Sci-Finance: The Great Cybernetic Experiment, Part 2
"In the game of life and evolution there are three players at the table: human beings, nature, and machines. I am firmly on the side of nature. But nature, I suspect, is on the side of machines."
—George Dyson, Darwin Among the Machines: The Evolution of Global Intelligence
In Part I of Sci-Finance, we looked at how the market has become a large-scale experiment for applying cutting edge science and technology previously considered the realm of science fiction. Things like evolutionary computing, neural networking, predictive modeling, cloud-scale machine learning, genetic algorithms, artificial intelligence, and neuroeconomics are the new tools of banking and finance.
We also identified how this experiment to track and predict the mathematical behavior of the market using complex computation is being conducted at the highest level of global governance as many of the world’s most powerful central bankers are technocrats straight out of MIT, the global focal point for much of today's sci-finance. Lastly, we highlighted the psychology endemic to financial authorities and experts that believe the key to managing the markets and saving the world from its greatest problems lies in financial engineering, high level computation, and, as Andrew Lo put it, harnessing the "collective intelligence" of the market—an intelligence we'll identify as cybernetic.
Source: Sandia National Laboratories
In Part II, we’ll take a closer look at the nature of this experiment, how it is evolving, and a possible outlook for the future. To begin, let’s return to some of the concepts mentioned in Part I—artificial markets, adaptable software agents, and machine learning—with a quote from Scott Patterson’s latest book, Dark Pools: High Speed Traders, A.I. Bandits, and the Threat to the Global Financial System. As he explains, the trading world is now one...
few outsiders could imagine, a worldwide matrix of dazzlingly complex algorithms, interlinked computer hubs the size of football fields, and high-octane trading robots guided by the latest advances in artificial intelligence...
The new pools [or electronic markets] evoked a water-filled world of frictionless trading... With electronic innovations...and the rise of algorithms that swam in their pools, the market was evolving like a living organism, shape-shifting into something entirely new. And the algorithms were changing, too. They were no longer the dumb single-cell virus-like creatures operating on simple orders. (Has Microsoft’s average price risen 1 percent? Buy.) They were learning how to adapt in the new pools, morphing into more advanced predators. Many were geared with advanced AI systems that could quickly detect hidden market signals using the high-bandwidth data feeds and react in a flash, learning and changing their behavior along the way.
AI Meets HFT: Humans Not Required
The notion that algorithms can actually learn, evolve, and become more “intelligent” isn’t controversial because it isn’t true, it is controversial because of the philosophical implications. In case there is any doubt, however, that firms aren’t using such technology, I encourage you to consider the following key points from an interview conducted by High Frequency Trading Review with Adam Afshar, CEO and President of Hyde Park Global:
- They are a “100% Robotic investment and trading firm based on Artificial Intelligence...Genetic Algorithms (GA) and other Evolutionary models to identify mispricings, arbitrage and patterns in electronic financial markets.”
- They employ "no analysts, portfolio managers or traders, ONLY scientists and engineers.”
- Their AI robotic trading platform "does not allow any human intervention"
- It monitors the news, prices, and volumes of "3,000+ stocks every second", a feat which their CEO says, "no human trader, analyst or portfolio manager or even legions of traders can do"
- The higher the frequency of trading the quicker it learns, evolves, and adapts
- It can scan up to hundreds of thousands of news stories each day to identify trends, sentiment, market psychology, etc. (Keep this in mind when we get to the Keynesian Beauty Contest)
A Complex Adaptive System
Many are unwilling to place a machine at equal or greater intelligence than themselves. Sure, it might beat us in mathematical calculations, strategy, or even riddles, but it still isn’t the same thing. There’s something magical about human consciousness that can’t be recreated in a machine, we are told. This is true; then again, we’re not talking about a single machine but, as Scott Patterson writes, a global sea of algorithms “evolving like a living organism, shape-shifting into something entirely new.” When we consider the self-organizing behavior of highly complex systems—that is, complex adaptive systems—this idea isn’t all that strange. To quote Wikipedia:
Typical examples of complex adaptive systems, include - the global macroeconomic network within a country or group of countries; stock market and complex web of cross border holding companies; social insect and ant colonies; the biosphere and the ecosystem; the brain and the immune system...The internet and cyberspace - composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system.
There are close relationships between the field of CAS and artificial life. In both areas the principles of emergence and self-organization are very important. The ideas and models of CAS are essentially evolutionary...biological views on adaptation...and simulation models in economics and social systems.
We must understand that what is evolving in the market—or how the market is itself evolving—is not merely one of machines but a complex interaction between humans and machines. Specifically, we’d call this type of interaction cybernetic, named after the science largely developed by MIT’s Norbert Weiner, which he called “the scientific study of control and communication in the animal and the machine.” Once again, quoting from Wikipedia:
Cybernetics [comes] from the Greek meaning to "steer" or "navigate." Contemporary cybernetics began as an interdisciplinary study connecting the fields of control systems, electrical network theory, mechanical engineering, logic modeling, evolutionary biology, neuroscience, anthropology, and psychology in the 1940s...It includes the study of feedback, black boxes and derived concepts such as communication and control in living organisms, machines and organizations including self-organization. Its focus is how anything (digital, mechanical or biological) processes information, reacts to information, and changes or can be changed to better accomplish the first two tasks.
Since cybernetics was used to understand many of the fundamental mechanisms at work in both biological and non-biological systems, and certainly how the two interact or combine, most people know of it indirectly through the popular use of the word “cyborg”—that is, a cybernetic organism. Given their widespread use in science fiction, cybernetic systems are typically not well understood or simply disregarded as, well, fictional. However, when we consider that prominent Nobel prize winning economists and well-respected financial engineers actually trace their fields to science fiction (see Part 1), it is clear that we shouldn’t throw the baby out with the bathwater.
Consider now the current nature of our market. It is a highly complex adaptive system composed of humans and machines processing information from their environment to make decisions and then continually adjust them according to incoming feedback and market behavior. When you also consider the role of central banks in using control and communication strategies to herd the market’s animal spirits, is it fair to call the market a cybernetic organism? Most definitely. We could even say it is the best and largest example of such a system so far ever developed. Call it a cyborg, the Frankenmarket, or what-have-you.
Let’s stitch this all together now. What is the market exactly? It is a highly complex, adaptive, cybernetic, information processing system undergoing rapid technological advancement. Is it evolving? Certainly. Question is, what is it evolving into?
Keynesian Beauty Contest
Before you start to think this all sounds too far-fetched, let’s connect some of these concepts back to one of the most famous descriptions of the market: a beauty contest.
The Keynesian beauty contest is the view that much of investment is driven by expectations about what other investors think, rather than expectations about the fundamental profitability of a particular investment. John Maynard Keynes, the most influential economist of the 20th century, believed that investment is volatile because investment is determined by the herd-like “animal spirits” of investors. Keynes observed that investment strategies resembled a contest in a London newspaper of his day that featured pictures of a hundred or so young women. The winner of the contest was the newspaper reader who submitted a list of the top five women that most clearly matched the consensus of all other contest entries. A naïve strategy for an entrant would be to rely on his or her own concepts of beauty to establish rankings. Consequently, each contest entrant would try to second guess the other entrants’ reactions, and then sophisticated entrants would attempt to second guess the other entrants’ second guessing. And so on. Instead of judging the beauty of people, substitute alternative investments. Each potential entrant (investor) now ignores fundamental value (i.e., expected profitability based on expected revenues and costs), instead trying to predict “what the market will do.” The results are (a) that investment is extremely volatile because fundamental value becomes irrelevant, and (b) that the most successful investors are either lucky or masters at understanding mob psychology... [Source]
First of all, why is this a correct description of how the financial markets operate? From both a macroeconomic and company-specific perspective, the main reason has to do with the fact that a large amount of data is subject to error, prone to revision, or outright manipulated. The second has to do with the problem of interpreting this data—something also prone to large variation. Thirdly, one cannot consider the implications of such data without also taking into account how central banks will respond to it—an extra layer of complexity that exacerbates the whole economic calculation problem further.
It should be pointed out that much of the complexity and error continually creeping into the market system would normally be corrected for if allowed to rebalance and achieve a more economically-anchored homeostasis. If it weren't for the crucial role central banking has played in pushing investor psychology further out on the risk curve, relying less on long-term interest and savings, but on a growing dependency of complex trading strategies that offer no real economic benefit, the market would not be evolving into the ugly and potentially destructive cybernetic organism it is today. Unfortunately, this experiment will go on until the mad scientists from MIT lose control. Thing is, it won't really be lost, but handed over.
The Forbidden Market
The evolution of the market into a vast cybernetic intelligence has been largely invisible so far. It is hard to distinguish because it is a creature of the collective mind, inextricable from society itself. It is being born, however, by the amplification of human intelligence in swarms of software evolving in the substrate of market psychology. When will we finally see it? Perhaps when we become a threat to its own existence.
Paul Krugman and Andrew Lo take the optimistic view of science fiction. I take another. Welcome to the Forbidden Market:
Doc Ostrow: Morbius was too close to the problem. The Krell had completed their project. Big machine. No instrumentalities. True creation.
Commander John J. Adams: Come on, Doc, let's have it.
Doc Ostrow: But the Krell forgot one thing.
Commander John J. Adams: Yes, what?
Doc Ostrow: Monsters, John. Monsters from the Id.
About Cris Sheridan
Cris Sheridan Archive
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