UCSB ENVS 106 - Lecture 13 Thinking in Systems Chapter 7 and Paper stuff_POST (1) (19 pages)

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Lecture 13 Thinking in Systems Chapter 7 and Paper stuff_POST (1)



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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 Our science itself from quantum theory to the mathematics of chaos leads us into irreducible uncertainty For any objective other than the most trivial we can t optimize we don t even know what to optimize We can t keep track If you can t understand predict and control what is there to do Systems thinking leads to another conclusion however waiting shining obvious as soon as we stop being blinded by the illusion of control It says that there is plenty to do of a different sort of doing The future can t be predicted but it can be envisioned and brought lovingly into being Systems can t be controlled but they can be designed and redesigned We can t surge forward with certainty into a world of no surprises but we can expect surprises and learn from them and even profit from them We can t impose our will on a system We can listen to what the system tells us and discover how its properties and our values can work together to bring forth something much better than could ever be produced by our will I already knew that in a way I had learned about dancing with great powers from whitewater kayaking from gardening from playing music from skiing All those endeavors require one to stay wide awake pay close attention participate flat out and respond to feedback It had never occurred to me that those same requirements might apply to intellectual work to management to government to getting along with people But there it was the message emerging from every computer model we made Living successfully in a world of systems requires more of us than our ability to calculate It requires our full humanity our rationality our ability to sort out truth Get the Beat of the System Observe the system watch it work 16 Learn its history 14 Make a time graph memory is fuzzy A anecdote simple Be aware of my brother who works in the prison system was quite misconceptions surprised to find out that homicides and actually all crime in general have gone down significantly in CA over the years Just because someone is in a given area does not mean that know the data or remember the past correctly Rate per 100 000 Population Ask those who ve been around longer what has happened Homicide Rate in California 1976 2015 12 10 8 6 4 2 0 1975 1980 1985 1990 1995 2000 2005 2010 Data From https oag ca gov sites all files agweb pdfs cjsc publications candd cd15 cd15 pd f 2015 Keep an intellectual curiosity rather than a diagnosis mindset Understand the system first and then think about whether or not your preferred solution is in fact a good one 1 Identify the Stock 2 Discuss inflows and outflows 3 Identify feedbacks 4 Identify systems traps And finally starting with history discourages the common and distracting tendency we all have to define a problem not by the system s actual behavior but by the lack of our favorite solution The problem is we need to find more oil The problem is we need to ban abortion The problem is we don t have enough salesmen The problem is how can we attract more growth to this town Listen to any discussion in your family or a committee meeting at work or among the pundits in the media and watch people leap to solutions usually solutions in predict control or impose your will mode without having paid any attention to what the system is doing and why it s doing it Expose your Mental Models to the Light of Day Draw out the diagram Makes the assumptions clear and forces them to rigorous analysis Can point to possible inconsistencies Prevents your mental model from sliding around Explain out your mental models with words Collect hypothesis about system behavior Test them against evidence This


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