And we are now launched, with a new preparation for a new course: a Royal Road into the millennia of global economic history, hopefully designed for both humanists and quants who want an introduction to learning to model pieces of the world economy throughout its history—from agriculture to attention economies—without needing to already have learned to be a coder first. (Why am I doing a new preparation the last semester before I go emeritus? Because I am a moron…)
< https://bcourses.berkeley.edu/courses/1551896 >
< https://braddelong.substack.com/t/quantitative-long-run-global-economic >
< https://braddelong.substack.com/p/quantitative-long-run-global-economic >
< https://substack.com/home/post/p-184363836 >
J. Bradford DeLong
delong@econ.berkeley.edu brad.delong@gmail.com delong@hey.com +1-925-708-0467
Tu 1-3 Evans 560 :: Th 1-3 Zoom :: additional mandatory office hours to be arranged
< https://bcourses.berkeley.edu/courses/1551896/discussion_topics/7205916?is_announcement=true > (repeats this weblog post)
The idea is to put all of the course materials below the paywall fold so that those who want to follow along and virtually participate—and ask questions and get answers!—can do so.
< https://bcourses.berkeley.edu/courses/1551896/assignments/9022294 >.
READING: Mandatory:
READING: Strongly Recommended: < https://bcourses.berkeley.edu/courses/1551896/files?preview=93579591 >.
AND THEN ANSWER THE FIVE QUESTIONS: < https://bcourses.berkeley.edu/courses/1551896/assignments/9022294 >.
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Consider the three short DeLong readings. What, in your view, is the single most important thing for success that I have overlooked in deciding to try to stand-up this new course this semester?
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Consider the Deming reading: How have you used GPT MAML LLM MAMLMs—General-Purpose Transformer Modern Advanced Machine-Learning Large-Language Models—in your university experience so far? To what extent do Deming’s concerns resonate with you? To what extent do you think they are largely off-base?
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How far did you get in the long DeLong Slouching Towards Utopia: The Economic History of the Twentieth Century reading? And what is the single thing in it you read that resonates in the front of your mind?
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What is the story of “Ulysses & the Sirens”, where does it come from, and do you find it important for you?
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What level of experience with the Berkeley standard data-science stack—Python Jupyter Notebooks, and so forth—have you had?
One paragraph answers for each. I need to have the answers to these by when I wake up Monday AM so that I can use them to plan the shape of Tuesday’s class. Hence no credit will be given for late answers.
COURSE DESCRIPTION: What, quantitatively, do we actually know about the long-run shape of global economic history? And what can we say about how reasonable counterfactual simulations of alternatives can add to our knowledge?
In our modern information-technology age, we can do many more kinds of analysis much more quickly and deeply and broadly than we ever could before. We ought to be able to use these tools beyond simple counting—the tools of sampling, estimation, forecasting, simulation, classification, and so on that make up what is now “data science” as the coming-together and then expansion of statistics with operations research and economics—to know more and to know in new ways.
This course will cover the standard long-run picture, as set out by Melissa Dell in her “History of Economic Growth” Harvard Econ 1342 and Robert Allen in his “Global Economic, Political & Social Development” NYU-Abu Dhabi SOCSC-UH 1011. It will try to cover much of:
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Our ancestors’ separation from chimpanzee ancestors six-million years ago,
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the evolution of behavioral modern humans (perhaps) a hundred-thousand years ago,
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the coming of agriculture ten-thousand years ago,
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the agrarian age and its societies of domination up to 1500,
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the first globalization and the societies of the imperial-commercial age up to 1770,
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then the coming of industrialization and full globalization in the following century;
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followed by, successively, the economies of the:
It will try to cover much of:
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the sources of and blockages to industrialization and economic growth across time and space,
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the origins and maintenance of large-scale inequality within and across societies,
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the social and political impacts of economic growth,
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the role of economic incentives and political institutions in underpinning and retarding economic progress,
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and other similar questions.
But it will cover it from a pedagogical-experimental angle that attempts to use “Data Science” estimation and counterfactual simulation tools to the max, to the extent that that can be done with a student body that has had little or no exposure to those tools beforehand.
And the hope is that in the process the course will generate teaching materials that will allow the course’s subsequent scaling-up to 150 people, or more—with enough of a Royal Road that students coming from the humanities-literary side of C.P. Snow’s “Two Cultures” will find it friendly and approachable and learn stuff, and enough analytical and intellectual depth that students coming from the STEM-numeroliteracy side of C.P. Snow’s “Two Cultures” will not find it trivial, and not be bored.
