An engine, not a camera

What Hacker News says

1. pashSep 2026
   Essentially all of economic theory is aimed at explaining, not predicting. The distinction between the two goals [0] is sometimes under-appreciated within the profession, and almost always under-appreciated outside of it.

   Most predictive tools in economics and finance have “surprisingly” little economic content; but once you understand the distinction between the two goals, it should be unsurprising that predictive models tend to make few economic assumptions, relying rather on general statistical techiniques or on econometrics that incorporate a minimum of theory [1]. From that understanding comes the humbling realization that predicting the future is quite difficult in a context in which the relevant processes are continually seeking an equilibrium that often implies unpredictability. [2]

   I’m not an economist, but I do a lot of applied financial-economic modeling. State-of-the-art LLMs are really, really terrible at economic intuition. They will hinder, not help, in formulating an economic model, which is a process of coming up with a set of modeling assumptions that lead to a useful (implicitly, tractable) model. LLMs are, however, quite good at math, and I’ve found them very useful in iterating through different sets of modeling assumptions to identify those that lead somewhere useful. Not having to work out all of the mathematical details myself, and thereby avoiding getting lost in the weeds and being better able to maintain a higher-level perspective on what I’m trying to accomplish, has accelerated my work immensely. But it’s a process of leading the LLM by the nose the whole time and asking it to fill in the details.

   I should note, thought, that if you indotend “AI” to mean more than LLMs, them yes, there is starting to be a lot of good work done on predictive economic models that use specialized neural networks as black-box functions to compute model quantities that are otherwise difficult to come up with, just as is also happening in applied physics and other fields.

   0. https://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf

   1. Many explanatory economic models refer to quantities that are fundamentally or practically unobservable or unidentifiable. Much of economics is built on models that were designed to provide a formal, logical basis for understanding the economic world, which is often quite unintuitive. (For example, many intelligent people uneducated in economics exhibit intuitions opposite of basic economic ideas like opportunity cost or comparative advantage.) Models of this sort have been very influential in determining the trajectory of economic thought, but they are often effectively impossible to calibrate to the real world.

   2. The most influential and effective economic ideas fall into a third class: ideas that have created their own reality by shaping the way people think in a way that gives rise to the results the models explain or predict. This phenomenon is most evident in finance, where ideas like the various forms of the efficient market hypothesis, the CAPM, and the Black–Scholes model and its follow-one have arguably provided a framework that has reshaped the ways financial practitioners behave to such an extent that financial markets now conform much more closely to what the models describe than was formerly the case. Donald MacKenzie’s book An Engine, Not a Camera is an excellent study of this phenomenon: https://mitpress.mit.edu/9780262633673/an-engine-not-a-camer...

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2. muggermuchNov 2022
   1. I would fiercely recommend reading “An Engine, Not A Camera” (Donald MacKenzie). Formally, it reads as a sociological study of the practice of finance - how financial markets shape society instead of simply capturing its needs and desires at large. More importantly, however, it made me think deeper on questions about fields of study that are (were?) distant and to question assumptions: a lot of ideas that we take for granted as absolute truths are simply consensus-derived and have no objective reality.

   2. On a similar theme, “The Lady Tasting Tea” (David Salsburg) was a wonderful history of the development of statistics as a mathematical discipline. I found it absolutely fascinating to map out how individual personalities were buffeted and shaped by larger historical events and movements (e.g. eugenics in the late 19th century, WW2, the rise of the Soviet Union, etc.) into asking questions of data that propelled statistics forward. Disciplines we think of as being “hard” (math, CS, statistics, physics) have been shaped by social forces in a non-linear non-Hegelian fashion, in fits and starts, quite contrary to the way they are presented in a pedagogical setting.

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3. bchjamSep 2021
   "An engine, not a camera" is a great book about this effect in financial markets.

   https://mitpress.mit.edu/books/engine-not-camera

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4. igorlevDec 2020
   "En Engine, Not a Camera", a great book and posits that this effect drives a large portion of modern (post-1970s) financial markets.

   https://www.goodreads.com/book/show/134474.An_Engine_Not_a_C...

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5. viburnumSep 2019
   There’s an excellent book called “An Engine, Not a Camera: How Financial Models Shape Markets” that lives up to its title.
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6. jfagerApr 2012
   If you ignore the absurdly stupid title, this is actually worth a quick read. It's excerpts from interviews with Myron Scholes and Ian Stewart woven into an oversimplified but not completely awful overview of Black-Scholes and its impact.

   If the question interests you, I recommend An Engine, Not a Camera by Donald MacKenzie. It goes into great detail about the history of Black-Scholes, its application in options trading, and how it (or, rather, financial modeling in general) tied into the 1987 crash and the failure of LTCM. I have a small writeup about it at http://jasonfager.com/1080-an-engine-not-a-camera/

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