The purpose of this essay is to defend the deductive-nomological model of explanation against a number of criticisms that have been made of it. It has traditionally been thought that scientific explanations were causal and that scientific explanations involved deduction from laws. In recent years, however, this three-fold identity has been challenged: there are, it is argued, causal explanations that are not scientific, scientific explanations that are not deductive, deductions from laws that are neither causal explanations nor scientific explanations, and (...) causal explanations that involve no deductions from laws. The aim of the present essay is to defend the traditional identities, and to show that the more recent attempts at invalidating them fail in their object. More specifically, this essay argues that a Humean version of the deductive-nomological model of explanation can be defended as the correct account of scientific explanation of individual facts and processes, and as the correct account of causal explanations of individual facts and processes. The deductive-nomological model holds that to explain an event E, say that a is G, one must find some initial conditions C, say that a is F, and a law or theory T such that T and C jointly entail E, and both are essential to the deduction. (shrink)

Since the middle of this century, the dominant prescriptive approach to decision theory has been a deductive viewpoint which is concerned with axioms of rational preference and their consequences. After summarizing important problems with the preference primitive, this paper argues for a constructive approach in which information is the foundation for decision-making. This approach poses comparability of uncertain acts as a question rather than an assumption. It is argued that, in general, neither preference nor subjective probability can be assumed given, (...) and that these need to be generated by using the relevant information available to the decision-agent in a given situation. A specific constructive model is discussed and illustrated with a real example from this viewpoint. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

In social and organizational research, theory is conventionally used to explain social phenomena. However, theory may be transformative in the sense that in using and testing the theory in a practical domain, researchers may attempt to help practitioners transform and improve their social practices and institutions. This idea is illustrated by a research-and-development project in Denmark, headed by the author, which used transformative theory to design professional conferences that are more conducive to participant learning and involvement than is the conventional, (...) lecture-based format. A number of learning techniques were derived from the theory and were tested as hypotheses: When implemented in thirty live conference experiments, did they contribute to learning, as specified by the theory? Used in this manner, transformative theory may supplement the aspirations motivating change agents by some of the well-known qualities of scientific research, including theoretical grounding, a coherent ontology, testable hypotheses, systematic evaluation, external validity, and theory–action consistency. (shrink)

The distinction between basic and applied research is notoriously vague, despite its frequent use in science studies and in science policy. In most cases it is based on such pragmatic factors as the knowledge and intentions of the investigator or the type of research institute. Sometimes the validity of the distinction is denied altogether. This paper suggests that there are two ways of distinguishing systematically between basic and applied research: (i) in terms of the utilities that define the aims of (...) inquiry, and (ii) by reference to the structure of the relevant knowledge claims. An important type of applied research aims at results that are expressed by techical norms (in von Wright's sense): if you wish to achieveA, and you believe you are in a situationB, then you should doX. This conception of design sciences allows us to re-evaluate many issues in the history, philosophy, and ethics of science. (shrink)

The article centres on a single document, the 1968 doctoral thesis of L. Bruce Archer. It traces Archer’s earlier publications and the sources that informed and inspired his thinking as a way of understanding his influential work at the Royal College of Art from 1962. Analysis suggests that Archer’s ambition for a rigorous ‘science of design’ inspired by linear algorithmic approaches was increasingly threatened with disruption by his experience of large, complex design projects. Reflecting on Archer’s engagement with other models (...) of designing, the article ends with Archer’s retrospective view and an account of his significantly altered opinions. Archer is located as both a theorist and someone fascinated by the commercial and practical aspects of designing. (shrink)