Analogies between classical statistical mechanics and quantum field theory played a pivotal role in the development of renormalization group methods for application in the two theories. This paper focuses on the analogies that informed the application of RG methods in QFT by Kenneth Wilson and collaborators in the early 1970's. The central task that is accomplished is the identification and analysis of the analogical mappings employed. The conclusion is that the analogies in this case study are (...) formal analogies, and not physical analogies. That is, the analogical mappings relate elements of the models that play formally analogous roles and that have substantially different physical interpretations. Unlike other cases of the use of analogies in physics, the analogical mappings do not preserve causal structure. The conclusion that the analogies in this case are purely formal carries important implications for the interpretation of QFT, and poses challenges for philosophical accounts of analogical reasoning and arguments in defence of scientific realism. Analysis of the interpretation of the cutoffs is presented as an illustrative example of how physical disanalogies block the exportation of physical interpretations from from statistical mechanics to QFT. A final implication is that the application of RG methods in QFT supports non-causal explanations, but in a different manner than in statistical mechanics. (shrink)

In May of 1973 we organized an international research colloquium on foundations of probability, statistics, and statistical theories of science at the University of Western Ontario. During the past four decades there have been striking formal advances in our understanding of logic, semantics and algebraic structure in probabilistic and statistical theories. These advances, which include the development of the relations between semantics and metamathematics, between logics and algebras and the algebraic-geometrical foundations of statistical theories (especially in the (...) sciences), have led to striking new insights into the formal and conceptual structure of probability and statistical theory and their scientific applications in the form of scientific theory. The foundations of statistics are in a state of profound conflict. Fisher's objections to some aspects of Neyman-Pearson statistics have long been well known. More recently the emergence of Baysian statistics as a radical alternative to standard views has made the conflict especially acute. In recent years the response of many practising statisticians to the conflict has been an eclectic approach to statistical inference. Many good statisticians have developed a kind of wisdom which enables them to know which problems are most appropriately handled by each of the methods available. The search for principles which would explain why each of the methods works where it does and fails where it does offers a fruitful approach to the controversy over foundations. (shrink)

Statistical learning theory is often associated with the principle of Occam’s razor, which recommends a simplicity preference in inductive inference. This paper distills the core argument for simplicity obtainable from statistical learning theory, built on the theory’s central learning guarantee for the method of empirical risk minimization. This core “means-ends” argument is that a simpler hypothesis class or inductive model is better because it has better learning guarantees; however, these guarantees are model-relative and so the (...) theoretical push towards simplicity is checked by our prior knowledge. (shrink)

This is an introductory book that targets the reader who has the ambition to apply economic analysis but may be missing a technical introduction to its mathematical techniques or seeks a structured elaboration of its philosophical principles. The book juxtaposes economic analysis with moral philosophy, political theory, egalitarianism, and other methodological principles and then passes to the details of methods such as model-building, derivatives, differential equations, statistical tests, and the use of computer programs.

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)

In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key (...) to untangling them is the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. (shrink)

Philosophy of medicine encompasses a broad range of methodological approaches and theoretical perspectives—from the uses of statistical reasoning and probability theory in epidemiology and evidence-based medicine to questions about how to recognize the uniqueness of individual patients in medical humanities, person-centered care, and values-based practice; and from debates about causal ontology to questions of how to cultivate epistemic and moral virtue in practice. Apart from being different ways of thinking about medical practices, do these different philosophical approaches have (...) anything in common? Are they committed to incompatible assumptions about the nature of science and its relationship to experience, value, and the art of medicine, or are different approaches nonetheless complementary? The chapter examines the questions these different approaches and perspectives raise and considers why so many theorists of medicine seek to find a “base” or “center” for medicine—as though there is, or should be, some general conceptual thread linking the things we call “usual clinical practice” in the real world. It further considers whether there are alternative approaches to the philosophy of medicine, which do not embody this philosophical assumption; and it suggests that a key tool for evaluating approaches must be their ability to contribute something of genuine value to clinical medicine. (shrink)

A major problem for interpersonal aggregation is how to compare utility across individuals; a major problem for decision-making under normative uncertainty is the formally analogous problem of how to compare choice-worthiness across theories. We introduce and study a class of methods, which we call statistical normalization methods, for making interpersonal comparisons of utility and intertheoretic comparisons of choice-worthiness. We argue against the statistical normalization methods that have been proposed in the literature. We argue, instead, in (...) favor of normalization of variance: we claim that this is the account that most plausibly gives all individuals or theories ‘equal say’. To this end, we provide two proofs that variance normalization has desirable properties that all other normalization methods lack, though we also show how different assumptions could lead one to axiomatize alternative statistical normalization methods. (shrink)

We give a probabilistic justification of the shape of one of the probability weighting functions used in Prospect Theory. To do so, we use an idea recently introduced by Herzog and Hertwig. Along the way we also suggest a new method for the aggregation of probabilities using statistical distances.

Chow's endorsement of a limited role for null hypothesis significance testing is a needed corrective of research malpractice, but his decision to place this procedure in a hypothetico-deductive framework of Popperian cast is unwise. Various failures of this version of the hypothetico-deductive method have negative implications for Chow's treatment of significance testing, meta-analysis, and theory evaluation.

In this paper, a problem for utility theory - that it would have an agent who was compelled to play “Russian Roulette’ with one revolver or another, to pay as much to have a six-shooter with four bullets relieved of one bullet before playing with it, as he would be willing to pay to have a six-shooter with two bullets emptied - is reviewed. A less demanding Bayesian theory is described, that would have an agent maximize expected values (...) of possible total consequence of his actions. And utility theory is located within that theory as valid for agents who satisfy certain formal conditions, that is, for agents who are, in terms of that more general theory, indifferent to certain dimensions of ‘risk’. Raiffa- and Savage-style arguments for its more general validity are then resisted. Addenda are concerned with implications for game theory, and relations between ‘utilities’ and ‘values’. (shrink)

Fictional models in science -- The hypothetical versus the fictional -- What is wrong with the new fictionalism of scientific models? -- Re-inflating the conception of scientific representation -- Idealization, confirmation, and scientific realism -- Laws and models in a theory of idealization -- Approximation and its measures -- Approximation, idealization, and the laws of nature -- Coordination of space and unity of science -- Gauge gravity and the unification of natural forces -- Models and theories II: issues and (...) applications -- Models and theories I: the semantic view revisited -- Explaining quantum spontaneous symmetry breaking -- Review: symmetries in physics: philosophical reflections -- Classical spontaneous symmetry breaking -- Spontaneous symmetry breaking and chance in a classical world -- Approximations, idealizations, and models in statistical mechanics -- Infinite systems in SM explanations: thermodynamic limit, renormalization (semi-) groups, and irreversibility -- Explaining the emergence of cooperative phenomena -- A possible unified understanding of probabilistic objects -- Particularism and holism revisited -- Potential, propensity and categorical realism -- The Aharonov-Bohm effect and the reality of wave packets -- Realism and spacetime: of arguments against metaphysical realism and manifold realism -- Gauge invariance, Cauchy problem, indeterminism, and symmetry breaking -- Is there a relativistic thermodynamics? a case study of the meaning of special relativity. (shrink)

This paper examines the subversive role of statistics paleontology at the end of the 19th and the beginning of the 20th centuries. In particular, I will focus on German paleontology and its relationship with statistics. I argue that in paleontology, the quantitative method was questioned and strongly limited by the first decade of the 20th century because, as its opponents noted, when the fossil record is treated statistically, it was found to generate results openly in conflict with the Darwinian (...) class='Hi'>theory of evolution. Essentially, statistics questions the gradual mode of evolution and the role of natural selection. The main objections to statistics were addressed during the meetings at the Kaiserlich-Königliche Geologische Reichsanstalt in Vienna in the 1880s. After having introduced the statistical treatment of the fossil record, I will use the works of Charles Léo Lesquereux, Joachim Barrande, and Henry Shaler Williams to compare the objections raised in Vienna with how the statistical treatment of the data worked in practice. Furthermore, I will discuss the criticisms of Melchior Neumayr, one of the leading German opponents of statistical paleontology, to show why, and to what extent, statistics were questioned in Vienna. The final part of this paper considers what paleontologists can derive from a statistical notion of data: the necessity of opening a discussion about the completeness and nature of the paleontological data. The Vienna discussion about which method paleontologists should follow offers an interesting case study in order to understand the epistemic tensions within paleontology surrounding Darwin’s theory as well as the variety of non-Darwinian alternatives that emerged from the statistical treatment of the fossil record at the end of the 19th century. (shrink)

To whatever extent the use of a behavioral, not an evidential, interpretation of decisions in the Lindley-Savage argument for Bayesian theory undermines its cogency as a criticism of typical standard practice, it also undermines the Neyman-Pearson theory as a support for typical standard practice. This leaves standard practice with far less theoretical support than Bayesian methods. It does nothing to resolve the anomalies and paradoxes of standard methods. (Similar statements apply to the common protestation that the (...) models are not real anyway.) The appropriate interpretation of tests as evidence, if possible at all, is difficult and counterintuitive. Any attempt to support tests as more than rules of thumb is doomed to failure. (shrink)

We owe most scientific knowledge to methods of inquiry that are never formally analyzed. The analysis of behavior does not call for hypothetico-deductive methods. Statistics, taught in lieu of scientific method, is incompatible with major features of much laboratory research. Squeezing significance out of ambiguous data discourages the more promising step of scrapping the experiment and starting again. As a consequence, psychologists have taken flight from the laboratory. They have fled to Real People and the human interest of (...) “real life,” to Mathematical Models and the elegance of symbolic treatments, to the Inner Man and the explanatory preoccupation with inferred internal mechanisms, and to Laymanship and its appeal to “common sense.” An experimental analysis provides an alternative to these divertissements.The “theories” to which objection is raised here are not the basic assumptions essential to any scientific activity or statements that are not yet facts, but rather explanations which appeal to events taking place somewhere else, at some other level of observation, described in different terms, and measured, if at all, in different dimensions. Three types of learning theories satisfy this definition: physiological theories attempting to reduce behavior to events in the nervous system; mentalistic theories appealing to inferred inner events; and theories of the Conceptual Nervous System offered as explanatory models of behavior. It would be foolhardy to deny the achievements of such theories in the history of science. The question of whether they are necessary, however, has other implications.Experimental material in three areas illustrates the function of theory more concretely. Alternatives to behavior ratios, excitatory potentials, and so on demonstrate the utility of rate or probability of response as the basic datum in learning. Functional relations between behavior and environmental variables provide an account of why learning occurs. Activities such as preferring, choosing, discriminating, and matching can be dealt with solely in terms of behavior, without referring to processes in another dimensional system. The experiments are not offered as demonstrating that theories are not necessary but to suggest an alternative. Theory is possible in another sense. Beyond the collection of uniform relationships lies the need for a formal representation of the data reduced to a minimal number of terms. A theoretical construction may yield greater generality than any assemblage of facts; such a construction will not refer to another dimensional system. (shrink)

The New Economic Windows Series, derived from Massimo Salzano's ideas and work, incorporates material from textbooks, monographs and conference proceedings that deals with both the theoretical and applied aspects of various sub-disciplines ...

Social judgement theory is particularly well suited to the study of medical judgements. Medical judgements characteristically involve decision making under uncertainty with inevitable error and an abundance of fallible cues. In medicine, as in other areas, SJT research has found wide variation among decision makers in their judgements and in the weighting of clinical information. Strategies inferred from case vignettes differ from physicians' self-described strategies and from the weights suggested by experts. These observations parallel recent findings of unexplained variation (...) in diagnosis and management in clinical practice that have been the source of concern in the medical community. The lens model provides one of the few methods for quantitatively analysing physicians' judgements. Contrary to what one might expect from the variation in strategies on paper cases, several studies suggest that, in practice, physicians' diagnostic judgements are highly accurate. Cognitive feedback has been less successful as a practical teaching tool than originally hoped, but some aspects of this methodology show promise, particularly in conjunction with the increasing emphasis on statistical decision support. All things considered, SJT has provided insight into physicians' decisions and gives the medical research community important tools for studying judgements in actual practice. (shrink)

This essay proposes a design perspective on argumentation, intended as complementary to empirical and critical scholarship. In any substantive domain, design can provide insights that differ from those provided by scientific or humanistic perspectives. For argumentation, the key advantage of a design perspective is the recognition that humanity’s natural capacity for reason and reasonableness can be extended through inventions that improve on unaided human intellect. Historically, these inventions have fallen into three broad classes: logical systems, scientific methods, and disputation (...) frameworks. Behind each such invention is a specifiable “design hypothesis”: an idea about how to decrease error or how to increase the quality of outcomes from reasoning. As problems in contemporary argumentation practice become more complex, design thinking rises in relevance and importance. A design research agenda in argumentation would focus on theorizing design innovations and on evaluating design hypotheses. (shrink)

Peirce is often credited with having formulated a pragmatic theory of truth. This can be misleading, if it is assumed that Peirce was chiefly interested in providing a metaphysical analysis of the immediate conditions under which a belief or proposition is true, or the conditions under which a proposition or belief is said to be madetrue. Cheryl Misak has exposed the subtleties in Peirce's discussion of truth, especially showing the difficulties faced by any ascription to him of an analytic (...) definition of truth. In this paper I follow Misak in urging that Peirce's contribution to the philosophical discussion about the nature of truth was not of that kind. What makes his pragmatic approach distinctive is that rather than attempting to state the nature of truth per se, it attempts to uncover the beliefs and expectations we commit ourselves to when we make specific claims that such and such is true or is the case. (shrink)

The book attempts to build a bridge across three cultures: mathematical statistics, quantum theory and chemometrical methods.

Escalation of commitment causes people to continue a failing course of action. We study the role of construal level in such escalation of commitment. Consistent with the widely held view of construal level as a primed effect, we employed a commonly used prime for manipulating this construct in a laboratory experiment. Our findings revealed that the prime failed to produce statistically significant differences in construal level, which was measured using the Behavior Identification Form. Furthermore, there was no effect of the (...) prime on escalation of commitment, or on constructs that have previously been linked to construal level such as the perceived importance of feasibility considerations relative to desirability considerations, and the number of pros and cons that subjects can think of.Interestingly, however, subjects’ actual construal level scores on the BIF were found to significantly affect escalation. Specifically, our findings show that people with a low construal level are less willing to continue a failing project. This relation is mediated by the perceived importance of the feasibility of the project relative to its desirability. For people with a low construal level, the perceived importance of feasibility relative to desirability is higher, which in turn makes escalation of commitment less likely.Our findings are consistent with prior research suggesting that priming construal level may not always be effective. Thus, while construal level has typically been regarded as a state-like variable that can be primed, in this study, we show that construal level may also be considered as a trait. (shrink)

In _Reliable Reasoning_, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory, the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts (...) to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT. (shrink)

The Philosophy of Quantitative Methods undertakes a philosophical examination of a number of important quantitative research methods within the behavioral sciences in order to overcome the non-critical approaches typically provided by textbooks. These research methods are exploratory data analysis, statistical significance testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. Further readings are provided to extend the reader's overall understanding of these methods.

According to the Bayesian view, scientific hypotheses must be appraised in terms of their posterior probabilities relative to the available experimental data. Such posterior probabilities are derived from the prior probabilities of the hypotheses by applying Bayes'theorem. One of the most important problems arising within the Bayesian approach to scientific methodology is the choice of prior probabilities. Here this problem is considered in detail w.r.t. two applications of the Bayesian approach: (1) the theory of inductive probabilities (TIP) developed by (...) Rudolf Carnap and other epistomologists and (2) the analysis of the multinational inferences provided by Bayesian statstics (BS). ... Zie: Summary. (shrink)

This essay demonstrates a previously unnoticed connection between formal and statistical learning theory with regard to Nelson Goodman’s new riddle of induction. Discussions of Goodman’s riddle in formal learning theory explain how conjecturing “all green” before “all grue” can enhance efficient convergence to the truth, where efficiency is understood in terms of minimizing the maximum number of retractions or “mind changes.” Vapnik-Chervonenkis (VC) dimension is a central concept in statistical learning theory and is similar to (...) Popper’s notion of degrees of testability. I show that for a class inductive problems of which Goodman’s riddle is one example, a reliable inductive method minimizes the maximum number of mind changes exactly if it always conjectures the hypothesis from the set with lowest VC dimension consistent with the data. I also discuss the relevance of these results to language invariance and curve fitting. (shrink)

Prospect theory emerged as one of the leading descriptive decision theories that can rationalize a large body of behavioral regularities. The methods for eliciting prospect theory parameters, such as its value function and probability weighting, are invaluable tools in decision analysis. This paper presents a new simple method for eliciting prospect theory’s value function without any auxiliary/simplifying parametric assumptions. The method is applicable both to choice under ambiguity (Knightian uncertainty) and risk (when events are characterized by (...) objective probabilities). Our new elicitation method is implemented in a simple paper-and-pencil experiment (via an iterative multiple price list format). This is one of the first experiments that elicits non-parametric prospect theory’s value function with salient rewards. The collected data generally confirm findings in the existing literature: the value function is S-shaped (concave in the gain domain and convex in the loss domain) though there is a weaker loss aversion on the aggregate level and a substantial heterogeneity in loss aversion on the individual level (41% loss averse, 6% loss neutral and 53% gain seeking). (shrink)

Theories and Methods is the one compulsory unit on the AEB and Interboard syllabuses. This guide outlines the main sociological perspectives, and discusses three main approaches: positivism, phenomenology and critical social research. The topic-book format should be suitable for linear and modular courses, and there are sample questions and skills advice.

This paper generalizes the simple regret model by Bell in Operations Research 30(5), 961-981 and Loomes and Sugden in The Economic Journal 92(368), 805-824 to cope with the situation in which decision outcomes are multi-attributed. We propose a model that combines the simple regret model for ex ante preferences and the additive difference representation for ex post preferences. We first present a necessary and sufficient axiomatization of our model in Savage’s framework. The proposed model is composed of three types of (...) functions. One is a value function for each attribute. The others are attribute-dependent and holistic regret-rejoicing functions that capture trade-offs among value-differences of chosen and forgone outcomes for each attribute and among all attributes, respectively. We then provide constructive procedures for those functions and methods for consistency checks of the procedures. We, finally, conduct an experiment to estimate those functions in the two attributes case by means of the constructive procedure and give an application of the model and our findings. (shrink)

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in (...) text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. (shrink)

My aim in this paper is to explain how universal statements, as they occur in scientific theories, are actually tested by observational evidence, and to draw certain conclusions, on that basis, about the way in which scientific theories are tested in general. 1 But I am pursuing that aim, ambitious enough in and of itself, in the service of even more ambitious projects, and in the first place: (a) to say what is distinctive about modern science, and especially modern physical (...) science, as a human intellectual activity; and (b) to show how this distinctiveness explains the unique status of modern science in human intellectual life. So I will begin by saying a few words about that larger project. One might doubt, first, whether that project is legitimate. Although everything is different from everything else, the question “What is distinctive about X?” is not necessarily well put, because X may not be anything—that is, anything distinct. And there are indeed many philosophers, otherwise of the most diverse intellectual backgrounds and tendencies, who would deny, in various ways, that modern science is a distinct thing, or that its status is unique. It seems to me that they deny something terrifyingly obvious— a fact which confronts us far more urgently than the fact that, say, ravens are black. I will put off further remarks about this until the end of the paper, however, because the discussion of my more limited present aim will focus precisely on the ways to tell when a question is well put, and whether there is such a (distinct) thing as X. That one’s methodological problems are also the subject of one’s investigation is a sign that that investigation is philosophical, although (or because) also a threat to its coherence. Second, some points about methodology. I am not a sociologist, or even a historian, nor (unlike some recent philosophers of science) will I pretend to be.. (shrink)

A comparison of Neyman's theory of interval estimation with the corresponding subjective Bayesian theory of credible intervals shows that the Bayesian approach to the estimation of statistical parameters allows experimental procedures which, from the orthodox objective viewpoint, are clearly biased and clearly inadmissible. This demonstrated methodological difference focuses attention on the key difference in the two general theories, namely, that the orthodox theory is supposed to provide a known average frequency of successful estimates, whereas the Bayesian (...) account provides only a coherent ordering of degrees of belief and a subsequent maximization of subjective expected utilities. To rebut the charge of allowing biased procedures, the Bayesian must attack the foundations of orthodox, objectivist methods. Two apparently popular avenues of attack are briefly considered and found wanting. The first is that orthodox methods fail to apply to the single case. The second is that orthodox methods are subject to a typical Humean regress. The conclusion is that orthodox objectivist methods remain viable in the face of the subjective Bayesian alternative — at least with respect to the problem of statistical estimation. (shrink)

This article explores some open questions related to the problem of verification of theories in the context of empirical sciences by contrasting three epistemological frameworks. Each of these epistemological frameworks is based on a corresponding central metaphor, namely: (a) Neo-empiricism and the gambling metaphor; (b) Popperian falsificationism and the scientific tribunal metaphor; (c) Cognitive constructivism and the object as eigen-solution metaphor. Each of one of these epistemological frameworks has also historically co-evolved with a certain statistical theory and method (...) for testing scientific hypotheses, respectively: (a) Decision theoretic Bayesian statistics and Bayes factors; (b) Frequentist statistics and p-values; (c) Constructive Bayesian statistics and e-values. This article examines with special care the Zero Probability Paradox (ZPP), related to the verification of sharp or precise hypotheses. Finally, this article makes some remarks on Lakatos’ view of mathematics as a quasi-empirical science. (shrink)

Researchers rely on psychometric principles when trying to gain understanding of unobservable psychological phenomena disconfounded from the methods used. Psychometric models provide us with tools to support this endeavour, but they are agnostic to the meaning researchers intend to attribute to the data. We define method effects as resulting from actions which weaken the psychometric structure of measurement, and argue that solution to this confounding will ultimately rest on testing whether data collected fit a psychometric model based on a (...) substantive theory, rather than a search for a model that best fits the data. We highlight the importance of taking the notions of fundamental measurement seriously by reviewing distinctions between the Rasch measurement model and more generalised 2PL and 3PL IRT models. We then present two lines of research that highlight considerations of making method effects explicit in experimental designs. First, we contrast the use of experimental manipulations to study measurement reactivity during the assessment of metacognitive processes with factor-analytic research of the same. The former suggests differential performance-facilitating and -inhibiting reactivity as a function of other individual differences, whereas factor-analytic research suggests a ubiquitous monotonically predictive confidence factor. Second, we evaluate differential effects of context and source on within-individual variability indices of personality derived from multiple observations, highlighting again the importance of a structured and theoretically grounded observational framework. We conclude by arguing that substantive variables can act as method effects and should be considered at the time of design rather than after the fact, and without compromising measurement ideals. (shrink)

This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a concept (...) introduced in the book's final section. This monograph will be of interest to philosophers and historians of science and students in related fields. Despite the mathematical nature of the topic, no statistical background is required, making the book a valuable read for anyone interested in the history of statistics and human cognition. (shrink)

The self-avoiding walk is a classical model in statistical mechanics, probability theory and mathematical physics. It is also a simple model of polymer entropy which is useful in modelling phase behaviour in polymers. This monograph provides an authoritative examination of interacting self-avoiding walks, presenting aspects of the thermodynamic limit, phase behaviour, scaling and critical exponents for lattice polygons, lattice animals and surfaces. It also includes a comprehensive account of constructive methods in models of adsorbing, collapsing, and pulled (...) walks, animals and networks, and for models of walks in confined geometries. Additional topics include scaling, knotting in lattice polygons, generating function methods for directed models of walks and polygons, and an introduction to the Edwards model.This essential second edition includes recent breakthroughs in the field, as well as maintaining the older but still relevant topics. New chapters include an expanded presentation of directed models, an exploration of methods and results for the hexagonal lattice, and a chapter devoted to the Monte Carlo methods. (shrink)

A. W. F. Edwards is one of the most influential mathematical geneticists in the history of the discipline. One of the last students of R. A. Fisher, Edwards pioneered the statistical analysis of phylogeny in collaboration with L. L. Cavalli-Sforza, and helped establish Fisher's concept of likelihood as a standard of statistical and scientific inference. In this book, edited by philosopher of science Rasmus Grønfeldt Winther, Edwards's key papers are assembled alongside commentaries by leading scientists, discussing Edwards's influence (...) on their own research and on thinking in their field overall. In an extensive interview with Winther, Edwards offers his thoughts on his contributions, their legacy, and the context in which they emerged. This book is a resource both for anyone interested in the history and philosophy of genetics, statistics, and science, and for scientists seeking to develop new algorithmic and statistical methods for understanding the genetic relationships between and among species both extant and extinct. (shrink)

The problem of how mathematics and physics are related at a foundational level is of interest. The approach taken here is to work towards a coherent theory of physics and mathematics together by examining the theory experiment connection. The role of an implied theory hierarchy and use of computers in comparing theory and experiment is described. The main idea of the paper is to tighten the theory experiment connection by bringing physical theories, as mathematical structures (...) over C, the complex numbers, closer to what is actually done in experimental measurements and computations. The method replaces C by C n which is the set of pairs, R n,I n, of n figure rational numbers in some basis. The properties of these numbers are based on those of numerical measurement outcomes for continuous variables. A model of space and time based on R n is discussed. The model is scale invariant with regions of constant step size interrupted by exponential jumps. A method of taking the limit n→∞ to obtain locally flat continuum-based space and time is outlined. Also R n based space is invariant under scale transformations. These correspond to expansion and contraction of space relative to a flat background. The location of the origin, which is a space and time singularity, does not change under these transformations. Some properties of quantum mechanics, based on C n and on R n space are briefly investigated. (shrink)

The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...) around tossing a coin with unknown bias and then placing bets. In this simulation, we programmed four players, inspired by Bayesian statistics, frequentist statistics, Jon Williamson’s version of Objective Bayesianism, and a player who simply extrapolates from observed frequencies to general frequencies. The last player functions as a benchmark: a statistical methodology should at least outperform a crude form of induction. We focused on the performance of these methodologies in guiding the players towards good decisions. Unlike an earlier simulation study of this type, we found no systematic difference in performance between the Bayesian and frequentist players, provided the Bayesian used a flat prior and the frequentist used a low confidence level. Unlike that study, we were able to use Big Data methods to mitigate problems of random error in the simulation results. The Williamsonian player, who is a novel element of our study, also had no systematic differences in their performance, provided that they use a low confidence level. These players performed similarly even in the very short run, when players were making different decisions. Our study indicates that all three methodologies should be taken seriously by philosophers and practitioners of statistics. However, the frequentist and Williamsonian performed poorly when their confidence levels were high, and the Bayesian was surprisingly harmed by biased priors, providing some unexpected lessons for these methodologies when facing this type of decision problem. (shrink)

Experimenters sometimes insist that it is unwise to examine data before determining how to analyze them, as it creates the potential for biased results. I explore the rationale behind this methodological guideline from the standpoint of an error statistical theory of evidence, and I discuss a method of evaluating evidence in some contexts when this predesignation rule has been violated. I illustrate the problem of potential bias, and the method by which it may be addressed, with an example (...) from the search for the top quark. A point in favor of the error statistical theory is its ability, demonstrated here, to explicate such methodological problems and suggest solutions, within the framework of an objective theory of evidence. (shrink)

The lack of "progress" in theory is often contrasted to progress in statistical methodology. The relation between the two bodies of thinking is itself problematic, however, for the particular advances in method that have occurred in quantitative sociology reflect a trade-off in which the results are characterized by the radical underdetermination of models by data and a high level of slack between measures and theoretical concepts. Both of these problems are usually understood as matters of "error," and thus (...) as potentially eliminable, but this claim is highly questionable, on grounds evident from Pearson's philosophy of science. The implications of large ineliminable error for the project of theory construction, particularly the "formal theory" of Blau and the California Positivists, is discussed. The ease of producing statistical results is an achievement, but one that leads away from theory rather than toward it, and, because the fact of underdetermination prevents these results from establishing clear cognitive superiority over the results of other "approaches," theoretical diversity is, in a sense, a consequence of the properties of the dominant statistical tradition in sociology. (shrink)

Many biologists, especially in ecology and evolution, analyze their data by estimating fits to a set of candidate models and selecting the best model according to the Akaike Information Criterion (AIC) or the Bayesian Information Criteria (BIC). When the candidate models represent alternative hypotheses, biologists may want to limit the chance of a false positive to a specified level. Existing model selection methodology, however, allows for only indirect control over error rates by setting a threshold for the difference in AIC (...) scores. We present a novel theoretical framework for parametric Neyman-Pearson (NP) model selection using information criteria that does not require a pre-data null and applies to three or more non-nested models simultaneously. We apply the theoretical framework to the Error Control for Information Criteria (ECIC) procedure introduced by Cullan et al. (J Appl Stat 47: 2565–2581, 2019), and we show it shares many of the desirable properties of AIC-type methods, including false positive and negative rates that converge to zero asymptotically. We discuss implications for the compatibility of evidentialist and severity-based approach to evidence in philosophy of science. (shrink)

Econometric Theory and Methods International Edition provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich (...) covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation. (shrink)

Research in recent years suggests that fairness concerns could mitigate hold-up problems. In this study, we report theoretical analysis and experimental evidence on an opposite possibility: that fairness concerns could also induce hold-up problems. In our setup, hold-up problems will not occur with purely self-interested agents, but theoretically could be induced by demand for distributional fairness among agents without sufficiently strong counteracting factors such as intention-based reciprocity. We observe a widespread occurrence of hold-up in our experiment. Relationship-specific investments occurred less (...) than half of the time, resulting in significant inefficiencies. Moreover, whenever a relationship-specific investment was made: (a) it was typically not reciprocated by the partner; (b) nor did the investor’s offers at the bargaining stage exhibit expectations for reciprocity. Consequently, the partner extracted all the additional expected payoff from relationship-specific investments. Further experimentation suggested that our results were driven by a fundamental lack of intention-based reciprocity in fairness concerns, rather than self-serving bias. (shrink)

This paper presents four theorems that connect continuity postulates in mathematical economics to solvability axioms in mathematical psychology, and ranks them under alternative supplementary assumptions. Theorem 1 connects notions of continuity (full, separate, Wold, weak Wold, Archimedean, mixture) with those of solvability (restricted, unrestricted) under the completeness and transitivity of a binary relation. Theorem 2 uses the primitive notion of a separately continuous function to answer the question when an analogous property on a relation is fully continuous. Theorem 3 provides (...) a portmanteau theorem on the equivalence between restricted solvability and various notions of continuity under weak monotonicity. Finally, Theorem 4 presents a variant of Theorem 3 that follows Theorem 1 in dispensing with the dimensionality requirement and in providing partial equivalences between solvability and continuity notions. These theorems are motivated for their potential use in representation theorems. (shrink)

In educational measurement, exploring the method of generating multiple high-quality parallel tests has become a research hotspot. One purpose of this research is to construct parallel forms item by item according to a seed test, using two proposed item selection heuristic methods [minimum parameters–information–distance method and minimum information–parameters–distance method ]. Moreover, previous research addressing test assembly issues has been limited mainly to situations in which the information curve of the item pool or seed test has a normal or skewed (...) distribution. However, in practice, the distributions of information curves for tests are diverse. These include multimodal distributions, the most common type of which is the bimodal distribution. Therefore, another main aim of this article is to extend the information curves of unimodal distributions to bimodal distributions. Thus, this study adopts simulation research to compare the results of two item, response, theory -based item matching methods using different information curve distributions for item pools or seed tests. The results show that the MPID and MIPD methods yield rather good performance in terms of both two statistical targets when the information curve has a unimodal distribution, and two new methods yield better performance than two existing methods in terms of test information functions target when the information curve has a bimodal distribution. (shrink)

Critical Thinking in Clinical Research explains the fundamentals of clinical research in a case-based approach. The core concept is to combine a clear and concise transfer of information and knowledge with an engagement of the reader to develop a mastery of learning and critical thinking skills. The book addresses the main concepts of clinical research, basics of biostatistics, advanced topics in applied biostatistics, and practical aspects of clinical research, with emphasis on clinical relevance across all medical specialties.