A famous saying, often attributed to the early twentieth-century economist John Maynard Keynes, is “the market can remain irrational longer than you can remain solvent.”
That’s where seasoned investors find themselves after a year in which the stock market recovered from a pandemic-induced crash to reach record highs and amateur investors on Reddit drove shares of video game retailer GameStop up 1500% over a two-week period.
The unfortunate reality is that the regulations governing today’s financial markets weren’t designed to deal with a world where the valuation of GameStop, AMC, Nokia, and other equities can be challenged by a coordinated effort on the part of amateurs on the internet.
“The Wall Street bets phenomena is just the latest in a series of events that show how communities formed through new technologies are altering our belief dynamics,” said Will Tracy, Vice President for Applied Complexity at the Santa Fe Institute. “Radical disagreements over ground truths are becoming the new norm.”
At SFI’s Applied Complexity roundtable in March, SFI External Professor and MIT economist Andrew Lo spoke about “market adaptation” and applied a complex ecological and evolutionary lens to the market’s behavior under COVID. This set the stage for breakout discussions focusing on the nature of the Game Stop phenomenon and how emergent engineering could help human social systems become more resilient in the face of changing environments.
One example of an emergent engineering approach that’s already used to regulate financial markets is a rule for pausing trading when signs of a massive drop are detected. SFI Professors Jessica Flack and Melanie Mitchell described this stock market “circuit breaker” in a 2020 article for Aeon magazine. They wrote that an even more explicit approach to the complex problem of timescale separation would be to slow down trading by limiting the magnitude or frequency of trades during a crisis, then allowing trading to return to normal when the environment is more predictable.
While their example may seem simple at first glance, designing systems that excel under uncertainty is not easy.
One of the major challenges is developing a greater understanding of why people in a network, such as the users of the Wall Street Bets subreddit, make the decisions that they do.
This is an area of investigation particularly well suited to the study of “belief dynamics,” another emerging field of complexity science that SFI researchers are advancing by creating quantitative frameworks to make sense of social survey data on a wide array of topics.
At the 12th annual meeting on Risk and Applied Complexity, co-hosted by SFI and the Swiss bank UBS last fall, SFI External Professor Simon DeDeo discussed how recent work in cognitive science has uncovered a diversity of explanatory values, or dimensions along which people judge explanations as better or worse.
His work in the area could ultimately help scientists paint a clearer picture of the drivers behind phenomena like the Wall Street Bets incident, and is already shedding light on the formation of conspiracy theories and extremist ideologies online.
“No one sticks $10,000 of their money into something by accident. They’re doing it because they have a story about the way the world works. And in the case of GameStop, that story is a shared one that they’ve developed with others in a hothouse online,” said DeDeo, who is also an assistant professor in the Department of Social and Decision Science at Carnegie Mellon. “We are interested in uncovering these stories, understanding their appeal, and seeing how people use them to make decisions.”
The intersection of belief dynamics and emergent engineering will be addressed again at SFI’s annual Fall Symposium on November 5 – 6, 2021.