Models of Humans and Models for Humans — Irony and Loops in Economics

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Models of Humans and Models for Humans — Notes on Irony and Loops in Economics

Irony in economics

“If [economics] is to be a science at all, [it] must be a mathematical science”, wrote nineteenth century economist William Stanley Jevons. Inspired by the cogency of Newtonian law, these economists imbued Homo sapiens with superhuman assumptions like complete knowledge and perfect foresight. They ossified the view that economic actors are in the business of maximization; and that their interactions would bring economic systems into equilibrium. Their theories and models brought elegance to a field still in its infancy.

Ironically, in the Maximum Principles in Analytical Economics, economist Paul Samuelson lamented that “there is nothing more pathetic than to have an economist… try to force analogies between the concepts of physics… [and] economics”. He recalls, for instance, his issue with “obscurely teleological” arguments in physics and how his “mathematical ear could not discern what tune was being played.” I wonder, however, if Samuelson foresaw the irony that he’d help perpetuate as a central figure of modern economic theory.

Economics is faddish

Indeed, “economics is faddish”, wrote Benoit Mandelbrot. And Herbert Simon, likewise, warned of the “serial one-thing-at-a-time character” that consumes the field—“unable to distribute its attention in a balanced fashion”. Not much has changed since their remarks. Many arcane papers in the recesses of economics and game theory continue to speak an alien language and use alien assumptions.

“There has always been a purist streak in economics that wants everything to follow neatly from greed, rationality, and equilibrium… The theory is neat, learnable, not terribly difficult, but just technical enough to feel like “science.” Moreover it is practically guaranteed to give laissez-faire-type advice, which happens to fit nicely with the general turn to the political right that began in the 1970s… But I do think it important that a few other, more eclectic, more data-sensitive approaches to macro-theory should remain in the profession’s gene pool.”

Robert Solow. (2009). The State of Macroeconomics.

Models of humans and models for humans

Moreover, as Kate Raworth observes in Doughnut Economics, “what had started as a model of man had turned into a model for man”. Economics, you see, is unlike physical law. People exist in a mutual network of feedback loops. Our beliefs, ideologies, and theories shape and are shaped by the behaviors that emerge.

Whether right or wrong, theories in physics will not change the planetary motions that we observe. But theories on markets and institutions can move social systems. This is especially so when beliefs reach critical mass—when many people come together to agree or reject a given set of prescriptions.

What then can we make of beliefs in self-interest and maximization? In behavioral economics, one study found, for instance, “that economic students were more likely than other students to be corruptible… if it led to a personal payout”.  Another study showed that U.S. college students, “after taking a course in economic game theory…, behaved more selfishly”.

Self-positing theories

Another compelling case can be found in the history of financial derivatives, when Fischer Black, Myron Scholes, and Robert Merton developed their Nobel-prize-winning Black-Scholes-Merton (BSM) model to price option contracts. But the evolution of the model’s power is rather surprising.

“At first”, as Raworth explains, “the formula’s predictions deviated widely—by 30% to 40%—from actual market prices”. But after a few years, “predicted prices differed by a mere 2% on average from actual market prices”. BSM predictions went from downright bad to pretty good.

Many theorists and practitioners saw this as evidence in support of market efficiency as transaction costs and other frictions melted away. After all, computers, competition, knowhow, and exchanges like the Chicago Board Options Exchange were still finding their foothold during that era.

But as Donald MacKenzie and Yuval Millo suggest in Constructing a Market, the BSM model may have also “succeeded empirically not because it discovered preexisting price patterns but because markets changed in ways that made its assumptions more accurate and because the theory was used in arbitrage”.

In other words, as more and more traders began to use the formula and believe in the same valuations, prices began to converge and move in turn. “Financial economics”, they write, “helped create in reality the kind of markets it posited in theory”.

Reflexive systems

Similarly, market beliefs can sometimes operate in paradoxical ways. Take, for example, an extreme case of the Efficient Market Hypothesis. If everybody believes that the hypothesis is true — that markets incorporate all available information — then the incentive to undertake price discovery disappears. Indeed, why bother if I cannot expect to profit from my hard work? Yet if nobody bothers to eliminate mispricing, how can markets be efficient?

In real life, of course, investors are heterogeneous. They differ in their beliefs, models, objectives, and methods. This is in part why we see such a diverse mix of active and passive investors in the market ecology. The point here, however, is to illustrate the unusual interplay that can take place between beliefs, incentives, behavior, and outcomes.

In light of all this, economic theory is in need of more systems thinking, Raworth suggests. This includes concepts like stocks and flows, positive and negative feedback loops, and delays (examples of delays include resistance to new information and inertia with regards to beliefs).

Trader George Soros has been talking about this for decades. He describes how uncertainty, fallibility and reflexivity in market systems can generate an assortment of feedback loops and loops within loops. To him, bubbles and crashes are endogenous to the market system.

Similarly, in How Nature Works, physicist Per Bak shows how phantom traffic jams can emerge without any accident or collision. Complex nonlinear interactions between the starts-and-stops of many cars can be enough to generate out-of-equilibrium behavior.

Raworth herself likens financial markets to “a flock of starlings cavorting in the sky”. “The obvious difference”, she says, “being that starlings never crash”. But that’s not true. Raworth’s simile is even better than she thinks. Flocks of birds can and do in fact crash.

Aspirational models

While physical laws do not care about our beliefs, economic theories do. When it comes to the social sciences, we cannot confine ourselves to passive study. We have to go beyond the early ideas of rational self-interest to incorporate history, ethics, systems, institutions, human nature, and feedback loops into our models.

As Andrew Lo reminds in Adaptive Markets, “we humans are not so much the ‘rational animal’ as we are the rationalizing animal”. Society is forever learning and reforming. The models we conjure up will, for better or worse, shape our beliefs and actions. We have to find better models to push our understanding and aspirations in the right direction.

“To today’s economics students I would simply say this: be watchful over the ideas that others try to sketch upon your mind… Pay attention to the pictures, especially the most fundamental ones, because they go in deep without you even realizing it… When it comes to new economic thinking, draw the change you want to see… It’s easy to get started. Just pick up a pencil and draw.”

Kate Raworth. (2017). Doughnut Economics.

Sources and further reading

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