Explaining with Models: The Role of Idealizations

Because they contain idealizations, scientific models are often considered to be misrepresentations of their target systems. An important question is therefore how models can explain the behaviours of these systems. Most of the answers to this question are representationalist in nature. Proponents of this view are generally committed to the claim that models are explanatory if they represent their target systems to some degree of accuracy; in other words, they try to determine the conditions under which idealizations can be made without jeopardizing the representational function of models. In this article, we first outline several forms of this representationalist view. We then argue that this view, in each of these forms, omits an important role of idealizations: that of facilitating the identification of the explanatory components within a model. Via examination of a case study from contemporary astrophysics, we show that one way in which idealizations can do this is by creating a comparison case that serves to highlight the relevant features of the target system.     

Motivating Williamson's Model Gettier Cases

Abstract

Williamson has a strikingly economical way of showing how justified true belief can fail to constitute knowledge: he models a class of Gettier cases by means of two simple constraints. His constraints can be shown to rely on some unstated assumptions about the relationship between reality and appearance. These assumptions are epistemologically non-trivial but can be defended as plausible idealizations of our actual predicament, in part because they align well with empirical work on the metacognitive dimension of experience.     

I—Varieties of Support and Confirmation of Climate Models

Today's climate models are supported in a couple of ways that receive little attention from philosophers or climate scientists. In addition to standard 'model fit', wherein a model's simulation is compared to observational data, there is an additional type of confirmation available through the variety of instances of model fit. When a model performs well at fitting first one variable and then another, the probability of the model under some standard confirmation function, say, likelihood, goes up more than under each individual case of fit alone. Thus, two instances of fit of distinct variables of a global climate model using distinct data sets considered collectively will provide stronger evidence for a model than either one of the instances considered individually. This has consequences for model robustness. Sets of models that produce robust results will, if their assumptions vary enough and they each are observationally sound, provide reasons to endorse common structures found in those models. Finally, independent empirical support for aspects and assumptions of the model provides an additional confirmational virtue for climate models, contrary to what is implied by some current philosophical writing on this topic.     

Don’t Blame the Idealizations

Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in a way that prevents those hypotheses from being false by virtue of their constituent idealizations.     

Scientific Research and Human Rights: A Response to Kitcher on the Limitations of Inquiry

In his recent work exploring the role of science in democratic societies Kitcher (Science in a democratic society. Prometheus Books, New York, 2011) claims that scientists ought to have a prominent role in setting the agenda for and limits to research. Against the backdrop of the claim that the proper limits of scientific inquiry is John Stuart Mill’s Harm Principle (Kitcher in Science, truth, and democracy. Oxford University Press, New York, 2001), he identifies the limits of inquiry as the point where the outcomes of research could cause harm to already vulnerable populations. Nonetheless, Kitcher argues against explicit limitations on unscrupulous research on the grounds that restrictions would exacerbate underlying social problems. I show that Kitcher’s argument in favor of dissuading inquiry through conventional standards is problematic and falls prey to the same critique he offers in opposition to official bans. I expand the conversation of limiting scientific research by recognizing that the actions that count as ‘science’ are located in the space between ‘thinking’ and ‘doing’. In this space, we often attempt to balance freedom of research, as scientific speech, against the disparate impact citizens might experience in light of such research. I end by exploring if such disparate impact justifies limiting research, within the context of the United States, under Title VII of the Civil Rights Act of 1964 or under international human rights standards more generally.     

Feyerabend’s well-ordered science: how an anarchist distributes funds

To anyone vaguely aware of Feyerabend, the title of this paper would appear as an oxymoron. For Feyerabend, it is often thought, science is an anarchic practice with no discernible structure. Against this trend, I elaborate the groundwork that Feyerabend has provided for the beginnings of an approach to organizing scientific research. Specifically, I argue that Feyerabend’s pluralism, once suitably modified, provides a plausible account of how to organize science. These modifications come from C.S. Peirce’s account of the economics of theory pursuit, which has since been corroborated by empirical findings in the social sciences. I go on to contrast this approach with the conception of a ‘well-ordered science’ as outlined by Kitcher (Science, truth, and democracy, Oxford University Press, New York, 2001), Cartwright (Philos Sci 73(5):981–990, 2006), which rests on the assumption that we can predict the content of future research. I show how Feyerabend has already given us reasons to think that this model is much more limited than it is usually understood. I conclude by showing how models of resource allocation, specifically those of Kitcher (J Philos 87:5–22, 1990), Strevens (J Philos 100(2):55–79, 2003) and Weisberg and Muldoon (Philos Sci 76(2):225–252, 2009), unwittingly make use of this problematic assumption. I conclude by outlining a proposed model of resource allocation where funding is determined by lottery and briefly examining the extent to which it is compatible with the position defended in this paper.

     

The Structure of Idealization in Biological Theories: The Case of the Wright-Fisher Model

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different ??degrees of contingency??. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model.     

Constructing reality with models

Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a model on the reality. By recognizing the physical constructive force of some scientific models, the merit of these models could be judged by how successful they are in the reality construction, rather than by the traditional criterion of model-world representation.

     

Laws and Models in a Theory of Idealization

I first give a brief summary of a critique of the traditional theories of approximation and idealization; and after identifying one of the major roles of idealization as detaching component processes or systems from their joints, a detailed analysis is given of idealized laws – which are discoverable and/or applicable – in such processes and systems (i.e., idealized model systems). Then, I argue that dispositional properties should be regarded as admissible properties for laws and that such an inclusion supplies the much needed connection between idealized models and the laws they `produce' or `accommodate'. And I then argue that idealized law-statements so produced or accommodated in the models may be either true simpliciter or true approximately, but the latter is not because of the idealizations involved. I argue that the kind of limiting-case idealizations that produce approximate truth is best regarded as approximation; and finally I compare my theory with some existing theories of laws of nature.We seem to trace [in KingLear] ... the tendency of imagination toanalyse and abstract, to decomposehuman nature into its constituentfactors, and then to construct beings in whomone or more of these factors isabsent or atrophied or only incipient     

SCIENTISTS AND RELIGIOUS COMMUNITIES: INVESTIGATING PERCEPTIONS,BUILDING UNDERSTANDING

The American Association for the Advancement of Science (AAAS) Dialogue on Science, Ethics, and Religion (DoSER) program has embarked on an exciting project, “Scientists and Religious Communities: Investigating Perceptions to Build Understanding.” The project will provide the first quantitative data on the underlying assumptions and concerns that shape national attitudes on science. A nationally representative survey conducted in collaboration with sociologists at Rice University has reached 10,000 people, including evangelical Christians, mainline Protestants, Catholics, and Jews. The survey probed how a broad range of religious people, particularly evangelical Christians, understands and thinks about science, and what they perceive about scientists. Scientists, broadly defined, were likewise surveyed to gauge their perceptions of how religious people regard science. The goal for AAAS is to increase understanding between the scientific and evangelical Christian communities and redefine this critical relationship. DoSER will bring together leaders from scientific and evangelical communities to discuss the implications of survey results and to use them for building better understanding and communication strategies. Building relationships between scientists and religious communities has the potential to create a new paradigm of understanding. Finding out what each group actually thinks, through a survey, is only the beginning.     

Intrinsic Ethics Regarding Integrated Assessment Models for Climate Management

In this essay we develop and argue for the adoption of a more comprehensive model of research ethics than is included within current conceptions of responsible conduct of research (RCR). We argue that our model, which we label the ethical dimensions of scientific research (EDSR), is a more comprehensive approach to encouraging ethically responsible scientific research compared to the currently typically adopted approach in RCR training. This essay focuses on developing a pedagogical approach that enables scientists to better understand and appreciate one important component of this model, what we call intrinsic ethics. Intrinsic ethical issues arise when values and ethical assumptions are embedded within scientific findings and analytical methods. Through a close examination of a case study and its application in teaching, namely, evaluation of climate change integrated assessment models, this paper develops a method and case for including intrinsic ethics within research ethics training to provide scientists with a comprehensive understanding and appreciation of the critical role of values and ethical choices in the production of research outcomes.     

A Virtue-Based Defense of Mathematical Apriorism

Mathematical apriorists usually defend their view by contending that axioms are knowable a priori, and that the rules of inference in mathematics preserve this apriority for derived statements—so that by following the proof of a statement, we can trace the apriority being inherited. The empiricist Philip Kitcher attacked this claim by arguing there is no satisfactory theory that explains how mathematical axioms could be known a priori. I propose that in analyzing Ernest Sosa’s model of intuition as an intellectual virtue, we can construct an “intuition–virtue” that could supply the missing explanation for the apriority of axioms. I first argue that this intuition–virtue qualifies as an a priori warrant according to Kitcher’s account, and then show that it could produce beliefs about mathematical axioms independent of experience. If my argument stands, this paper could provide insight on how virtue epistemology could help defend mathematical apriorism on a larger scale.     

Analytic Idealizations and the Disavowed: Winnicott,His Patients,and Us

This paper explores the dynamics of mutual idealization within the analytic dyad. While the subject of idealization is not a new one, very little has been written about the analyst's own participation in patients' idealizations or her vulnerability to idealizing the patient. I use both published and unpublished materials to muse about coconstructed idealizations as they appear to have coalesced in Winnicott's treatment of Masud Khan and Harry Guntrip. Because the notion that we might be involved in being idealized by our patients—or in idealizing them—collides with our professional vision, we tend to be highly resistant to acknowledging these dynamics and often turn to denigration when they are unmasked. I argue for the ubiquity of idealization's dynamics and against the demonization of Winnicott.     

The effect of consequential thinking on trust game behavior

Contrary to rational Expected Monetary Value (EMV) predictions that no money will be transferred in Trust Games, in experiments players make positive transfers. Theorists have proposed modifying the Sender's utility function while retaining utility‐maximization assumptions to account for this behavior. Such accounts assume that Senders can grasp the possible outcomes of their choices, their probabilities, and utilities. In reality, however, Senders' choices are unexpectedly complex, and the assumption that they approximate expected utility maximization is highly implausible. Instead, we suggest that Senders are guided by general propensities to trust others. Two experiments examine the effect of inducing consequential thought on Sender behavior. One induced consequential thought directly; the other did so indirectly. The amount sent was significantly reduced following either manipulation. This suggests that models of Sender behavior in Complex Trust Games should not assume that participants routinely engage in consequential thinking (CT) of the depth that would be required for utility maximization. Copyright © 2008 John Wiley & Sons, Ltd.     

Five arguments on the rationality of suicide terrorists

This is a critical review of five arguments against the scholarly consensus that individual suicide terrorists are rational actors. The first three arguments are analytic, meaning that they hold that suicide terrorism is inherently irrational, based on assumptions regarding the nature of the perpetrators' interests as individuals. The fourth argument is empirical, based on alleged evidence of suicide terrorist psychopathology. And the fifth argument is pragmatic, based on the strategic implications of ‘conceding’ the status of rationality to enemies. This article highlights weaknesses in these arguments and considers assessing the rationality of suicide terrorists by measuring their act to cultural or community goals subject to a division of labor principle.     

Atheists and Agnostics Negotiate Religion and Family

Through in‐depth interviews with scientists at elite academic institutions—those particularly likely to have no firm belief in God—we provide insight into the motives scientists who are not religious have for joining a religious group and the struggle faced by these individuals in reconciling personal beliefs with what they consider the best interests of their families. Narratives stress the use of resources from identities as scientists to provide their children with religious choices consistent with science and in negotiating spousal influence and a desire for community. Findings expand the religious socialization and identities literatures by widening the range of understanding of the strategies parents utilize to interface with religious communities as well as lead to more nuanced public understanding of how atheist and agnostic scientists relate to religious communities.     

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R ² for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.     

Fictional Models and Fictional Representations

Scientific models consist of fictitious elements and assumptions. Various attempts have been made to answer the question of how a model, which is sometimes viewed as a fiction, can explain or predict the target phenomenon adequately. I examine two accounts (the counterfactual dependency view and the idealization view) of models-as-fictions which are aiming at disentangling the myth of representing the reality by fictional models. I argue that both views have their own weaknesses in spite of many virtues. I propose to re-evaluate the problems of representation from a novel perspective in which some of the model representations can be regarded as fictional representations. I argue that this type of model representation is credible despite being a fictional representation of the reality.