Kevin's Reviews > Thinking in Systems: A Primer

Thinking in Systems by Donella H. Meadows
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it was amazing
bookshelves: research-methodologies, 2-brilliant-intros-101, 1-how-the-world-works

How to see the the big picture 101: how often do you get the sense that we are too consumed with micro issues to see the looming macro tidal waves that will wash away our elaborate sand castles into oblivion?

--I have been semi-consciously learning and applying “systems thinking” as a survival mechanism while exploring the dismal realms of economics and geopolitics (in particular: global division of labor and market externalities i.e. environment, cheap labor, reproductive labor, violence).
--Along the way, banging my head against conservatives and liberals (mostly Western) regarding their micro priorities (overspending on welfare, what changes are “affordable”), macro assumptions (surely capitalism raises all boats?!) and macro omissions (imperialism’s unequal exchange in the global division of labor, military industrial complex, job precarity with mass automation, mass extinction in the Anthropocene).
--So, this primer by an environmental scientist (lead author of The Limits to Growth in 1972) is a necessary intervention to help give shape to vaporous intuitions.

The Good:
1) Systems Modeling 101: external forces (which get a lot of attention due to visibility) trigger internal system structures (hidden, key to systems modeling) which lead to behaviors.
--Example: the book’s cover is a slinky, which has a unique internal structure responsible for its unique behavior when an external force is applied.
--Example: all the attention on specific politicians “causing” economic booms/busts instead of the economic system’s inherently-volatile structures.
--Conceptualize a system composed of stocks (measured at a specific moment in time) and flows (change over time). A system is more than just the sum of its parts, thus investigate the interactions between the parts and how they are layered to produce system functions.
--“Archetypes”: common structures that produce characteristic behaviors.
--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 in system: perception (of feedback information), response, and 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.) and how they can fail.
--Resilience (many feedback loops with redundancy to provide elasticity as opposed to the brittleness of reductionism), self-organization (ability to learn/evolve resilience), and hierarchy (in the sense of a division of labor into subsystems to reduce the amount of information needed by each part to contribute to system goals).
--System “traps” that can lead to failure include: 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 monopolization of capitalism), “Tragedy of the Commons” (externalizing costs, more on this later), cheating on rules, perverse system goals, etc.
--The author ranks 12 opportunities, leverage points for system change: the key takeaway is that reforms (altering parameters, buffers, delays, physical structures of stocks/flows) rank as low leverage tinkering. Medium leverage points are promoting self-organization, and changing structural rules, information flow, feedback loops (balancing, re-enforcing)... High leverage points are changing system goals, the paradigm 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, honor 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”. The author describes this as only seeing the tip of the iceberg, and uses the illustrative example of the mass media news industry churning out events while offering minimal systems analysis. Another interesting example I’d like more details on is how economists’ “econometrics” analyses focus on trying to find relations between flows, instead of between flows and stocks.
--Also: non-existent/false boundaries: World-Systems Analysis’ Immanuel Wallerstein stresses this as well (World-Systems Analysis: An Introduction), on 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 assumption of “perfect information” for rational decision-making. Ben Goldacre provides an accessible introduction to heuristics (mental short-cuts and the problems they cause) in Bad Science: Quacks, Hacks, and Big Pharma Flacks and I Think You'll Find it's a Bit More Complicated Than That.
--Of course, the direct application to Earth System science: Facing the Anthropocene: Fossil Capitalism and the Crisis of the Earth System .

The Questionable:
--I get the distinct impression that the author is a product of the Cold War, in particular America’s Red Scare. Thus, there a paradox with a) her systems approach to environmentalism (which must recognize capitalism’s role in exponential growth and externalizing costs, i.e. pollution) and b) her apolitical (liberal) political economy. I do not deduce this from her technical work on systemics modeling, but from some of her real-world examples. I am willing to set this aside as the author using generic examples to communicate to a Western mainstream audience, but there are several examples (regarding banking, social housing, national debt, market prices, immigration, and population) that seem suspect if we were to shine our systems thinking spotlight on them.
--A more relevant example is the description of the “Tragedy of the Commons”. I am more interested in the political nature of this topic, particularly the assumptions on the social power relations of property rights, so this needs to be unpacked further:
-Re-enchanting the World: Feminism and the Politics of the Commons
-Think Like a Commoner: A Short Introduction to the Life of the Commons
-Governing the Commons: The Evolution of Institutions for Collective Action
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Reading Progress

May 25, 2018 – Shelved
August 2, 2019 – Started Reading
August 18, 2019 – Finished Reading

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