University of Toronto E g reduce to a set of equations governing interactions Statistics measure average behaviour of a very large number of instances E g gas pressure results from averaging random movements of zillions of atoms Error tends to zero when the number of instances gets this large You cannot eliminate the observer Most truths are relative Most views are complementary Behaviour that is not random enough for statistical analysis Example systems Purposefulness openness hardness General systems theory Originally developed for biological systems E g to understand the human body and the phenomena of life Describing systems Basic ideas Choosing a boundary Treat inter related phenomena as a system Study the relationships between the pieces and the system as a whole Don t worry if we don t fully understand each piece Describing behaviour 1 Easterbrook 2004 University of Toronto But sometimes neither of these work Systems that are too interconnected to be broken into parts Defining Systems Elements of a system description How scientist understand the world Reductionism break a phenomena down into its constituent parts Basic Principles Everything is connected to everything else University of Toronto Department of Computer Science Department of Computer Science Relativism Achieving objectivity in scientific inquiry 1 Eliminate the observer Truth is relative to many things The meanings of the words we use E g ways of measuring that have no variability across observers E g law of gravity depends on correct understanding of mass distance force etc 2 Distinguish between scientific reasoning and value based judgement The assumptions we make about context Science is supposed to be value free but how do scientists choose which theories to investigate 2 Easterbrook 2004 Role of the Observer Department of Computer Science General Systems Theory Lecture 3 What is a system University of Toronto Department of Computer Science E g law of gravity not applicable at subatomic level or near the speed of light E g Which is the step function For complex systems this is not possible Cannot fully eliminate the observer E g Probe effect measuring something often changes it E g Hawthorne effect people react to being studied Our observations biased by past experience We look for familiar patterns to make sense of complex phenomena E g try describing someone s accent Achieving objectivity in systems thinking Time Study the relationship between observer and observations Time Look for observations that make sense from many perspectives Easterbrook 2004 3 Easterbrook 2004 4 1 University of Toronto University of Toronto Department of Computer Science Relativism is everywhere The principle of complementarity Truth often depends on the observer Emergent properties of a system are not predictable from studying the parts E g the idea of a state is an abstraction All our descriptions of the world are partial filtered by Purpose of a system is a property of the relationship between system environment Our perceptual limitations Our cognitive ability Our personal values and experience What is the purpose of General Motors A University A birthday party Weltanshaungen worldviews The set of categories we use for understanding the world The language we develop for describing what we observe Ethno centrism or ego centrism E g ask the observers for increasingly detailed observations 5 6 Easterbrook 2004 University of Toronto Department of Computer Science Department of Computer Science Elements of a system Ackoff s definition A system is a set of two or more elements that satisfies the following conditions The behaviour of each element has an effect on the behaviour of the whole The behaviour of the elements and their effect on the whole are interdependent However subgroups of elements are formed each has an effect on the behaviour of the whole and none has an independent effect on it are likely to be complementary But this is not always possible feasible Definition of a system of the same system Complementarity should disappear if we can remove the partiality E g In the land of the blind the one eyed man is king But what use would visually oriented descriptions be in this land Easterbrook 2004 Redundant if one observer s description can be reduced to the other Equivalent if redundant both ways Independent if there is no overlap at all in their descriptions Complementary if none of the above hold Any two partial descriptions The tendency to assume one s own category system is superior University of Toronto Complementarity Two observers descriptions of system may be Our Weltanshaungen permeate everything Raw observation is too detailed We systematically ignore many details Whose ability to predict are we talking about Department of Computer Science Boundary Can decompose a system into parts Often not sharply defined For each subsystem the remainder of the system is its environment Each part is also a system Also known as an interface Subsystems are inter dependent Environment Part of the world with which the system can interact Other views because the parts of the system are so interconnected Observable Interactions How the system interacts with its environment Wieringa A system is any actual or possible part of reality that if it exists can be observed E g inputs and outputs suggests the importance of an observer Weinberg A system is a way of looking at the world Control Mechanism How the behaviour of the system is regulated to allow it to endure System and environment are interrelated Weinberg A system is a collection of parts none of which can be changed on its own Subsystems Separates a system from its environment Often a natural mechanism Emergent Properties Properties that hold of a system but not of any of the parts Properties that cannot be predicted from studying the parts Systems don t really exist Just a convenient way of describing things like sets Easterbrook 2004 7 Easterbrook 2004 8 2 University of Toronto University of Toronto Department of Computer Science Conceptual Picture of a System Department of Computer Science Hard vs Soft Systems Hard Systems Soft Systems The system is precise is hard to define precisely well defined is an abstract idea quantifiable depends on your perspective No disagreement about Not easy to get agreement Where the boundary is The system doesn t really exist What the interfaces are Calling something a system helps us to understand it The internal structure Identifying the
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