledge. Among other strategies, paleontology maintains a division of labor between data-makers and theory-makers. This distinction serves to justify the omission of preparators’ nonstandard, individualized techniques from scientific publications. This separation has benefits for both scientists and technicians; however, it restricts knowledge production by preventing scientists from understanding how the pieces of nature they study were processed into researchable specimens. (shrink)

A model-theoretic realist account of science places linguistic systems and their corresponding non-linguistic structures at different stages or different levels of abstraction of the scientific process. Apart from the obvious problem of underdetermination of theories by data, philosophers of science are also faced with the inverse (and very real) problem of overdetermination of theories by their empirical models, which is what this article will focus on. I acknowledge the contingency of the factors determining the nature – and (...) choice – of a certain model at a certain time, but in my terms, this is a matter about which we can talk and whose structure we can formalise. In this article a mechanism for tracing "empirical choices" and their particularized observational-theoretical entanglements will be offered in the form of Yoav Shoham's version of non-monotonic logic. Such an analysis of the structure of scientific theories may clarify the motivations underlying choices in favor of certain empirical models (and not others) in a way that shows that "disentangling" theoretical and observation terms is more deeply model-specific than theory-specific. This kind of analysis offers a method for getting an articulable grip on the overdetermination of theories by their models – implied by empirical equivalence – which Kuipers' structuralist analysis of the structure of theories does not offer. (shrink)

I examine the argument that scientific theories are typically 'underdetermined' by the data, an argument which has often been used to combat scientific realism. I deal with two objections to the underdetermination argument: (i) that the argument conflicts with the holistic nature of confirmation, and (ii) that the argument rests on an untenable theory/data dualism. I discuss possible responses to both objections, and argue that in both cases the proponent of underdetermination can respond (...) in ways which are individually plausible, but that the best response to the first objection conflicts with the best response to the second. Consequently underdetermination poses less of a problem for scientific realism than has often been thought. (shrink)

The anti-exceptionalist debate brought into play the problem of what are the relevant data for logical theories and how such data affects the validities accepted by a logical theory. In the present paper, I depart from Laudan's reticulated model of science to analyze one aspect of this problem, namely of the role of logical data within the process of revision of logical theories. For this, I argue that the ubiquitous nature of logical data is responsible (...) for the proliferation of several distinct methodologies for logical theories. The resulting picture is coherent with the Laudanean view that agreement and disagreement between scientific theories take place at different levels. From this perspective, one is able to articulate other kinds of divergence that considers not only the inferential aspects of a given logical theory, but also the epistemic aims and the methodological choices that drive its development. (shrink)

The Polish researcher in the field of logic and philosophy, Jan Woleński, in one of his recent articles, "Metalogical Observations About the Underdetermination of Theories by Empirical Data," logically formalized two weak and strong versions of the underdetermination of theories by empirical data (or UT by abbreviation) and with these formalization has metalogically analyzed these two versions. Finally he has deducted that the weak version is defensible while the strong version is not. In this paper we (...) will critically study Woleński’s analysis of the strong version of UT. (shrink)

In this paper, I argue that the role of values in theory selection is an important issue within medical education. I review the underdetermination argument, which is the idea within philosophy of science that the data serving as evidence for theories are by themselves not sufficient to support a theory to the exclusion of alternatives. There are always various explanations compatible with the data, and we ultimately appeal to certain values as our grounds for choosing (...) one theory over another. I explore some of the ways contemporary feminist philosophers have chosen to grapple with the problem of underdetermination and proposed solutions to systematize how values might be incorporated into theory choice, drawing primarily from the work of Helen Longino and Elizabeth Anderson. I conclude by discussing how value‐laden inquiry should be incorporated within medical education to promote reflection towards medicine’s normative underpinnings. (shrink)

: Where there are cases of underdetermination in scientific controversies, such as the case of the molecular clock, scientists may direct the course and terms of dispute by playing off the multidimensional framework of theory evaluation. This is because assessment strategies themselves are underdetermined. Within the framework of assessment, there are a variety of trade-offs between different strategies as well as shifting emphases as specific strategies are given more or less weight in assessment situations. When a strategy (...) is underdetermined, scientists can change the dynamics of a controversy by making assessments using different combinations of evaluation strategies and/or weighting whatever strategies are in play in different ways. Following an underdetermination strategy does not end or resolve a scientific dispute. Consequently, manipulating underdetermination is a feature of controversy dynamics and not controversy closure. (shrink)

The three basic kinds of theory development are expansion, contraction and revision by empirical evidence. Under empiricist assumptions, the history of scientific evidence can be represented by a sequence of true and cumulatively increasing evidence sets which in the limit determine the complete structure of the world. Under these assumptions it turns out that purely universal hypotheses are falsifiable with certainty, but verifiable only in the limit, ∀-∃-hypotheses are falsifiable in the limit but not verifiable in the limit, (...) and ∀-∃-∀-hypotheses are neither nor. In the consequence, hypotheses with complex quantification structure can only be confirmed probabilistically. While these results are based on “empiricist” assumptions, the revision of theories which contain theoretical concepts requires either a given partition of possible hypotheses out of which the most promising one is chosen, or it requires steps of abductive belief revision. Revision of scientific theories is based on a Lakatosian preference structure, following the idea that in case of a conflict between theory and data, only peripherical parts of the theory are revised, while the theory’s core is saved from revision as long as possible. Surprisingly, the revision of a false theory by true empirical evidence does not necessarily increase the theory’s truthlikeness. Moreover, increase in empirical adequacy does not necessarily indicate progress in theoretical truthlikeness; a well-known attempt to justify this inference is Putnam’s no-miracles argument. (shrink)

Scientific realism is the view that our best scientific theories can be regarded as (approximately) true. This is connected with the view that science, physics in particular, and metaphysics could (and should) inform one another: on the one hand, science tells us what the world is like, and on the other hand, metaphysical principles allow us to select between the various possible theories which are underdetermined by the data. Nonetheless, quantum mechanics has always been regarded as, at (...) best, puzzling, if not contradictory. As such, it has been considered for a long time at odds with scientific realism, and thus a naturalized quantum metaphysics was deemed impossible. Luckily, now we have many quantum theories compatible with a realist interpretation. However, scientific realists assumed that the wave-function, regarded as the principal ingredient of quantum theories, had to represent a physical entity, and because of this they struggled with quantum superpositions. In this paper I discuss a particular approach which makes quantum mechanics compatible with scientific realism without doing that. In this approach, the wave-function does not represent matter which is instead represented by some spatio-temporal entity dubbed the primitive ontology: point-particles, continuous matter fields, space-time events. I argue how within this framework one develops a distinctive theory-construction schema, which allows to perform a more informed theory evaluation by analyzing the various ingredients of the approach and their inter-relations. (shrink)

Quine has attempted to support his indeterminacy thesis by invoking the assumption that two different physical theories could both be compatible with all possible data. His argument ought to work even if the translation of non-Theoretical sentences is determinate. But this enables us to see that the underdetermination of theory need not produce any indeterminacy in the translation of theory.

Consciousness is defined as operating with the meanings of representations, which are what arises in mind under the influence of a stimulus (primary representations) as well as what arises as a result of their transformation (secondary, combined representations). In a first approximation, a representation is expressed by words. The concept of “representation” is a special case of the concept of “information-certainty”, which is the result of distinction. Any distinction is a distinction by a specific attribute and representation is the value (...) of the attribute. In relative ontology, distinction is an essential condition of being that is formalized quantitatively through the operation of ontological subtraction, which is a quantitative expression of representation in relation to consciousness. The finite set of representations for one attribute can always be sorted in ascending order of this attribute values. In the order constructed, each representation corresponds to an attribute number, the only difference of which from a number in mathematics is that the one is always associated with a specific attribute, and the other is not associated with any attribute. The meaning of a representation expressed by an attribute number has a place in an ordered quantitative series of other representations of the same attribute, which is the basis of distinction. Operating with meanings is operating with attributive numbers-meanings. This method of the meaning describing is called the relative method. All operations with the meanings of representations are performed in a multidimensional cognitive space. Each axis of the cognitive space corresponds to an attribute by which representations differ. On each axis there are attribute numbers corresponding to the meanings of representations for the attribute. This approach is a quantitative method of describing of all experimental processes of consciousness, which allows to build a scientific, i.e. quantitative, theory of consciousness with laws written down in a quantitative way. Based on this, it is possible to construct a quantitative calculation of this theory predictions. Such a prediction allows to make an accurate (unambiguous) experimental verification. Due to the quantitative way of describing the operation of meanings, such a theory of consciousness is easily modeled in information technologies, since the latter are also based on the numerical nature of all their processes. The main difference between the relative method and the vast majority of methods using information technologies for text processing is that the relative method, firstly, is not statistical, and secondly, conveys the meaning of the text fully and most adequately. The relative method is effective in operating not only with text data, but also with any data of a different nature, for example, with video data, audio data. (shrink)

The extra value of novel confirmation over accommodation is explained based on an analysis of the underdetermination of scientific theory building. Novel confirmation can provide information on the number of possible scientific alternatives to a predictively successful theory. This information, in turn, can raise the probability that the given theory is true or will be empirically viable in the future.

Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the mappings (...) between these scientific theories and computational systems in their scope are analyzed in terms of suitable specializations of the notions of model of experiment and model of data. Furthermore, the extensively mechanized character of model-based reasoning in model checking is highlighted by a comparison with proof procedures adopted by other formal methods in computer science. Finally, potential epistemic benefits flowing from the application of model checking in other areas of scientific inquiry are emphasized in the context of computer simulation studies of biological information processing. (shrink)

There are two ways that we might respond to the underdetermination of theory by data. One response, which we can call the agnostic response, is to suspend judgment: "Where scientific standards cannot guide us, we should believe nothing". Another response, which we can call the fideist response, is to believe whatever we would like to believe: "If science cannot speak to the question, then we may believe anything without science ever contradicting us". C.S. Peirce recognized these (...) options and suggested evading the dilemma. It is a Logical Maxim, he suggests, that there could be no genuine underdetermination. This is no longer a viable option in the wake of developments in modern physics, so we must face the dilemma head on. The agnostic and fideist responses to underdetermination represent fundamentally different epistemic viewpoints. Nevertheless, the choice between them is not an unresolvable struggle between incommensurable worldviews. There are legitimate considerations tugging in each direction. Given the balance of these considerations, there should be a modest presumption of agnosticism. This may conflict with Peirce's Logical Maxim, but it preserves all that we can preserve of the Peircean motivation. (shrink)

The underdetermination of theory by data obtains when, inescapably, evidence is insufficient to allow scientists to decide responsibly between rival theories. One response to would-be underdetermination is to deny that the rival theories are distinct theories at all, insisting instead that they are just different formulations of the same underlying theory; we call this the identical rivals response. An argument adapted from John Norton suggests that the response is presumptively always appropriate, while another from Larry (...) Laudan and Jarrett Leplin suggests that the response is never appropriate. Arguments from Einstein for the special and general theories of relativity may fruitfully be seen as instances of the identical rivals response; since Einstein’s arguments are generally accepted, the response is at least sometimes appropriate. But when is it appropriate? We attempt to steer a middle course between Norton’s view and that of Laudan and Leplin: the identical rivals response is appropriate when there is good reason for adopting a parsimonious ontology. Although in simple cases the identical rivals response need not involve any ontological difference between the theories, in actual scientific cases it typically requires treating apparent posits of the various theories as mere verbal ornaments or computational conveniences. Since these would-be posits are not now detectable, there is no perfectly reliable way to decide whether we should eliminate them or not. As such, there is no rule for deciding whether the identical rivals response is appropriate or not. Nevertheless, there are considerations that suggest for and against the response; we conclude by suggesting two of them. (shrink)

The notion of so-called "postulated ontology" appears in the context of a well- -known thesis of the underdetermination of scientific theories by empirical data. It is argued in the paper, that the conviction of the existence of some kind of relation between a given theory and ontological ideas can be derived from this thesis, regardless of its particular form. Therefore, certain solutions to classical philosophical questions can be obtained, in principle, by careful inspection of scientific (...) achievements. However, if the thesis of underdetermination holds, such philosophical solutions are not imposed by science itself. In order to arrive at some kind of ontology based on science, it seems necessary to accept certain philosophical presuppositions in the first place. This, and the fact that scientific theories change in time show that although such a kind of ontology is possible, and perhaps desirable, it can never be ultimate. (shrink)

It is often claimed that science should be "value-free in that ethical, political, and social values have no legitimate role in the justification of scientific theories. Although such values may influence which hypotheses are pursued, or whether some application of scientific theories is desirable, they play no legitimate role in scientific reasoning. ;I argue against the view that all science ought to be value-free. Examining a range of cases from biology, epidemiology, pathology, and atmospheric sciences I show (...) that ethical and political values can operate in cases widely agreed to be "good science." Drawing on recent work by Helen Longino and Phillip Kitcher, I argue that in many research contexts the goals of scientific research cannot be separated from ethical and political interests. As a result, there are at least four ways ethical and political value judgments can play a legitimate role in the justification of scientific theories. First, in certain research contexts, ethical and political values help justify and apply criteria for theory choice . Second, ethical and political aims of inquiry can give scientists reason to adopt conceptual frameworks that contain ethical or political normative content. In these contexts, ethical and political value judgments play a legitimate role as auxiliary hypotheses in generating evidential relations. Third, when scientific research has implications for public policy, scientists must weigh ethical values in determining which risks are "acceptable" in classifying data. Finally, ethical and political considerations can be relevant to evaluating whether an interpretation of data is biased. ;A consequence of my view is that, in some contexts, "value-free" science is not only impossible, but also epistemologically irresponsible. Good science will sometimes require scientists to weigh the ethical and political values at stake in their research. As a result, scientists should be trained to think critically about the ethical and political dimensions of their work. (shrink)

There are two ways that we might respond to the underdetermination of theory by data. One response, which we can call the agnostic response, is to suspend judgment: `Where scientific standards cannot guide us, we should believe nothing.' Another response, which we can call the fideist response, is to believe whatever we would like to believe: `If science cannot speak to the question, then we may believe anything without science ever contradicting us.' C.S. Peirce recognized these (...) options and suggested evading the dilemma. It is a Logical Maxim, he suggests, that there could be no genuine underdetermination. This is no longer a viable option in the wake of developments in modern physics, so we must face the dilemma head on. The agnostic and fideist responses to underdetermination represent fundamentally different epistemic viewpoints. Nevertheless, the choice between them is not an unresolvable struggle between incommensurable worldviews. There are legitimate considerations tugging in each direction. Given the balance of these considerations, there should be a modest presumption of agnosticism. This may conflict with Peirce's Logical Maxim, but it preserves all that we can preserve of the Peircean motivation. 1. Peirce's Logical Maxim 2. The concept of underdetermination 3. Our dilemma 4. Endgame. (shrink)

Current discussion of scientific realism and antirealism often cites Pierre Duhem’s argument for the underdetermination of theory choice by evidence. Participants draw on an account of his underdetermination thesis that is familiar, but incomplete. The purpose of this article is to complete the familiar account. I argue that a closer look at Duhem’s The aim and structure of physical theory suggests that the rationale for his underdetermination thesis comes from his philosophy of scientific (...) language. I explore how an understanding of physical laws as symbolic is meant to support the thesis. In the course of my argument, I point out that Duhemian underdetermination is not meta-practical but grounded in the practice of science, specifically in the scientist’s use of instruments and measurement techniques. Measurement has a significant limitation, according to Duhem: it always involves approximation and a degree of experimental error. Consequently, it cannot overcome the gap between the ordinary, concrete language of observation and the mathematical language of science. Moreover, Duhem argues that the use of instruments in experiment invokes whole groups of theories. I contend that, ultimately, this reliance on auxiliary assumptions—which makes possible the use of instruments—is the foundation of his thesis and that recognizing this completes the familiar account of his underdetermination argument.Author Keywords: Duhem; Suppes; Underdetermination; Measurement; Physical theory. (shrink)

This paper examines the epistemological significance of the present situation of underdetermination in quantum mechanics. After analyzing this underdetermination at three levels---formal, ontological, and methodological---the paper considers implications for a number of variants of the thesis of scientific realism in fundamental physics and reassesses Lakatos‘ characterization of progress in physical theory in light of the present situation. Next, this paper considers the implications of underdetermination for Weinberg’s ‘‘dream of a final theory.’’ Finally, the paper (...) concludes by suggesting how one might still think of realism and progress in fundamental physics despite the possibility of persistent underdetermination in quantum mechanics. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical (...) sciences, and argue that they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

The curse of dimensionality is one of the most prominent challenge that data scientists face when trying to make valuable inferences. It is an epistemic problem that hits particularly hard in "Big Data" research contexts, where the volume of the data set is particularly large. The way in which we tackle with this problem sheds light on the notion of scientific evidence. Yet, it is virtually absent from the current philosophical literature. In this paper, I aim (...) to broaden the focus of that literature by showing that the dimensions in which the data are embedded are an integral part of scientific evidence. This is an aspect of scientific evidence that is often hidden behind the traditional observation/theory dichotomy that we often encounter in philosophy of science. Dimensions are abstract objects. They are not observables like tables and chairs, nor are they entries in a data set. Ultimately, I aim to show that empirical adequacy is not merely a matter of finding the model that best fit the relevant observational data. It is also a matter of finding the model that best fit the relevant observational data inside the relevant dimensions. (shrink)

The underdetermination of theory by data argument (UD) is traditionally construed as an argument that tells us that we ought to favour an anti-realist position over a realist position. I argue that when UD is constructed as an argument saying that theory choice is to proceed between theories that are empirically equivalent and adequate to the phenomena up until now, the argument will not favour constructive empiricism over realism. A constructive empiricist cannot account for why scientists (...) are reasonable in expecting one theory to be empirically adequate rather than another, given the criteria he suggests for theory choice. (shrink)

According to the no miracles argument, scientific realism provides the only satisfactory explanation of the predictive success of science. It is argued in the present article that a different explanatory strategy, based on the posit of strong limitations to the underdetermination of scientific theory building by the available empirical data, offers a more convincing understanding of scientific success.

More than a hundred years ago, the American philosopher C. S. Peirce suggested the idea of pragmatism as a logical criterion to analyze what words and concepts express through their practical meaning. Many words have been spent on creative processes and reasoning, especially in the case of scientific practices. In fact, philosophers have usually offered a number of ways of construing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, and (...) thus not analyzable. The “computational turn” gave us a new way to understand creative processes in a strictly pragmatic sense. Artificial Intelligence and Cognitive Science tools allow us to test concepts and ideas previously conceived in abstract terms. It is in the perspective of these actual models that we find the central role of abduction in the explanation of creative reasoning in science. What I call theoretical abduction certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper. (shrink)

Whether a collection of scientific data can be explained only by a unique theory or whether such data can be equally explained by multiple theories is one of the more contested issues in the history and philosophy of science. This paper argues that the case for multiple explanations is strengthened by the widespread failure of models in mathematical logic to be unique, i.e., categorical. Science is taken to require replicable and explicit public knowledge; this necessitates an (...) unambiguous language for its transmission. Mathematics has been chosen as the vehicle to transmit scientific knowledge, both because of its “unreasonable effectiveness” and because of its unambiguous nature, hence the vogue of axiomatic systems. But mathematical logic tells us that axiomatic systems need not refer to uniquely defined real structures. Hence what is accepted as science may be only one of several possibilities. (shrink)

Attempts by 20th century philosophers of science to define inductive concepts and methods concerning the support provided to scientific theories by empirical data have been unsuccessful. Although 20th century philosophers of science largely ignored statistical methods for testing theories, when they did address them they argued against rather than for their use. In contrast, this study demonstrates that traditional statistical methods used for validating computer simulation models provide tests of the scientific theories that those models may embody. (...) This study shows that these statistical methods are applicable regardless of whether the scientific theory or model is deterministic, stochastic, or chaos-based in nature. Traditional statistical methods, such as hypothesis testing, test theories by providing measures of their goodness-of-fit with the portion of the world the theories describe. Nothing more is needed to portray the agreement between theory and the world. Furthermore, the statistical measures of goodness-of-fit are not ampliative inductive measures. Therefore, the ultimate value and direction of the 20 th century ampliative inductive program within philosophy of science is thrown into question. This study also suggests that advanced statistical, visualization, and hybrid quantitative qualitative methods hold promise for offering scientists additional methods for testing scientific theories through validating their computer model representations. As a prerequisite to the above discussions, this study proves that scientific theories, when applied to concrete physical systems, can indeed be represented by computer models. (shrink)

Complex adaptation systems are the main development direction of China’s current green innovation research. New material industry is one of the key entry points to accelerate the construction of modern industrial system and promote innovation, green, and efficient development. Under the requirements of China’s current low-carbon development, China’s green innovation system is developing rapidly. Green innovation complicates traditional innovation models and their functions and improves economic development. The purpose of this paper is to study the theoretical analysis framework of applying (...) complex adaptive systems and to analyze and calculate the characteristics of the current status of green innovation systems in multiple industries in China’s manufacturing industry. Analysis is as follows. The experimental group and the control group are set for comparison, and the data are organized by applying complex adaptive system theory and data analysis methods. Experimental data show that the coordinated development of the green innovation systems in various industries in China is not balanced, with a difference of more than three times; the parameters of the various green innovation systems within the industry are unbalanced, and the difference is more than five times. The experimental results show that the green innovation subject can strategically interact by adjusting the ratio of resource allocation through the constructed green innovation system model. The green innovation system with complex adaptive system theory is conducive to the development of the green system. It can save about 23% of green innovation time and improve efficiency by about 15%, to promote the development of green innovation in China. (shrink)

Four empirically equivalent versions of general relativity, namely standard GR, Lorentz-invariant gravitational theory,and the gravitational gauge theories of the Lorentz and translation groups, are investigated in the form of a case study for theory underdetermination. The various ontological indeterminacies (both underdetermination and inscrutability of reference) inherent in gravitational theories are analyzed in a detailed comparative study. The concept of practical underdetermination is proposed, followed by a discussion of its adequacy to describe scientific progress.

According to standard scientific realism, science seeks truth and we can justifiably believe that our successful theories achieve, or at least approximate, that goal. In this paper, I discuss the implications of the following competitor thesis: Any theory we may favor has competitors such that we cannot justifiably deny that they are approximately true. After defending that thesis, I articulate three specific threats it poses for standard scientific realism; one is epistemic, the other two are axiological (that (...) is, pertaining to the claim that science seeks truth). I also flag an additional axiological “challenge,” that of how one might justify the pursuit of a primary aim, such as truth. Bracketing epistemic realism, I argue that the axiological threats can be addressed by embracing a refined realist axiological hypothesis, one that specifies a specific subclass of true claims sought in science. And after identifying three potential responses to the axiological “challenge,” I contend that, while standard axiological realism appears to lack the resources required to utilize any of the responses, the refined realist axiology I embrace is well suited to each. (shrink)

Duhem—Quine underdetermination plays a constructive role in epistemology by pinpointing the impact of non-empirical virtues or cognitive values on theory choice. Underdetermination thus contributes to illuminating the nature of scientific rationality. Scientists prefer and accept one account among empirical equivalent alternatives. The non-empirical virtues operating in science are laid open in such theory choice decisions. The latter act as an epistemological test tube in making explicit commitments to how scientific knowledge should be like.

The empirical underdetermination of theories is a philosophical problem which until the last century has not seriously troubled actual science. The reason is that confirmation does not depend only on empirical consequences, and theoretical virtues allow to choose among empirically equivalent theories. Moreover, I argue that the theories selected in this way are not just pragmatically or aesthetically better, but more probably true. At present in quantum mechanics not even theoretical virtues allow to choose among many competing theories and (...) interpretations, but this is because none of them possess those virtues to a sufficient degree. However, first, we can hope for some future advancement. Second, even if no further progress came forth, all the most credited competitors agree on a substantial core of theoretical assumptions. Therefore underdetermination does not show that we cannot be realist on unobservable entities in general, but at most that in particular fields our inquiry may encounter some de facto limits. (shrink)

Experiment, instrumentation, and procedures of measurement, the body of practices and technologies forming the technical culture of science, have received at most a cameo appearance in most histories. For the history of science is almost always written as the history of theory. Of course, the interpretation of science as dominated by theory was the main pillar of the critique, launched by Kuhn, Quine, Hanson, Feyerabend, and others, of the positivist and logical empiricist traditions in the philosophy of science. (...) Against Carnap, Hempel, Nagel, and Popper, who accorded observation reports an independent status either as a source of inductive support or as a basis for the falsification of scientific theories, Hanson and Kuhn emphasized the theory-ladenness of observation. They made this central point – that all observation is shaped by reference to theory – the cornerstone of a full-blown philosophy of science by buttressing it with two additional lines of argument: First, theories are always underdetermined by the data, several theories being compatible with the same set of data. Hence, choice between theories is never a matter of empirical support, but always turns around conceptual issues. Second, statements derived from theory never confront nature alone; they are always clothed in a web of interrelated beliefs. Thus, theories, their associated observation languages, and the entire technical culture they support must be accepted or rejected as wholes. (shrink)

Isaac Newton's Scientific Method examines Newton's argument for universal gravity and his application of it to resolve the problem of deciding between geocentric and heliocentric world systems by measuring masses of the sun and planets. William L. Harper suggests that Newton's inferences from phenomena realize an ideal of empirical success that is richer than prediction. Any theory that can achieve this rich sort of empirical success must not only be able to predict the phenomena it purports to explain, (...) but also have those phenomena accurately measure the parameters which explain them. Harper explores the ways in which Newton's method aims to turn theoretical questions into ones which can be answered empirically by measurement from phenomena, and to establish that propositions inferred from phenomena are provisionally accepted as guides to further research. This methodology, guided by its rich ideal of empirical success, supports a conception of scientific progress that does not require construing it as progress toward Laplace's ideal limit of a final theory of everything, and is not threatened by the classic argument against convergent realism. Newton's method endorses the radical theoretical transformation from his theory to Einstein's. Harper argues that it is strikingly realized in the development and application of testing frameworks for relativistic theories of gravity, and very much at work in cosmology today. (shrink)