Many in philosophy understand truth in terms of precise semantic values, true propositions. Following Braun and Sider, I say that in this sense almost nothing we say is, literally, true. I take the stand that this account of truth nonetheless constitutes a vitally useful idealization in understanding many features of the structure of language. The Fregean problem discussed by Braun and Sider concerns issues about application of language to the world. In understanding these issues I propose an alternative modeling tool (...) summarized in the idea that inaccuracy of statements can be accommodated by their imprecision. This yields a pragmatist account of truth, but one not subject to the usual counterexamples. The account can also be viewed as an elaborated error theory. The paper addresses some prima facie objections and concludes with implications for how we address certain problems in philosophy. (shrink)

This paper challenges “traditional measurement-accuracy realism”, according to which there are in nature quantities of which concrete systems have definite values. An accurate measurement outcome is one that is close to the value for the quantity measured. For a measurement of the temperature of some water to be accurate in this sense requires that there be this temperature. But there isn’t. Not because there are no quantities “out there in nature” but because the term ‘the temperature of this water’ fails (...) to refer owing to idealization and failure of specificity in picking out concrete cases. The problems can be seen as an artifact of vagueness, and so doing facilitates applying Eran Tal’s robustness account of measurement accuracy to suggest an attractive way of understanding vagueness in terms of the function of idealization, a way that sidesteps the problems of higher order vagueness and that shows how idealization provides a natural generalization of what it is to be vague. (shrink)

The science of metrology characterizes the concept of precision in exceptionally loose and open terms. That is because the details of the concept must be filled in—what I call narrowing of the concept—in ways that are sensitive to the details of a particular measurement or measurement system and its use. Since these details can never be filled in completely, the concept of the actual precision of an instrument system must always retain some of the openness of its general characterization. The (...) idea that there is something that counts as the actual precision of a measurement system must therefore always remain an idealization, a conclusion that would appear to hold very broadly for terms and the concepts they express. (shrink)

Bogen and Woodward argued the indirect connection between data and theory in terms of their conception of “phenomena.” I outline and elaborate on their presentation. To illuminate the connection with contemporary thinking in terms of models, I distinguish between phenomena tokens, representations of which can be identified with data models, and phenomena types that can be identified with relatively low-lying models or aspects of models in the model hierarchy. Throughout I stress the role of idealization in these considerations.

Knowledge requires truth, and truth, we suppose, involves unflawed representation. Science does not provide knowledge in this sense but rather provides models, representations that are limited in their accuracy, precision, or, most often, both. Truth as we usually think of it is an idealization, one that serves wonderfully in most ordinary applications, but one that can terribly mislead for certain issues in philosophy. This article sketches how this happens for five important issues, thereby showing how philosophical method must take into (...) account the idealized nature of our familiar conception of truth. (shrink)

The causal argument for physicalism is anayzed and it's key premise--the causal closure of physics--is found wanting. Therefore, a hidden premise must be added to the argument to gain its conclusion, but the hidden premise is indistinguishable from the conclusion of the causal argument. Therefore, it begs the question on physicalism.

I argue that we cannot adequately characterize idealization and abstraction and the distinction between the two on the grounds that they have distinct semantic properties. By doing so, on the one hand, we focus on the conceptual products of the two processes in making the distinction and we overlook the importance of the nature of the thought processes that underlie model-simplifying assumptions. On the other hand, we implicitly rely on a sense of abstraction as subtraction, which is unsuitable for explicating (...) scientific model construction. Instead, I argue that a sense of abstraction as extraction is more suitable. Finally, I suggest a different way to distinguish the two processes that avoids these problems. Namely, that both idealization and abstraction could be understood as particular modes of application of the same cognitive process: selective attention. (shrink)

The argument from causal closure of the physical is usually considered the most powerful argument in favor of the ontological doctrine of physicalism. Many authors, most notably Papineau, assume that CCP implies that physicalism is supported by physics. I demonstrate, however, that physical science has no bias in the ontological debate between proponents of physicalism and dualism. I show that the arguments offered for CCP are effective only against the accounts of mental causation based on the action of the mental (...) forces of a Newtonian nature, i.e. those which manifest themselves by causing accelerations. However, it is conceivable and possible that mental causation is manifested through the redistribution of energy, momentum and other conserved quantities in the system, brought about by altering the state probability distribution within the living system and leading to anomalous correlations of neural processes. After arguing that a probabilistic, interactionist model of mental causation is conceivable, which renders the argument from causal closure of the physical ineffective, I point to some basic features that such a model must have in order to be intelligible. At the same time, I indicate the way that conclusive testing of CCP can be done within the theoretical framework of physics. (shrink)

In previous publications I have argued that much scientific activity should be thought of as involving the operation of distributed cognitive systems. Since these contributions to the cognitive study of science appear in venues not necessarily frequented by philosophers of science, I begin with a brief introduction to the notion of a distributed cognitive system. I then describe what I take to be an exemplary case of a scientific distributed cognitive system, the Hubble Space Telescope (HST). I do not here (...) reargue the case for conceiving of systems like the HST as distributed cognitive systems. Rather, I examine a question that arises once one has adopted the perspective of distributed cognitive systems, namely, the role of agency in a distributed cognitive system. Here I argue, contrary to several advocates of distributed cognitive systems, that we should regard the human components of distributed cognitive systems as the only sources of agency within such systems. In particular, we should not extend notions of agency to such systems as a whole. (shrink)

Numerous articles in top IS journals note as a limitation and lack of generalizability that their findings are specific to a certain type of technology, culture, and so on. We argue that this generalizability concern is about limited scope. The IS literature notes this preference for generalizability as a characteristic of good science and it is sometimes confused with statistical generalizability. We argue that such generalizability can be in conflict with explanation or prediction accuracy. An increase in scope can decrease (...) explanation or prediction accuracy. Thus, in sciences such as cancer research, where explanation and prediction accuracy are highly valued, the cancer accounts have become increasingly narrower. IS thinking has not yet benefitted from these considerations. Whether generalizability is valued should be linked with the research aims. If the aim is practical applicability through explanation or prediction accuracy, then “limited” generalizability could be a strength rather than a weakness. (shrink)

We respond to Lee’s (forthcoming) commentary on our article “Demystifying the Influential IS Legends of Positivism” (Siponen & Tsohou [S&T], 2018). Lee offers four arguments against our analyses and conclusions in S&T (2018). First, because logical positivism (LP) has been discredited, he contends it cannot be used as a normative standard in IS. We clarify that our conclusions in S&T (2018) point to (1) the lack of justification for certain IS beliefs, and (2) a misunderstanding rather than legitimacy of LP (...) as a philosophy of science. Second, Lee argues that IS researchers characterizing positivism never said they were following the tenets of LP. We provide evidence to show some influential papers on positivism in IS research indicated they were indeed following LP. Third, Lee offers an alternative explanation for the emergence and nature of IS positivism. His explanation has merit, and it can be accommodated in S&T’s (2018) account of positivism in IS. Unfortunately, his explanation does not account for certain problems in the IS discipline’s use of positivism. In S&T (2018), we provide a plausible explanation for these problems. Finally, we discuss the implications of S&T’s (2018) findings for the need to better understand the philosophical assumptions underlying “IS positivism.” We also counter Lee’s arguments that our conclusions in S&T (2018) should not make a difference to the future of IS research. (shrink)

Positivism has been used to establish a standard that Information Systems (IS) research must meet to be scientific. According to such positivistic beliefs in IS, scientific research should: 1) be generalizable, 2) focus on stable independent variables, 3) have certain ontological assumptions, and 4) use statistical or quantitative methods rather than qualitative methods. We argue that logical positivist philosophers required none of these. On the contrary, logical positivist philosophers regarded philosophizing in general and ontological considerations in particular as nonsense. Moreover, (...) the positivists’ preferred empirical research method was not a survey, but rather a qualitative observation recorded by field notes. In addition, positivist philosophers required neither statistical nor nonstatistical generalizability. At least some positivist philosophers also acknowledged the study of singular cases as being scientific. Many research orientations (e.g., single-setting research, examination of change, qualitative research) that are deemed “unscientific” by positivism in IS seem to be “scientific” (in principle) according to logical positivism. In turn, generally speaking, what has been justified as scientific by positivism in IS (e.g., requirements of statistical or nonstatistical generalizability, surveys, independent variables, ontological views) were either not required by logical positivists or were regarded as nonsensical by logical positivists. Furthermore, given that positivism is sometimes associated (or confused) with logical empiricism in IS, we also briefly discuss logical empiricism. Finally, realizing that certain influential, taken-forgranted assumptions that underlie IS research are unwarranted could have ground-breaking implications for future IS research. (shrink)

The paper tries to provide an alternative to Hempel’s approach to scientific laws and scientific explanation as given in his D-N model. It starts with a brief exposition of the main characteristics of Hempel’s approach to deductive explanations based on universal scientific laws and analyzes the problems and paradoxes inherent in this approach. By way of solution, it analyzes the scientific laws and explanations in classical mechanics and then reconstructs the corresponding models of explanation, as well as the types of (...) scientific laws appearing in it. Finally, it compares this reconstruction with the approaches of J. Woodward and C. Hitchcock, C. Liu and with the views of M. Thalos on analytic mechanics. (shrink)