We propose an analysis of the notion of model as crucially related to the notion of point of view. A model in this sense would always suggest a certain way of looking at a real system, a certain way of thinking about it and a certain way of acting upon it. We focus on System Dynamics as a paradigmatic case with respect to many of the features and problems we can find in the field of modelling and simulation. We analyse (...) in detail some of those features. All of them would be present in many other cases of construction and use of models. Furthermore, they would support the thesis that a model can be fruitfully understood as offering a point of view capable of improving our own points of view over a certain system. The point of view offered by the model could include both non-conceptual and conceptual contents, it would have a complex structure and behaviour, and it would have direct consequences on the decisions made by the subjects adopting that point of view. (shrink)

It is proposed that the Darwinian theoretical approach and account of living systems has not yet been clearly given. A first approximation to this is attempted, focussing on behavior in evolving environments. A theoretical terminology is defined emphasizing the mutuality of organism and environment and the existence of biologically theoretical entities.

This essay presents a philosophical and computational theory of the representation of de re, de dicto, nested, and quasi-indexical belief reports expressed in natural language. The propositional Semantic Network Processing System (SNePS) is used for representing and reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring in intentional contexts and representing uses of indicators by another speaker) pose problems for natural-language representation and reasoning systems, because--unlike pure indicators--they cannot be replaced by coreferential NPs without changing the meaning of the (...) embedding sentence. Therefore, the referent of the quasi-indicator must be represented in such a way that no invalid coreferential claims are entailed. The importance of quasi-indicators is discussed, and it is shown that all four of the above categories of belief reports can be handled by a single representational technique using belief spaces containing intensional entities. Inference rules and belief-revision techniques for the system are also examined. (shrink)

I show how a robot with what looks like a hard problem of consciousness might emerge from the earnest attempt to make a robot that is smart and self-reflective. This problem arises independently of any assumption to the effect that the robot is conscious, but deserves to be thought of as related to the human problem in virtue of the fact that (1) the problem is one the robot encounters when it tries to naturalistically reduce its own subjective states (2) (...) it seems incredibly difficult from the robot’s own naturalist perspective and, most importantly, (3) it invites the robot to engage in the exact same metaphysical responses as humans offer to the problem of consciousness. Despite the fact that it invites the robot to consider extravagant metaphysical solutions, the problem I explore is purely algorithmic. The robot cannot complete its naturalist physicalist reduction as a matter of algorithmic fact, whether or not the naturalist physicalist reduction would be correct as a matter of metaphysical fact. It is hoped that by reproducing the familiar seeming problem in an artificial context, a greater understanding of the human problem of consciousness can be achieved. (shrink)

Metaphors play a crucial role in the understanding of science. Since antiquity, metaphors have been used in technical texts to describe structures unknown or unnamed; besides establishing a terminology of science, metaphors are also important for the expression of concepts. However, a concise terminology to classify metaphors in the language of science has not been established yet. But in the context of studying the history of a science and its concepts, a precise typology of metaphors can be helpful. Metaphors have (...) a lot in common with models in science, as has been observed already. In this paper, therefore, I suggest a typology of metaphor in ancient science to fill this terminological gap by using concepts applied to the classification of models in science, as coined by Rom Harré. I propose to differentiate between homeoconceptual metaphors and paraconceptual metaphors. Furthermore, functional and structural aspects of metaphors in ancient science are taken into account. Case studies from ancient texts displaying metaphors in ancient science are presented and classified following the outlined typology of metaphors. (shrink)

Scientific inquiry has led to immense explanatory and technological successes, partly as a result of the pervasiveness of scientific theories. Relativity theory, evolutionary theory, and plate tectonics were, and continue to be, wildly successful families of theories within physics, biology, and geology. Other powerful theory clusters inhabit comparatively recent disciplines such as cognitive science, climate science, molecular biology, microeconomics, and Geographic Information Science (GIS). Effective scientific theories magnify understanding, help supply legitimate explanations, and assist in formulating predictions. Moving from their (...) knowledge-producing representational functions to their interventional roles (Hacking 1983), theories are integral to building technologies used within consumer, industrial, and scientific milieus. -/- This entry explores the structure of scientific theories from the perspective of the Syntactic, Semantic, and Pragmatic Views. Each of these views answers questions such as the following in unique ways. What is the best characterization of the composition and function of scientific theory? How is theory linked with world? Which philosophical tools can and should be employed in describing and reconstructing scientific theory? Is an understanding of practice and application necessary for a comprehension of the core structure of a scientific theory? How are these three views ultimately related? (shrink)

Many debates about consciousness appear to be endless, in part because of conceptual confusions preventing clarity as to what the issues are and what does or does not count as evidence. This makes it hard to decide what should go into a machine if it is to be described as 'conscious'. Thus, triumphant demonstrations by some AI developers may be regarded by others as proving nothing of interest because the system does not satisfy *their* definitions or requirements specifications.

Wittgenstein famously ended his Tractatus Logico-Philosophicus (Wittgenstein 1922) by writing: "Whereof one cannot speak, one must pass over in silence." (Wovon man nicht sprechen kann, darüber muss man schweigen.) In that earliest work, Wittgenstein gives no clue about whether this aphorism applied to animal minds, or whether he would have included philosophical discussions about animal minds as among those displaying "the most fundamental confusions (of which the whole of philosophy is full)" (1922, TLP 3.324), but given his later writings on (...) language and thought, it seems a plausible hypothesis. Years later he wrote in the Philosophical Investigations (1968, p. 223) another aphoristic statement: "If a lion could speak, we would not understand him." (Wenn der Löwe sprechen könnte, wir könnten ihn nicht verstehen.) Decades of philosophical discussion about what Wittgenstein meant by these remarks illustrate another point: when philosophers speak, we do not fully understand them. (shrink)

The work done in the philosophy of modeling by Vaihinger (1876), Craik (1943), Rosenblueth and Wiener (1945), Apostel (1960), Minsky (1965), Klaus (1966) and Stachowiak (1973) is still almost completely neglected in the mainstream literature. However, this work seems to contain original ideas worth to be discussed. For example, the idea that diverse functions of models can be better structured as follows: in fact, models perform only a single function – they are replacing their target systems, but for different purposes. (...) Another example: the idea that all of cognition is cognition in models or by means of models. Even perception, reflexes and instincts (animal and human) can be best analyzed as modeling. The paper presents an analysis of the above-mentioned work. (shrink)

We are engineers, and our view of consciousness is shaped by an engineering ambition: we would like to build a conscious machine. We begin by acknowledging that we may be a little disadvantaged, in that consciousness studies do not form part of the engineering curriculum, and so we may be starting from a position of considerable ignorance as regards the study of consciousness itself. In practice, however, this may not set us back very far; almost a decade ago, Crick wrote: (...) 'Everyone has a rough idea of what is meant by consciousness. It is better to avoid a precise definition of consciousness because of the dangers of premature definition. Until the problem is understood much better, any attempt at a formal definition is likely to be either misleading or overly restrictive, or both'. This seems to be as true now as it was then, although the identification of different aspects of consciousness by Block has certainly brought a degree of clarification. On the other hand, there is little doubt that consciousness does seem to be something to do with the operation of a sophisticated control system, and we can claim more familiarity with control systems than can most philosophers, so perhaps we can make up some ground there. (shrink)

As brightly shown by Mainzer [24], the science of complexity has many distinct origins in many disciplines. Those various origins has led to “an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the nonlinear interactions of microscopic elements” (ibid.). This paper suggests that the parallel and strong expansion of modeling and simulation - especially after the Second World War and the subsequent development of computers - is a rationale which also can be counted as an explanation of (...) this emergence. With the benefit of hindsight, one can find three periods in the methodologies of modeling in the empirical sciences: 1st the simple modeling of the simple, 2nd the simple modeling of the complex, 3rd the complex modeling and simulation of the complex. Our main thesis is that the current spreading (since the 90’s) of complex computer simulations of systems of models (where a simulation is no more a step by step calculus of a unique logico-mathematical model) is another promising dimension of the science of complexity. Following this claim, we propose to distinguish three different types of computer simulations in the context of complex systems’ modeling. Finally, we show that these types of simulations lead to three different types of weak emergence, too. (shrink)

http://www.cs.bham.ac.uk/research/projects/cogaff/62-80.html#1967-01.

Models are the ultimate results of all (scientific, non-scientific, and anti-scientific) kinds of cognition. Therefore, philosophy of cognition should start with the following fundamental distinction: there are models, and there are means of model-building. Laws of nature and theories are useful only as a means of model-building. If it's true that models are the ultimate results of cognition, then shouldn't we try reordering the field, starting with the notion of model? In this way, couldn't we obtain a unified and more (...) productive picture - a model-based model of cognition? (shrink)