ASPECTS OF THE NOVEL is a unique attempt to examine the novel afresh, rejecting the traditional methods of classification by chronology or subject-matter. Forster pares down the novel to its essential elements as he sees them: story, people, plot, fantasy, prophecy, pattern and rhythm. He illustrates each aspect with examples from their greatest exponents, not hesitating as he does so to pass controversial judgement on the works of, among others, Sir Walter Scott, Charles Dickens and Henry James. Full of (...) Forster's renowned wit and perceptiveness, ASPECTS OF THE NOVEL offers a rare insight into the art of fiction from one of our greatest novelists. 'His is a book to encourage dreaming.' Virginia Woolf. (shrink)

Recent solutions to the curve-fitting problem, described in Forster and Sober ([1995]), trade off the simplicity and fit of hypotheses by defining simplicity as the paucity of adjustable parameters. Scott De Vito ([1997]) charges that these solutions are 'conventional' because he thinks that the number of adjustable parameters may change when the hypotheses are described differently. This he believes is exactly what is illustrated in Goodman's new riddle of induction, otherwise known as the grue problem. However, the 'number of (...) adjustable parameters' is actually a loose way of referring to a quantity that is not language dependent. The quantity arises out of Akaike's theorem in a way that ensures its language invariance. (shrink)

Ketelaar and Ellis have provided a remarkably clear and succinct statement of Lakatosian philosophy of science and have also argued compellingly that the neo-Darwinian theory of evolution fills the Lakatosian criteria of progressivity. We find ourselves in agreement with much of what Ketelaar and Ellis say about Lakatosian philosophy of science, but have some questions about (1) the place of evolutionary psychology in a Lakatosian framework, and (2) the extent to which evolutionary psychology truly predicts new findings.

Alienation and Connection addresses social constructs that perpetuate alienation through suffering. The contributors discuss how alienation through suffering in a variety of contexts can be transformed into connection and reconnection: human relationship with the environment, economic and social systems that disconnect and reconnect, cultural constructs that divide or can heal, encountered difference that brings opportunity, and various manifestations of personal pain that can be survived and even overcome.

Scientific discovery is often regarded as romantic and creative - and hence unanalyzable - whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought (...) processes used to discover scientific laws. The programs - BACON, DALTON, GLAUBER, and STAHL - are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. "Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science. Pat Langley is an Associate Professor in the Department of Information and Computer Science at the University of California, Irvine.Herbert Simon is a Professor in the Departments of Psychology, Computer Science, and Philosophy at Carnegie-Mellon University. Gary L. Bradshaw is an Assistant Professor in the Department of Psychology and Institute of Cognitive Science at the University of Colorado, Boulder. Jan M. Zytkow is an Associate Professor in the Computer Science Department at Wichita State University. (shrink)

Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light (...) on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific realism. * Both of us gratefully acknowledge support from the Graduate School at the University of Wisconsin-Madison, and NSF grant DIR-8822278 (M.F.) and NSF grant SBE-9212294 (E.S.). Special thanks go to A. W. F. Edwards.William Harper. Martin Leckey. Brian Skyrms, and especially Peter Turney for helpful comments on an earlier draft. (shrink)

The distinction itself is best explained as follows. At the empirical level (at the bottom), there are curves, or functions, or laws, such as PV = constant the Boyle’s example, or a = M/r 2 in Newton’s example. The first point is that such formulae are actually ambiguous as to the hypotheses they represent. They can be understood in two ways. In order to make this point clear, let me first introduce a terminological distinction between variables and parameters. Acceleration and (...) distance (a and r) are variables in Newton’s formula because they represent quantities that are more or less directly measured. The distinction between what is directly measured and what it is not is to be understood relative the context. All I mean is that values of acceleration and distance are determined independently of the hypothesis, or theory, under consideration. I do not mean that their determination involves no kind of inference at all. For instance, acceleration is the instantaneous change in velocity per unit time, and this is not something that is directly determined from raw data that records the position of the moon at consecutive points in time. It is consistent with that raw data that the motion of the moon is actually discontinuous, so that the moon has no acceleration. So, there are definitely theoretical assumptions make about the moon’s motion that are used to estimate the moon’s acceleration at a particular time. But these assumptions are not unique to Newton’s theory. The same assumptions are also made by the rival hypotheses under consideration. In fact, the existence of quantities such as instantaneous acceleration is only called into question by the far more recent theory of quantum mechanics. Likewise, in the case of Boyle’s law, there is no controversy in viewing the volume of the trapped air as being determined in a way that does not make use of the theory that Boyle is introducing. (shrink)

Puzzle solving in normal science involves a process of accommodation—auxiliary assumptions are changed, and parameter values are adjusted so as to eliminate the known discrepancies with the data. Accommodation is often contrasted with prediction. Predictions happen when one achieves a good fit with novel data without accommodation. So, what exactly is the distinction, and why is it important? The distinction, as I understand it, is relative to a model M and a data set D, where M is a set of (...) equations with adjustable parameters (i. e., M is a family of equations with no free parameters). Definition: Model M predicts data D if and only if either (a) all members of M fit D well, or (b) a particular predictive hypothesis is selected from M by fitting M to other data, and the fitted model fits D well. M merely accommodates D if and only if (i) M does not predict D, and (ii) the predictive hypothesis selected from M using other data does not fit D well. There will be cases in which a model M neither predicts nor accommodates D. These are the cases in which we are willing to say that data falsifies the model. So, the distinction between prediction and accommodation applies only when there is no falsification. (shrink)

Whewell, William (b Lancaster, England, 24 May 1794; d Cambridge, England, 6 March 1866) Born the eldest son of a carpenter, William Whewell rose to become Master of Trinity College, Cambridge and a central figure in Victorian science. After attending the grammar school at Heversham in Westmorland, Whewell entered Trinity College, Cambridge and graduated Second Wrangler. He became a Fellow of the College in 1817, took his M.A. degree in 1819, and his D.D. degree in 1844.

The usual construction of models of NFU (New Foundations with urelements, introduced by Jensen) is due to Maurice Boffa. A Boffa model is obtained from a model of (a fragment of) Zermelo–Fraenkel with Choice (ZFC) with an automorphism which moves a rank: the domain of the Boffa model is a rank that is moved. “Most” elements of the domain of the Boffa model are urelements in terms of the interpreted NFU. The main result of this paper is that the restriction (...) of the membership relation of the original model of set theory with automorphism to the domain of the Boffa model is first-order definable in the language of NFU. In particular, all information about the extensions in the original model of the urelements of the model of NFU is definable in terms of NFU. A corollary (answering a question of Thomas Forster) is that the urelements in a Boffa model are not homogeneous. (shrink)

"Forster was the first to lay the foundation of the world view that has now become dominant thanks to the progress of positive knowledge. He rebelled with all the power of his thought against the philosophical systems then in favor and counterposed to the subjective speculations of philosophy the logic of experience and the direct witness of common sense." This was the characterization of Georg Forster given by D. I. Pisarev. Upon reading the works of Forster one (...) cannot but agree that their author was possessed of an extraordinary intellect and that he ranks with the outstanding materialist philosophers. A passionate striving to remake the existing order of things characterized his entire life and activity. He took an active part in the French Revolution, and was one of the organizers of the commune in Mainz. Forster was the author of a number of philosophical works that had an enormous influence upon his contemporaries and upon the development of philosophical thought in general. (shrink)

“Only connect …,” the epigraph of Forster's Howards End, offers itself as a model of moral diplomacy. The efficacy of genuine human connection—whether it takes the form of creative action or of decent human relations—in containing and civilizing force is an idea that informs the novel's conception of what constitutes and ensures civilized life. Forster regarded propriety and convention as expressions of force and so applauded any assault on conventional feeling as an act of moral heroism. This essay (...) introduces the third installment of the Common Knowledge symposium “Peace by Other Means”: it explores and indeed extols the moral efficacy of connection in containing force and resolving conflicts, but it also contemplates the obstacles to connection, which Forster dramatizes with his characteristic honesty. (shrink)

The article explores E.M. Forster’s story The Machine Stops as an example of dystopian literature and its possible associations with the use of technology and with today’s cyber culture. Dystopian societies are often characterized by dehumanization and Forster’s novel raises questions about how we live in time and space; and how we establish relationships with the Other and with the world through technology. We suggest that the fear of technology depicted in dystopian literature indicates a fear that machines (...) are mimicking the roles that humans already play in relational encounters. Our relationship with machines frequently suggests a classical “I-it” situation. However, a genuine dialogue is where there is no master and where communication and understanding are achieved through the encounter and through openness to difference and to change. The article examines the ways machines and automata are imagined and become part of lived human existence, in the light of Martin Buber’s philosophy of dialogue and Merleau-Ponty’s phenomenology of perception and otherness. The problem seems to be how everyday technological interfaces can change the way we first perceive the world and the possibility that with certain types of mediation there is a loss of connection with the Other. It is argued that understanding dialogical conditions could help turn the relationship with technology into something more humane. Literature such as Forster’s is considered as an example of such a dialogical condition, suggesting ways of dealing with human dilemmas by exploring the field of possibilities. (shrink)