Susan G. Sterrett

In this paper I discuss the relationship between model, theories, and laws in the practice of experimental scale modeling. The methodology of experimental scale modeling, also known as physical similarity, differs markedly from that of other kinds of models in ways that are important to issues in philosophy of science. Scale models are not discussed in much depth in mainstream philosophy of science. In this paper, I examine how scale models are used in making inferences. The main question I address in this talk is ``How are fundamental laws involved in the construction of, and inferences drawn from, experimental scale models?'' We shall see that there is a refreshing alternative to the mainstream view that models can serve only as intermediaries between theory and experiment. Using the methodology of scale models, one can use observations on one piece of the world to make inferences about another piece of the world, without involving an intermediate abstract model about which one reasons. The philosophical significance of that point to philosophy of science is that the method of physical similarity, which provides the basis for inferences based upon scale models, is a qualitatively different way in which fundamental laws can be used in analogical reasoning that is truly informative. Finally, as this method provides a formal basis for case-based reasoning, it may be helpful in formalizing methods used in some of the so-called ``special sciences''.

How are beliefs efficacious? One answer is: via rational intentional action. But there are other ways that beliefs are efficacious. This dissertation examines these other ways, and sketches an answer to the question of how beliefs are efficacious that takes into account how beliefs are involved in the full range of behavioral disciplines, from psychophysiology and cognition to social and economic phenomena. The account of how beliefs are efficacious I propose draws on work on active accounts of perception. I develop an account based on a proposal sketched by the cognitive scientist Ulrich Neisser. Neisser sketched an active account of perception, on which dynamic anticipatory schemata direct an organism's exploration and action, and are in turn revised as a result of exploration and action. This notion of schema has roots in nineteenth century neurophysiology and in Frederick Bartlett's subsequent work on memory. Neisser appealed to it to unite what he thought was right about information-processing accounts of perception with what he thought was right about ecological accounts of perception. The point that we must anticipate in order to perceive has been recognized by philosophers in the form of the "theory-ladenness of observation." I extend the concept of anticipatory schema to include its role in social perception and social interaction; the concept of anticipatory schema provides a more interactive account of the role of expectations in the maintenance and existence of social institutions, and can be used to enrich the account of convention David Lewis provided. I also show that the concept of rational expectations, which explains the neutrality of money in terms of the efficacy of anticipatory expectations, is compatible with the proposed account of how beliefs are efficacious. I discuss how the proposal accounts for the three main modes by which beliefs can be efficacious: via their role in causing intentional action, via their role in causing economic phenomena and the existence and maintenance of social institutions, and via their role in causing unintentional physiological responses, including anticipatory physiological responses that can enable perception, cause involuntary actions and give rise to the placebo effect.

I address questions about values in model-making in engineering, specifically: Might the role of values be attributable solely to interests involved in specifying and using the model? Selected examples illustrate the surprisingly wide variety of things one must take into account in the model-making itself. The notions of system , and physically similar systems are important and powerful in determining what is relevant to an engineering model. Another example illustrates how an idea to completely re-characterize, or reframe, an engineering problem arose during model-making.I employ a qualitative analogue of the notion of physically similar systems. Historical cases can thus be drawn upon; I illustrate with a comparison between a geoengineering proposal to inject, or spray, sulfate aerosols, and two different historical cases involving the spraying of DDT . The current geoengineering proposal is seen to be like the disastrous and counterproductive case, and unlike the successful case, of the spraying of DDT. I conclude by explaining my view that model-making in science is analogous to moral perception in action, drawing on a view in moral theory that has come to be called moral particularism

This paper investigates the following proposal about machine intelligence: that behaviour in which a habitual response that would have been inappropriate in a certain unfamiliar situation is overridden and replaced by a more appropriate response be considered evidence of intelligence. The proposal was made in an earlier paper (Sterrett 2000) and arose from an analysis of a neglected test for intelligence hinted at in Turing's legendary 'Computing Machinery and Intelligence'; it was also argued there that it was a more principled test of machine intelligence than straightforward comparisons with human behaviour. The present paper first summarizes the previous claim then looks at writings about intelligence, or the lack of it, in animals and machines by various writers (Descartes, Hume, Darwin and James). It is then shown that, despite their considerable differences regarding fundamental things such as what kinds of creatures are intelligent and the relationship between reason, instinct and behaviour, all of these writers would regard behaviour that meets the proposed criterion as evidence of intelligence. Finally, some recent work in employing logic and reinforcement learning in conjunction with 'behaviour-based' principles in the design of intelligent agents is described; the significance for the prospect of machine intelligence according to the proposed criterion is discussed

I survey a broad variety of models with an eye to asking what kind of model each is in the following sense: in virtue of what is each of them regarded as a model? It will be seen that when we classify models according to the answer to this question, it comes to light that the notion of model predominant in philosophy of science covers only some of the kinds of models used in scientific contexts. The notion of a model predominant in philosophy of science requires that a model be related to something formal, such as equations or statements. Not all the examples provided in the brief survey in this paper fit that notion of a model. I identify another kind of model that ought to be included in philosophical and foundational studies of scientific models, which I call a “piece of the world” kind of model, to contrast with a “realm of thought” kind of model. These models also have formal methodologies associated with them, and, hence, analytic philosophers of science can embrace them without abandoning the rigor that has characterized the discipline.

. Some disagreements have arisen in the last few years regarding the role played by material properties when modeling, simulating and experimenting on physical systems (Morrison 2008, Parker (forthcoming), Winsberg (forthcoming), Guala 2002, 2005; Morgan 2005). The question has proven more involved than it first appears. A number of significant and correct points have already been made, but some confusions remain. In this paper I attempt to sort them out. After pointing out the importance of some distinctions that need to be drawn, I explain the ways in which material properties matter in modeling and simulations.

The year that Ludwig Wittgenstein was born in Vienna, 1889, nearby developments already underway portended two major changes of the coming century: the advent of controlled heavier-than-air flight and the mass production of musical sound recordings. Before they brought about major social changes, though, these innovations appeared in Europe in the form of children’s toys. Both a rubber-band-powered model helicopter-like toy employing an ingenious solution to the problem of control, and a working toy gramophone with which music could be reproduced from hard discs, appeared in Europe in time for Ludwig’s childhood. And, both innovations reappear in his work as an adult. The relationship between the advent of heavier-than-air flight and Wittgenstein’s claim in Tractatus Logico-Philosophicus that a proposition is a picture or model is a topic in its own right, and I discuss it in separate works. 1 In this essay, I consider the way Wittgenstein employed the development of sound recordings in discussing logical form in the Tractatus

Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There are hopes it can be adapted to other contexts besides that game show, in the role of a collaborator of, rather than a competitor to, humans. Another, different, research project --- an artificial intelligence program put into operation in 2010 --- is the machine learning program NELL (Never Ending Language Learning), which continuously ‘learns’ by ‘reading’ massive amounts of material on millions of web pages. Both of these recent endeavors in artificial intelligence rely to some extent on the integration of human guidance and feedback at various points in the machine’s learning process. In this paper, I examine Turing’s remarks on the development of intelligence used in various kinds of search, in light of the experience gained to date on these projects.