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Thinking in Systems: A Primer
Meadows’ Thinking in Systems, is a concise and crucial book offering insight for problem solving on scales ranging from the personal to the global. Edited by the Sustainability Institute’s Diana Wright, this essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life.
Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking.
While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner.
In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions.
Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking.
While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner.
In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions.
218 pages, Paperback
First published January 1, 2008
About the author
Donella H. Meadows
17 books292 followersDonella H. "Dana" Meadows was a pioneering American environmental scientist, teacher, and writer. She was educated in science, receiving a B.A. in chemistry from Carleton College in 1963, and a Ph.D. in biophysics from Harvard in 1968. After a year-long trip with her husband, Dennis Meadows, from England to Sri Lanka and back, she became, along with him, a research fellow at MIT, as a member of a team in the department created by Jay Forrester, the inventor of system dynamics as well as the principle of magnetic data storage for computers. She taught at Dartmouth College for 29 years, beginning in 1972.
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Displaying 1 - 30 of 1,279 reviews
December 10, 2017
As a collection of guidelines for understanding and intervening in problematic situations this book is quite useful. But I have never liked it because of two reasons, one internal to the book and one related to its effects in the outside world.
As a primer, it’s perfectly fine that a book skids over some of the finer points of the theory. But my feeling is that this informality hides a quite damaging conceptual incoherence. Epistemologically the book oscillates between a naive realism (there is complexity out there and we can model it more or less faithfully) and a muddled constructivism (we can’t really know what is out there but models are a useful construct to structure our interaction with the world with the aim to progressively learn about how to deal with the friction and problems in that world). Then the book is anchored in the normative perspective of an engineer who is interested in the dynamics of depletion of natural resources. It is inevitable that this professional perspective engenders a very distinctive (but always disputable) way of evaluating systems behavior. It is equally inevitable that it reflects a rather obvious position on the political spectrum. Finally the lack of conceptual clarity extends to some of the pivotal notions in the book. It remains, for instance, unclear how desirable systems behavior, resilience and self-organization are conceptually linked. Also, readers may be surprised by the progressively narrowing focus to how social systems may suffer from actors’ bounded rationality.
My other misgivings have to do with the way this book is at the root of some of the ‘systems traps’ that it wants to help defeat. Many people will read the book in the conviction that this is more or less what there is to say about systems thinking. In fact its scope is quite narrow. There is much more to be said about ‘systems thinking and doing’ than the MIT-centered school of system dynamics leads us to believe. By omitting references to other, ‘competing’ (or complementary) approaches the book puts the bar for aspiring learners rather low, leading to a premature sense of gratification of readers’ curiosity for systemic insights (the ‘eroding goals’ trap). Furthermore and related to the previous point, if readers’ attention is the stock that authors and publishers are competing for, then the net effect of this book’s ever-increasing popularity is that it crowds out other, contrasting accounts of systems thinking (the ’success to the successful’ trap). In other words, the success of the MIT brand of system dynamics is obscuring other valuable contributions in the systems domain to such an extent that it is becoming a liability rather than an asset (considered against the background of potential gains in intellectual capital that could be realized by other systems approaches).
So I'm giving two stars to underline my reservations. But I won't dispute that 'Thinking in Systems" contains quite a few useful ideas and from that point of view I could have granted it 3,5 stars too.
As a primer, it’s perfectly fine that a book skids over some of the finer points of the theory. But my feeling is that this informality hides a quite damaging conceptual incoherence. Epistemologically the book oscillates between a naive realism (there is complexity out there and we can model it more or less faithfully) and a muddled constructivism (we can’t really know what is out there but models are a useful construct to structure our interaction with the world with the aim to progressively learn about how to deal with the friction and problems in that world). Then the book is anchored in the normative perspective of an engineer who is interested in the dynamics of depletion of natural resources. It is inevitable that this professional perspective engenders a very distinctive (but always disputable) way of evaluating systems behavior. It is equally inevitable that it reflects a rather obvious position on the political spectrum. Finally the lack of conceptual clarity extends to some of the pivotal notions in the book. It remains, for instance, unclear how desirable systems behavior, resilience and self-organization are conceptually linked. Also, readers may be surprised by the progressively narrowing focus to how social systems may suffer from actors’ bounded rationality.
My other misgivings have to do with the way this book is at the root of some of the ‘systems traps’ that it wants to help defeat. Many people will read the book in the conviction that this is more or less what there is to say about systems thinking. In fact its scope is quite narrow. There is much more to be said about ‘systems thinking and doing’ than the MIT-centered school of system dynamics leads us to believe. By omitting references to other, ‘competing’ (or complementary) approaches the book puts the bar for aspiring learners rather low, leading to a premature sense of gratification of readers’ curiosity for systemic insights (the ‘eroding goals’ trap). Furthermore and related to the previous point, if readers’ attention is the stock that authors and publishers are competing for, then the net effect of this book’s ever-increasing popularity is that it crowds out other, contrasting accounts of systems thinking (the ’success to the successful’ trap). In other words, the success of the MIT brand of system dynamics is obscuring other valuable contributions in the systems domain to such an extent that it is becoming a liability rather than an asset (considered against the background of potential gains in intellectual capital that could be realized by other systems approaches).
So I'm giving two stars to underline my reservations. But I won't dispute that 'Thinking in Systems" contains quite a few useful ideas and from that point of view I could have granted it 3,5 stars too.
September 6, 2022
Seeing the Big Picture 101:
Preamble:
--How often do you get the sense that we are too consumed with surface-level micro issues to see the looming macro tidal waves that will wash away our elaborate sandcastles into oblivion?
--Prior to reading this book several years ago, I was semi-consciously learning and applying “systems thinking” as a survival mechanism while exploring the dismal realms of economics (esp. market externalites of social relations: global division of labour/violence/environment/social reproduction/crises) and geopolitics (beyond the “enlightened” liberal-imperialism paradigm: https://youtu.be/6jKcsHv3c74 ).
--After reading:
i) Industrial capitalism: Capital: A Critique of Political Economy, Volume 1
ii) Finance capitalism: The Bubble and Beyond
iii) Capitalism's imperialism: Capital and Imperialism: Theory, History, and the Present
iv) starting to explore ecological economics (Less is More: How Degrowth Will Save the World) and biophysical economics, i.e. energy (The New Economics: A Manifesto).
...I decided to update this review of systems science 101 by the most progressive of the Cold War-era US technocrats (which I’ll critique and supplement) behind "The Limits to Growth" (first ed. 1972): Donella H. Meadows.
Highlights:
1) Systems Modeling 101:
--External forces (visible, thus gets the attention) trigger internal system structures (hidden, but key to systems thinking) which lead to behaviors:
-ex. The book’s cover is a slinky, which has a unique internal structure responsible for its unique behavior when an external force is applied.
-ex. All the attention on specific politicians “causing” economic booms/busts instead of the capitalist economic system’s inherently-volatile structures.
--Conceptualize a system composed of stocks (quantities measured at a specific moment in time) and flows (change over time). A complex system (basically anything worth examining in the messy real-world) is more than just the sum of its parts (more visible; fundamental failing of mainstream economics), thus investigate the interactions between the parts (more hidden; where the magic happens) to understand the system behaviors and purposes.
--Feedback loops: where changes in stock affects the flow in/out of the same stock. There can be “balancing” feedback loops that have a regulatory, stabilizing behavior, and “re-enforcing” feedback loops that snowballs into an avalanche in the direction of change.
--Delays/buffers in system from perception (of feedback info)/response/delivery.
--Non-renewable (stock-limited) and renewable (flow-limited) stocks and their critical thresholds.
2) System Resilience, Traps, and Opportunities:
--The exciting application of systems modeling is understanding how complex systems can work so well (ex. human body, ecosystem, etc.):
a) Resilience: many feedback loops with redundancy to provide elasticity as opposed to the brittleness of reductionist “efficiency”.
b) Self-organization: ability to learn/evolve resilience.
c) Hierarchy: in the sense of a division-of-labor into subsystems to reduce the amount of info needed by each part (ex. organ) to contribute to system goals (ex. health of body).
--And how system can fail via “traps”: addiction (shifting the burden to deny signals and opt for short-term relief), drift to low performance (the feedback of eroding goals/perception), escalation (arms race feedback, policy resistance), success to the successful (ecology’s “Competitive Exclusion Principle”, Marxian market monopolization), so-called “Tragedy of the Commons” (externalizing costs, see later), cheating on rules, perverse system goals, etc.
--Meadows ranks 12 opportunities in terms of leverage points for system behavior change (for an application of this in economics, see Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist):
a) The liberal economic/political/social reforms we are so familiar with involve low-leverage tinkering: altering parameters, buffers, delays, physical structures of stocks/flows, etc.
b) Medium leverage points: promoting self-organization, changing structural rules/information flow/feedback loops (balancing, re-enforcing), etc.
c) High leverage points: changing system goals, the paradigm (liberalism?) that the system goals rest on, and untethering from singular paradigms (Kuhn's The Structure of Scientific Revolutions).
3) The Paradox of Systems Modeling – the more you learn, the more questions you have:
--“Everything we think we know of the world is a model”. Every model is an incomplete representation to illustrate a perspective, with assumptions often hidden. Furthermore, resilient systems are self-organizing/dynamic/nonlinear while reductionism weakens systems, so systems modeling must acknowledge uncertainty, honour indeterminacy, and constantly observe and adapt.
--The fallacy of relying on event-based analysis: “it’s endlessly engrossing to take in the world as a series of events, and constantly surprising, because that way of seeing the world has almost no predictive or explanatory value”. Meadows uses the illustrative example of the mass media news industry churning out events without structural analysis (my favorite critical journalists call this “churnalism”).
--Let me add that systems science’s focus on the complexities and contradictions of interactions was recognized in Classical political economy. Indeed, Marx pushed this the furthest with a critical and dialectical approach in his unfinished Capital project Volume 1 = market logic’s commodification/abstraction of structural social relations/use-values into market exchange-values represented by visible prices, the growth logic of the circulation of capital and its potential blockages, money-power, exploitation of surplus value from labour in production, technological change and market competition under the logic of capital, violent expropriation “so-called primitive accumulation”, etc.).
…Meadows’ Cold War liberalism is blatantly clear in her list of influences, but she still has a brief acknowledgement of Marx (regarding market competition). Today’s mainstream (Neoclassical) economics originated from a rejection of Classical political economy after witnessing Marx’s critical direction. Mainstream economics were hostile to Meadows' “Limits to Growth” scientists/engineers (esp. award-winning fool William D. Nordhaus; liberals bashing liberals, good times); Steve Keen, who has spent his academic life debunking the Neoclassical paradigm (intro: Can We Avoid Another Financial Crisis?), recommends students to skip mainstream “economics” classes, study systems science (+ real-world accounting, which hilariously is not taught in “economics”) for the technical skills to apply to real-world economic questions: The New Economics: A Manifesto.
--Of course, the direct application to Earth System science: Facing the Anthropocene: Fossil Capitalism and the Crisis of the Earth System and Keen's colleague Lenton's Earth System Science: A Very Short Introduction.
--Non-existent/false boundaries: World-Systems Analysis: An Introduction also stresses how academic disciplines can be stifling and arbitrary.
--“Bounded rationality”: citing Herbert A. Simon) on the phenomenon of rational but limited information, to put a wrench in the Neoclassical assumption of “perfect information” for rational decision-making:
a) See popular science writer/epidemiologist Ben Goldacre for an intro to heuristics (mental short-cuts) and how to avoid their shortcomings using good statistics/evidence-based methodologies (esp. I Think You'll Find It's a Bit More Complicated Than That).
b) Meadows/Raworth (Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist) also considers science’s reductionist spillover bypassing the holism embedded in certain heuristics.
c) Strange how science has provided us such revelatory glimpses of the physical world, while capitalism has abstracted so much of the social world (The Global Minotaur: America, the True Origins of the Financial Crisis and the Future of the World Economy).
…I find most compelling a synthesis where science is recognized as a set of tools (methodologies, institutions) while ideologies are carefully critiqued (social goals + assumptions).
-Ex. disease and capitalist destruction of ecosystems: Dead Epidemiologists: On the Origins of COVID-19
-Ex. agroecology: A People’s Green New Deal
-Ex. Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge, and the Teachings of Plants.
…It’s a shame Western science is plagued with undiagnosed liberalism; looking forward to The Dialectical Biologist.
The Questionable:
--Finally, let’s reapply systems thinking to Meadows’ own assumptions. Symptoms of undiagnosed liberalism include:
a) “Tragedy of the Commons” + “overpopulation”: to debunk this “Tragedy” as much more applicable to capitalist “open access” rather than social “Commons”, see https://youtu.be/xcwXME-PNuE ...Pretty cringe to list anti-immigration/pro-eugenics/Neomalthusian Garrett Hardin in your influences list, but even the most “enlightened” liberalism cannot reform itself out of capitalism so it cannot stop exploiting poor and coloured people; poor coloured people are the first to go. Details: Too Many People?: Population, Immigration, and the Environmental Crisis
b) “communist Soviet Union”: Cold War liberals forget the structure of industrialization (West took centuries of colonialism/settler colonialism/slavery while USSR took decades), decolonization (where liberals were and continue to be the colonizers: The Darker Nations: A People's History of the Third World), anti-fascism (where liberals were fascist sympathizers while USSR was invaded: Blackshirts and Reds: Rational Fascism and the Overthrow of Communism) and the capitalist military industrial complex (the basis of the Cold War that turned USSR into “siege socialism” and derailed Third World Industrialization). “Socialism” became big “government”, which was a mess since capitalism requires plenty of “government” (ex. military industrial complex), with the non-option of “traditional societies”/“gift-giving cultures”. Western readers can start to transcend the liberal capitalist paradigm with:
-some hand-holding starting with a focus on the Global North: Another Now: Dispatches from an Alternative Present
…combined with Global South:
-The Agrarian Question in the Neoliberal Era: Primitive Accumulation and the Peasantry
-Struggle Makes Us Human: Learning from Movements for Socialism
Preamble:
--How often do you get the sense that we are too consumed with surface-level micro issues to see the looming macro tidal waves that will wash away our elaborate sandcastles into oblivion?
--Prior to reading this book several years ago, I was semi-consciously learning and applying “systems thinking” as a survival mechanism while exploring the dismal realms of economics (esp. market externalites of social relations: global division of labour/violence/environment/social reproduction/crises) and geopolitics (beyond the “enlightened” liberal-imperialism paradigm: https://youtu.be/6jKcsHv3c74 ).
--After reading:
i) Industrial capitalism: Capital: A Critique of Political Economy, Volume 1
ii) Finance capitalism: The Bubble and Beyond
iii) Capitalism's imperialism: Capital and Imperialism: Theory, History, and the Present
iv) starting to explore ecological economics (Less is More: How Degrowth Will Save the World) and biophysical economics, i.e. energy (The New Economics: A Manifesto).
...I decided to update this review of systems science 101 by the most progressive of the Cold War-era US technocrats (which I’ll critique and supplement) behind "The Limits to Growth" (first ed. 1972): Donella H. Meadows.
Highlights:
1) Systems Modeling 101:
--External forces (visible, thus gets the attention) trigger internal system structures (hidden, but key to systems thinking) which lead to behaviors:
-ex. The book’s cover is a slinky, which has a unique internal structure responsible for its unique behavior when an external force is applied.
-ex. All the attention on specific politicians “causing” economic booms/busts instead of the capitalist economic system’s inherently-volatile structures.
--Conceptualize a system composed of stocks (quantities measured at a specific moment in time) and flows (change over time). A complex system (basically anything worth examining in the messy real-world) is more than just the sum of its parts (more visible; fundamental failing of mainstream economics), thus investigate the interactions between the parts (more hidden; where the magic happens) to understand the system behaviors and purposes.
--Feedback loops: where changes in stock affects the flow in/out of the same stock. There can be “balancing” feedback loops that have a regulatory, stabilizing behavior, and “re-enforcing” feedback loops that snowballs into an avalanche in the direction of change.
--Delays/buffers in system from perception (of feedback info)/response/delivery.
--Non-renewable (stock-limited) and renewable (flow-limited) stocks and their critical thresholds.
2) System Resilience, Traps, and Opportunities:
--The exciting application of systems modeling is understanding how complex systems can work so well (ex. human body, ecosystem, etc.):
a) Resilience: many feedback loops with redundancy to provide elasticity as opposed to the brittleness of reductionist “efficiency”.
b) Self-organization: ability to learn/evolve resilience.
c) Hierarchy: in the sense of a division-of-labor into subsystems to reduce the amount of info needed by each part (ex. organ) to contribute to system goals (ex. health of body).
--And how system can fail via “traps”: addiction (shifting the burden to deny signals and opt for short-term relief), drift to low performance (the feedback of eroding goals/perception), escalation (arms race feedback, policy resistance), success to the successful (ecology’s “Competitive Exclusion Principle”, Marxian market monopolization), so-called “Tragedy of the Commons” (externalizing costs, see later), cheating on rules, perverse system goals, etc.
--Meadows ranks 12 opportunities in terms of leverage points for system behavior change (for an application of this in economics, see Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist):
a) The liberal economic/political/social reforms we are so familiar with involve low-leverage tinkering: altering parameters, buffers, delays, physical structures of stocks/flows, etc.
b) Medium leverage points: promoting self-organization, changing structural rules/information flow/feedback loops (balancing, re-enforcing), etc.
c) High leverage points: changing system goals, the paradigm (liberalism?) that the system goals rest on, and untethering from singular paradigms (Kuhn's The Structure of Scientific Revolutions).
3) The Paradox of Systems Modeling – the more you learn, the more questions you have:
--“Everything we think we know of the world is a model”. Every model is an incomplete representation to illustrate a perspective, with assumptions often hidden. Furthermore, resilient systems are self-organizing/dynamic/nonlinear while reductionism weakens systems, so systems modeling must acknowledge uncertainty, honour indeterminacy, and constantly observe and adapt.
--The fallacy of relying on event-based analysis: “it’s endlessly engrossing to take in the world as a series of events, and constantly surprising, because that way of seeing the world has almost no predictive or explanatory value”. Meadows uses the illustrative example of the mass media news industry churning out events without structural analysis (my favorite critical journalists call this “churnalism”).
--Let me add that systems science’s focus on the complexities and contradictions of interactions was recognized in Classical political economy. Indeed, Marx pushed this the furthest with a critical and dialectical approach in his unfinished Capital project Volume 1 = market logic’s commodification/abstraction of structural social relations/use-values into market exchange-values represented by visible prices, the growth logic of the circulation of capital and its potential blockages, money-power, exploitation of surplus value from labour in production, technological change and market competition under the logic of capital, violent expropriation “so-called primitive accumulation”, etc.).
…Meadows’ Cold War liberalism is blatantly clear in her list of influences, but she still has a brief acknowledgement of Marx (regarding market competition). Today’s mainstream (Neoclassical) economics originated from a rejection of Classical political economy after witnessing Marx’s critical direction. Mainstream economics were hostile to Meadows' “Limits to Growth” scientists/engineers (esp. award-winning fool William D. Nordhaus; liberals bashing liberals, good times); Steve Keen, who has spent his academic life debunking the Neoclassical paradigm (intro: Can We Avoid Another Financial Crisis?), recommends students to skip mainstream “economics” classes, study systems science (+ real-world accounting, which hilariously is not taught in “economics”) for the technical skills to apply to real-world economic questions: The New Economics: A Manifesto.
--Of course, the direct application to Earth System science: Facing the Anthropocene: Fossil Capitalism and the Crisis of the Earth System and Keen's colleague Lenton's Earth System Science: A Very Short Introduction.
--Non-existent/false boundaries: World-Systems Analysis: An Introduction also stresses how academic disciplines can be stifling and arbitrary.
--“Bounded rationality”: citing Herbert A. Simon) on the phenomenon of rational but limited information, to put a wrench in the Neoclassical assumption of “perfect information” for rational decision-making:
a) See popular science writer/epidemiologist Ben Goldacre for an intro to heuristics (mental short-cuts) and how to avoid their shortcomings using good statistics/evidence-based methodologies (esp. I Think You'll Find It's a Bit More Complicated Than That).
b) Meadows/Raworth (Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist) also considers science’s reductionist spillover bypassing the holism embedded in certain heuristics.
c) Strange how science has provided us such revelatory glimpses of the physical world, while capitalism has abstracted so much of the social world (The Global Minotaur: America, the True Origins of the Financial Crisis and the Future of the World Economy).
…I find most compelling a synthesis where science is recognized as a set of tools (methodologies, institutions) while ideologies are carefully critiqued (social goals + assumptions).
-Ex. disease and capitalist destruction of ecosystems: Dead Epidemiologists: On the Origins of COVID-19
-Ex. agroecology: A People’s Green New Deal
-Ex. Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge, and the Teachings of Plants.
…It’s a shame Western science is plagued with undiagnosed liberalism; looking forward to The Dialectical Biologist.
The Questionable:
--Finally, let’s reapply systems thinking to Meadows’ own assumptions. Symptoms of undiagnosed liberalism include:
a) “Tragedy of the Commons” + “overpopulation”: to debunk this “Tragedy” as much more applicable to capitalist “open access” rather than social “Commons”, see https://youtu.be/xcwXME-PNuE ...Pretty cringe to list anti-immigration/pro-eugenics/Neomalthusian Garrett Hardin in your influences list, but even the most “enlightened” liberalism cannot reform itself out of capitalism so it cannot stop exploiting poor and coloured people; poor coloured people are the first to go. Details: Too Many People?: Population, Immigration, and the Environmental Crisis
b) “communist Soviet Union”: Cold War liberals forget the structure of industrialization (West took centuries of colonialism/settler colonialism/slavery while USSR took decades), decolonization (where liberals were and continue to be the colonizers: The Darker Nations: A People's History of the Third World), anti-fascism (where liberals were fascist sympathizers while USSR was invaded: Blackshirts and Reds: Rational Fascism and the Overthrow of Communism) and the capitalist military industrial complex (the basis of the Cold War that turned USSR into “siege socialism” and derailed Third World Industrialization). “Socialism” became big “government”, which was a mess since capitalism requires plenty of “government” (ex. military industrial complex), with the non-option of “traditional societies”/“gift-giving cultures”. Western readers can start to transcend the liberal capitalist paradigm with:
-some hand-holding starting with a focus on the Global North: Another Now: Dispatches from an Alternative Present
…combined with Global South:
-The Agrarian Question in the Neoliberal Era: Primitive Accumulation and the Peasantry
-Struggle Makes Us Human: Learning from Movements for Socialism
Read
November 9, 2018This is a nice basic text about systems. The layout is clear. The diagrams are helpful. The volume is an introduction. Much of it overlaps with what is in The Limits to Growth The 30Year Update but without the specific focus. The opening chapters here I felt could have been boiled down, I found myself skipping and sliding over paragraphs, but if you completely new to systems thinking the slow pace is probably helpful.
In chapter four Meadows argues that one of the reasons why economic modelling is flawed because it concentrates on flows not stocks (eg production rather than productive capacity) and doesn't consider the dynamic between them. There's a nice discussion of bounded rationality as opposed to the complete information assumed to be available to homo economicus due to the time delays inherent in information feedback and the absence of data which means that in real life we get tragedy of the commons type events occurring.
Chapter five on systems traps I thought was particularly good, with aninteresting comparison on population policy in post world war two Romania, Hungary and Sweden. Attempts in Romania to increase the birth rate by banning abortion led to an increase in deaths from back-street abortions and an increase of children abandoned at orphanages. While in Hungary there was a focus on building instead to counter people delaying having families due to a lack of housing - ie addressing a cause rather than a symptom of the problem.
There's a nice rehabilitation of President Carter who attempted to deal with the Oil Shock in the USA by introducing a tax on imported oil that would rise in proportion to the amount of oil imported and with illegal immigration by investing in development in Mexico instead. This rather sums up the difficulty that you face through thinking in systems. Because the results are counter-intuitive the feel good message of a President Reagan that addresses symptoms are more easily grasped than policies that address causes of problems be it addiction to oil or differing levels of development in North America having an effect like osmosis on populations.
In chapter four Meadows argues that one of the reasons why economic modelling is flawed because it concentrates on flows not stocks (eg production rather than productive capacity) and doesn't consider the dynamic between them. There's a nice discussion of bounded rationality as opposed to the complete information assumed to be available to homo economicus due to the time delays inherent in information feedback and the absence of data which means that in real life we get tragedy of the commons type events occurring.
Chapter five on systems traps I thought was particularly good, with aninteresting comparison on population policy in post world war two Romania, Hungary and Sweden. Attempts in Romania to increase the birth rate by banning abortion led to an increase in deaths from back-street abortions and an increase of children abandoned at orphanages. While in Hungary there was a focus on building instead to counter people delaying having families due to a lack of housing - ie addressing a cause rather than a symptom of the problem.
There's a nice rehabilitation of President Carter who attempted to deal with the Oil Shock in the USA by introducing a tax on imported oil that would rise in proportion to the amount of oil imported and with illegal immigration by investing in development in Mexico instead. This rather sums up the difficulty that you face through thinking in systems. Because the results are counter-intuitive the feel good message of a President Reagan that addresses symptoms are more easily grasped than policies that address causes of problems be it addiction to oil or differing levels of development in North America having an effect like osmosis on populations.
March 8, 2015
Ever read a book that you're sad to finish because you borrowed it from the library, rather than bought it? Also, you were sad you couldn't write notes in the margins or highlight passages? Yeah, that's this right here.
This is essential reading for anyone, and I say that without hyperbole. You should do it especially if you're in business, technology, or policy (god, especially policy) but also just generally if you live on this planet and care about a thing. I think perhaps it puts a lot of people off because of two things:
One, the perceived dryness of the title/topic, but there's no worry here, as Meadows conveys complex ideas simply and engagingly.
Two, the sustainability emphasis that runs through it. However, even though it takes an environmentalist stance, you can completely discard that to embrace the bigger picture of the book - which is, well, to embrace the bigger picture.
As I read this I was floored by the constant application to my life. Pretty remarkable considering the bulk of the writing was done around 2001 and it wasn't published until 2008. The passage about how we tend to focus on the play of a system but not the space it has to play in made me think about the infrastructure challenges we have at work. The bit about the volatility that can result from removing delays from a system is powerfully and scarily echoed in the story of the 2010 flash crash (well-told in the Radiolab episode "Speed"). And the whole thing about a system constantly reinforcing itself even if you change the players within it is the plot of The Wire.
While it can be daunting to think about challenges in this way, it's really the only way to do it if we want to solve those challenges.
This is essential reading for anyone, and I say that without hyperbole. You should do it especially if you're in business, technology, or policy (god, especially policy) but also just generally if you live on this planet and care about a thing. I think perhaps it puts a lot of people off because of two things:
One, the perceived dryness of the title/topic, but there's no worry here, as Meadows conveys complex ideas simply and engagingly.
Two, the sustainability emphasis that runs through it. However, even though it takes an environmentalist stance, you can completely discard that to embrace the bigger picture of the book - which is, well, to embrace the bigger picture.
As I read this I was floored by the constant application to my life. Pretty remarkable considering the bulk of the writing was done around 2001 and it wasn't published until 2008. The passage about how we tend to focus on the play of a system but not the space it has to play in made me think about the infrastructure challenges we have at work. The bit about the volatility that can result from removing delays from a system is powerfully and scarily echoed in the story of the 2010 flash crash (well-told in the Radiolab episode "Speed"). And the whole thing about a system constantly reinforcing itself even if you change the players within it is the plot of The Wire.
While it can be daunting to think about challenges in this way, it's really the only way to do it if we want to solve those challenges.
March 25, 2012
The world is unspeakably complex and unfortunately our inferior lizard-evolved brains are nowhere near capable of comprehending this. The world is complex and that is why our Hollywood movies have sucky plots, our politicians say idiotic things that idiotic people believe, and the word "accurate economist" is an oxymoron.
So here is the progression/evolution of a man who learns about the complexity of the world. He starts by watching Hollywood movies and Fox News and thinks that the world is black and white. There are good guys and there are bad guys. Good guys beat the bad guys. We win. Then, you go out in the real world and find out that things aren't that easy to pigeonhole. There are bad guys who do good things. There are good guys who do bad things. Good guys promise to do things for everyone but never do them, or even make things worse. The man lays out simple plans in his head and tries to execute on them in the real world but they inevitably fail. "Why don't things work out for me?" he thinks. And through years of failure, he comes to learn that the good guys are sometimes the bad guys and the bad guys are sometimes the good guys too. The rules that we believe to be real - "Good guys win" and "Working hard always leads to results" - are not so, and our efforts turn out so often to be naught because of the mysterious working processes of some Wizard of Oz behind the curtain screen. But the hard truth is that though the working processes are indeed mysterious, there is no single person to bash or blame. There are only the actions of everyone around us.
It could take years to learn this way of the world, but if you read this book you will learn this in hours. This book is not an engineering book and it is unfortunate that Donella gave it such a bland engineering-oriented name. This is a book of philosophy and life learnings. A book about complexity, written with Zen-style simplicity and brevity yet every word is packed with meaning. You read this book not because you want to learn about "systems" (who cares about that?!) but you want to learn about why things don't always work out the way you want them to.
So here is the progression/evolution of a man who learns about the complexity of the world. He starts by watching Hollywood movies and Fox News and thinks that the world is black and white. There are good guys and there are bad guys. Good guys beat the bad guys. We win. Then, you go out in the real world and find out that things aren't that easy to pigeonhole. There are bad guys who do good things. There are good guys who do bad things. Good guys promise to do things for everyone but never do them, or even make things worse. The man lays out simple plans in his head and tries to execute on them in the real world but they inevitably fail. "Why don't things work out for me?" he thinks. And through years of failure, he comes to learn that the good guys are sometimes the bad guys and the bad guys are sometimes the good guys too. The rules that we believe to be real - "Good guys win" and "Working hard always leads to results" - are not so, and our efforts turn out so often to be naught because of the mysterious working processes of some Wizard of Oz behind the curtain screen. But the hard truth is that though the working processes are indeed mysterious, there is no single person to bash or blame. There are only the actions of everyone around us.
It could take years to learn this way of the world, but if you read this book you will learn this in hours. This book is not an engineering book and it is unfortunate that Donella gave it such a bland engineering-oriented name. This is a book of philosophy and life learnings. A book about complexity, written with Zen-style simplicity and brevity yet every word is packed with meaning. You read this book not because you want to learn about "systems" (who cares about that?!) but you want to learn about why things don't always work out the way you want them to.
May 23, 2019
It's not the first book on Systems Theory I've read, but even if this one is described as a "primer", it was not time wasted (definitely).
It starts very low-level (stacks & flows), but don't get discouraged by that - w/o some foundations it's really hard to get a proper grasp of the what ST is. All this stuff is supported with nice, simple examples expressed with stacks+flows notation. You learn about balancing, reinforcing, delays, corrective flows, feedback, renewable vs non-renewable stocks, ... - and the best part comes around half of the book: author describes some sort of "patterns" (archetypes) of systems frequently spotted in the wild. This is incredible & enlightening: you go through examples from politics, sociology, economy which map well-known RL situations to ST. Don't get me wrong - if e.g. particular scenario is doomed to fail you'll know it (because you've seen in failing in the real-life), now you know WHY such systems fail (not THIS system fail) - it *clicks* as something corresponding to a pattern. Neat stuff.
The last 20% is not as entertaining, but even keeping that in mind, the book is truly worth reading (4.5 stars). Epic stuff. TBH the more I learn about ST, the more critical I find it in my everyday's work (as an Architect, Manager, Leader).
It starts very low-level (stacks & flows), but don't get discouraged by that - w/o some foundations it's really hard to get a proper grasp of the what ST is. All this stuff is supported with nice, simple examples expressed with stacks+flows notation. You learn about balancing, reinforcing, delays, corrective flows, feedback, renewable vs non-renewable stocks, ... - and the best part comes around half of the book: author describes some sort of "patterns" (archetypes) of systems frequently spotted in the wild. This is incredible & enlightening: you go through examples from politics, sociology, economy which map well-known RL situations to ST. Don't get me wrong - if e.g. particular scenario is doomed to fail you'll know it (because you've seen in failing in the real-life), now you know WHY such systems fail (not THIS system fail) - it *clicks* as something corresponding to a pattern. Neat stuff.
The last 20% is not as entertaining, but even keeping that in mind, the book is truly worth reading (4.5 stars). Epic stuff. TBH the more I learn about ST, the more critical I find it in my everyday's work (as an Architect, Manager, Leader).
July 22, 2018
An attempt to make holism rigorous; given holism's deep intuitive appeal for people, the attempt is worthy. But I was hostile to this at first – mostly because her field helped breed a generation of pseuds who use ‘reductionism’ as an insult (rather than as a straightforward fact, or a useful way of thinking, instances of which denote the highest achievements of the species). Let's get clear:
So stated, there is no conflict between good old reduction and shiny systems thinking. But Meadows distils the juicy bits into <200pp here, and freely admits that systems theory has an intractable indeterminacy built into it, and says this, too:
Can it resolve empirical questions the way physics does, though? In saying, probably rightly, that a flow could go either way, depending on the state of the rest of the system and neighbouring systems, you lose or sideline crucial power to find out a single cause's influence, and thereby know more or less exactly what to do to the system. In other places, knowledge comes from isolating causes. A reductionist can agree with all the clever diagrams in this, happily concede that they illustrate the gnarly problems of collective action and feedback and other ecosystems very clearly - and not give up their peerlessly successful method / ontological stance at all.
“REDUCTIONISM” (to the pseud): The claim that complicated or immeasurable things do not exist.
“SYSTEMS THEORY” (to the pseud): The only way of understanding things: as a whole. Everything else omits and so isn't full.
REDUCTIONISM (ontology): The claim that complicated things are made of simpler things. Only the simplest of them are physically real; the rest are mental models of their interactions.*
REDUCTIONISM (methodology): The attempt to isolate causes and treat phenomena in terms of their most basic units (whether quark, string, person, transaction).
SYSTEMS THEORY: When things get together, they exhibit features the individual things don’t.
So stated, there is no conflict between good old reduction and shiny systems thinking. But Meadows distils the juicy bits into <200pp here, and freely admits that systems theory has an intractable indeterminacy built into it, and says this, too:
Ever since the Industrial Revolution, Western society has benefited from science, logic, and reductionism over intuition and holism. Psychologically and politically we would much rather assume that the cause of a problem is “out there,” rather than “in here.” It’s almost irresistible to blame something or someone else, to shift responsibility away from ourselves, and to look for… the technical fix that will make a problem go away.
Serious problems have been solved by focusing on external agents — preventing smallpox, increasing food production, moving large weights and many people rapidly over long distances. Because they are embedded in larger systems, however, some of our “solutions” have created further problems… Hunger, poverty, environmental degradation, economic instability, unemployment, chronic disease, drug addiction, and war, for example, persist in spite of the analytical ability and technical brilliance that have been directed toward eradicating them. No one deliberately creates those problems, no one wants them to persist, but they persist nonetheless.
That is because they are intrinsically systems problems – undesirable behaviors characteristic of the system structures that produce them. They will yield only as we reclaim our intuition, stop casting blame, see the system as the source of its own problems, and find the courage and wisdom to restructure it.
Can it resolve empirical questions the way physics does, though? In saying, probably rightly, that a flow could go either way, depending on the state of the rest of the system and neighbouring systems, you lose or sideline crucial power to find out a single cause's influence, and thereby know more or less exactly what to do to the system. In other places, knowledge comes from isolating causes. A reductionist can agree with all the clever diagrams in this, happily concede that they illustrate the gnarly problems of collective action and feedback and other ecosystems very clearly - and not give up their peerlessly successful method / ontological stance at all.
* Also
PHYSICALISM: Everything is made of physical things. (However, the physical may be stranger than you think.)
June 11, 2012
I recommend Thinking in Systems because it has changed the way I understand and relate to my world. Published after Donella Meadow's death, it introduces Systems Thinking by way of definition, illustration and application.
In Part 1, System Structure and Behaviour, Meadows uses two graphical tools to analyse systems: stock and flow diagrams to show system structure; and charts mapping stock or flow levels over time to explore system behaviour for specific scenarios. The diagrams can be used to display "balancing" (aka "negative") and "reinforcing" (aka "positive") feedback loops, and the charts to explore how these might play out.
While some of the systems might seem simplistic, they build up understanding of a key Systems Thinking insight, that systems generate their own behaviour. And if you're ever wondered why the "heroes and villains" style of explanation only works in retrospect, this is a damn good explanation.
Chapter two, The Zoo, is a library of common system structures and their behaviour. Those of us from the software world will be reminded of a patterns library. Again, these patterns illustrate a deeper insight, that "systems with similar feedback structures produce similar dynamic behaviors, even if the outward appearance of these systems is completely dissimilar." (p 51)
In Part 2, Systems and Us, Meadows applies Systems Thinking to our world. Many of the examples are dated, but I found myself thinking how applicable these patterns and insights were to topics I was currently encountering - for example, I can't help thinking she would have loved the way that Kanban reflects a systems learning, that the ability of people and organisations to execute tasks degrades rapidly as the number of tasks rises beyond a critical limit.
Of course one natural and urgent interest in systems behaviour is how to change it. If worshipping heroes and lynching villains isn't going to reform systems that may exhibit non-linear, perverse or self-preserving behaviour, what is?
In Part 3, Creating Change in System and in our Philosophy, Meadows gives us a dozen leverage points for changing systems, starting with the simplest and ending with the most powerful. She finishes with a list of "systems wisdoms" - attitudes and values that she and others she respects have adopted to make them more effective at understanding and changing the systems we live in.
Like many of the other reviewers, I wish I'd read this book a long time ago. It has its limitations - I'd love to see more recent examples, and can't help wondering if there are any open-source Systems modelling resources. But for me this is a book of timeless value for anyone interested in a better understanding of their world and their options in it.
In Part 1, System Structure and Behaviour, Meadows uses two graphical tools to analyse systems: stock and flow diagrams to show system structure; and charts mapping stock or flow levels over time to explore system behaviour for specific scenarios. The diagrams can be used to display "balancing" (aka "negative") and "reinforcing" (aka "positive") feedback loops, and the charts to explore how these might play out.
While some of the systems might seem simplistic, they build up understanding of a key Systems Thinking insight, that systems generate their own behaviour. And if you're ever wondered why the "heroes and villains" style of explanation only works in retrospect, this is a damn good explanation.
Chapter two, The Zoo, is a library of common system structures and their behaviour. Those of us from the software world will be reminded of a patterns library. Again, these patterns illustrate a deeper insight, that "systems with similar feedback structures produce similar dynamic behaviors, even if the outward appearance of these systems is completely dissimilar." (p 51)
In Part 2, Systems and Us, Meadows applies Systems Thinking to our world. Many of the examples are dated, but I found myself thinking how applicable these patterns and insights were to topics I was currently encountering - for example, I can't help thinking she would have loved the way that Kanban reflects a systems learning, that the ability of people and organisations to execute tasks degrades rapidly as the number of tasks rises beyond a critical limit.
Of course one natural and urgent interest in systems behaviour is how to change it. If worshipping heroes and lynching villains isn't going to reform systems that may exhibit non-linear, perverse or self-preserving behaviour, what is?
In Part 3, Creating Change in System and in our Philosophy, Meadows gives us a dozen leverage points for changing systems, starting with the simplest and ending with the most powerful. She finishes with a list of "systems wisdoms" - attitudes and values that she and others she respects have adopted to make them more effective at understanding and changing the systems we live in.
Like many of the other reviewers, I wish I'd read this book a long time ago. It has its limitations - I'd love to see more recent examples, and can't help wondering if there are any open-source Systems modelling resources. But for me this is a book of timeless value for anyone interested in a better understanding of their world and their options in it.
July 13, 2016
This book was meant to grasp the basics of systems thinking, which it does but its writing style is not clear enough. Also some examples are clules and not accuratly relevant.
The number of pages could be reduced by at least 40%, without harming the content delivery. If it was will written, it would be an outstanding book, but unfortunately this is not the case.
The number of pages could be reduced by at least 40%, without harming the content delivery. If it was will written, it would be an outstanding book, but unfortunately this is not the case.
May 28, 2022
I was happy to discover that a lot of the BS I've heard described as "systems thinking" is directly contradicted by what Meadows actually wrote (see quotes and lessons below). Meadows emphasizes the importance of starting with objective data as opposed to individuals' anecdotal impressions, and she extols the scientific method in general.
In defense of the author, this book was published posthumously so maybe it's a collection of half-baked thoughts that weren't supposed to be shared. Much of it, as the author admits up front, is just common sense along the lines of "you can't grow a forest overnight."
A worldview based on “Feedback, non-linearity and systems responsible for their own behavior” I think would be pretty obvious to anyone with a background in biology, or public health or a number of other fields. Some of the book verges on mystical mumbo jumbo. My biggest problem though was that I kept waiting for the example where some country or company was all messed up and they called in the MIT Systems Thinkers who pointed out to them how their thinking was all wrong and they needed to think the other way and then things went on happily ever after. That example never came, let alone any rigorous evidence for some specific technique.
Quotes: (may not be 100% accurate because I did audio.)
- “I know from bitter experience that because they are so counterintuitive, when I do discover a system’s leverage points, hardly anybody will believe me. Very frustrating. Especially for those who yearn … to make the world work better. …99% of our attention goes to parameters but there’s not a lot of leverage in them. It’s not that parameters aren’t important; they can be, especially in the short-term and to the individual who’s standing directly in the flow. … Spending more on police doesn’t make crime go away."
The take-home lessons are listed below.
1. Get the beat of the system. "… social system …Watch it work; get its history; ask people who have been around a long time to tell you what has happened. If possible, find or make a time graph of actual data from the system. People’s memories are not always reliable when it comes to timing. …you won’t believe how many wrong turns it helps you avoid… … Starting with the behavior of the system forces you to focus on facts, not theories. It keeps you from falling too quickly into your own beliefs or misconceptions or those of others. … People will swear that rainfall is decreasing, say, but when you look at the data, you find that what is really happening is that variability is increasing. … Watching what really happens instead of listening to people’s theories of what happens can explode many careless causal hypotheses. … One can ask what’s working well here. … 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 don’t have enough salesmen.” ..
2. Expose your mental models to the light of day. "Get your model out there where it can be viewed. .. Consider all [models] to be plausible until you find some evidence that allows you to rule one out. Getting models out into the light of day, making them as rigorous as possible, testing them against the evidence, and being willing to scuttle them if they are no longer supported is nothing more than practicing the scientific method, something that is done too seldom, even in science, and is done hardly at all in social science, or management, or government or everyday life."
3. Honor, respect, and distribute information. … "most of what goes wrong in systems goes wrong because of biased, late or missing information. If I could I would add an eleventh commandment to the first ten: thou shalt not distort, delay or withhold information. … Information is power. … It’s no wonder that our social systems so often run amok."
4. Use language with care and enrich it with systems concepts… "The first step in respecting language is keeping it as concrete, meaningful and truthful as possible."
5. Pay attention to what is important, not just what is quantifiable.
6. Make feedback policies for feedback systems. "The best policies contain feedback loops… These are policies that design learning into the management process. An example was the historic Montreal Protocol to protect the ozone layer of the stratosphere. In 1987, when that protocol was signed, there was no certainty about the danger to the ozone layer, about the rate at which it was degrading, or about the specific effect of different chemicals. … it also required monitoring the situation, and reconvening an international congress to change the phase-out schedule if the damage to the ozone layer turned out to be more or less than expected. Just three years later, in 1990, the schedule had to be hurried forward and more chemicals added to it because the damage was turning out to be much greater than what was foreseen in 1987. That was a feedback policy…."
7. Go for the good of the whole.
8. Listen to the wisdom of the system. "…Don’t be an unthinking intervener… Before you charge in to make things better, pay attention to the value of what’s already there."
9. Locate responsibility within the system.
10. Stay humble—stay a learner. "The thing to do when you don’t know is not to bluff, and not to freeze, but to learn. The way you learn is by experiment. … it means making mistakes and what’s worse, admitting them. .. It takes a lot of courage to embrace your errors."
11. Celebrate complexity.
12. Expand time horizons.
13. Defy the disciplines.
14. Expand the boundary of caring.
15. Don’t erode the goal of goodness.
In defense of the author, this book was published posthumously so maybe it's a collection of half-baked thoughts that weren't supposed to be shared. Much of it, as the author admits up front, is just common sense along the lines of "you can't grow a forest overnight."
A worldview based on “Feedback, non-linearity and systems responsible for their own behavior” I think would be pretty obvious to anyone with a background in biology, or public health or a number of other fields. Some of the book verges on mystical mumbo jumbo. My biggest problem though was that I kept waiting for the example where some country or company was all messed up and they called in the MIT Systems Thinkers who pointed out to them how their thinking was all wrong and they needed to think the other way and then things went on happily ever after. That example never came, let alone any rigorous evidence for some specific technique.
Quotes: (may not be 100% accurate because I did audio.)
- “I know from bitter experience that because they are so counterintuitive, when I do discover a system’s leverage points, hardly anybody will believe me. Very frustrating. Especially for those who yearn … to make the world work better. …99% of our attention goes to parameters but there’s not a lot of leverage in them. It’s not that parameters aren’t important; they can be, especially in the short-term and to the individual who’s standing directly in the flow. … Spending more on police doesn’t make crime go away."
The take-home lessons are listed below.
1. Get the beat of the system. "… social system …Watch it work; get its history; ask people who have been around a long time to tell you what has happened. If possible, find or make a time graph of actual data from the system. People’s memories are not always reliable when it comes to timing. …you won’t believe how many wrong turns it helps you avoid… … Starting with the behavior of the system forces you to focus on facts, not theories. It keeps you from falling too quickly into your own beliefs or misconceptions or those of others. … People will swear that rainfall is decreasing, say, but when you look at the data, you find that what is really happening is that variability is increasing. … Watching what really happens instead of listening to people’s theories of what happens can explode many careless causal hypotheses. … One can ask what’s working well here. … 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 don’t have enough salesmen.” ..
2. Expose your mental models to the light of day. "Get your model out there where it can be viewed. .. Consider all [models] to be plausible until you find some evidence that allows you to rule one out. Getting models out into the light of day, making them as rigorous as possible, testing them against the evidence, and being willing to scuttle them if they are no longer supported is nothing more than practicing the scientific method, something that is done too seldom, even in science, and is done hardly at all in social science, or management, or government or everyday life."
3. Honor, respect, and distribute information. … "most of what goes wrong in systems goes wrong because of biased, late or missing information. If I could I would add an eleventh commandment to the first ten: thou shalt not distort, delay or withhold information. … Information is power. … It’s no wonder that our social systems so often run amok."
4. Use language with care and enrich it with systems concepts… "The first step in respecting language is keeping it as concrete, meaningful and truthful as possible."
5. Pay attention to what is important, not just what is quantifiable.
6. Make feedback policies for feedback systems. "The best policies contain feedback loops… These are policies that design learning into the management process. An example was the historic Montreal Protocol to protect the ozone layer of the stratosphere. In 1987, when that protocol was signed, there was no certainty about the danger to the ozone layer, about the rate at which it was degrading, or about the specific effect of different chemicals. … it also required monitoring the situation, and reconvening an international congress to change the phase-out schedule if the damage to the ozone layer turned out to be more or less than expected. Just three years later, in 1990, the schedule had to be hurried forward and more chemicals added to it because the damage was turning out to be much greater than what was foreseen in 1987. That was a feedback policy…."
7. Go for the good of the whole.
8. Listen to the wisdom of the system. "…Don’t be an unthinking intervener… Before you charge in to make things better, pay attention to the value of what’s already there."
9. Locate responsibility within the system.
10. Stay humble—stay a learner. "The thing to do when you don’t know is not to bluff, and not to freeze, but to learn. The way you learn is by experiment. … it means making mistakes and what’s worse, admitting them. .. It takes a lot of courage to embrace your errors."
11. Celebrate complexity.
12. Expand time horizons.
13. Defy the disciplines.
14. Expand the boundary of caring.
15. Don’t erode the goal of goodness.
February 21, 2022
Some books argue theses or tell stories. Others encompass an entire worldview. Thinking in Systems is the latter. It almost doesn’t even make sense for this to be a book. It’s a way of looking at people and the world. It’s a ~ mindset ~. The idea is basically: nothing exists in isolation; everything in the world is being influenced by its environment, and exerting influence in turn. We can understand basically everything—governments, the natural environment, the body’s metabolism, corporations, culture, all of human foible and folly—through the lens of systems, incentives, and feedback loops.
Here’s another way to express this mindset: people have pattern-seeking brains that enable them to pass down ideas through culture; culture shapes people’s beliefs and values; people’s beliefs and values cause them to create artifacts and participate in institutions; institutions are generated through the interactions of complex networks of people; people’s interactions are mediated by their social context, their psychology, and their culture; their culture in turn shapes their values and beliefs, and on and on.
Once you start looking at things this way, it’s hard to stop. In particular, you quickly realize that if you want to change the way the world works, you need to be very precise about where in the system you are intervening. Convincing an elected official to change their mind about something is a rather minor intervention. Electing a new official is bigger. Changing the entire structure of government is massive. Setting up educational and cultural institutions that universally imbue egalitarian values—now that’s a systems-level change.
A nice quote that stood out: “If you destroy a factory but don’t destroy the underlying thought patterns that created it, soon enough there will be a new factory to replace it.” Nice.
I also really liked that idea that reading the news is harmful because it provides an event-level, not systems-level, view. Theorizing about the world based on the news is like theorizing climate change based on each individual day’s weather report. If you really want to understand what’s going on, you need to look at the reasons people do what they do, not merely the descriptions of their behavior.
All that said: this type of book makes me uneasy. It makes me uneasy because I feel like this way of looking at the world has to be earned. I worry that there are a bunch of people who read a book like this then style themselves “systems thinkers”, without actually having a deep understanding of any particular thing. If you want to become a systems thinker, don’t read a book like this. Pick a system—the federal reserve, the climate, peat bogs, healthcare—and study how it works in immense depth. Historians don’t study “systems”, they study specific groups of people, cultures, or events. Donella Meadows herself did not study “systems”, she studied overpopulation and the environment.
It seems to me, then, that systems thinking is the “gift” you get to earn at the end of this work. After years or even decades of working with something in great detail, you can then generate abstractions like “systems thinking”. It reminds me a bit of the book The Lessons of History, by Will and Ariel Durant. It’s a short book that makes broad, sweeping claims about history, but it really only has any merit because the two authors spent their entire lifetime writing detailed, hyper-specific books about particular events and people. I would be very nervous if someone skipped the details and tried to get straight to the takeaway lessons. In other words: beware premature abstraction.
A final example: a lot of popular science/math content is about high-level theorems or big ideas in physics. These things have value, of course. But profound insights about algebraic geometry or quantum physics are the gift you get at the end of putting in the real work: calculations. Real mathematicians and physicists spend most of their time doing calculations, playing with toy examples, and wading through other details. Grant Sanderson talked about this in a recent video, in which he shows the “cleaned-up YouTube way” to prove a particular theorem (elegant, abstract), and the more “honest” way (lots of calculations).
My takeaway: If you want to be a good systems thinker like Donella Meadows, the best way is to be like Donella Meadows: find a field you are passionate about and go all-in on understanding its incentive structures, inner logic, and feedback loops in great detail. Maybe that’s what they mean by “do what I do, not what I say”: advice is the reward you get to dish out at the end. But you gotta put in the work first.
Here’s another way to express this mindset: people have pattern-seeking brains that enable them to pass down ideas through culture; culture shapes people’s beliefs and values; people’s beliefs and values cause them to create artifacts and participate in institutions; institutions are generated through the interactions of complex networks of people; people’s interactions are mediated by their social context, their psychology, and their culture; their culture in turn shapes their values and beliefs, and on and on.
Once you start looking at things this way, it’s hard to stop. In particular, you quickly realize that if you want to change the way the world works, you need to be very precise about where in the system you are intervening. Convincing an elected official to change their mind about something is a rather minor intervention. Electing a new official is bigger. Changing the entire structure of government is massive. Setting up educational and cultural institutions that universally imbue egalitarian values—now that’s a systems-level change.
A nice quote that stood out: “If you destroy a factory but don’t destroy the underlying thought patterns that created it, soon enough there will be a new factory to replace it.” Nice.
I also really liked that idea that reading the news is harmful because it provides an event-level, not systems-level, view. Theorizing about the world based on the news is like theorizing climate change based on each individual day’s weather report. If you really want to understand what’s going on, you need to look at the reasons people do what they do, not merely the descriptions of their behavior.
All that said: this type of book makes me uneasy. It makes me uneasy because I feel like this way of looking at the world has to be earned. I worry that there are a bunch of people who read a book like this then style themselves “systems thinkers”, without actually having a deep understanding of any particular thing. If you want to become a systems thinker, don’t read a book like this. Pick a system—the federal reserve, the climate, peat bogs, healthcare—and study how it works in immense depth. Historians don’t study “systems”, they study specific groups of people, cultures, or events. Donella Meadows herself did not study “systems”, she studied overpopulation and the environment.
It seems to me, then, that systems thinking is the “gift” you get to earn at the end of this work. After years or even decades of working with something in great detail, you can then generate abstractions like “systems thinking”. It reminds me a bit of the book The Lessons of History, by Will and Ariel Durant. It’s a short book that makes broad, sweeping claims about history, but it really only has any merit because the two authors spent their entire lifetime writing detailed, hyper-specific books about particular events and people. I would be very nervous if someone skipped the details and tried to get straight to the takeaway lessons. In other words: beware premature abstraction.
A final example: a lot of popular science/math content is about high-level theorems or big ideas in physics. These things have value, of course. But profound insights about algebraic geometry or quantum physics are the gift you get at the end of putting in the real work: calculations. Real mathematicians and physicists spend most of their time doing calculations, playing with toy examples, and wading through other details. Grant Sanderson talked about this in a recent video, in which he shows the “cleaned-up YouTube way” to prove a particular theorem (elegant, abstract), and the more “honest” way (lots of calculations).
My takeaway: If you want to be a good systems thinker like Donella Meadows, the best way is to be like Donella Meadows: find a field you are passionate about and go all-in on understanding its incentive structures, inner logic, and feedback loops in great detail. Maybe that’s what they mean by “do what I do, not what I say”: advice is the reward you get to dish out at the end. But you gotta put in the work first.
August 1, 2019
It'a a good book to get a general understanding of what Systems Thinking really is.
My key takeaways after reading this book:
* When you create some process/initiative - add a paragraph about the way your thing will evolve over time - what kind of feedback will make it better and what kind of feedback will make it worse. Make a thought experiment in each direction and think of how your system is going to learn over time.
* Most of the time systems are complex :)
* There are a lot of systems traps like Policy resistance, The tragedy of commons, Success to the Successful and the entire chapter about it was my favourite part of this book.
* The most interesting system trap for me was "drift to low performance". TLDR:
1. We tend to believe bad news more than good news
2. We believe our current performance is worse than it actually is
3. We adjust our performance goal based on our perceived current state
4. We adjust our corrective action based on our performance goal
5. Our actual performance gets worse
* Introducing new law and policies in order to fix something might take us in a completely undesired direction. I was aware of that, but after reading this book I feel even more informed about it
My key takeaways after reading this book:
* When you create some process/initiative - add a paragraph about the way your thing will evolve over time - what kind of feedback will make it better and what kind of feedback will make it worse. Make a thought experiment in each direction and think of how your system is going to learn over time.
* Most of the time systems are complex :)
* There are a lot of systems traps like Policy resistance, The tragedy of commons, Success to the Successful and the entire chapter about it was my favourite part of this book.
* The most interesting system trap for me was "drift to low performance". TLDR:
1. We tend to believe bad news more than good news
2. We believe our current performance is worse than it actually is
3. We adjust our performance goal based on our perceived current state
4. We adjust our corrective action based on our performance goal
5. Our actual performance gets worse
* Introducing new law and policies in order to fix something might take us in a completely undesired direction. I was aware of that, but after reading this book I feel even more informed about it
January 24, 2018
This is an absolutely fundamental book if you want to understand and influence the world. I appreciate the simplicity of the language and the humility of the presentation. Just because you have a model, that doesn't mean that you know what to do. Or that what you do will have just the intended consequences. Or even the intended consequences at all.
April 7, 2017
Wow. This book was incredible. When I picked it up I honestly had no idea how much it'd end up pulling me in.
I love how Meadows approached a very complex scientific and mathematical subject and broke it down into easy-to-understand diagrams and concepts. Reading it at times was almost like reading poetry mixed with a text book, especially near the end.
What I enjoy most about her way of thinking is that it arms you with a practical lens for viewing the world. She also advocates for constantly questioning and observing the systems in which you're living to identify different leverage points to help you achieve your purpose whatever it may be.
In the end, I learned just as much about a beautiful dance-like way of approaching life as I did about approaching the analysis and manipulation of complex systems.
I love how Meadows approached a very complex scientific and mathematical subject and broke it down into easy-to-understand diagrams and concepts. Reading it at times was almost like reading poetry mixed with a text book, especially near the end.
What I enjoy most about her way of thinking is that it arms you with a practical lens for viewing the world. She also advocates for constantly questioning and observing the systems in which you're living to identify different leverage points to help you achieve your purpose whatever it may be.
In the end, I learned just as much about a beautiful dance-like way of approaching life as I did about approaching the analysis and manipulation of complex systems.
May 1, 2016
This book deserves a star more if the concepts and the ideas in it a completely new to you. For me, unfortunately, too much of this was too long-winded considering that it concluded with concepts that are extremely well-known to me.
The best part of this book is the first few chapters; where the basic concepts and vocabulary is explained. Stock. Flow. Balancing and Strengthening feedback-loops. After that it spends too many words for too simple concepts making it fairly boring in the latter parts, at least if you've got a decent grounding in related concepts already. I think this book would be better at half the length. I ended up skimming the last half of the book; there just wasn't enough that was new to me there to hold my interest, relative to how many words were spent.
The best part of this book is the first few chapters; where the basic concepts and vocabulary is explained. Stock. Flow. Balancing and Strengthening feedback-loops. After that it spends too many words for too simple concepts making it fairly boring in the latter parts, at least if you've got a decent grounding in related concepts already. I think this book would be better at half the length. I ended up skimming the last half of the book; there just wasn't enough that was new to me there to hold my interest, relative to how many words were spent.
September 14, 2020
"Thinking in systems" just became one of my favorite books of all time. Reading it can shift your perspective and change the way you think. Learning to think in systems expands your horizon, and we very much need it in a world where we are constantly looking at the immediate present and immediate surroundings.
The author illustrates her concepts marvelously with stories and graphs. The book is an immensely enjoyable read and I would highly recommend it to anyone who wants to better understand the world or just wants to upgrade his/her thinking skills.
The author illustrates her concepts marvelously with stories and graphs. The book is an immensely enjoyable read and I would highly recommend it to anyone who wants to better understand the world or just wants to upgrade his/her thinking skills.
September 8, 2015
Easy to read introduction into systems theory with several practical implementations which often goes against the naive way.
December 23, 2011
If you're interested in "limits to growth," climate change, peak oil, and things like that, you should at least take a look at this book. It is, as the title advertises, a "primer," so anyone can read it, and it is very readable. It isn't real technical (and technical people may find it not technical enough), but the results are important and often surprising.
Donella Meadows is one of the original authors of the "Limits to Growth" study in 1972, and she shows the kind of systems reasoning that went into the study. It generally boosts my confidence that the authors were pretty well informed for an analysis of the whole issue of limits to growth for their day, and in fact even today.
The book does not cover specific issues, but uses issues as examples; it is really about method and approach to issues and problems in our society. She discusses what a system is, why systems surprise us, and some favorite patterns in systems that lead us astray, how to get systems to work better, and some practical advice on how to deal with systems and "systems thinking." She talks about the "tragedy of the Commons" which Garrett Hardin made famous. There's the trap of the "drift to low performance." Intervening to overcome problems may lead to something like "addiction," in the sense that we become totally dependent on the intervention.
Two interesting examples stand out. She discusses why sailboat races progressed from a way to have fun with normal sailboats, to a highly specialized competition in which boats are useless for any other purpose except competition in carefully-defined sailboat races. She also discusses why maintaining inventory in the face of a changing demand, may actually lead NOT to a more stable inventory level, but to an increasing oscillation in inventory -- a result which I couldn't argue with, but that I found totally non-intuitive.
Some people may find this all very obvious. Well, some of it is, and some of it isn't, and I think that Meadows knows this and is doing the best she can to explain this fact. Part of it is obvious. When you see the diagrams and the explanations, the flows and stocks and arrows, it all looks pretty simple. But part of it is not only NOT obvious, it's very hard to explain or even see in the first place. The problem is getting to the point where you HAVE flows and diagrams and arrows. The problem is figuring out how much of systems analysis is "art," and how much is "science," and getting the knack of knowing the difference and when you need one and not the other.
One key problem in analyzing systems, which she points out, is that we never quite know where they begin or where they end. Figuring out the "boundaries" is exactly a key problem in dealing with systems. So you can't just approach systems analysis by laying out the elements and definitions and drawing some general conclusions. You need to know when you've really identified all the elements in a system, and that, unfortunately, is an art and not a science, which her last chapter deals with in a whimsical way.
Donella Meadows is one of the original authors of the "Limits to Growth" study in 1972, and she shows the kind of systems reasoning that went into the study. It generally boosts my confidence that the authors were pretty well informed for an analysis of the whole issue of limits to growth for their day, and in fact even today.
The book does not cover specific issues, but uses issues as examples; it is really about method and approach to issues and problems in our society. She discusses what a system is, why systems surprise us, and some favorite patterns in systems that lead us astray, how to get systems to work better, and some practical advice on how to deal with systems and "systems thinking." She talks about the "tragedy of the Commons" which Garrett Hardin made famous. There's the trap of the "drift to low performance." Intervening to overcome problems may lead to something like "addiction," in the sense that we become totally dependent on the intervention.
Two interesting examples stand out. She discusses why sailboat races progressed from a way to have fun with normal sailboats, to a highly specialized competition in which boats are useless for any other purpose except competition in carefully-defined sailboat races. She also discusses why maintaining inventory in the face of a changing demand, may actually lead NOT to a more stable inventory level, but to an increasing oscillation in inventory -- a result which I couldn't argue with, but that I found totally non-intuitive.
Some people may find this all very obvious. Well, some of it is, and some of it isn't, and I think that Meadows knows this and is doing the best she can to explain this fact. Part of it is obvious. When you see the diagrams and the explanations, the flows and stocks and arrows, it all looks pretty simple. But part of it is not only NOT obvious, it's very hard to explain or even see in the first place. The problem is getting to the point where you HAVE flows and diagrams and arrows. The problem is figuring out how much of systems analysis is "art," and how much is "science," and getting the knack of knowing the difference and when you need one and not the other.
One key problem in analyzing systems, which she points out, is that we never quite know where they begin or where they end. Figuring out the "boundaries" is exactly a key problem in dealing with systems. So you can't just approach systems analysis by laying out the elements and definitions and drawing some general conclusions. You need to know when you've really identified all the elements in a system, and that, unfortunately, is an art and not a science, which her last chapter deals with in a whimsical way.
February 24, 2021
This is one of those books that where it was almost useless to highlight valuable statements because I was highlighting multiple things every page. Meadows does not go into the mathematics of systems theory. As the title suggests, she focuses on the key ideas so that the reader learns to think about systems and their common properties.
One of the key takeaways from this book -- if I had to choose just one -- is that systems have common properties that apply regardless of their type. There are ways of thinking about environmental, human, technological, and other systems that show their deep similarities and give insights into their differences.
Overall, this book was readable and should be a required read for anyone who designs or influences systems, big or small.
One of the key takeaways from this book -- if I had to choose just one -- is that systems have common properties that apply regardless of their type. There are ways of thinking about environmental, human, technological, and other systems that show their deep similarities and give insights into their differences.
Overall, this book was readable and should be a required read for anyone who designs or influences systems, big or small.
September 27, 2018
Although I had looked at Systems Thinking in the past, I did it very superficially. This book really helped me understand more about it and gave me knowledge I could apply immediately in my work environment.
As I’ve been told before reading the book, I would see systems everywhere. And that’s exactly what happened while going through the book and after I finished. Systems Thinking makes you look at things in a different way and gives you tools to better deal and influence the environment around you.
As I’ve been told before reading the book, I would see systems everywhere. And that’s exactly what happened while going through the book and after I finished. Systems Thinking makes you look at things in a different way and gives you tools to better deal and influence the environment around you.
June 7, 2020
This gets technical pretty quickly but I think the overall ideas and way of thinking make this a must-read for anyone working with large-scale systems today (I consider a large swathe of knowledge workers in this category.)
January 24, 2018
It is the one of the areas I’ve been interested for quite a while. But I could not find concise introductory book about system thinking for social science and/or policymaking. I think, in spite of its limitations, this book is as close as you could get. It is not technical. It explains the basics very clearly. It could be claimed, that the book is representing only one part of the rapidly developing and diverse field. But I would argue, that it does not go deep into the specifics of any field, while providing the starting point.
Donella Meadows, professor of MIT, she has written the manuscript in1992, but never came to publishing it. It has been published after her death in 2001, I believe. While reading, i felt she did not have a chance to edit it properly. It is a mixture of a basic system theory, her views on certain social issues, personal experiences and even sometimes more general philosophic thoughts. So you have to be a patient reader to get what you want out of it. I’ve almost ended up reading it twice. But it is very rewarding at the end. You start see systems everywhere.
Some of her examples are obviously very outdated. In spite of this, I did not feel her explanations and approach has aged. She came up with the suggested methodology, how to change social systems and this is already quite a lot.
What is the system?
It is something which is more than the sum of its parts and it is source of its own behaviour. She gives more formal definition: “a set of elements or parts that is coherently organised and interconnected in a pattern or structure that produces a characteristic set of behaviours often classified as its function or purpose”.
The difference between a system and a set of objects:
1) there are connections (called flows) between the elements (often called stocks or agents) in the system;
2) there are certain feedback mechanisms communicated through these connections. Respectively, a change in one part of the system would course the change in another part. The main point here, that the system would course its own behaviour by itself. So it would function in time (“dynamic” is the scientific word).
Non controversial examples is a thermostat, or a human body. Apparently, in the 90s to consider an individual, society or economy as a systems examples was controversial to the extent that she uses the word “heretical”…
In the book she briefly describes different types of systems. She also brings natural resources into social system.
What is thinking in systems?
It is a critical tool applying the knowledge of systems to the chosen object of research in order to understand it better or solve related problems. Practically, it is done through building a model of the underlying system. It is just one type of analysis, the “lens” how to understand the world around us. The book delves deeper into how it works and describes the properties of the systems.
What are feedbacks?
Feedback loop is much easier to understand intuitively than define. But it is a chain of rules or physical laws which defines how the initial change in the element (stock in the system speak) of the system courses the next change at the same element. It comes in two varieties:
- balancing feedback - there is a defined goal and the current state is compared to this goal (the targeted temperature in thermostat vs the current temperature for example). This type of feedback makes the system more stable, but more resistant to change as well.
- reinforcing feedback loop - it is self-enfacing mechanism when the initial change triggers even bigger change. This type of feedback left alone is leading to the exponential growth or runaway collapses of the system over time. The example could be a bank balance with the compounded interest rate; population growth of rabbits or escalation of a political conflict.
Or, I dare say, a rating on GR website - the higher the rating, the more people would read the book, the more people would rate it positively - the more rating would grow - the more people would read and…. you’ve got the picture. But unfortunately, for some wonderful, but underrated books, it works in negative way as well.
What a boundaries?
“there are no separate systems. The world is continuum. Where to draw a boundary around a system depends on the purpose of the discussion.” It is very important point which illustrates the relevance of systems thinking to our life. She notes: “Ideally we would have the mental flexibility to find the appropriate boundary for thinking about each new problem. We are rarely that flexible. We get attached to the boundary our minds happen to be accustomed to. To think how many arguments have to do with boundaries- national trade ethnic, boundaries between public and private responsibility, and boundaries between the religious ch and poor, polluters and pollutees, people alive now and people who cone in the future.” This lack of mental flexibility is the source of a lot of unnecessary conflicts and mistakes, systems or not…
What are “systems’ traps”?
Systems could be counter-intuitive. More often than not a system would “surprise” you - come up with the results which you would not expect. This is because the majority of systems are complex - contain subsystems. And there are a lot of connections and feedbacks. Feedbacks might pull the performance in an unexpected direction. Or, the goals of subsystems might contradict to each other and “may not lead to decisions that further the welfare of the system of the whole”.
The bad “surprises” she calls “systems traps”. She defines about 9 different traps encountered in the social systems and the recommended ways out. They are all very important, but I’ve picked up just three to give you a jest of what is all about:
- Escalation - When the state of one stock (agent) is determined by trying to surpass the state of another stock (agent) and vice versa. “It is a reinforcing feedback loop carrying the system in an arms race, a wealth race, a smear campaign, escalating loudness, escalating violence. The escalation is exponential and can lead to extremes surprisingly quickly. If nothing is done, the spiral will be stopped by someone collapse.” As the way out, once side could refuse to compete unilaterally. Or some balancing feedback loop could be negotiated to compensate for the reinforcing one.
- Success to the Successful - “The winners of a competition are systematically rewarded with the means to win again, a reinforcing feedback loop is created. And if it is allowed to proceed uninhibited, the winners eventually take all, while the losers are eliminated.” One might recognise the monopolisations of the industries and the mechanism for inequality, currently big issues on the social radar. As the way out - diversification, strict limitation of maximum fraction of a winning pie (antitrust law for example); periodically levelling the playing field for everyone; policies that device the rewards for success that do not bias the next round of competition.
- Seeking a Wrong Goal - if the system has got a poorly defined or plainly wrong goal, it would obediently tick-tack towards it. Way out - define the goal carefully and redefine if wrong; not to try accept the goal just so it is easy to measure.
What to do with all of this?
You can answer this question on two levels. On the personal level, the system thinking is a good tool to understand the world around you; that everything is interconnected and some of your decisions might come back and affect you in a surprising way. On the other hand, you always have an ability to learn and to change (to adapt in system speak).
On the level of society and social science, it is much more complicated of course:
“Self-organising, 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 unrealisable.”
In plain speak, no-one can control such system and no-one would be able to predict what it would do in future. To add insult to injury: “Social systems are the external manifestation of cultural thinking patterns and of profound human needs emotions strength and weakness. Changing them is not simple.”
Why to bother then?
Because, though the systems cannot be controlled and predicted, but it can be understood (to some extent)! The new system can be designed and the existing ones can be re-designed.
Redesigning of the existing systems is probably the most interesting bit. For this she defines “Leverage points”, the terms borrowed from Physics - places in the system where a small change could lead to a large shift in its behaviour.
Leverage points (levels of impact from the weakest to the strongest):
1. Numbers/parameters - characteristic of rates of flow, levels of stock, buffers. They are the least effective in terms lasting impact on the system behaviour. Examples are tax rates, spending rates, caps on ambient air quality, minimal wage and cap at prices. Even such things like firing people and hiring the new ones, including politicians — but of the structure is the same and info flows are the same would not help. She compares in with shifting chars on Titanic in order to restore the balance.
“Whatever cap we put on campaign contributions, it does not clean up politics. The Fed’s fiddling with the interest rate has not made business cycles go away. (We always forget that during upturns and are shocked by the downturns). Spending more on police or education does not make crime go away.”
Only one exception is when the goal of the whole system is expressed as a numerical value. But more about it below.
2. Nitty-gritty stuff (my term) - basically the other elements embedded in the system:
- buffers of stock - more buffer more stability, but if it is too big - lack of flexibility.
- physical structures and networks
- delays in feedback loops - the system cannot response to short-term changes if it has got a long-term delays; but her advice is counter-intuitive - slow done the system rather than shorten delay in many cases is better.
- balancing feedback loops - regulate the strength of them; I liked very much her example for this feedback:
“Democracy - system was invented to put self-correcting feedback between the people and their government. The people informed what their elected representative do, respond by voting those representative out of the office.” She farther notes “the process depends on the free, full, unbiased flow of information back and forth between electorate and leaders. Billions of dollars are spent to limit, and bias and dominate this flow of information.”
- reinforcing feedbacks are sources of growth, explosion and collapse with they are unchecked - so should have some sort of balancing feedback attached
- missing feedbacks altogether - the lack of accountability. The one of her examples is to depose a bit of production waste to the garden of the relevant CEO would add a missing feedback.
3. Self-organisation or evolution - divine creator (if there is one) does not have to produce evolutionary miracles. “He, she or it just has to write marvellously clever rules for self-organisation.” The current development on AI comes to mind here. But she means more social rules i guess.
4. Goals of the system - to change the purpose of system’s function. She has to put it above self-organisation in terms of effectiveness because of “diversity destroying push for control”.
Her example is a corporation (in theory could be any organisation or institution). To make profit is just a rule. The real goal “to make more under control of a corporation so it is shielded from uncertainty.”. It is a goal of any living population but only a bad one when it isn’t balanced by a higher level balancing feedback that never let “an upstart power-loop-driven entity to control the world.” And on the level of the goal, the parameters do have a big impact. The same the individual if he/she can change the goal of the system by her decision. It brings to my mind the old debate about the role of an individual in the historical process… It certainly makes sense from the system perspective…
5. Paradigm is the highest leverage point. It is unstated assumptions in the minds of the society and the deepest set of beliefs how the world works. Her teacher, Jay Forrester came up with this observation:
“It doesn’t matter how tax law of a country is written. There is a shared idea in the minds of the society about what a fair distribution of the tax load is. Whatever the laws say, by fair means of foul, by complications, cheating, exemptions or deductions, by constant shipping at the rules, actual tax payments will push right up against the accepted idea of “fairness”.
The assumptions are unstated because everyone knows them. So they are shared social agreement about the nature of reality. More examples from her:
- One can “own” the land.
- Money have real meaning and measure something real; so people who paid less are literally worth less. …
I would add from myself two more:
- the business profit is real and that measures the added value;
- nationality is real and defines the identity.
Paradigms are sources of systems. From them the systems are built. How too change it - by building the model of the system which would take us outside the system and “forces” us to see it as a whole. Thought experiments then! Sometimes people like Einstein, Smith, Lenin hit the leverage points and managed to totally transform the system.
But in general, it is the hardest thing to do and she talks about it quite a lot.
Though one could disagree with her views on certain political issues, it should not stop the reader benefiting from systems thinking she describes. The time was well spent for me.
QUOTES
Whatever cap we put on campaign contributions, it does not clean up politics. The Fed’s fiddling with the interest rate has not made business cycles go away. (We always forget that during upturns and are shocked by the downturns). Spending more on police or education does not make crime go away.
Balancing feedback should be adequate to the goal - The power of a big industry calls for the power of big government to hold it in check; a global economy makes global regulations necessary.
If the goal is to bring more and more of the world under the control of one particular central planning system (the empire of Genghis Khan, the Church, the Peoples Republic of China, Wal-Mart, Disney), then everything further down the list, physical stocks and flows, feedback loops, information flows and even self-organisation would conform that goal.
Allowing species to go extinct is a systems crime, just as randomly eliminating all copies of particular science journals of a particular kind of scientist would be. The same could be said of human cultures of course, which are the store of behavioural repertoires, accumulated over not billions, but hundreds of thousands of years. They are a stock out of which social evolution can arise. Unfortunately, people appreciate the precious evolutionary potential of cultures even less that they understand the preciousness of every genetic variation in the world’s ground squirrels. I guess that’s because one aspect of almost every culture is the belief in the utter superiority of that culture.
Donella Meadows, professor of MIT, she has written the manuscript in1992, but never came to publishing it. It has been published after her death in 2001, I believe. While reading, i felt she did not have a chance to edit it properly. It is a mixture of a basic system theory, her views on certain social issues, personal experiences and even sometimes more general philosophic thoughts. So you have to be a patient reader to get what you want out of it. I’ve almost ended up reading it twice. But it is very rewarding at the end. You start see systems everywhere.
Some of her examples are obviously very outdated. In spite of this, I did not feel her explanations and approach has aged. She came up with the suggested methodology, how to change social systems and this is already quite a lot.
What is the system?
It is something which is more than the sum of its parts and it is source of its own behaviour. She gives more formal definition: “a set of elements or parts that is coherently organised and interconnected in a pattern or structure that produces a characteristic set of behaviours often classified as its function or purpose”.
The difference between a system and a set of objects:
1) there are connections (called flows) between the elements (often called stocks or agents) in the system;
2) there are certain feedback mechanisms communicated through these connections. Respectively, a change in one part of the system would course the change in another part. The main point here, that the system would course its own behaviour by itself. So it would function in time (“dynamic” is the scientific word).
Non controversial examples is a thermostat, or a human body. Apparently, in the 90s to consider an individual, society or economy as a systems examples was controversial to the extent that she uses the word “heretical”…
In the book she briefly describes different types of systems. She also brings natural resources into social system.
What is thinking in systems?
It is a critical tool applying the knowledge of systems to the chosen object of research in order to understand it better or solve related problems. Practically, it is done through building a model of the underlying system. It is just one type of analysis, the “lens” how to understand the world around us. The book delves deeper into how it works and describes the properties of the systems.
What are feedbacks?
Feedback loop is much easier to understand intuitively than define. But it is a chain of rules or physical laws which defines how the initial change in the element (stock in the system speak) of the system courses the next change at the same element. It comes in two varieties:
- balancing feedback - there is a defined goal and the current state is compared to this goal (the targeted temperature in thermostat vs the current temperature for example). This type of feedback makes the system more stable, but more resistant to change as well.
- reinforcing feedback loop - it is self-enfacing mechanism when the initial change triggers even bigger change. This type of feedback left alone is leading to the exponential growth or runaway collapses of the system over time. The example could be a bank balance with the compounded interest rate; population growth of rabbits or escalation of a political conflict.
Or, I dare say, a rating on GR website - the higher the rating, the more people would read the book, the more people would rate it positively - the more rating would grow - the more people would read and…. you’ve got the picture. But unfortunately, for some wonderful, but underrated books, it works in negative way as well.
What a boundaries?
“there are no separate systems. The world is continuum. Where to draw a boundary around a system depends on the purpose of the discussion.” It is very important point which illustrates the relevance of systems thinking to our life. She notes: “Ideally we would have the mental flexibility to find the appropriate boundary for thinking about each new problem. We are rarely that flexible. We get attached to the boundary our minds happen to be accustomed to. To think how many arguments have to do with boundaries- national trade ethnic, boundaries between public and private responsibility, and boundaries between the religious ch and poor, polluters and pollutees, people alive now and people who cone in the future.” This lack of mental flexibility is the source of a lot of unnecessary conflicts and mistakes, systems or not…
What are “systems’ traps”?
Systems could be counter-intuitive. More often than not a system would “surprise” you - come up with the results which you would not expect. This is because the majority of systems are complex - contain subsystems. And there are a lot of connections and feedbacks. Feedbacks might pull the performance in an unexpected direction. Or, the goals of subsystems might contradict to each other and “may not lead to decisions that further the welfare of the system of the whole”.
The bad “surprises” she calls “systems traps”. She defines about 9 different traps encountered in the social systems and the recommended ways out. They are all very important, but I’ve picked up just three to give you a jest of what is all about:
- Escalation - When the state of one stock (agent) is determined by trying to surpass the state of another stock (agent) and vice versa. “It is a reinforcing feedback loop carrying the system in an arms race, a wealth race, a smear campaign, escalating loudness, escalating violence. The escalation is exponential and can lead to extremes surprisingly quickly. If nothing is done, the spiral will be stopped by someone collapse.” As the way out, once side could refuse to compete unilaterally. Or some balancing feedback loop could be negotiated to compensate for the reinforcing one.
- Success to the Successful - “The winners of a competition are systematically rewarded with the means to win again, a reinforcing feedback loop is created. And if it is allowed to proceed uninhibited, the winners eventually take all, while the losers are eliminated.” One might recognise the monopolisations of the industries and the mechanism for inequality, currently big issues on the social radar. As the way out - diversification, strict limitation of maximum fraction of a winning pie (antitrust law for example); periodically levelling the playing field for everyone; policies that device the rewards for success that do not bias the next round of competition.
- Seeking a Wrong Goal - if the system has got a poorly defined or plainly wrong goal, it would obediently tick-tack towards it. Way out - define the goal carefully and redefine if wrong; not to try accept the goal just so it is easy to measure.
What to do with all of this?
You can answer this question on two levels. On the personal level, the system thinking is a good tool to understand the world around you; that everything is interconnected and some of your decisions might come back and affect you in a surprising way. On the other hand, you always have an ability to learn and to change (to adapt in system speak).
On the level of society and social science, it is much more complicated of course:
“Self-organising, 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 unrealisable.”
In plain speak, no-one can control such system and no-one would be able to predict what it would do in future. To add insult to injury: “Social systems are the external manifestation of cultural thinking patterns and of profound human needs emotions strength and weakness. Changing them is not simple.”
Why to bother then?
Because, though the systems cannot be controlled and predicted, but it can be understood (to some extent)! The new system can be designed and the existing ones can be re-designed.
Redesigning of the existing systems is probably the most interesting bit. For this she defines “Leverage points”, the terms borrowed from Physics - places in the system where a small change could lead to a large shift in its behaviour.
Leverage points (levels of impact from the weakest to the strongest):
1. Numbers/parameters - characteristic of rates of flow, levels of stock, buffers. They are the least effective in terms lasting impact on the system behaviour. Examples are tax rates, spending rates, caps on ambient air quality, minimal wage and cap at prices. Even such things like firing people and hiring the new ones, including politicians — but of the structure is the same and info flows are the same would not help. She compares in with shifting chars on Titanic in order to restore the balance.
“Whatever cap we put on campaign contributions, it does not clean up politics. The Fed’s fiddling with the interest rate has not made business cycles go away. (We always forget that during upturns and are shocked by the downturns). Spending more on police or education does not make crime go away.”
Only one exception is when the goal of the whole system is expressed as a numerical value. But more about it below.
2. Nitty-gritty stuff (my term) - basically the other elements embedded in the system:
- buffers of stock - more buffer more stability, but if it is too big - lack of flexibility.
- physical structures and networks
- delays in feedback loops - the system cannot response to short-term changes if it has got a long-term delays; but her advice is counter-intuitive - slow done the system rather than shorten delay in many cases is better.
- balancing feedback loops - regulate the strength of them; I liked very much her example for this feedback:
“Democracy - system was invented to put self-correcting feedback between the people and their government. The people informed what their elected representative do, respond by voting those representative out of the office.” She farther notes “the process depends on the free, full, unbiased flow of information back and forth between electorate and leaders. Billions of dollars are spent to limit, and bias and dominate this flow of information.”
- reinforcing feedbacks are sources of growth, explosion and collapse with they are unchecked - so should have some sort of balancing feedback attached
- missing feedbacks altogether - the lack of accountability. The one of her examples is to depose a bit of production waste to the garden of the relevant CEO would add a missing feedback.
3. Self-organisation or evolution - divine creator (if there is one) does not have to produce evolutionary miracles. “He, she or it just has to write marvellously clever rules for self-organisation.” The current development on AI comes to mind here. But she means more social rules i guess.
4. Goals of the system - to change the purpose of system’s function. She has to put it above self-organisation in terms of effectiveness because of “diversity destroying push for control”.
Her example is a corporation (in theory could be any organisation or institution). To make profit is just a rule. The real goal “to make more under control of a corporation so it is shielded from uncertainty.”. It is a goal of any living population but only a bad one when it isn’t balanced by a higher level balancing feedback that never let “an upstart power-loop-driven entity to control the world.” And on the level of the goal, the parameters do have a big impact. The same the individual if he/she can change the goal of the system by her decision. It brings to my mind the old debate about the role of an individual in the historical process… It certainly makes sense from the system perspective…
5. Paradigm is the highest leverage point. It is unstated assumptions in the minds of the society and the deepest set of beliefs how the world works. Her teacher, Jay Forrester came up with this observation:
“It doesn’t matter how tax law of a country is written. There is a shared idea in the minds of the society about what a fair distribution of the tax load is. Whatever the laws say, by fair means of foul, by complications, cheating, exemptions or deductions, by constant shipping at the rules, actual tax payments will push right up against the accepted idea of “fairness”.
The assumptions are unstated because everyone knows them. So they are shared social agreement about the nature of reality. More examples from her:
- One can “own” the land.
- Money have real meaning and measure something real; so people who paid less are literally worth less. …
I would add from myself two more:
- the business profit is real and that measures the added value;
- nationality is real and defines the identity.
Paradigms are sources of systems. From them the systems are built. How too change it - by building the model of the system which would take us outside the system and “forces” us to see it as a whole. Thought experiments then! Sometimes people like Einstein, Smith, Lenin hit the leverage points and managed to totally transform the system.
But in general, it is the hardest thing to do and she talks about it quite a lot.
Though one could disagree with her views on certain political issues, it should not stop the reader benefiting from systems thinking she describes. The time was well spent for me.
QUOTES
Whatever cap we put on campaign contributions, it does not clean up politics. The Fed’s fiddling with the interest rate has not made business cycles go away. (We always forget that during upturns and are shocked by the downturns). Spending more on police or education does not make crime go away.
Balancing feedback should be adequate to the goal - The power of a big industry calls for the power of big government to hold it in check; a global economy makes global regulations necessary.
If the goal is to bring more and more of the world under the control of one particular central planning system (the empire of Genghis Khan, the Church, the Peoples Republic of China, Wal-Mart, Disney), then everything further down the list, physical stocks and flows, feedback loops, information flows and even self-organisation would conform that goal.
Allowing species to go extinct is a systems crime, just as randomly eliminating all copies of particular science journals of a particular kind of scientist would be. The same could be said of human cultures of course, which are the store of behavioural repertoires, accumulated over not billions, but hundreds of thousands of years. They are a stock out of which social evolution can arise. Unfortunately, people appreciate the precious evolutionary potential of cultures even less that they understand the preciousness of every genetic variation in the world’s ground squirrels. I guess that’s because one aspect of almost every culture is the belief in the utter superiority of that culture.
August 21, 2018
This book should be made compulsory reading for pretty much everyone. Thinking in systems is one of the most powerful mental models to ingrain, the lack of which is the source of innumerable problems.
May 8, 2020
A thought provoking read that introduces the "systems" lens to the world. Some of the concepts need a bit better explanations, but overall it was an enjoyable and compelling read.
July 27, 2019
It is human instinct to recoil from complexity and uncertainty. But there is no avoiding them, especially in a world of interconnected and emergent complexity. This is why we need systems thinking, and Meadows has written a concise and powerful introduction to the subject. Her expertise guides the reader as she distills a ton of accumulated wisdom into strident prose. Humanity is lost in the labyrinth of complex systems, and this book is like an Ariadne's thread that can help the reader navigate and survive the Minotaur's labyrinth.
Meadows's own specialty is an understanding of the limits of ecological systems. She was the primary author of The Limits to Growth. This primer echoes many of the themes of that book, and of later sustainability literature, but the book's lessons are just as fruitfully applied outside of the ecological domain. Some of the best applied sections deal with governmental, industrial, infrastructural, financial, and educational systems. A lot of those insights 1) challenge mainstream thinking in useful ways and 2) are broadly applicable into many disciplines.
This primer is a mixture of theory and practice. The more theoretical sections might sound dry, but they are just as useful as the more applied sections. They offer tools to think about old problems and come up with new solutions in a manner that is admirably well structured and easy to understand. Without excessive obfuscation or quantitative overload, the author explains the introductory central themes of the MIT complex systems approach, such as feedback mechanisms, stocks & flows, system growth, carrying capacity, sustainability, diversity, etc.
The book does not waste your time and it is jam-packed full of keen observations. Towards the end, the author engages in some surprisingly potent and provocative musings that fire up the imagination. Her engineering discipline is fused with a holistic and humanistic spirit. Perhaps the most powerful lessons are contained in the later chapters that teach double lessons on when and how to successfully act on leverage points that can transform systems, but also when and how to learn to live with uncertainty. The practical ability to discern what lies within our power, and what does not, is the key insight of the book, and indeed of complex systems thinking more broadly.
As a primer on complex systems, Meadows's book is fantastic. However, as an introduction to how to think about economics, the book succeeds only partially. She gives a good introduction to the ecological limits of resource use, but her models are based on rather simplistic engineering analysis. Compared to some other schools of complex systems thinking, the MIT school discussion of markets, governments, and the commons relies on rather mechanistic, thermodynamic, and engineering-based metaphors. As a result, she fails to appreciate the holistic complex adaptive systems models of market behaviour and cultural self-organization of, say, Hayek, Burke, Schumpeter, or the Ostroms. These would have brought important insights to the debate. She fails to appreciate the full extent of the evolutionary, self-organizing nature of market societies. She seems ignorant of the long history of complex systems thinking in political economy, from Smith to Marshall, before Herbert Simon. Simon's contribution to the subject, of course, cannot be ignored, but Meadows's calumny against economic thinking is very narrow-minded.
While the book fails as a primer on evolutionary thinking in economics, it wholly succeeds as a general primer on systems thinking more broadly. Its focus on ecological thinking and sustainability makes it very timely, but its lessons carry into a far range of human and social problems. The special importance of general systems thinking should be recognized in relation to the global climate crisis, the financial crisis, the immigration crisis, the crisis of the welfare state sustainability, the economic crisis caused by technological development, etc.... Meadows has done the intellectual community a service by providing a readable introduction to the topic.
Meadows's own specialty is an understanding of the limits of ecological systems. She was the primary author of The Limits to Growth. This primer echoes many of the themes of that book, and of later sustainability literature, but the book's lessons are just as fruitfully applied outside of the ecological domain. Some of the best applied sections deal with governmental, industrial, infrastructural, financial, and educational systems. A lot of those insights 1) challenge mainstream thinking in useful ways and 2) are broadly applicable into many disciplines.
This primer is a mixture of theory and practice. The more theoretical sections might sound dry, but they are just as useful as the more applied sections. They offer tools to think about old problems and come up with new solutions in a manner that is admirably well structured and easy to understand. Without excessive obfuscation or quantitative overload, the author explains the introductory central themes of the MIT complex systems approach, such as feedback mechanisms, stocks & flows, system growth, carrying capacity, sustainability, diversity, etc.
The book does not waste your time and it is jam-packed full of keen observations. Towards the end, the author engages in some surprisingly potent and provocative musings that fire up the imagination. Her engineering discipline is fused with a holistic and humanistic spirit. Perhaps the most powerful lessons are contained in the later chapters that teach double lessons on when and how to successfully act on leverage points that can transform systems, but also when and how to learn to live with uncertainty. The practical ability to discern what lies within our power, and what does not, is the key insight of the book, and indeed of complex systems thinking more broadly.
As a primer on complex systems, Meadows's book is fantastic. However, as an introduction to how to think about economics, the book succeeds only partially. She gives a good introduction to the ecological limits of resource use, but her models are based on rather simplistic engineering analysis. Compared to some other schools of complex systems thinking, the MIT school discussion of markets, governments, and the commons relies on rather mechanistic, thermodynamic, and engineering-based metaphors. As a result, she fails to appreciate the holistic complex adaptive systems models of market behaviour and cultural self-organization of, say, Hayek, Burke, Schumpeter, or the Ostroms. These would have brought important insights to the debate. She fails to appreciate the full extent of the evolutionary, self-organizing nature of market societies. She seems ignorant of the long history of complex systems thinking in political economy, from Smith to Marshall, before Herbert Simon. Simon's contribution to the subject, of course, cannot be ignored, but Meadows's calumny against economic thinking is very narrow-minded.
While the book fails as a primer on evolutionary thinking in economics, it wholly succeeds as a general primer on systems thinking more broadly. Its focus on ecological thinking and sustainability makes it very timely, but its lessons carry into a far range of human and social problems. The special importance of general systems thinking should be recognized in relation to the global climate crisis, the financial crisis, the immigration crisis, the crisis of the welfare state sustainability, the economic crisis caused by technological development, etc.... Meadows has done the intellectual community a service by providing a readable introduction to the topic.
April 12, 2021
"Remember, always, that everything you know, and everything everyone knows, is only a model."
I always wanted to read something about system thinking. I was not sure I wanted to take a deep dive but sure I needed to know more about it. The ability to take a step back and look at the system as a whole with all the powers at play is an important skill. This book looked for me as a good first step.
“There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion.”
The book is divided into 3 sections.
First part covers the basics. The examples are simple so everybody can understand the idea of how we can model a system but not deep enough to give you something one could work with. After reading this part I dont think I would be able to start creating the diagrams that describe systems however at the end of the book there is reference for more advanced books. It is after all "A Primer" so no problem for me.
Second part describes the characteristics of a system, why systems surprise us and most common traps. I love this part. This is clearly a part that gives you the most applicable knowledge after the reading. I love the simplicity of explanation. Many ideas here (like bounded rationality) I was understanding intuitively but never thought about it in a more aware way. The traps in a system are something I can clearly start identifying in my personal as well professional life. I love it!
Last part for me is the weakest although I think it is very important - where to find leverage and how to change the system. Author lists all the points where people should look for leverage in order to change the system for better and avoid the pitfalls of pushing the system in the wrong direction. The general idea of changing the systems is still a bit vague for me therefore I won't write about it much.
"Let's face it, the universe is messy. It is nonlinear, turbulent, and chaotic. It is dynamic. It spends its time in transient behavior on its way to somewhere else, not in mathematically neat equilibria. It self-organizes and evolves. It creates diversity, not uniformity. That's what makes the world interesting, that's what makes it beautiful, and that's what makes it work."
I like this book. It's short, very concise and easy to read. Great first step into system thinking which got me even more curious. For sure I will grab more books from this space at some point.
I always wanted to read something about system thinking. I was not sure I wanted to take a deep dive but sure I needed to know more about it. The ability to take a step back and look at the system as a whole with all the powers at play is an important skill. This book looked for me as a good first step.
“There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion.”
The book is divided into 3 sections.
First part covers the basics. The examples are simple so everybody can understand the idea of how we can model a system but not deep enough to give you something one could work with. After reading this part I dont think I would be able to start creating the diagrams that describe systems however at the end of the book there is reference for more advanced books. It is after all "A Primer" so no problem for me.
Second part describes the characteristics of a system, why systems surprise us and most common traps. I love this part. This is clearly a part that gives you the most applicable knowledge after the reading. I love the simplicity of explanation. Many ideas here (like bounded rationality) I was understanding intuitively but never thought about it in a more aware way. The traps in a system are something I can clearly start identifying in my personal as well professional life. I love it!
Last part for me is the weakest although I think it is very important - where to find leverage and how to change the system. Author lists all the points where people should look for leverage in order to change the system for better and avoid the pitfalls of pushing the system in the wrong direction. The general idea of changing the systems is still a bit vague for me therefore I won't write about it much.
"Let's face it, the universe is messy. It is nonlinear, turbulent, and chaotic. It is dynamic. It spends its time in transient behavior on its way to somewhere else, not in mathematically neat equilibria. It self-organizes and evolves. It creates diversity, not uniformity. That's what makes the world interesting, that's what makes it beautiful, and that's what makes it work."
I like this book. It's short, very concise and easy to read. Great first step into system thinking which got me even more curious. For sure I will grab more books from this space at some point.
February 24, 2017
An eye-opener in every sense. An absolute essential for anyone aiming towards even the slightest understanding of this incredibly complex world we live in.
February 3, 2021
Great primer for systems thinking. I was looking for a book that would make core concepts clear with a secondary focus on mathematical explanation. This is perfect. The author is a well known polymath that weaves multiple branches of study in a holistic picture of systems.
“If a factory is torn down but the rationality that produced it is left standing, then that rationality will simply produce a new factory. If a revolution brings down a government, but the systematic patterns of thought that brought about that government are left standing, then those patterns will repeat themselves. There is so much talk about the system, so little understanding” - this is the book's opening quote from Zen and the art of motorcycle maintenance. Sets the tone perfectly.
Random highlights that stood out for me:
Our intuition often points to the wrong levers in a system to pull when trying to impact behavior. Also, often, even if we find the right lever, we pull in the wrong direction
Events are the most visible part of a system but patterns of behavior are more important in understanding how the system is working. So behavior based models are more useful than event based models
Nonlinearities are common in feedback loops, where it the impact of a change becomes disproportionate over time. You can’t notice it when it is small, but it becomes intolerable as it increases even linearly. There are breaking points when the hockey stick takes off
Drawing boundaries too narrowly make a systems problem worse. eg: building a highway to reduce traffic clogging causes housing to be developed along highways putting even more cars on the highways further worsening traffic clogs
There will always be limits to growth. They can be self imposed. If not, they will be system imposed
Bounded rationality - we make decisions without knowing full information, based on what is visible within a system. The logic that seems perfectly reasonable when acting within a subsystem but is counterproductive to the goal of the overall system when observed in whole
When trying to change the behavior of a system, replacing actors constrained by bounded rationality of incentives, information, goals and stresses will not help. Like changing the President of a country plagued by a broken healthcare system or changing the manager of a team setup for failure with wrong incentives, constraints or lack of information
And a great summary of the three attributes of sustainable systems. It is fascinating to think of how this applies to your job, your team, your team of teams, your department
Resilience - feedback loops that can repair and restore when one of them fails. eg: fire resistant flora, one extinct species replaced by another filling its niche. Because resilience is not always obvious, people often sacrifice them for more measurable attributes. eg: cutting the pandemic task force, cutting dev prod/tech debt reduction efforts
Self organization - capacity of a system to make its own structure more complex. eg: fractals constructing snowflakes, petals on a flower. Self organization needs a certain amount of disorder to allow for freedom & experimentation. This is often sacrificed for short term productivity and control. This leads to loss of learning and diversity. eg: concrete towers vs a growing tree
Hierarchy - ability to organize multiple subsystems into a more complex system. eg: organs making a creature, cells building into an organ. Hierarchy allows subsystems to operate autonomously, reducing the amount of information that has to pass through each subsystem while keeping the overall system resilient and stable
Relationships within each subsystem are denser and richer than across subsystems
Hierarchies are partially decomposable, but not all function can be retained by individual parts. Relationships between each subsystem are equally important as each component subsystem
Hierarchies evolve from the bottom up. Upper layers of hierarchy exist to serve lower layers
When a subsystem’s goals dominate the overall system, there is suboptimization. When a team member shoots for individual glory over team results, when a cell breaks out of growth constraints and turns into cancer - systems fail
“If a factory is torn down but the rationality that produced it is left standing, then that rationality will simply produce a new factory. If a revolution brings down a government, but the systematic patterns of thought that brought about that government are left standing, then those patterns will repeat themselves. There is so much talk about the system, so little understanding” - this is the book's opening quote from Zen and the art of motorcycle maintenance. Sets the tone perfectly.
Random highlights that stood out for me:
Our intuition often points to the wrong levers in a system to pull when trying to impact behavior. Also, often, even if we find the right lever, we pull in the wrong direction
Events are the most visible part of a system but patterns of behavior are more important in understanding how the system is working. So behavior based models are more useful than event based models
Nonlinearities are common in feedback loops, where it the impact of a change becomes disproportionate over time. You can’t notice it when it is small, but it becomes intolerable as it increases even linearly. There are breaking points when the hockey stick takes off
Drawing boundaries too narrowly make a systems problem worse. eg: building a highway to reduce traffic clogging causes housing to be developed along highways putting even more cars on the highways further worsening traffic clogs
There will always be limits to growth. They can be self imposed. If not, they will be system imposed
Bounded rationality - we make decisions without knowing full information, based on what is visible within a system. The logic that seems perfectly reasonable when acting within a subsystem but is counterproductive to the goal of the overall system when observed in whole
When trying to change the behavior of a system, replacing actors constrained by bounded rationality of incentives, information, goals and stresses will not help. Like changing the President of a country plagued by a broken healthcare system or changing the manager of a team setup for failure with wrong incentives, constraints or lack of information
And a great summary of the three attributes of sustainable systems. It is fascinating to think of how this applies to your job, your team, your team of teams, your department
Resilience - feedback loops that can repair and restore when one of them fails. eg: fire resistant flora, one extinct species replaced by another filling its niche. Because resilience is not always obvious, people often sacrifice them for more measurable attributes. eg: cutting the pandemic task force, cutting dev prod/tech debt reduction efforts
Self organization - capacity of a system to make its own structure more complex. eg: fractals constructing snowflakes, petals on a flower. Self organization needs a certain amount of disorder to allow for freedom & experimentation. This is often sacrificed for short term productivity and control. This leads to loss of learning and diversity. eg: concrete towers vs a growing tree
Hierarchy - ability to organize multiple subsystems into a more complex system. eg: organs making a creature, cells building into an organ. Hierarchy allows subsystems to operate autonomously, reducing the amount of information that has to pass through each subsystem while keeping the overall system resilient and stable
Relationships within each subsystem are denser and richer than across subsystems
Hierarchies are partially decomposable, but not all function can be retained by individual parts. Relationships between each subsystem are equally important as each component subsystem
Hierarchies evolve from the bottom up. Upper layers of hierarchy exist to serve lower layers
When a subsystem’s goals dominate the overall system, there is suboptimization. When a team member shoots for individual glory over team results, when a cell breaks out of growth constraints and turns into cancer - systems fail
August 8, 2020
5 out of 5.
The book introduces a reader into world of systems, interconnected things. This is done in a very reader-friendly manner, starting with examples you can find in your daily life. Next, it builds up on top the already gathered knowledge introducing systems' archetypes, principles, traps. In this way you learn a lot about thinking in systems and perceiving reality in a system-aware way.
I'm amazed not only by the content of the book but also the way author conveyed the message. In less than 200 pages she was able to explain complex behaviors and add a meaningful appendix which summarizes the content from multiple angles.
I know, it's a primer but this book is much to short.
The book introduces a reader into world of systems, interconnected things. This is done in a very reader-friendly manner, starting with examples you can find in your daily life. Next, it builds up on top the already gathered knowledge introducing systems' archetypes, principles, traps. In this way you learn a lot about thinking in systems and perceiving reality in a system-aware way.
I'm amazed not only by the content of the book but also the way author conveyed the message. In less than 200 pages she was able to explain complex behaviors and add a meaningful appendix which summarizes the content from multiple angles.
I know, it's a primer but this book is much to short.
March 25, 2021
I’m not sure how « Thinking In systems” ranks in terms of books about systems but the idea of systems is really incredible.
It allows seeing and modeling the complexity of the world with something else than flows.
The most interesting learnings for me : defining a system, modeling its structure, understanding balancing and reinforcing feedback loops, looking at the evolution of a stock over time to understand mechanisms. The book also includes the most effective ways to “control” a system which are game changers
It allows seeing and modeling the complexity of the world with something else than flows.
The most interesting learnings for me : defining a system, modeling its structure, understanding balancing and reinforcing feedback loops, looking at the evolution of a stock over time to understand mechanisms. The book also includes the most effective ways to “control” a system which are game changers
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