Online research in philosophy

In “General Information in Relevant Logic” (Synthese 167, 2009), the semantics for relevant logic is interpreted in terms of objective information . Objective information is potential data that is available in an environment. This paper explores the notion of objective information further. The concept of availability in an environment is developed and used as a foundation for the semantics, in particular, as a basis for the understanding of the information that is expressed by relevant implication. It is also used to understand the nature of misinformation. A form of relevant logic—called “LOI” for “logic of objective information”—is presented and the relationship between the justification of its proof theory and the semantics is discussed. This relationship is rather reciprocal. Intuitive features of the logic are used to interpret and justify aspects of the model theory and intuitive aspects of the model theory are used to interpret and justify features of the logic. Information conditions are presented for the connectives and the way that they fit into the theory of information is discussed.

This paper is an empirical critique of causal accounts of scientific explanation. Drawing on explanations which rely on nonlinear dynamical modeling, I argue that the requirement of causal relevance is both too strong and too weak to be constitutive of scientific explanation. In addition, causal accounts obscure how the process of mathematical modeling produces explanatory information. I advance three arguments for the inadequacy of causal accounts. First, I argue that explanatorily relevant information is not always information about causes, even in cases where the explanandum has an identifiable causal history. Second, I argue that treating theoretical explanations as reductions from general causal laws does not accurately describe the types of "top-down" explanations typical of dynamical modeling. Finally, I argue that causal/mechanical accounts of explanation are intrinsically vulnerable to the irrelevance problem.

We present an information theoretic account of models as scientific representations, where scientific models are understood as information carrying artifacts. We propose that the semantics of models should be based on this information coupling of the model to the world. The information theoretic account presents a way of avoiding the need to refer to agents' intentions as constitutive of the semantics of scientific representations, and it provides a naturalistic account of model semantics, which can deal with the problems of asymmetry, relevance and circularity that afflict other currently popular naturalistic proposals.

Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, and more. Elaborations are suggested about their precise formulations, mutual relationships, and truth-aptness.

In his article Open Problems in the Philosophy of Information [1] Luciano Floridi presented a Philosophy of Information research program in the form of eighteen open problems, covering the following fundamental areas: Information definition, information semantics, intelligence/cognition, informational universe/nature and values/ethics. We revisit Floridi’s program, highlighting some of the major advances, commenting on unsolved problems and rendering the new landscape of the Philosophy of Information (PI) emerging at present. As we analyze the progress of PI we try to situate Floridi’s program in the context of scientific and technological development that have been made last ten years. We emphasize that Philosophy of Information is a huge and vibrant research field, with its origins dating before Open Problems, and its domains extending even outside their scope. In this paper, we have been able only to sketch some of the developments during the past ten years. Our hope is that, even if fragmentary, this review may serve as a contribution to the effort of understanding the present state of the art and the paths of development of Philosophy of Information as seen through the lens of Open Problems.

http://www.diva-portal.org/mdh/theses/abstract.xsql?dbid=153.

There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates some interesting areas of application of the revised definition.

The distinction between the modeling of information and the modeling of data in the creation of automated systems has historically been important because the development tools available to programmers have been wedded to machine oriented data types and processes. However, advances in software engineering, particularly the move toward data abstraction in software design, allow activities reasonably described as information modeling to be performed in the software creation process. An examination of the evolution of programming languages and development of general programming paradigms, including object-oriented design and implementation, suggests that while data modeling will necessarily continue to be a programmer's concern, more and more of the programming process itself is coming to be characterized by information modeling activities.

Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and expressible by the system user as well as the system developer. This is particularly desirable in connection with certain intimate relationships, such as being the same as or being a part of.

http://www.springerlink.com/content/45454q54j3151304/fulltext.pdf.