Online research in philosophy

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.

While information structure has traditionally been viewed as a singlepartition of information within an utterance, there are opposing viewsthat identify multiple such partitions in an utterance. The existenceof alternative proposals raises questions about the notion ofinformation structure and also its relation to discoursestructure. Exploring various linguistic aspects, this paper supports thetraditional view by arguing that there is no information structure partition within a subordinate clause.

The purpose of this paper is to look at some existing methods of semantic information quantification and suggest some alternatives. It begins with an outline of Bar-Hillel and Carnap’s theory of semantic information before going on to look at Floridi’s theory of strongly semantic information. The latter then serves to initiate an in-depth investigation into the idea of utilising the notion of truthlikeness to quantify semantic information. Firstly, a couple of approaches to measure truthlikeness are drawn from the literature and explored, with a focus on their applicability to semantic information quantification. Secondly, a similar but new approach to measure truthlikeness/information is presented and some supplementary points are made.

The paper offers the foundations of the theory of information media. Information media are dynamical systems with additional macrostructure of information-carrying states and information-preserving transformations. The paper also defines the notion of information media network as a system of information media connected by information transformations. It is demonstrated that many standard examples of information-containing and processing systems are captured by the general notion of information medium. The paper uses the theory (and informal discussion) of information media to motivate a structural approach to the information in media. The idea is that the notion of information transformation should be regarded as more primitive than the notion of informational state. Thus in information systems, especially in the context of information technology, information is secondary while information transformation is primary.

Examination of the systemic properties and forms of interaction that characterize classification and categorization reveals fundamental syntactic differences between the structure of classification systems and the structure of categorization systems. These distinctions lead to meaningful differences in the contexts within which information can be apprehended and influence the semantic information available to the individual. Structural and semantic differences between classification and categorization are differences that make a difference in the information environment by influencing the functional activities of an information system and by contributing to its constitution as an information environment.

What is it that one thinks or believes when one thinks or believes something? A mental formula? A sentence in some natural language? Its truth conditions? Or perhaps an abstract proposition? The current story of content is fairly ecumenical. It says that a number of aspects, some mental, other semantic, go into our understanding of content. Yet the current story is incomplete. It leaves out a very important aspect of content, one which I call incremental information. It is information in a specific format, information as a limited or local increment, structured by a number of underlying parameters. It is in the form of such increments that information drives cognition and behavior. This is why, perhaps of all aspects of content, it is incremental information which matters most when we want to understand cognitive attitudes and performances. This in turn must have an impact on our philosophical notions of content, propositional attitudes, inference, justification and knowledge.

The book focuses on relations between information and computation. Information is a basic structure of the world, while computation is a process of the dynamic change of information. In order for anything to exist for an individual, the individual must get information on it, either by means of perception or by re-organization of the existing information into new patterns and networks in the brain. With the advent of World Wide Web and a prospect of semantic web, the ways of information supply for individuals, networks of humans and machines and for humanity as a whole are becoming strategically important in a number of ways. Information becomes pivotal for communication, research, education systems, government, businesses and basic functioning of everyday life. At the same time, information may be understood only if we understand its dynamics - time changes of informational structure, that is, we should understand information processing and its primary form - computation. As there is no information without (physical) representation, the dynamics of information is implemented on different levels of granularity by different physical processes, including the level of computation performed by computing machines. There are a lot of open problems of the nature of information and computation, as well as their relationships. How exactly is information dynamics implemented in computational systems, machines as well as living organisms? Are computers processing only data or information and knowledge as well? How does information processing relate to knowledge management and sciences, especially to science of information itself? What do we know of computational processes in machines and living organisms and how these processes are related? What can we learn from natural computational processes that can be useful for information systems and knowledge management? These and similar problems related to information and computation are treated in the book.

This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents five tentative laws of information, valid at multiple scales, which are generalizations of Darwinian, cybernetic, thermodynamic, and complexity principles. These are further used to discuss the notions of life and cognition and their evolution.

This paper addresses one of the fundamental problems of the philosophy of information: How does semantic information emerge within the underlying dynamics of the world?—the dynamical semantic information problem. It suggests that the canonical approach to semantic information that defines data before meaning and meaning before use is inadequate for pre-cognitive information media. Instead, we should follow a pragmatic approach to information where one defines the notion of information system as a special kind of purposeful system emerging within the underlying dynamics of the world and define semantic information as the currency of the system. In this way, systems operating with semantic information can be viewed as patterns in the dynamics—semantic information is a dynamical system phenomenon of highly organized systems. In the simplest information systems, the syntax, semantics, and pragmatics of the information medium are co-defined. It proposes a new more general theory of information semantics that focuses on the interface role of the information states in the information system—the interface theory of meaning. Finally, with the new framework, it addresses the debate between weakly semantic and strongly semantic accounts of information, siding with the strongly semantic view because the pragmatic account developed here is a better generalization of it.