Stafford Beer in memoriam – ‘an argument of change’ three decades on.

Purpose This paper is written in memory of the late Stafford Beer. The paper engages with only one dimension of the whole man: Stafford Beer as the diagnostician and prognostician of the social conditions that he so keenly observed. Design/methodology/approach The paper revisits a talk that Stafford Beer gave, over three decades ago, to administrators of the UK National Health Service (NHS). It uses the content of the talk, entitled “Health and Quiet Breathing”, to diagnose the problems that have been encountered in the development of NHS information management strategies. The paper concludes with some brief personal recollections of Stafford Beer as a friend and as a teacher. Findings The paper finds Stafford Beer’s managerial cybernetics to be a useful tool in understanding many of the problems that have beset NHS information management strategies: lack of operational research, problems in the commodification of information, financial scandal, and bureaucracy. In its examination of these issues, the paper recognises Stafford Beer’s status as a legatee of not only Norbert Wiener, but also of the great philosophers. Value The paper demonstrates how the problem-orientation of Stafford Beer’s managerial cybernetics continues to be fresh and relevant to today’s society and provides a brief portrait of him both as a friend and as a teacher

Is the Viable System Model of organization inimical to the concept of human freedom?

This paper examines the sensitivity of Stafford Beer’s Viable System Model of organization to the concept of human freedom. The paper notes the many critics who have suggested that the Viable System Model is inimical to human freedom and their especial reference to its application to the social economy of Chile in the early 1970s. Drawing on the work of philosophers, a conceptual analysis of freedom is provided that suggests a complex ordinary language usage of the term. At least three determinants of freedom, that are logically independent of one another, are identified as being of relevance to its ordinary usage. The paper finds that these determinants are implicitly addressed and acknowledged within Beer’s own writings, but that they are ignored by the critics of the Viable System Model and that this makes for a lack of clarity and precision in the debate. The paper also applies a further criterion, formulated in political philosophy, to judge whether the leadership of the government that applied the Viable System Model to the Chilean social economy was itself hostile to political freedom or democracy. This application of the criterion suggests that they were not

Evolution and Revolution: The Drama of Realtime Complementarity

This article is by design a response to Alastair M. Taylor's "For Philosophers and Scientists: A General Systems Paradigm." That work is an advance over stage theories. But its focus on modernization tacitly accepts marginalization. Its focus on an undifferentiated evolving human species disregards intra- and intersocietal conflicts. Its uncritical talk of societal energy shifts obscures the reality of conquest and exploitation. If general systems theory is to be truly objective, it should take into account world-around system imbalance and the relevance of Newton's Third Law. (Publisher omitted title of this article and used only its subtitle.

Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems

Spartan Daily, March 2, 1971

Volume 58, Issue 76https://scholarworks.sjsu.edu/spartandaily/5484/thumbnail.jp

Detection of the Security Vulnerabilities in Web Applications

The contemporary organizations develop business processes in a very complex environment. The IT&C technologies are used by organizations to improve their competitive advantages. But, the IT&C technologies are not perfect. They are developed in an iterative process and their quality is the result of the lifecycle activities. The audit and evaluation processes are required by the increased complexity of the business processes supported by IT&C technologies. In order to organize and develop a high-quality audit process, the evaluation team must analyze the risks, threats and vulnerabilities of the information system. The paper highlights the security vulnerabilities in web applications and the processes of their detection. The web applications are used as IT&C tools to support the distributed information processes. They are a major component of the distributed information systems. The audit and evaluation processes are carried out in accordance with the international standards developed for information system security assurance.security, vulnerability, web application, audit