Can computer systems ever be considered moral agents? This paper considers two factors that are explored in the recent philosophical literature. First, there are the important domains in which computers are allowed to act, made possible by their greater functional capacities. Second, there is the claim that these functional capacities appear to embody relevant human abilities, such as autonomy and responsibility. I argue that neither the first (Domain-Function) factor nor the second (Simulacrum) factor gets at the central issue in the (...) case for computer moral agency: whether they can have the kinds of intentional states that cause their decisions and actions. I give an account that builds on traditional action theory and allows us to conceive of computers as genuine moral agents in virtue of their own causally efficacious intentional states. These states can cause harm or benefit to moral patients, but do not depend on computer consciousness or intelligence. (shrink)

In this paper the basic aim of the so‐called ‘strong programme’ in the sociology of knowledge is examined. The ‘strong programme’ is considered (and rightly so) as an extreme version of the anti‐realist view of science. While the problem of scientific realism has normally been dealt with from the point of view of the ‘context of justification’ of theories, the paper focuses on the issues raised by law‐discovery. In this context Herbert Simon's views about the existence of a ‘logic of (...) scientific discovery’ are discussed and criticized. The main thesis of the paper is that if the structure of both discovery and prediction is properly understood, then the basic anti‐realist claims become untenable. A fortiori, the ‘strong programme’ appears to be unable to explain some basic features of the structure of science. (shrink)

Based on architectural projects which interpret literature as program we discuss design reasoning when no routine models of problem solving apply. We address three aspects of formulation: defining the design charge so that it can be retrospectively stated independent of the actual proposal; defining a language of formal operations; and defining the intrinsic aims of design that are only intimated through the proposal itself. The coherence of the project is a function of the way in which formal properties interact, and (...) the way in which they sustain analogical or metaphorical relationships to text: how the patterns of subdivision, connection, differentiation, positioning, movement or perception associated with built space relate to textual figures, concepts, structure, or narrative. The possibility of constructing architectural meaning in this way implies an underlying model of space as a morphic language which works primarily through the constitution of generic and significant relationships rather than the combination of previously objectified elements. The gradual articulation of the design charge is mediated by a process of diagramming. Diagrams express as spatial constructions the conditions and concepts abstracted from text; also, they act as notations of constructive operations which are themselves spatial. Diagrams can be abstractive or pictorial, dense or discrete. They document two aspects of an integral process of reasoning: First, an exploration of how concepts, whether directly, analogically or metaphorically transferred from text to shape, may relate to produce a more complex idea; second, how formal properties co-vary and how an emergent design proposal engages and activates a field of formal possibility. (shrink)

This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress (...) should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”. (shrink)

Un problème est dit ici «mouvoir» la recherche, quand c'est sur lui que se concentre l'énergie du chercheur et que c'est son traitement qui fait «avancer» la recherche.Une entreprise de recherche, une « e-r »,commej'écrirai souvent, est considérée ici comme une unité individuée et je postule qu'il y a toujours au moins un « niveau » où l'on peut se placer pour qu'il soit légitime de considérer une e-r comme une unité individuée.

Most management researchers pause at the threshold of objective right and wrong. Their hesitation is understandable. Values imply a “subjective,” personal dimension, one that can invite religious and moral interference in research. The dominant epistemological camps of positivism and subjectivism in management stumble over the notion of moral objectivity. Empirical research can study values in human behavior, but hard-headed scientists should not assume that one value can be objectively better than another. In this article, we invite management researchers to rethink (...) this presumption. We show how accepting at least a limited form of moral objectivity, namely, an epistemic orientation that seeks objective moral reasons, can benefit management research by 1. guiding research practice; 2. using patterns of moral objectivity as clues for formulating empirical hypotheses for psychological explanations; and 3. adding prescriptive power to empirical theories. (shrink)

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental (...) program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories. (shrink)

Collaborative learning with cases characteristically involves discussing and developing shared explanations. We investigated the argumentation scheme which learners use in constructing shared explanations over evidence. We observed medical students attempting to explain how a judge had arrived at his verdict in a case of medical negligence. The students were learning within a virtual learning environment and their communication was computer mediated. We identify the dialogue type that these learners construct and show that their argumentation conforms with an abductive form of (...) argumentation scheme (‘inference to the best explanation’). We also assessed the students’ learning and propose that it is related to particular features of this argumentation scheme. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information‐processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher‐level mechanisms from their components and situating them in their environments. (shrink)

The research on which the present paper makes a point in aimed at designing a cognitive model of Albert Einstein's discovery that is based on fundamental Einstein's publications and placed, ideally, at a meso-level, between macro-historical and micro-cognitive reconstructions (e.g. protocol analysis). As in a cognitive-historical analysis, we will trace some discovery heuristics in the construction of representations, that are on a continuum with those we employ in ordinary problem solving. Firstly, some theory-specific, reflexive heuristics—named orientative heuristics—are traced: inner perfection, (...) explain-or-assume, explanatory correspondence, and covariance/invariance. Then, other well-known abstractive heuristics as analogical and imagistic reasoning, thought experiment, limiting case analysis (e.g. Nersessian 1992) are shown occurring in Einstein's key-publications. A sketch of a socio-cognitive model for his discovery is then presented following two suggestions: (a) an idea of Van Fraassen about discovery phases, and (b) the Humean distinction between beliefs and ideas. (shrink)

The main thrust of the argument of this thesis is to show the possibility of articulating a method of construction or of synthesis--as against the most common method of analysis or division--which has always been (so we shall argue) a necessary component of scientific theorization. This method will be shown to be based on a fundamental synthetic logical relation of thought, that we shall call inversion--to be understood as a species of logical opposition, and as one of the basic monadic (...) logical operators. Thus the major objective of this thesis is to This thesis can be viewed as a response to Larry Laudan's challenge, which is based on the claim that ``the case has yet to be made that the rules governing the techniques whereby theories are invented (if any such rules there be) are the sorts of things that philosophers should claim any interest in or competence at.'' The challenge itself would be to show that the logic of discovery (if at all formulatable) performs the epistemological role of the justification of scientific theories. We propose to meet this challenge head on: a) by suggesting precisely how such a logic would be formulated; b) by demonstrating its epistemological relevance (in the context of justification) and c) by showing that a) and b) can be carried out without sacrificing the fallibilist view of scientific knowledge. OBJECTIVES: We have set three successive objectives: one general, one specific, and one sub-specific, each one related to the other in that very order. (A) The general objective is to indicate the clear possibility of renovating the traditional analytico-synthetic epistemology. By realizing this objective, we attempt to widen the scope of scientific reason or rationality, which for some time now has perniciously been dominated by pure analytic reason alone. In order to achieve this end we need to show specifically that there exists the possibility of articulating a synthetic (constructive) logic/reason, which has been considered by most mainstream thinkers either as not articulatable, or simply non-existent. (B) The second (specific) task is to respond to the challenge of Larry Laudan by demonstrating the possibility of an epistemologically significant generativism. In this context we will argue that this generativism, which is our suggested alternative, and the simplified structuralist and semantic view of scientific theories, mutually reinforce each other to form a single coherent foundation for the renovated analytico-synthetic methodological framework. (C) The third (sub-specific) objective, accordingly, is to show the possibility of articulating a synthetic logic that could guide us in understanding the process of theorization. This is realized by proposing the foundations for developing a logic of inversion, which represents the pattern of synthetic reason in the process of constructing scientific definitions. (shrink)