UCSB ENVS 106 - Lecture 13 Thinking in Systems Chapter 7 and Paper stuff_POST (19 pages)

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Lecture 13 Thinking in Systems Chapter 7 and Paper stuff_POST



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Week Tuesday Thursday 26 Sep 0 28 Sep Intro to Course 3 Oct 1 Merchants of Doubt 5 Oct Merchants of Doubt 10 Oct 2 Merchants of Doubt 12 Oct Merchants of Doubt 17 Oct 3 Merchants of Doubt 20 Oct Merchants of Doubt 24 Oct 4 26 Oct UEE Thinking in Systems Pappeerr 11 D DU Pa Thinking in Systems 31 Oct 5 Thinking in Systems 2 Nov Thinking in Systems 7 Nov 6 Thinking in Systems 9 Nov Thinking in Systems 14 Nov 7 16 Nov UEE Climate Psychology Pappeerr 22 D DU Pa Climate Psychology 21 Nov 8 Climate Action 23 Nov Thanksgiving 28 Nov 9 Climate Action 30 Nov Framing 5 Dec 10 ES 106 Tentative Schedule Words that Work 7 Dec Review for Final Exam Learn how people have been misled on environmental issues who some major players are and what tactics have been and continue to be used to distort the science behind environmental issues Develop systems thinking insights and methods into analysis of environmental problems and solutions incorporating concepts such as dynamic equilibrium feedback oscillation and resilience Cognition and the Environment Understand how humans process environmental information Learn to effectively communicate environmental problems and implement solutions Study the psychological mechanisms and methods that underlie environmental skepticism denial particularly on climate Chapter 7 Living in a World of Systems The real trouble with this world of ours is not that it is an unreasonable world nor even that it is a reasonable one The commonest kind of trouble is that it is nearly reasonable but not quite Life is not an illogicality yet it is a trap for logicians It looks just a little more mathematical and regular than it is G K Chesterton People who are raised in the industrial world and who get enthused about systems thinking are likely to make a terrible mistake They are likely to assume that here in systems analysis in interconnection and complication in the power of the computer here at last is the key to prediction and control This mistake is likely because the mind set of the industrial world assumes that there is a key to prediction and control I assumed that at first too We all assumed it as eager systems student sat the great institution called MIT More or less innocently enchanted by what we could see through our new lens we did what many discoverers do We exaggerated our findings We did so not with any intent to deceive others but in the expression of our Self organizing nonlinear feedback systems are inherently unpredictable They are not controllable They are understandable only in the most general way The goal of foreseeing the future exactly and preparing for it perfectly is unrealizable The idea of making a complex system do just what you want it to do can be achieved only temporarily at best We can never fully understand our world not in the way our reductionist science has led us to expect



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