An introduction to the model-theoretic approach in the philosophy of science is given and it is argued that this program is further enhanced by the introduction of partial structures. It is then shown that this leads to a natural and intuitive account of both "iconic" and mathematical models and of the role of the former in science itself.

An introduction to the model-theoretic approach in the philosophy of science is given and it is argued that this program is further enhanced by the introduction of partial structures. It is then shown that this leads to a natural and intuitive account of both "iconic" and mathematical models and of the role of the former in science itself.

We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that abstraction through information hiding is a (...) primary factor in computer science progress and success through an examination of the ubiquitous role of information hiding in programming languages, operating systems, network architecture, and design patterns. (shrink)

Models are of central importance in many scientific contexts. The centrality of models such as inflationary models in cosmology, general-circulation models of the global climate, the double-helix model of DNA, evolutionary models in biology, agent-based models in the social sciences, and general-equilibrium models of markets in their respective domains is a case in point (the Other Internet Resources section at the end of this entry contains links to online resources that discuss these models). Scientists spend significant amounts of time building, (...) testing, comparing, and revising models, and much journal space is dedicated to interpreting and discussing the implications of models. In short, models are one of the principal instruments of modern science. (shrink)

Introduction: Science and Common Sense Long before the beginnings of modern civilization, men ac- quired vast funds of information about their environment. ...

It is argued that a number of important, and seemingly disparate, types of representation are species of a single relation, here called structural representation, that can be described in detail and studied in a way that is of considerable philosophical interest. A structural representation depends on the existence of a common structure between a representation and that which it represents, and it is important because it allows us to reason directly about the representation in order to draw conclusions about the (...) phenomenon that it depicts. The present goal is to give a general and precise account of structural representation, then to use that account to illuminate several problems of current philosophical interest — including some that do not initially seem to involve representation at all. In particular, it is argued that ontological reductions (like that of the natural numbers to sets), compositional accounts of semantics, several important sorts of mental representation, and (perhaps) possible worlds semantics for intensional logics are all species of structural representation and are fruitfully studied in the framework developed here. (shrink)

A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.

We consider the case where inconsistencies are present between a system and its corresponding model, used for automatic verification. Such inconsistencies can be the result of modeling errors or recent modifications of the system. Despite such discrepancies, we can still attempt to perform automatic verification. In fact, as we show, we can sometimes exploit the verification results to assist in automatically learning the required updates to the model. In a related previous work, we have suggested the idea of black box (...) checking, where verification starts without any model, and the model is obtained while repeated verification attempts are performed. Under the current assumptions, an existing inaccurate model is used to expedite the updates. We use techniques from black box testing and machine learning. We present an implementation of the proposed methodology called AMC . We discuss some experimental results, comparing various tactics of updating a model while trying to perform model checking. (shrink)

Taking Brian Cantwell Smith’s study, “Limits of Correctness in Computers,” as its point of departure, this article explores the role of models in computer science. Smith identifies two kinds of models that play an important role, where specifications are models of problems and programs are models of possible solutions. Both presuppose the existence of conceptualizations as ways of conceiving the world “in certain delimited ways.” But high-level programming languages also function as models of virtual (or abstract) machines, while low-level programming (...) languages function as models of causal (or physical) machines. The resulting account suggests that sets of models embedded within models are indispensable for computer programming. (shrink)

Provability, Computability and Reflection.

Frederick Suppe has come to enjoy a position of undisputed leadership in the post-positivistic philosophy of science.

The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of ...

The notion of program verification appears to trade upon an equivocation. Algorithms, as logical structures, are appropriate subjects for deductive verification. Programs, as causal models of those structures, are not. The success of program verification as a generally applicable and completely reliable method for guaranteeing program performance is not even a theoretical possibility.