Image similarity metric, also known as metric learning in computer vision, is a significant step in various advanced image tasks. Nevertheless, existing well-performing approaches for image similarity measurement only focus on the image itself without utilizing the information of other modalities, while pictures always appear with the described text. Furthermore, those methods need human supervision, yet most images are unlabeled in the real world. Considering the above problems comprehensively, we present a novel visual similarity metric model named PTF-SimCM. It (...) adopts a self-supervised contrastive structure like SimSiam and incorporates a multimodal fusion module to utilize textual modality correlated to the image. We apply a cross-modal model for text modality rather than a standard unimodal text encoder to improve late fusion productivity. In addition, the proposed model employs Sentence PIE-Net to solve the issue caused by polysemous sentences. For simplicity and efficiency, our model learns a specific embedding space where distances directly correspond to the similarity. Experimental results on MSCOCO, Flickr 30k, and Pascal Sentence datasets show that our model overall outperforms all the compared methods in this work, which illustrates that the model can effectively address the issues faced and enhance the performances on unsupervised visual similarity measuring relatively. (shrink)

As a relatively young profession, public relations seeks a realistic ethics foundation. A continuing debate in public relations has pitted journalistic/objectivity ethics against the advocacy ethics that may be more appropriate in an adversarial society. As the journalistic/objectivity influence has waned, the debate has evolved, pitting the advocacy/adversarial foundation against the two-way symmetrical model of public relations, which seeks to build consensus and holds that an organization itself, not an opposing public, sometimes may need to change to build a (...) productive relationship. A similar battle between adversarial advocacy and symmetry occurred during the emergence of rhetoric in the Athens of the 4th century B.C. Plato and Aristotle favored adversarial/advocacy rhetoric, whereas Isocrates favored a symmetrical rhetoric. Four criteria of comparison of those rhetorics are examined: success of the respective schools, success of the respective graduates, the evaluation of later Roman rhetoricians, and the impact on the future of education. History shows that Isocrates's symmetrical rhetoric clearly was more effective than its adversarial/advocacy rivals. Recent studies of the two-way symmetrical model concur, indicating that it may well be the most effective foundation for public relations ethics. (shrink)

This paper compares and contrasts three groups that conducted biological research at Yale University during overlapping periods between 1910 and 1970. Yale University proved important as a site for this research. The leaders of these groups were Ross Granville Harrison, Grace E. Pickford, and G. Evelyn Hutchinson, and their members included both graduate students and more experienced scientists. All produced innovative research, including the opening of new subfields in embryology, endocrinology and ecology respectively, over a long period of time. Harrison's (...) is shown to have been a classic research school; Pickford's and Hutchinson's were not. Pickford's group was successful in spite of her lack of departmental or institutional position or power. Hutchinson and his graduate and post-graduate students were extremely productive but in diverse areas of ecology. His group did not have one focused area of research or use one set of research tools. The paper concludes that new models for research groups are needed, especially for those, like Hutchinson's, that included much field research. (shrink)

This paper reviews computational models of surprise, with a specific focus on the authors’ probabilistic, contrast model. The contrast model casts surprise, and its intensity, as emerging from the difference between the probability of the surprising event and the probability of the highest expected‐event in a given situation. Strong arguments are made for the central role of surprise in creativity and learning by natural and artificial agents.

Mathematical models provide explanations of limited power of specific aspects of phenomena. One way of articulating their limits here, without denying their essential powers, is in terms of contrastive explanation.

How do philosophers of science make use of historical case studies? Are their accounts of historical cases purpose-built and lacking in evidential strength as a result of putting forth and discussing philosophical positions? We will study these questions through the examination of three different philosophical case studies. All of them focus on modeling and on Vito Volterra, contrasting his work to that of other theoreticians. We argue that the worries concerning the evidential role of historical case studies in philosophy are (...) partially unfounded, and the evidential and hermeneutical roles of case studies need not be played against each other. In philosophy of science, case studies are often tied to conceptual and theoretical analysis and development, rendering their evidential and theoretic/hermeneutic roles intertwined. Moreover, the problems of resituating or generalizing local knowledge are not specific to philosophy of science but commonplace in many scientific practices—which show similarities to the actual use of historical case studies by philosophers of science. (shrink)

It is widely recognized that scientific theories are often associated with strictly inconsistent models, but there is little agreement concerning the epistemic consequences. Some argue that model inconsistency supports a strong perspectivism, according to which claims serving as interpretations of models are inevitably and irreducibly perspectival. Others argue that in at least some cases, inconsistent models can be unified as approximations to a theory with which they are associated, thus undermining this kind of perspectivism. I examine the arguments for (...) perspectivism, and contend that its strong form is defeasible in principle, not merely in special cases. The argument rests on the plausibility of scientific knowledge concerning non-perspectival, dispositional facts about modelled systems. This forms the basis of a novel suggestion regarding how to understand the knowledge these models afford, in terms of a contrastive theory of what-questions. (shrink)

In the final decades of the 20th century, the majority of modern Sinophone scholars believed that Confucianism was an outdated and obsolete ideology that was not only unsuitable for the development...

Chakravartty puts forward a view of scientific knowledge that conceives of properties attributed to objects by scientific models as dispositions. Those dispositions refer to the capacity of an object to behave differently in different circumstances. This pluralism of behaviour is intended to show that perspectivalism does not exclude the possibility of non-perspectival knowledge. To support this claim, he offers an analogy between conflicting models and contrastive explanations. I examine the strength of the purported analogy between conflicting models and contrastive explanations. (...) Then, I analyse this dispositionalist account of properties in order to assess whether ontological pluralism can meet the challenge that conflicting models pose to the scientific realism. To conclude, I contend that to accomplish this goal, a more detailed account of dispositional properties should be provided, as well as a theory of explanatory relevance. (shrink)

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...) as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice. (shrink)

We often explain by citing an absence or an omission. Apart from the problem of assigning a causal role to such apparently negative factors as absences and omissions, there is a puzzle as to why only some absences and omissions, out of indefinitely many, should figure in explanations. In this paper we solve this ’many absences problem’ by using the contrastive model of explanation. The contrastive model of explanation is developed by adapting Peter Lipton’s account. What initially appears (...) to be only a trivial amendment to Lipton’s Difference Condition enables us both to offer a much more satisfactory solution to the ’many absences problem’ than David Lewis did, and also to explain why explanation in terms of absences and omissions should be so common. (shrink)

The study investigated the influence of resilience and dispositional optimism on, first, emotional distress and, second, the intention to self-isolate, experienced by people with a lower and higher illness risk, during the lockdown imposed in Spain during the first COVID-19 wave. These effects were investigated against the background of the Health Belief Model. A convenience sample of N = 325 participants completed an online survey including an ad-hoc questionnaire measuring the HBM core factors: Perceived health threat, and perceived quarantine (...) benefits and costs. Self-efficacy and perceived social pressure were also measured. Based on reviews regarding pandemic outbreaks, quarantine benefits were conceptualized as the perceived effectiveness and solidary contribution of self-isolating in line with the quarantine protocols. Quarantine “psychosocial” costs were conceptualized as a composite of perceived boredom, loneliness, and economic concerns. Findings revealed an asymmetrical pattern of results so that people at higher risk were more distressed by the perceived severity of getting infected whereas people at lower risk were more distressed by the psychosocial costs. Moreover, resilience and optimism were more “protective” against distress within the lower and higher risk groups, respectively. In addition, quarantine benefits and self-efficacy promoted the intention to self-isolate within both groups. However, optimism hindered such intention. This finding is discussed in the light of links between dispositional optimism and optimistic bias; the underestimation of experiencing negative events, which can relax the perceived health risk. Based on these findings, communication campaigns should prioritize information about the effectiveness of the implemented preventive behaviors rather than the costs of not implementing them, and be cautionary in encouraging excessive optimism. (shrink)

I argue that the contrast between models and theories is important for public policy issues. I focus especially on the way a mathematical model explains just one aspect of the data.

Explanatory contrastivists hold that we often explain phenomena of the form p rather than q. In this paper, I present a new, social‐epistemological model of contrastive explanation—accountabilism. Specifically, my view is inspired by social‐scientific research that treats explanations fundamentally as accounts; that is, communicative actions that restore one's social status when charged with questionable behaviour. After developing this model, I show how accountabilism provides a more comprehensive model of contrastive explanation than the causal models of contrastive explanation (...) that are currently en vogue. (shrink)

This book reports on the contrastive-semantic investigation of sadness expressions between English and Chinese, based on two monolingual general corpora and a parallel corpus. The exploration adopts a unique theoretical approach which integrates corpus-linguistic theories on meaning (as a social construct, usage and paraphrase) with a corpus-linguistic lexical model. It employs a new complex but workable methodology which combines computational tools with manual examination to tease meaning out of corpus evidence, to compare and contrast lexical items that do (...) not match up neatly between languages. It looks at sadness expressions both within and across languages in terms of three corpus-linguistic structural categories, i.e. colligation, collocation and semantic association/preference, and paraphrase (both explicit and implicit) to capture their subtle nuances of meaning, disclose the culture-specific conceptualisations encoded in them, and highlight their respective cultural distinctiveness of emotion. By presenting multidisciplinary original work, Sadness Expressions in English and Chinese will be of interest to researchers in corpus linguistics, contrastive lexical semantics, psychology, bilingual lexicography and language pedagogy. (shrink)

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of (...) a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda. (shrink)

In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate (...) an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object. (shrink)

I provide a theory of causation within the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. I suggest that, if this theory is true, then we should understand a cause as something which (...) transmits deviant or non-inertial behavior to its effect. (shrink)

According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids (...) many of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

Dennett, a philosopher, and Griffin, an ethologist, have recently presented influential arguments promoting the extended use of intentional language by students of animal behavior. This essay seeks to elucidate and to contrast the claims made by each of these authors, and to evaluate their proposals primarily from the perspective of a practicing comparative psychologist or ethologist. While Griffin regards intentional terms as explanatory, Dennett assigns them a descriptive function; the issue of animal consciousness is central to Griffin's program and (...) only tangentially related to Dennett's. The philosopher's arguments are founded upon a more coherent metaphysics, but Dennett neglects to substantiate his claim that animal competences can be most readily modelled by artificial intelligence specialists when they are described in intentional terms. Both authors assume that some examples of animal behavior should not be given an intentional characterization, but neither provides adequate guidelines for the identification of cases belonging to this negative set. (shrink)

Many models in economics are very unrealistic. At the same time, economists put a lot of effort into making their models more realistic. I argue that in many cases, including the Modigliani-Miller irrelevance theorem investigated in this paper, the purpose of this process of concretization is explanatory. When evaluated in combination with their assumptions, a highly unrealistic model may well be true. The purpose of relaxing an unrealistic assumption, then, need not be to move from a false model (...) to a true one. Instead, it may be providing an explanation of some phenomenon by invoking the factor that figures in the assumption. This idea is developed in terms of the contrastive account of explanation. It is argued that economists use highly unrealistic assumptions to determine a contrast that is worth explaining. The process of concretization also motivates new explanatory questions. A high degree of explanatory power, then, may well be due to a high number of unrealistic assumptions. Thus, highly unrealistic models can be powerful explanatory engines. Key Words: concretization • explanation • explanatory power • idealization • model • Modigliani-Miller theorem • unrealistic assumption. (shrink)

This paper constructs a model of metaphysical indeterminacy that can accommodate a kind of ‘deep’ worldly indeterminacy that arguably arises in quantum mechanics via the Kochen-Specker theorem, and that is incompatible with prominent theories of metaphysical indeterminacy such as that in Barnes and Williams (2011). We construct a variant of Barnes and Williams's theory that avoids this problem. Our version builds on situation semantics and uses incomplete, local situations rather than possible worlds to build a model. We evaluate (...) the resulting theory and contrast it with similar alternatives, concluding that our model successfully captures deep indeterminacy. (shrink)

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to (...) have different epistemic characters. Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion. (shrink)

[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...) was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

The thesis of underdetermination presents a major obstacle to the epistemological claims of scientific realism. That thesis is regularly assumed in the philosophy of science, but is puzzlingly at odds with the actual history of science, in which empirically adequate theories are thin on the ground. We propose to advance a case for scientific realism which concentrates on the process of scientific reasoning rather than its theoretical products. Developing an account of causal–explanatory inference will make it easier to resist the (...) thesis of underdetermination. For, if we are not restricted to inference to the best explanation only at the level of major theories, we will be able to acknowledge that there is a structure in data sets which imposes serious constraints on possible theoretical alternatives. We describe how Differential Inference, a form of inference based on contrastive explanation, can be used in order to generate causal hypotheses. We then go on to consider how experimental manipulation of differences can be used to achieve Difference Closure, thereby confirming claims of causal efficacy and also eliminating possible confounds. The model of Differential Inference outlined here shows at least one way in which it is possible to ‘reason from the phenomena’. (shrink)

ABSTRACT Contrastive and deviant/default accounts of causation are becoming increasingly common. However, discussions of these accounts have neglected important questions, including how the context determines the contrasts, and what shared knowledge is necessary for this to be possible. I address these questions, using organic chemistry as a case study. Focusing on one example—nucleophilic substitution—I show that the kinds of causal claims that can be made about an organic reaction depend on how the reaction is modelled, and argue that paying attention (...) to the various ways that reactions are modelled has important implications for our understanding of causation. _1_ Introduction _2_ General Contrastive Causal Claims in Organic Chemistry _3_ Deviant Causal Claims in Organic Chemistry _4_ Nucleophilic Substitution Reactions _5_ The Causal Modelling Tradition _5.1_ The type/token distinction _6_ Competing Reactions _6.1_ Type- and token-causal claims, variables, and values of variables _7_ Disambiguation of ‘Reaction’ _8_ Reaction Kinds _9_ Specific Reactions _9.1_ Specific reactions and token-causal claims _9.2_ Specific reactions and type-causal claims _10_ Implications _10.1_ Kinds of causal claim _10.2_ Contrastive and deviant causal claims _10.3_ Model relativity. (shrink)

In an influential paper, Wendy Parker argues that agreement across climate models isn’t a reliable marker of confirmation in the context of cutting-edge climate science. In this paper, I argue that while Parker’s conclusion is generally correct, there is an important class of exceptions. Broadly speaking, agreement is not a reliable marker of confirmation when the hypotheses under consideration are mutually consistent—when, e.g., we’re concerned with overlapping ranges. Since many cutting-edge questions in climate modeling require making distinctions between mutually consistent (...) hypotheses, agreement across models will be generally unreliable in this domain. In cases where we are only concerned with mutually exclusive hypotheses, by contrast, agreement across climate models is plausibly a reliable marker of confirmation. (shrink)

One striking feature of the contemporary modelling practice is its interdisciplinary nature. The same equation forms, and mathematical and computational methods, are used across different disciplines, as well as within the same discipline. Are there, then, differences between intra- and interdisciplinary transfer, and can the comparison between the two provide more insight on the challenges of interdisciplinary theoretical work? We will study the development and various uses of the Ising model within physics, contrasting them to its applications to socio-economic (...) systems. While the renormalization group methods justify the transfer of the Ising model within physics – by ascribing them to the same universality class – its application to socio-economic phenomena has no such theoretical grounding. As a result, the insights gained by modelling socio-economic phenomena by the Ising model may remain limited. (shrink)

We argue that an approach that treats short-term memory as activated long-term memory is not inherently in conflict with information recycling in a limited-capacity or working-memory store, or with long-term storage based on the processing in such a store. Language differences aside, real model differences can only be assessed when the contrasting models are formulated precisely.

A common argument for evidentialism is that the norms of assertion, specifically those bearing on warrant and assertability, regulate belief. On this assertoric model of belief, a constitutive condition for belief is that the believing subject take her belief to be supported by sufficient evidence. An equally common source of resistance to these arguments is the plausibility of cases in which a speaker, despite the fact that she lacks warrant to assert that p, nevertheless attributes to herself the belief (...) that p. In the following, I will outline a variety of ways a speaker may contrastively attribute a belief to herself. In light of what these contrastive statements communicate, cases of attributing beliefs with little or no warrant to oneself offer no substantive counter-example to the evidentialist argument from assertion. (shrink)

This book reports on the contrastive-semantic investigation of sadness expressions between English and Chinese, based on two monolingual general corpora and a parallel corpus. The exploration adopts a unique theoretical approach which integrates corpus-linguistic theories on meaning (as a social construct, usage and paraphrase) with a corpus-linguistic lexical model. It employs a new complex but workable methodology which combines computational tools with manual examination to tease meaning out of corpus evidence, to compare and contrast lexical items that do (...) not match up neatly between languages. It looks at sadness expressions both within and across languages in terms of three corpus-linguistic structural categories, i.e. colligation, collocation and semantic association/preference, and paraphrase (both explicit and implicit) to capture their subtle nuances of meaning, disclose the culture-specific conceptualisations encoded in them, and highlight their respective cultural distinctiveness of emotion. By presenting multidisciplinary original work, Sadness Expressions in English and Chinese will be of interest to researchers in corpus linguistics, contrastive lexical semantics, psychology, bilingual lexicography and language pedagogy. (shrink)

Philosophy and model theory frequently meet one another. Philosophy and Model Theory aims to understand their interactions -/- Model theory is used in every ‘theoretical’ branch of analytic philosophy: in philosophy of mathematics, in philosophy of science, in philosophy of language, in philosophical logic, and in metaphysics. But these wide-ranging appeals to model theory have created a highly fragmented literature. On the one hand, many philosophically significant mathematical results are found only in mathematics textbooks: these are (...) aimed squarely at mathematicians; they typically presuppose that the reader has a significant background in mathematics; and little clue is given as to their philosophical significance. On the other hand, the philosophical applications of these results are then scattered across disconnected pockets of papers, separated by decades or more. -/- The first aim of Philosophy and Model Theory, then, is to consider the philosophical uses of model theory. On a technical level, we try to show how philosophically significant results connect to one another, and also to state the best version of a result for philosophical purposes. On a philosophical level, we show how similar dialectical situations arise repeatedly, across fragmented debates in varied philosophical areas. -/- The second aim of Philosophy and Model Theory, though, is to consider the philosophy of model theory. Model theory itself is rarely taken as the subject matter of philosophising (contrast this, say, with the philosophy of biology, or the philosophy of set theory). But model theory is a beautiful part of pure mathematics, and worthy of philosophical study in its own right. (shrink)

This essay compares and contrasts nine different conceptual models of God: atheism, agnosticism, deism, theism, pantheism, polytheism, henotheism, panentheism, and eschatological panentheism. This essay justifies employment of the model method in theology based on commitments within philosophical hermeneutics, philosophy of science, and the theological understanding of divine transcendence. The result is an array of conceptual models of the divine which have reference, but which make indirect rather than literal claims. Of the analyzed models, this essay defends “eschatological panentheism” as (...) the most satisfying model for Christian constructive theology. This paper was delivered during the APA Pacific 2007 Mini-Conference on Models of God. (shrink)

A review of the literature indicates that linear models are frequently used in situations in which decisions are made on the basis of multiple codable inputs. These models are sometimes used normatively to aid the decision maker, as a contrast with the decision maker in the clinical vs statistical controversy, to represent the decision maker "paramorphically" and to "bootstrap" the decision maker by replacing him with his representation. Examination of the contexts in which linear models have been successfully employed (...) indicates that the contexts have the following structural characteristics in common: each input variable has a conditionally monotone relationship with the output; there is error of measurement; and deviations from optimal weighting do not make much practical difference. These characteristics ensure the success of linear models, which are so appropriate in such contexts that random linear models may perform quite well. 4 examples involving the prediction of such codable output variables as GPA and psychiatric diagnosis are analyzed in detail. In all 4 examples, random linear models yield predictions that are superior to those of human judges. (shrink)

We introduce ‘model migration’ as a species of cross-disciplinary knowledge transfer whereby the representational function of a model is radically changed to allow application to a new disciplinary context. Controversies and confusions that often derive from this phenomenon will be illustrated in the context of econophysics and phylogeographic linguistics. Migration can be usefully contrasted with concept of ‘imperialism’, that has been influentially discussed in the context of geographical economics. In particular, imperialism, unlike migration, relies upon extension of the (...) original model via an expansion of the domain of phenomena it is taken to adequately described. The success of imperialism thus requires expansion of the justificatory sanctioning of the original idealising assumptions to a new disciplinary context. Contrastingly, successful migration involves the radical representational re-interpretation of the original model, rather than its extension. Migration thus requires ‘re-sanctioning’ of new ‘counterpart idealisations’ to allow application to an entirely different class of phenomena. Whereas legitimate scientific imperialism should be based on the pursuit of some form of ontological unification, no such requirement is need to legitimate the practice of model migration. The distinction between migration and imperialism will thus be shown to have significant normative as well as descriptive value. (shrink)

Less salient, lower contrast disks appear to be more numerous than more salient, higher contrast disks when intermingled in equal numbers into the same display, but they are equal in perceived numerosity when segregated into different displays. Comparative judgements indicate that the apparent numerosity of the lower contrast disks is unaffected by being intermingled with high contrast disks, whereas the high contrast disks are reduced in numerosity by being intermingled with the low contrast ones. (...) Here, we report that this illusion also occurs for absolute judgements of the numerosities of displays of from 20 to 80 disks. A model based on luminance-difference contrast normalization explains the illusory loss of high-contrast items along with veridical perception of the low-contrast ones. The model correctly predicts that perceived numerosity is linearly related to the square-root of the number of disks, with the extent of the illusion depending on an attentionally-weighted function of contrast and assimilation. (shrink)

The paper introduces, compares and contrasts formal models of source reliability proposed in the epistemology literature, in particular the prominent models of Bovens and Hartmann and Olsson :127–143, 2011). All are Bayesian models seeking to provide normative guidance, yet they differ subtly in assumptions and resulting behavior. Models are evaluated both on conceptual grounds and through simulations, and the relationship between models is clarified. The simulations both show surprising similarities and highlight relevant differences between these models. Most importantly, however, our (...) evaluations reveal that important normative concerns arguably remain unresolved. The philosophical implications of this for testimony are discussed. (shrink)