Degrees of Belief as Basis for Scientific Reasoning?

Bayesianism is the claim that scientific reasoning is\ud probabilistic, and that probabilities are adequately interpreted\ud as an agent"s actual subjective degrees of belief\ud measured by her betting behaviour.\ud Confirmation is one important aspect of scientific reasoning.\ud The thesis of this paper is the following: Given that\ud scientific reasoning (and thus confirmation) is at all\ud probabilistic, the subjective interpretation of probability has\ud to be given up in order to get right confirmation, and thus\ud scientific reasoning in general

Knowledge-sourcing of R&D workers in different job positions: contextualising external personal knowledge networks

This paper systematically examines the role of external personal knowledge networks vis-à-vis alternative sources of work-related knowledge. Specific hypotheses on whether the importance of external personal networks vary for job positions, knowledge functions and sources of competitiveness are examined. The study is based on a survey and interviews with 105 R&D workers in 46 Information Technology (IT) firms in the Greater Cambridge Region (UK).The results show that alternative sources of knowledge are considerably more important than external personal networks. Specifically, the results confirm the hypothesis that the lower the job position, the less important are external personal networks. The most frequent type of knowledge that is uniquely available through personal networks concerns business knowledge of senior managers rather than technological knowledge. Furthermore, the analysis supports the view that external personal networks are most important for exploratory keeping up-to-date than for problem solving. Finally, the paper shows that external personal networks are more important for firms whose competitiveness is primarily driven by being cutting-edge in technology.Overall, the results suggest that academic theorising and R&D management/policy on external personal networks needs to be more context-sensitive and would benefit from differentiating between job positions, knowledge functions and types of firm competitiveness

Reply to Crupi et al.’s ‘Confirmation by Uncertain Evidence’ ([2008])

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Structural Equations and Beyond

Recent accounts of actual causation are stated in terms of extended causal models. These extended causal models contain two elements representing two seemingly distinct modalities. The first element are structural equations which represent the “(causal) laws” or mechanisms of the model, just as ordinary causal models do. The second element are ranking functions which represent normality or typicality. The aim of this paper is to show that these two modalities can be unified. I do so by formulating two constraints under which extended causal models with their two modalities can be subsumed under so called “counterfactual models” which contain just one modality. These two constraints will be formally precise versions of Lewis’ (1979) familiar “system of weights or priorities” governing overall similarity between possible worlds

Towards a Precise Semantics for Object-Oriented Modeling Techniques

In this paper we present a possible way how a precise semantics of object oriented modeling techniques can be achieved and what the possible benefits are .We outline the main modeling techniques used in the SysLab project sketch how a precise semantics can be given and how this semantics can be used during the development process.Comment: 6 pages, 0 figure