The final semester of my Master's program is beginning. It is going to be a really busy semester—classes, teaching, research, job hunting—and hope I still have the time to write these each week. I will try, but they may get shorter.
I'm in two classes: Physical-Chemical Processes in Wastewater Treatment, and Life-Cycle Analysis (LCA) of Civil Systems. The first is a standard-fare class on how contemporary plants treat wastewater before it gets discharged; the second is about how to assess and compare the lifecycles of infrastructure systems.
They are both broadly about "system dynamics," but in very different forms. The focus of the wastewater processes class is on man-made water treatment plants—highly engineered closed systems in big windowless buildings across the country. These systems are designed to be efficient, reliable, and predictable: completely controlled. We can model them as fairly straightforward chemical systems, with fairly predictable outputs, using basic stoichiometry and differential equations. These systems are complicated, but they are not complex.
The second class, meanwhile, seeks a much broader scope. It asks: when we build a wastewater treatment plant, what are the known costs, and then, what are the externalities that we can assess? What are the unintended consequences? There is, of course, a case of the unknown unknowns: much of the "work" of effective and responsible LCA is to turn unrecognized unknowns into known, even quantifiable entities.
I'm taking the class partially to be able to put "LCA" on my résumé (hire me, agencies dealing with coastal infrastructure!!!) but more-so because I am curious how this professor teaches the process of uncovering the 4th quadrant (one of Taleb's terms for the unknown unknowns). And, for systems that are infrastructure-scale (federal water projects, electricity distribution systems, transportation networks, etc), where do we draw the bounds around the system?
(This is the farce of a lot of sustainability success stories—they are successful only because they draw a line in the sand exactly around the achievable scope of success; the failures have been externalized. For instance, American recycling programs are only a success the waste leaving the country means success; they're a failure if you nail down how much material actually gets turned into something new.)
"The system is always larger than itself," I like to say. An aqueduct is not only an aqueduct, but the material/industrial ecology that led to its construction, the water running through it, the people who built it, the people who maintain it, the knowledge systems that support it, the people who pay for it, the people who reap its benefit, the people who bear its wounds, the ecosystems it supports, the ecosystems it destroys, the roads it re-routs, its mark on the landscape, the legacy it leaves. And more. But somewhere you draw the line.
One of my favorite conversation topics goes something like, "if you were running a school, what would you want every student to learn?" Unequivocally, as cheesy as it sounds, I think "systems thinking" should be on the docket for every kid. What are the tools for systems thinking? Calculus notation is typical but awful—fortunately we have more and more digital interfaces for symbolic manipulation, but it is often still limited to technical, rational systems. Broader systems, as complicated, open-ended tangles of relationships, may lend themselves to other modes of expression and understanding, but still, what's a methodology for choosing a methodology? Any finite methodology will be finite in scope. (Unless "think of everything" is a methodology.)
Dr Tsing, in "The Mushroom At The End Of The World" talks about the bias, common in scientists, to favor clear and discrete cause-effect relationships for research, a bias against "descriptive" modes of research. But in systems where it is difficult to grasp all the ins and all the outs, or systems where the limits of the system itself are fuzzy, descriptive science is the best we can ask for. Accordingly, most of the LCA class is open-ended writing and case studies.
(How many times will I reference Tsing's TMATEOTW in this newsletter? Time will tell. I love how the book, and Odell's "How To Do Nothing," seem to have captured a lot of people I consider peers. Making publics.)
I am a collection of feedback loops,
Lukas
I'm in two classes: Physical-Chemical Processes in Wastewater Treatment, and Life-Cycle Analysis (LCA) of Civil Systems. The first is a standard-fare class on how contemporary plants treat wastewater before it gets discharged; the second is about how to assess and compare the lifecycles of infrastructure systems.
They are both broadly about "system dynamics," but in very different forms. The focus of the wastewater processes class is on man-made water treatment plants—highly engineered closed systems in big windowless buildings across the country. These systems are designed to be efficient, reliable, and predictable: completely controlled. We can model them as fairly straightforward chemical systems, with fairly predictable outputs, using basic stoichiometry and differential equations. These systems are complicated, but they are not complex.
The second class, meanwhile, seeks a much broader scope. It asks: when we build a wastewater treatment plant, what are the known costs, and then, what are the externalities that we can assess? What are the unintended consequences? There is, of course, a case of the unknown unknowns: much of the "work" of effective and responsible LCA is to turn unrecognized unknowns into known, even quantifiable entities.
I'm taking the class partially to be able to put "LCA" on my résumé (hire me, agencies dealing with coastal infrastructure!!!) but more-so because I am curious how this professor teaches the process of uncovering the 4th quadrant (one of Taleb's terms for the unknown unknowns). And, for systems that are infrastructure-scale (federal water projects, electricity distribution systems, transportation networks, etc), where do we draw the bounds around the system?
(This is the farce of a lot of sustainability success stories—they are successful only because they draw a line in the sand exactly around the achievable scope of success; the failures have been externalized. For instance, American recycling programs are only a success the waste leaving the country means success; they're a failure if you nail down how much material actually gets turned into something new.)
"The system is always larger than itself," I like to say. An aqueduct is not only an aqueduct, but the material/industrial ecology that led to its construction, the water running through it, the people who built it, the people who maintain it, the knowledge systems that support it, the people who pay for it, the people who reap its benefit, the people who bear its wounds, the ecosystems it supports, the ecosystems it destroys, the roads it re-routs, its mark on the landscape, the legacy it leaves. And more. But somewhere you draw the line.
One of my favorite conversation topics goes something like, "if you were running a school, what would you want every student to learn?" Unequivocally, as cheesy as it sounds, I think "systems thinking" should be on the docket for every kid. What are the tools for systems thinking? Calculus notation is typical but awful—fortunately we have more and more digital interfaces for symbolic manipulation, but it is often still limited to technical, rational systems. Broader systems, as complicated, open-ended tangles of relationships, may lend themselves to other modes of expression and understanding, but still, what's a methodology for choosing a methodology? Any finite methodology will be finite in scope. (Unless "think of everything" is a methodology.)
Dr Tsing, in "The Mushroom At The End Of The World" talks about the bias, common in scientists, to favor clear and discrete cause-effect relationships for research, a bias against "descriptive" modes of research. But in systems where it is difficult to grasp all the ins and all the outs, or systems where the limits of the system itself are fuzzy, descriptive science is the best we can ask for. Accordingly, most of the LCA class is open-ended writing and case studies.
(How many times will I reference Tsing's TMATEOTW in this newsletter? Time will tell. I love how the book, and Odell's "How To Do Nothing," seem to have captured a lot of people I consider peers. Making publics.)
I am a collection of feedback loops,
Lukas