What is Systems Science? Systems Science constitutes a somewhat fuzzily defined academic domain, that touches virtually all traditional disciplines, from mathematics, technology and biology to philosophy and the social sciences. It is more specifically related to the recently developing "sciences of complexity", including AI, neural networks, dynamical systems, chaos, and complex adaptive systems. Systems science argues that however complex or diverse the world that we experience, we will always find different types of organization in it, and such organization can be described by concepts and principles which are independent from the specific domain at which we are looking. Hence, if we would uncover those general laws, we would be able to analyze and solve problems in any domain, pertaining to any type of system. The systems approach distinguishes itself from the more traditional analytic approach by emphasizing the interactions and connectedness of the different components of a system. Although the systems approach in principle considers all types of systems, it in practices focuses on the more complex, adaptive, self-regulating systems which we might call "cybernetic". Many of the concepts used by system scientists come from the closely related approach of cybernetics: information, control, feedback, communication... In its present incarnation of "second- order cybernetics", its emphasis is on how observers construct models of the systems with which they interact*. Systems theory studies organization independent of the substrate in which it is embodied and is focused on the structure of systems and their models. ... from Principia Cybernetica Web http://pespmc1.vub.ac.be/ * This interaction demands the study of uncertainty, complexity, knowledge discovery, informatioin, decision making, formal concept analysis, dynamics and evolution. Common characteristics across systems • uncertainty • complexity • knowledge discovery • information • decision making • formal concept analysis • dynamics and evolution Control systems, Economic systems, Socio-technical systems , Biological systems , Ecological systems, Psychological systems, ... Cybernetic Cognitive Science: • Data is values, symbols, numbers etc. e.g., 20 • Data can have degrees of structure and context, 20C • Information is that which reduces uncertainty • Knowledge can be quantified as the conditional uncertainty of an action from R, given a disturbance in D: H(R|D). (The uncertainty or entropy H is calculated in the normal way , but using conditional probabilities P(R|D)). H(R|D) = 0 represents the case of no uncertainty or complete knowledge, where the action is completely determined by the disturbance. H(R|D) = H(R) represents complete ignorance • System - < A, R > (states, variables, source and data systems, behaviour systems, structure systems, metasystems) • Computation - rewriting strings according to rules • Machine - an entity which exhibits behavior • Computer - a machine that does computation. • Luck - random chance acting in your favour. .