Susan G. Sterrett

Readers who’ve wished to know more about the genesis of Remembering: a study in experimental and social psychology (Bartlett, 1932), will find in Brady Wagoner’s The Constructive Mind a treasure trove. Remembering was originally published in 1932, and is probably Frederic Bartlett’s most well known work today. Wagoner does not focus on Bartlett’s contributions to memory studies, though, but on the promise that Bartlett’s work holds for the field of psychology today, especially the study of thinking. Although rich in historical detail, The Constructive Mind is not only a work in history of psychology, but a critique—and, hence, implicitly, a programmatic work. Wagoner describes himself as a cultural psychologist, and this book as “the first extended and integrative reconstruction of Bartlett’s work and legacy.” The work also serves, he says, as “a case study in the diffusion and reconstruction of ideas in a science” (p. 5).

The role of mathematics in scientific practice is too readily relegated to that of formulating equations that model or describe what is being investigated, and then finding solutions to those equations. I survey the role of mathematics in: 1. Exact solutions of differential equations, especially conformal mapping; and 2. Simulations of solutions to differential equations via numerical methods and via agent-based models; and 3. The use of experimental models to solve equations (a) via physical analogies based on similarity of the form of the equations, such as Prandtl’s soap-film method, and (b) the method of physically similar systems. Two major themes emerge: First, the role of mathematics in science is not well described by deduction from axioms, although it generally involves deductive reasoning. Creative leaps, the integration of experimental or observational evidence, synthesis of ideas from different areas of mathematics, and insight regarding analogous forms are required to find solutions to equations. Second, methods that involve mappings or transformations are in use in disparate contexts, from the purely mathematical context of conformal mapping where it is mathematical objects that are mapped, to the use of concrete physical experimental models, where one concrete thing is shown to correspond to another.

In this paper, I enrich the context of Wittgenstein's Tractatus given over a decade ago in my book Witttgenstein Flies A Kite (and related earlier works dating from 2000). I've since located a sketch reprinted from a 1914 Paris magazine showing a lawyer using a model bus and dolls to depict a traffic accident; I present it here along with a discussion of the modelmaker movement of that time. The modelmaker movement was a movement at the intersection of popular culture and technical expertise that really needs to be understood and recognized in discussing Wittgenstein's use of Modell and Bild. I discuss its role in relation to experimental models used in scientific research. Other new aspects presented here include: the very special role of model-fl ying clubs (known in Germany as Modell-Flugverein); the use of scientific forensics in courts of law, really just beginning then (c. 1914), and a part of popular culture as well; the significance of more recent work by others on Boltzmann's personal interest in flight, and on the widespread but now-forgotten discussion of dimensional analysis in the history of physics. I conclude that all these lend support to the views on the Tractatus I laid out in my book, and summarize and elaborate on some of them here, inasmuch as space permits. More generally, I argue that the philosophical community interested in interpreting Wittgenstein's early works stands to gain from becoming better acquainted with the scientific and technological developments of the milieu in which they were conceived.

Twenty years ago in "Turing's Two Tests for Intelligence" I distinguished two distinct tests to be found in Alan Turing's 1950 paper "Computing Machinery and Intelligence": one by then very well-known, the other neglected. I also explained the significance of the neglected test. This paper revisits some of the points in that paper and explains why they are even more relevant today. It also discusses the value of tests for machine intelligence based on games humans play, giving an analysis of some twentieth century TV game shows and how they relate to the tests for machine intelligence in Turing's paper and in some other tests for machine intelligence that have been proposed since. Their value in distinguishing between 'wise' and simply ‘clever’ AI is discussed.

This chapter describes discussions by scientists in Wittgenstein’s milieu relevant to problems Wittgenstein was pondering after he had decided to devote himself to solving the problems of logic. The chapter opens just after his father has died, and Wittgenstein’s investigations into logic were bringing him to examine notions of mirroring and corresponding. It discusses Ludwig Boltzmann’s views on differential equations, mental models, experimental models, and debates with Ostwald on the use of models in the kinetic theory of gases. Work on similarity by various scientists developed from insights by Newton and Galileo is surveyed. Questions about equations analogous to those Wittgenstein was pondering about propositions in early 1914 would receive an answer by the end of 1914—by a physicist who had studied thermodynamics with Ostwald, and formalized the concept of “physically similar systems.”

This chapter describes discussions by scientists in Wittgenstein’s milieu relevant to problems Wittgenstein was pondering after he had decided to devote himself to solving the problems of logic. The chapter opens just after his father has died, and Wittgenstein’s investigations into logic were bringing him to examine notions of mirroring and corresponding. It discusses Ludwig Boltzmann’s views on differential equations, mental models, experimental models, and debates with Ostwald on the use of models in the kinetic theory of gases. Work on similarity by various scientists developed from insights by Newton and Galileo is surveyed. Questions about equations analogous to those Wittgenstein was pondering about propositions in early 1914 would receive an answer by the end of 1914—by a physicist who had studied thermodynamics with Ostwald, and formalized the concept of “physically similar systems.”

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.

Putting Wittgenstein's writing into an historical context that includes scientific and technological developments as well as cultural and intellectual works can be helpful in understanding some of Wittgenstein's works. I focus on the Tractatus Logico-Philosophicus in particular in this paper, and on topics related to pictures and models: the development of audio recording technologies, the development of miniature scale models that were both aesthetically pleasing and scientifically useful, particularly in the forensics of traffic accidents, and the culmination of a centuries-long effort to articulate the method behind the use of physical modeling, i.e., the formulation of a concept presented in 1914 and dubbed "physically similar systems."

This chapter concerns dimensions as the term is used in the physical sciences today. Some key points made are: Quantities of the same kind have the same dimension; but that two quantities have the same dimension does not necessarily mean they are of the same kind. The dimension of a quantity is not determined for a single quantity in isolation, but relative to a system of quantities and the relations that hold between them. Dimensions, units, and quantities are distinct notions. In this article, I explain how dimensions, units, and quantities are involved in the design of coherent systems of units; the account involves the equations of physics. When the use of a coherent system of units can be presumed, dimensional analysis is a powerful logico-mathematical method for deriving equations and relations in physics, and for parameterizing equations in terms of dimensionless parameters, which allows identifying physically similar systems. The source of the information yielded by dimensional analysis is not yet well understood in philosophy of physics. This chapter aims to reveal the role of dimensions not only in applications of dimensional analysis to obtain information by involving the principle of dimensional homogeneity, but to the role of dimensions in encoding information about physical relationships in the language of dimensions, specifically via the feature of coherence of a system of units. Philosophers of mathematics and philosophers of science have been concerned to address the question of the effectiveness of mathematics in science. It is argued here that no philosophical analysis of the question of the applicability of mathematics to science is complete without including dimensions and dimensional analysis in the picture.

The proposed revision to the International System of Units contains two features that are bound to be of special interest to those concerned with foundational questions in philosophy of science. These are that the proposed system of international units can be defined without drawing a distinction between base units and derived units, and without restricting the means by which the value of the quantities associated with the units are to be established. In this paper, I address the question of the role of base units in light of the New SI: Do the "base units" of the SI play any essential role anymore, if they are neither at the bottom of a hierarchy of definitions themselves, nor the only units that figure in the statements fixing the numerical values of the "defining constants"? The answer I develop and present also shows why it is important to retain the distinction between dimensions and quantities. I argue for an appreciation of the role of dimensions in understanding issues related to systems of units.

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.

The concept of similar systems arose in physics, and appears to have originated with Newton in the seventeenth century. This chapter provides a critical history of the concept of physically similar systems, the twentieth century concept into which it developed. The concept was used in the nineteenth century in various fields of engineering, theoretical physics and theoretical and experimental hydrodynamics. In 1914, it was articulated in terms of ideas developed in the eighteenth century and used in nineteenth century mathematics and mechanics: equations, functions and dimensional analysis. The terminology physically similar systems was proposed for this new characterization of similar systems by the physicist Edgar Buckingham. Related work by Vaschy, Bertrand, and Riabouchinsky had appeared by then. The concept is very powerful in studying physical phenomena both theoretically and experimentally. As it is not currently part of the core curricula of STEM disciplines or philosophy of science, it is not as well known as it ought to be.

The topic of this Handbook entry is the relationship between similarity and dimensional analysis, and some of the philosophical issues involved in understanding and making use of that relationship. Discusses basics of the relationship between units, dimensions, and quantities. It explains the significance of dimensionless parameters, and explains that similarity of a physical systems is established by showing equality of a certain set of dimensionless parameters that characterizes the system behavior. Similarity is always relative -- to some system behavior. Other topics discussed: generalization of the notion of similarity, the difference between relative similarity and partial similarity; how the notion of similarity in science differs from similarity as it has been discussed in recent philosophy. Philosophers' views discussed: R. Giere, N. Goodman, P. Bridgman, and B. Ellis.

On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication of intelligence. This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one''s habitual responses; thus the test''s applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human''s linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test'' has been dismissed.

Experimental engineering models have been used both to model general phenomena, such as the onset of turbulence in fluid flow, and to predict the performance of machines of particular size and configuration in particular contexts. Various sorts of knowledge are involved in the method - logical consistency, general scientific principles, laws of specific sciences, and experience. I critically examine three different accounts of the foundations of the method of experimental engineering models (scale models), and examine how theory, practice, and experience are involved in employing the method to obtain practical results. Models of machines and mechanisms can be (and generally are) involved in establishing criteria for similar phenomena, which provide guidance in using events to model other events. Conversely, models of phenomena such as events that model other events can be (and generally are) involved in experimentation on models of machines. I conclude that often it is not more detailed models or the more precise equations they engender that leads to better understanding, but rather an insightful use of knowledge at hand to determine which similarity principles are appropriate in allowing us to infer what we do not know from what we are able to observe.

Turing wrote that the “guiding principle” of his investigation into the possibility of intelligent machinery was “The analogy [of machinery that might be made to show intelligent behavior] with the human brain.” [10] In his discussion of the investigations that Turing said were guided by this analogy, however, he employs a more far-reaching analogy: he eventually expands the analogy from the human brain out to “the human community as a whole.” Along the way, he takes note of an obvious fact in the bigger scheme of things regarding human intelligence: grownups were once children; this leads him to imagine what a machine analogue of childhood might be. In this paper, I’ll discuss Turing’s child-machine, what he said about different ways of educating it, and what impact the “bringing up” of a child-machine has on its ability to behave in ways that might be taken for intelligent. I’ll also discuss how some of the various games he suggested humans might play with machines are related to this approach.

Today I want to talk about an element in the milieu in which Ludwig Wittgenstein conceived the Tractatus Logico-Philosophicus that has not been recognized to date: the generalization of the methodology of experimental scale models that occurred just about the time he was writing it. I find it very helpful to keep in mind how this kind of model portrays when reading the Tractatus — in particular, when reading the statements about pictures and models, such as:That a picture is a fact ,That a picture is a model of reality ,That the “pictorial relationship” that makes a picture a picture is part of that picture , andThat a picture must have its pictorial form in common with reality in order to able to depict it

I argue here that Frege’s eventual view on the relation between sentences and the thoughts they express is that, ideally, a sentence expresses exactly one thought, and a thought is expressed by exactly one (canonical) sentence. This may clash with some mainstream views of Frege, for it has the consequence of de-emphasizing the philosophical significance of the question of how it is possible for someone to regard one sentence as true yet regard another sentence that expresses the same thought as false. This account of Frege was developed by taking a long-range look at his writings over the course of his life.

Demonstrating the different roles that logic plays in the disciplines of computer science, mathematics, and philosophy, this concise undergraduate textbook covers select topics from three different areas of logic: proof theory, computability theory, and nonclassical logic. The book balances accessibility, breadth, and rigor, and is designed so that its materials will fit into a single semester. Its distinctive presentation of traditional logic material will enhance readers' capabilities and mathematical maturity. The proof theory portion presents classical propositional logic and first-order logic using a computer-oriented (resolution) formal system. Linear resolution and its connection to the programming language Prolog are also treated. The computability component offers a machine model and mathematical model for computation, proves the equivalence of the two approaches, and includes famous decision problems unsolvable by an algorithm. The section on nonclassical logic discusses the shortcomings of classical logic in its treatment of implication and an alternate approach that improves upon it: Anderson and Belnap's relevance logic. Applications are included in each section. The material on a four-valued semantics for relevance logic is presented in textbook form for the first time. Aimed at upper-level undergraduates of moderate analytical background, Three Views of Logic will be useful in a variety of classroom settings. Gives an exceptionally broad view of logic. Treats traditional logic in a modern format. Presents relevance logic with applications. Provides an ideal text for a variety of one-semester upper-level undergraduate courses

In "The Status and Future of the Turing Test" (Moor, 2001), which appeared in an earlier issue of this journal, James Moor remarks on my paper "Turing's Two Tests for Intelligence." In my paper I had claimed that, whatever Turing may or may not have thought, the test described in the opening section of Turing's now legendary 1950 paper "Computing Machinery and Intelligence" is not equivalent to, and in fact is superior to, the test described in a passage that occurs much later in Turing's paper (i.e., in Section 5 of Turing, 1950). I'm pleased Moor chose to give such prominence to my point, and very happy to see that he recognized that my claim was a normative one about the superiority of one test over another, rather than a claim about Turing's intentions. However, as I think the way he describes my point could lead to misunderstandings, I'd like to clarify the points I made. One major clari?cation is which two tests I am contrasting; another is that the difference in overall structure of the two tests is of philosophical signi?cance

Summary Analogue models are actual physical setups used to model something else. They are especially useful when what we wish to investigate is difficult to observe or experiment upon due to size or distance in space or time: for example, if the thing we wish to investigate is too large, too far away, takes place on a time scale that is too long, does not yet exist or has ceased to exist. The range and variety of analogue models is too extensive to attempt a survey. In this article, I describe and discuss several different analogue model experiments, the results of those model experiments, and the basis for constructing them and interpreting their results. Examples of analogue models for surface waves in lakes, for earthquakes and volcanoes in geophysics, and for black holes in general relativity, are described, with a focus on examining the bases for claims that these analogues are appropriate analogues of what they are used to investigate. A table showing three different kinds of bases for reasoning using analogue models is provided. Finally, it is shown how the examples in this article counter three common misconceptions about the use of analogue models in physics.

Ernst Mach is the only person whom Einstein included on both the list of physicists he considered his true precursors, and the list of the philosophers who had most affected him. Einstein scholars have been less generous in their estimation of Mach's contributions to Einstein's work, and even amongst the more generous of them, Mach's great achievements in physics are seldom mentioned in this context. This is odd, considering Mach was nominated for the Nobel Prize in Physics three times. In this paper, I examine some of Mach's work in physics that bears conceptually on Einstein's 1905 paper on Special Relativity ("On The Electrodynamics of Moving Bodies"). Mach was the first to give the correct explanation of the Doppler Effect, and he presented it in a way that Einstein echoes in his 1905 paper: laying out two apparently contradictory principles and showing how both can be retained. It is also notable that Mach's explanation was explicit about not relying on the existence of a medium of transmission for the propagation of light waves. In his work on supersonic shock waves, Mach invokes the constancy of the velocity of sound (i.e., its independence of the motion of the sound source) , just as he had invoked the constancy of the velocity of light in his work on the Doppler Effect for Light. I examine the analogies between light and sound that were drawn upon by Einstein and Mach, as well as one analogy that Einstein could have, but did not make: Cherenkov radiation, or "singing electrons", i.e., cases in which the sound of light in the medium of transmission is exceeded, which results in an optical analogue of supersonic shock waves

The analogy Darwin drew between artificial and natural selection in "On the Origin of Species" has a detailed structure that has not been appreciated. In Darwin’s analogy, the kind of artificial selection called Methodical selection is analogous to the principle of divergence in nature, and the kind of artificial selection called Unconscious selection is analogous to the principle of extinction in nature. This paper argues that it is the analogy between these two different principles familiar from his studies of artificial selection and the two different principles he claims are operative in nature that provides the main structure and force of the analogy he uses to make his case for the power of natural selection to produce new species. Darwin’s statements explicitly distinguishing between these two kinds of principles at work in nature occur prominently in the text of the Origin. The paper also shows that a recent revisionist claim that Darwin did not appeal to the efficacy of artificial selection is mistaken