The Web of System Performance

1986

2009

Presented is research articulating a novel technology progressing resource management within self-organizing systems. Examining both Cybernetic and Autonomic Computing techniques we evolve a set-theory oriented, atomically-derived, emergent model that reflects an algorithmic decomposition of Beer's recursive, multi-agent Viable System Model, pertinent by its composition of multiple and independent entities, sharing one or more objectives. Integrated management promotes each subsystem as a whole within a closed ecological meta-boundary. The relationships between subsystems is demonstrated via syntax subscripts, while the relationship linking recursive levels is recognized via superscripts. The resultant design grammar endorses autonomy versus governance, exploiting cybernetic, biological and mathematical metaphors, crucially seeking inherent learning and control through system-environment interplay. Focusing on interactions and interrelationships , the self-organizing environments exhibit evolution of systemic elements, conserving yet managing resources provided by each entity. Research ultimately aspires augmentation of the Autonomic Computing state of the art into the original field of Viable Computing Systems.

1991

International journal of systems and society, 2017

In this article, the author briefly summarise the characteristics of the science of complexity or post-Bertalanffy General Systems. The author discusses the shift from considering systems as acquiringpropertiesduetotheirexplicitorsupposeddesign,toself-organised,emergentsystems.

2010

It is a widely held assumption that computers process information. When finding out that natural systems manifest information processes, it is hypothesised that natural systems too are computers. This can be called the quintessence of the “computational turn”, however, it is a non sequitur. This chapter draws upon the ontological distinction of strict determinism and less-than-strict determinism. It contends that artificial devices like computers work on the basis of strict determinism, while natural systems to the extent as they self-organise work on the basis of less-than-strict determinism. Strict determinism is a derivative of less-than-strict determinism. Thus the chapter concludes that concerning computers and natural, self-organising systems the assumption of the computational turn is wrong. It is the other way round: computers play a restricted, though essential and indispensable, part within self-organising (natural and social) contexts.

2015

This scientific article speaks about generations of computers, PC history, saving data, Von Neumann architecture, input/output peripherals, software instructions, programs, mainframe, minicomputers, microcomputers, supercomputers, libraries and operating systems, computer networks and Internet, introduction to the world of computers, evolution of computer systems, from the literature specialized in computer science. Computers are divided into: mechanical computers-water and gas meters, electromechanical computers-electricity meters, electronic computers (I generation of computers, II generation of computers, III generation of computers, IV generation of computers), optical computers and biological computers. After the highest prevalence, electronic computers are divided into: analogue-electronic computers, digital electronic-computers and hybrid electronic computers.

ArXiv, 2019

The Turing Machine is the paradigmatic case of computing machines, but there are others, such as Artificial Neural Networks, Table Computing, Relational-Indeterminate Computing and diverse forms of analogical computing, each of which based on a particular underlying intuition of the phenomenon of computing. This variety can be captured in terms of system levels, re-interpreting and generalizing Newell's hierarchy, which includes the knowledge level at the top and the symbol level immediately below it. In this re-interpretation the knowledge level consists of human knowledge and the symbol level is generalized into a new level that here is called The Mode of Computing. Natural computing performed by the brains of humans and non-human animals with a developed enough neural system should be understood in terms of a hierarchy of system levels too. By analogy from standard computing machinery there must be a system level above the neural circuitry levels and directly below the knowle...