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Scientific modeling is a creative activity that can benefit from computational support. This chapter reports five challenges that arise in developing such aids, as illustrated by PROMETHEUS, a software environment that supports the construction and revision of explanatory models. These challenges include the paucity of relevant data, the need to incorporate prior knowledge, the importance of comprehensibility, an emphasis on explanation, and the practicality of user interaction. The responses to these challenges include the use of quantitative processes to encode models and background knowledge, as well as the combination of AND/OR search through a space of model structures with gradient descent to estimate parameters. This chapter reports our experiences with PROMETHEUS on three scientific modeling tasks and some lessons we have learned from those efforts. This chapter concludes by noting additional challenges that were not apparent at the outset of our work.

This chapter reflects upon three modeling scenarios — the aesthetics, the mimetic (Swann as model), and the scientific — in order to explore some key concepts and structures: repetition and reproduction, sameness and difference, and affective and objective relationships with the law. These pairs are all variations generated by the self-model pair, and offer different perspectives on the dialogue between the particular and the general in A la recherche.

Fictionalism about mathematics is the view that we can use mathematics in science and yet not believe that our mathematical claims are true. This chapter critically evaluates some of the most important versions of fictionalism. It begins by considering the leading options for making sense of literary fictions and argues that engaging with a literary fiction does not present any evidence for how things are in the real world. This point is then used to criticize fictionalism about mathematics and a related fictionalist position about scientific models. Another objection is that the fictionalist has yet to clarify the rules for exporting well-supported claims from the fiction in the scientific case. This suggests that fictionalists are not able to make sense of the contributions delineated in part one of the book.

Introduces the background to the scientism criticized in the main body of the work, and discusses its relation to various forms of reductionism and physicalism. (This summarizes, in part, detailed work in the author's earlier book, The Disorder of Things.) There is also a discussion of the essential role of models in scientific theory.

This chapter provides a guide to scientific evidence and explores how field experiments can be used as a scientific standard for determining what place-based policies to adopt, refine, or abandon. It also discusses what types of evidence to rely on when experiments are not possible for various ethical or pragmatic reasons. Experimentation is at the very heart of science, and relying on a scientific model for deciding how, and in what forms, the built environment should be modified is a dynamic process that can ultimately inform the efficient and effective expenditures of limited resources by policy makers. Rather than provide a treatise on the scientific method and the value of experiments, the chapter provides a short discussion of the benefits of different methods of evaluation and focuses more attention on the utility of a science-based policy agenda for changing places. The scientific model allows people to evaluate the influence that environments may have on health and safety while also encouraging them to pursue discoveries of innovative new place-based strategies that can achieve the greatest health and safety benefits at relatively low costs.

One of the most contentious issues with which the emerging naturalism had to grapple was the question of what, if anything, was beyond science’s grasp. Both naturalists and theists accused each other of making unsupportable claims of absolute knowledge about the world, and of intellectual arrogance. However, in practice, both sides agreed quite closely about the limits of scientific investigation and knowledge, and tied the establishment of those limits to their naturalistic or theistic worldview. One important example was the question of the origin of the universe. Surprisingly, human ignorance of this question was justified in very similar terms by both groups. Maxwell and Huxley undertook sophisticated analyses of the limits of science - Huxley through his articulation of agnosticism, Maxwell through his work on scientific models. The limits of science question was not solely a rhetorical debate, and appeared in important ways in scientific practice.

Because no large-scale bioterrorist attack has happened in the modern age, planning for bioterrorism requires imagination. Simulations, ranging from computer-generated models to large-scale role-playing events involving thousands of actors, have gained credibility as a scientific tool for calculating the outcomes of violent events. Expanding use of simulation raises questions about how modeling and scenario planning gain tenacity in the current political climate, used by planners and policymakers to generate knowledge of the future. In bioterrorism preparedness, simulations and scenarios matter because they rationalize political actions that manage human life, individually and collectively, in the present moment. A case study of a terrorism training center in Playas, New Mexico, demonstrates the material and political effects of scenarios and simulation.

Many philosophers have drawn parallels between scientific models and fictions. This chapter is concerned with a recent version of this analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an ineliminable role in the practice of modeling that cannot be captured by other accounts. The approach thus leaves open what we should say about the ontological status of model systems, and here advocates differ among themselves, defending a variety of realist or anti-realist positions. I argue that this debate over the ontological status of model systems is misguided. If model systems are the kinds of objects fictional realists posit, they can play no role in explaining the epistemology of modeling for an advocate of this approach. So they are at best superfluous. Defenders of the approach should focus on developing an account of the epistemological role of imagining model systems.

There has been growing interest in the idea that model descriptions should be thought of as similar to stories, and model systems should be thought of as akin to fictional characters. But if model systems were (like) fictional characters, what would they be? Two prominent approaches to fictional discourse have been pursued in the literature on models: realist approaches, which take models to be abstract objects that (in some sense) fit the model descriptions, and anti-realist approaches, which typically hold that the relevant discourse involves pretense in a way that enables us to deny that we ever refer to models. Both of these views have problems well known in the literature on fiction. These problems have motivated a third, increasingly popular approach to the ontology of fiction: an artifactual approach, according to which our (external) discourse about fiction refers to abstract artifacts. This approach has been little considered in the literature on scientific models—but this chapter argues that it has important advantages over the familiar alternatives. Most notably, an artifactualist approach can retain the advantages of the pretense view while giving a far more straightforward account of external historical, theoretical, and critical discourse about models. In short, bearing in mind the full range of discourse about models gives us reason to accept that there are model systems, where these are considered as a kind of abstract artifact. The main perceived drawback to artifactualist views is their supposed “ontological costs.” In closing, the chapter suggests why ontological qualms of this sort should be discounted.

This chapter examines how both tabulated data and metaphors functioned in early mercantile treatises as tools for conceptualising economic systems. It finds in treatises by Gerard Malynes and Edward Misselden the desire for what Mary Hesse, in her work on scientific models, terms the ‘perfect metaphor’ (a metaphor that corresponds exactly to the system it describes) and proposes reformulating the insights of economist-cum-rhetorician Deirdre McCloskey into a strategy for reading these metaphor-models. Whereas economic writers came to favour numerical data over metaphor because of the apparent ability of the former to offer an unmediated view of economic activity, the stage undermined any notion of an unmediated, non-metaphoric view, as East India Company merchant Walter Mountfort found when he attempted to incorporate information from the tables of Thomas Mun's A Discourse of Trade into his play The Launching of the Mary. In attempting to present Mun's tables as perfectly transparent windows on to the world of trade, Mountfort struggled against the mechanics of theatrical representation. However, the competing metaphoric descriptions of Dorotea Constance in the play's more theatrically effective plot offer a glimpse of how the stage might function to question, rather than transmit, economic models.

According to the fiction view of models, scientific models are akin to places and characters in literary fiction. The chapter introduces this view and develops a specific version of the view based on the pretense account of fiction. It then turns to the question of how models represent their targets and formulates an account of representation based on the notions of denotation, exemplification, keying up, and imputation. The notion of denotation, it is argued, is usually borrowed from language and so an account of scientific representation can pursue a reductive strategy as regards denotation. Finally, it is pointed out that the fiction view of models in no way implies that models promulgate falsities and that the fiction therefore does not undermine the authority of science.

One of the chief aims of science is understanding. The primary way that we achieve understanding of natural phenomena is by constructing explanations of how and why the phenomena occur as they do. The explanations provided by science are inherently uncertain. Due to the complexity of the phenomena being explained and our limitations as humans, scientists rely on models when constructing explanations. By their vary nature, models are uncertain because they essentially involve idealizations (abstractions or distortions of the facts) for the purpose of simplification. Although scientists legitimately infer that the best explanation of a given phenomenon is true, this method of inference is always uncertain for at least two reasons. The first is simply that the data being explained are limited (i.e., there is always more data that could have been gathered). The second is that there are always alternative explanations that might later be discovered.

Chapter 3 introduces the use of mathematical models and modeling practices in contemporary biological and cognitive sciences. The familiar Lotka–Volterra model of predator–prey relations is used to explain these practices and show how they promote the extensions of predicates, including psychological predicates, into new and often unexpected domains. It presents two models of cognitive capacities that were developed to explain human behavioral data: Ratcliff’s drift-diffusion model of decision-making and Sutton and Barto’s temporal difference model of reinforcement learning. These are now used for fruit flies and neural populations. It also discusses contemporary and ongoing attempts to revise psychological concepts in response to empirical discovery.

This chapter examines Hans Blumenberg's speech “World Pictures and World Models,” which he gave after he was appointed full professor at the University of Gießen in 1960. In the modern age, the scientific world model and the cultural self-understanding are no longer congruent. Philosophy's task is not just to explicate this divergence but also to dismantle remaining monist and exclusive world pictures. Blumenberg then locates philosophy's critical function in reducing total expectations of meaning — even if, as is true for his later works, modernity's loss of meaning may itself be mourned. He explains that the task that falls to philosophy within the association of academic fields can be traced back to its function in the spiritual economy of humans in general. The countless definitions that have been given for philosophy's achievements in its history have a basic formula at their core: philosophy is the emerging consciousness of humans about themselves.

This chapter discusses the extent of an addict’s responsibility in drug-related behavior. It is argued that addicts cannot be held responsible for the range of activities in which they must engage in order to procure, prepare, and consume drugs, at least with regard to much of the behavior. Consequently, the view that addictive behavior is not responsible behavior is also defended here. Currently, there exists two influential ways of understanding responsibility for addictive behavior. According to the moral model, addictive behavior is under the control of the agent; the behavior is considered normal, but the goal of the behavior is abnormal. The second model is the medical or scientific model, which posits that addictive behavior is grossly abnormal and, therefore, not responsible behavior. The account of addiction offered in the chapter differs from that offered by most proponents of this view.

This chapter discusses the discovery of chaos and how it has created a new paradigm in scientific modeling. On one hand, it implies new fundamental limits on the ability to make predictions; on the other, the determinism inherent in chaos implies that many random phenomena are more predictable than had originally been thought. Information found and shelved in the past because it was deemed unexplainable and too complicated can now be explained in terms of simple laws. Surprisingly, chaos allows order to be found in diverse systems. This facet of chaos has led to a revolution affecting many different branches of science. The chaos discussed in this chapter starts with the notion that existence of random behavior in very simple systems motivates a reexamination of the sources of randomness even in large systems.

This chapter starts out from the idea that semantics is a “special science” whose aim, like that of chemistry or ecology, is to identify systematic, high-level patterns in a fundamentally physical world. I defend an approach to this task on which sentences are associated with sets of possible worlds (of some kind). These sets of worlds, however, are not postulated for the compositional treatment of intensional contexts; they are not meant to capture what is intuitively asserted or communicated by an utterance; nor are they supposed to shed light on the cognitive processes that underlie our linguistic competence. Instead, their job description is to capture certain regularities in the interactions between subjects using the relevant language. I also raise some questions about how the relevant worlds might be construed.