Scientific Explanation and Methodology of Science

The following sections are included:

• PREFACE
• ORGANIZATION
• CONTENTS

The aim of this paper is to show that a new understanding of fundamentality can be applied successfully in classical cosmology based on General Relativity. We are thereby able to achieve an account of cosmological time asymmetry as an intrinsic and fun-damental property of the universe. First, we consider Price's arguments against the fundamental status of time-asymmetry (Price (1996, 2002, 2011)). We show that these arguments have some force, but their force depends on understanding fundamentality as law-likeness. Second, we show that alternative approaches attempting to explain time directedness either by applying an anthropic strategy based on a multiverse approach, or by using the empirical fact of accelerated expansion of the universe, equally fail to provide a fundamental explanation of time directedness. In the third part, we present our own new concept of fundamentality based on properties of the solution space of fundamental laws. We demonstrate how this new concept of fundamentality is effective in understanding the cosmological asymmetry.

Which aspects of our experience of time can special relativity be used to explain? In this paper I focus on those aspects of our experience of time associated with presentism. Special relativity has been used to argue against presentism, and to insist that those aspects of our experience of time that are central to the presentist thesis are to be “explained away” as somehow illusory. In this paper I offer a version of special relativistic presentism that has the potential to explain (rather than explain away) these aspects of our experience of time, including the short duration of the present and temporal becoming.

The following sections are included:

• Introduction
• Challenges to Thought Experiments
• Dennett's Intuition Pumps
• Dennett and Conservative (Folk) Concepts
• Weird Spacetimes
• Bibliography

Philosophical discussions of rule-following in the later Wittgenstein (1953, 1967) are an important source of inspiration for the development of views on the social nature of scientific knowledge. Two major opposing views in this inquiry — Bloor's sociology of scientific knowledge (SSK) (1983, 1991, 1992, 1997) and Lynch's (1992, 1993) ethnomethodological studies of work (ESW) — represent two positions derived from two different readings of Wittgenstein's later writings on rule-following. The aim of this paper is two-fold. One is to re-examine the noted Bloor-Lynch debate by considering Kusch's (2004) recent discussion of this debate. Another is to show that a new semantic framework of rule-following ascriptions based on a cognitive approach to the study of generics can be provided such that SSK and ESW are compatible in it (Leslie, 2009; Cheng, 2011).

The following sections are included:

• Introduction
• Basic Dynamical Systems: the Toy Model
• Initial Condition Ensemble Forecasting
• Model Error
• Weather and Climate Modelling
• Ways Out?
• References

From perspectives of contextual structure, elements and features, this paper analyses the contextualist basis of scientific explanation. It is articulated that contextual analysis specifies the boundary conditions of syntactics, semantics, and pragmatics, clarifies the essence and value-orientation, constructs a conversational platform, and unveils the progress condition and historical continuity of scientific explanation.

Two popular models of scientific discovery, abduction and the inference to the best explanation (IBE), presuppose that the reason for accepting a hypothetical explanation A comes from the epistemic and/or explanatory force manifested in the fact that observed fact C is an inferred consequence of A. However, not all discoveries take this top-down procedure from A to C, in which the result of discovery A implies the observed fact C. I contend that discovery can be modeled as a bottom-up procedure based on inductive and analogical rules that lead us to infer from C to A. I take the theory of Dignaga, an Indian medieval logician, as a model of this bottom-up approach. My argument has three panels: 1) this bottom-up approach applies to both commonsense and scientific discovery without the assumption that C has to be an inferred consequence of A; 2) this bottom-up approach helps us get around problems that crop up in applying abduction and/or IBE, which means that scientific discovery need not to be modeled exclusively by top-down approaches; and 3) the existence of the bottom-up approach requires a pluralist attitude towards modeling of scientific discovery.

Although many today speak of ours as a knowledge society, ignorance seems to flourish all around us. Even in the United States, considered one of the most advanced of societies, millions question some of the most established results of science (such as evolution, global warming, and the benefits of childhood immunization) while they fail to question other results (such as many of the claims about the safety and effectiveness of drugs offered by the pharmaceutical industry). What's more, only a small fraction (less than one-sixth) of the information gathered in the U.S. each year is even made public (the rest is kept secret by industry or government), and the information that is made public often varies in content depending on the sources consulted. And the problem, many say, is getting worse…

The following sections are included:

• Introduction
• Why Most Models in Science Are Not Fictional
• Typically Fictional Models in Science
• Modeling the Unobservable
• Fictional Models for the Unobservable?
• References

The nature of mathematics has been the subject of heated debate among mathematicians and philosophers throughout the ages. The realist and anti-realist positions have had longstanding debate over this problem, but some of the most important recent development has focused on the interpretations; each of the above positions has its own interpretation of the nature of mathematics. I argue in this paper a contextualist interpretation of mathematics, it elucidates the essential features of mathematical context. That is, being integral and having concrete structure, mathematical context is a recontextualizational process with determinate boundary.

The search for understanding is a major aim of science. Traditionally, understanding has been undervalued in the philosophy of science because of its psychological underpinnings; nowadays, however, it is widely recognized that epistemology cannot be divorced from psychology as sharp as traditional epistemology required. This eliminates the main obstacle to give scientific understanding due attention in philosophy of science. My aim in this paper is to describe an account of scientific understanding as an emergent feature of our mastering of different (causal) explanatory frameworks that takes place through the mastering of scientific practices. Different practices lead to different kinds of representations. Such representations are often heterogeneous. The integration of such representations constitute understanding.

Approximation of laws is an important theme in the philosophy of science. If we can make sense of the idea that two scientific laws are “close” to each other, then we can also analyze such methodological notions as approximate explanation of laws, approximate reduction of theories, approximate empirical success of theories, and approximate truth of laws. Proposals for measuring the distance between quantitative scientific laws were given in Niiniluoto (1982, 1987). In this paper, these definitions are reconsidered as a response to the interesting critical remarks by Liu (1999).

Bas van Fraassen's brand of empiricism, as articulated in his well-known constructive empiricism and his more recent empiricist structuralism, has been developed using the so-called semantic approach to scientific theories. This approach has allowed van Fraassen to espouse quite a robust form of non-realism about unobservable entities while avoiding some of the problems that contributed to the demise of logical empiricism. I shall argue, however, that in other respects van Fraassen replicates many of the serious shortcomings associated with the earlier tradition — despite the fact that his choice of language sometimes obscures those similarities. Because van Fraassen's empiricist structuralism is much less well-known than his constructive empiricism, the rather lengthy first section of this paper provides an overview and initial assessment of that position. Subsequent sections deal with the ways in which van Fraassen's very sophisticated form of empiricism not only fails to address some of the most serious shortcomings of logical empiricism but actually reinforces them. The chief focus is on the failure to address issues that arise from the historical and social contexts within which science is pursued — this despite a vocabulary rich in references to perspectivity, pragmatics, context, use, and aims.

The usual Bayesian approach to understanding the confirmation of scientific theories is inadequate. The problem lies not with Bayesian epistemology, but with a simplistic equation of the subjective, individualistic evidential relevance relation that Bayesianism attempts to capture and the more objective relevance relation of confirmation.

Unary Operator Theory (UOT) systematically researches unary operators by analyzing the unification between the formal universality of mathematical logic and the adaptability of the natural language. At first, it focuses on Special Theory of Relativity of Function (STRF) which is only useful for equivalent transformation between the non-truth-valuedfunction formed by non-functional unary operators and truth-valued-function formed by functional binary connectives in two-truth-valued classical propositional logic. Moreover, according to the STRF, there are new interpretations about problems both in philosophical logic and in philosophy of science which could not be solved only by the logic of truth-function in the past.

Production is an essential notion for theory of mechanism, and to clarify its meaning brings about lots of difficulties. This article argues for an event-property theory which characterizes production as a particular property of event. That theory may provide a prospective solution for those difficulties.

C. G. Hempel, one of the logical empiricists, who builds up his probability explanation model by using the empiricist view of probability, this model encountered many difficulties in the scientific explanation in which Hempel is difficult to make a reasonable defense. Based on the bayesian probability theory, the Bayesian probability model provides an approach of a subjective probability explanation based on the subjective probability, using the subjectivist view of probability. On the one hand, this probability model establishes the epistemological status of the subject in the social science; On the other hand, it provides a feasible explanation model for the social scientific explanation, which has important methodological significance.

Elliott Sober's well-known challenge to the principle of the common cause — and to its generalization, the causal Markov condition — appeals to the apparent positive correlation between two causally unconnected quantities: Venetian sea levels and British bread prices. In this paper we examine Kevin Hoover's and Daniel Steel's opposite evaluations of Sober's case. We argue that the difference in their assessments results from a difference in their choice of units and populations for statistical modeling. Our analysis suggests yet another diagnosis of Sober's counterexample: the failure of the causal Markov condition in the population chosen by Sober and Steel is due to the presence of causal relations that hold between the relevant properties across units. Such inter-unit causation is left unrepresented in causal models congenial to statistical analysis, because statistics does not deal with inter-unit relationships once the units are fixed. Accordingly, the causal Markov condition is formulated in terms of causal structures that depict intra-unit causal relations only. It is therefore worth highlighting a methodological principle for causal inference: the units should be so chosen that they do not interfere with each other, a principle that, fortunately, is often observed in practice.