Social entropy theory and analysis applied to communication
Social entropy theory is derived from Shannon’s Mathematical Theory of Communication and it proposes that the evolution and structure of social systems at any scale can be studied using synthetic indicators, such as entropy. Such indicators can tell two main things: 1. what is the relative state of organization and 2. how much energy is available for social action for a given system. The explanatory power of social entropy theory is derived from the fact that its central indicator, social entropy, is at the same time a meaningful measure of information writ large. Social entropy was construed by Shannon, and discussed by his successors, as a measure of how “in-formed” (i.e., structured, organized, non-random) the world of symbols and social interactions is. As such, it is not just a simple indicator, but it can become a powerful explanatory tool, which can play the role of either independent or dependent analysis tool.
This paper is written in three main sections. In the first and third, W. W. is responsible both for the ideas and the form. The middle section, namely “2), Communication Problems of Level A” is an interpretation of mathematical papers by Dr. Claude E. Shannon of the Bell Telephone Laboratories. Dr. Shannon’s work roots back, as von Neumann has pointed out, to Boltzmann’s observation, in some of his work on statistical physics (1894), that entropy is related to “missing information,” inasmuch as it is related to the number of alternatives which remain possible to a physical system after all the macroscopically observable information concerning it has been recorded. L. Szilard (Zsch. f. Phys. Vol. 53, 1925) extended this idea to a general discussion of information in physics, and von Neumann (Math. Foundation of Quantum Mechanics, Berlin, 1932, Chap. V) treated information in quantum mechanics and particle physics. Dr. Shannon’s work connects more directly with certain ideas developed some twenty years ago by H. Nyquist and R. V. L. Hartley, both of the Bell Laboratories; and Dr. Shannon has himself emphasized that communication theory owes a great debt to Professor Norbert Wiener for much of its basic philosophy. Professor Wiener, on the other hand, points out that Shannon’s early work on switching and mathematical logic antedated his own interest in this field; and generously adds that Shannon certainly deserves credit for independent development of such fundamental aspects of the theory as the introduction of entropic ideas. Shannon has naturally been specially concerned to push the applications to engineering communication, while Wiener has been more concerned with biological application (central nervous system phenomena, etc.)
The terms employed in information theory, as developed by Shannon, ‘Wiener and others (cf. 6, pp. 47-49, for a bibliography), provide at least the beginnings of an arbitrary metalanguage for talking about communication. They have the advantage of
extreme generality and. although the lexicon has to be extended a bit to cover human communication situations. the mechanistic nature of the language serves as a partial safeguard against unjustified implicit assumptions. This is particularly useful in a field that deals with human activities. like human communications. where everyone has a ready explanation of any phenomenon. but in terms from the lay language that are usually loaded with diverse and sometimes contradictory connotations. While it is not our intention to try to impose a new language, we have tried to select terms which avoid identification with any particular human communication situation (and thus pressure toward conviction by analogy). Also. we have tried to define these terms as precisely as possible. and we have associated these terms with a set of measurement operations. We have concluded that a relatively small number of basic measures can be used’ to describe a relatively large variety of communication situations, thus increasing the comparability and economy of such descriptions. This also means that. the lexicon is considerably larger than the number of measurement operations. It is important to point out at the outset. however. that what follows in this paper is not a predictive or explanatory theory of the human communication process but rather a descriptive model with measurement implications. This is for the most· part true of information theory as it has been applied to human communication situations. However. in developing any theory of human communication, such arbitrarily defined terms as used here can become the constructs whose interrelationships are specified by the principles of the theory, and the measurement operations associated with such terms can facilitate tests of the theory.
A number of entropy models of social systems have been developed recently. Unfortunately, the complementarity of these approaches remains largely unanalyzed, due to terminological and conceptual differences among them.
Sorin A. Matei et al.
Collaboration and Communication in Online Environments: A Social Entropy Approach
Paper presented at the NCA Annual Conference, San Antonio, Texas, November, 2005
A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.
A DEMOCRACY OF UNEQUALS: SOCIAL DIFFERENTIATION, PARTICIPATION INEQUALITY AND THE COLLABORATIVE IDEAL ONLINE –
Doctoral Dissertation, 2010
This dissertation examines social self-structuring processes and group collaboration online with a special focus on their learning effects. Review of empirical and theoretical literature suggests that functional differentiation and participation inequalities are a constant phenomenon in small and large groups, both online and off. This dissertation examines what effects these inequalities may have on group performance, especially with respect to learning. An optimal unevenness range is proposed in the construct model wherein a curvilinear relationship exists between participation evenness and learning outcomes. A key element of Shannon’s mathematical theory of communication, the entropy function, is proposed as a measure of interaction in a wiki online collaborative environment. The author’s hypothesis of a curvilinear relationship between relative group participation and learning gain is informed by a broad range of literature from small group communication, system-level analyses, recent examinations of mass collaboration and open source software development, collaborative learning theory, developmental psychology, visual feedback technology, and information theory. A quasi-experimental design was proposed as the best way to test the hypothesis and answer relevant research questions. The procedure included 170 participants on teams of 4-11 co-constructing Purdue-related topics within three broad groupings, 1) a regular wiki, 2) the entropy-enabled Visible Effort Wiki, and 3) offline. Variant entropy levels and control variables were correlated with learning gain, as measured by pretests and posttests. Perceived measures were added as well. Research results and data analysis showed the overall model to be statistically significant at the group level. All predictor variables, including entropy, technical competence, and effort level, and excepting knowledge of Purdue University, were significant. On the individual, perceived level the overall model showed a significant linear effect and all predictor variables, except knowledge of Purdue, were significant. Results support a curvilinear relationship between participation levels and learning for objective measures, and overall findings showed a clear relationship between these two variables.