Smallville, Agent Based Modeling, and Capital Markets

Google and Stanford cooked up something intriguing—a virtual village called Smallville, populated by agents running on the #ChatGPT API.

The researchers witnessed interesting emergent behavior, from coordination and communication to downright adorable interactions among the village’s wholesome residents.

Smallville even comes with cute graphics. But beyond the little sprites organizing Valentine’s Parties (yes, that’s what happens in Smallville): this experiment made me think of my time, a long time ago and in a City far away, in Capital Markets.

Smallville (courtesy Ars Technica)

Sidebar

Derivatives are a vast market. And derivatives, like options, are priced using a somewhat arcane mathematical field called Stochastic Calculus – the Black-Scholes equation being a famous example.

The underlying assumption is that markets behave randomly, and Stochastic Calculus provides a way of modeling this behavior. But – this approach can have problems. Even the famous creators of the Black-Scholes equation spectacularly blew up their fund LTCM.


Enter Agent Based Modelling (ABM): a nifty but niche approach that relies on simulating the behavior of market participants via Agents. The idea is that these simulations provide a better insight into how the market may evolve under different conditions.

Smallville shows us that LLM-driven agents are a possibility. Is it a stretch to envision specialized LLMs, trained on financial data, being used in ABM to predict how a particularly temperamental market might behave?

If you are a quantitative analyst on a sell-side firm looking to market-make a particularly exotic derivative, an LLM-powered approach may be viable. Or at least less boring than reaching for the Stochastic Calculus textbook.

The future might find traders armed with their own simulated worlds to forecast the price of, oh, let’s say, a derivative on the price of an exotic tulip of a non-fungible JPEG of a smoking Ape.. who knows?

PS – The painting is called “The Copenhagen Stock Exchange” by P.S. Krøyer. You can see why an agent-based approach to simulating capital markets is a .. possibility..

Generative Models and the “Grey Goo Problem”

Generative AI models may be causing a “Grey Goo” problem with art, publishing, and user-generated content. 

Thomas Jane encounters the Protomolecule in The Expanse

The Grey Goo Problem is a thought experiment where self-replicating nano-robots consume all available resources leading to a catastrophic scenario. This scenario is a popular science fiction trope (see comments).

Several publishers and user-generated content sites like StackOverflow have been impacted by a flood of AI-generated content in the last few months. Clarkesworld, a science fiction magazine, stopped accepting submissions last week. Even LinkedIn is overrun by ChatGPT-generated “thought leadership.” 

Tools like ChatGPT need high-quality training data to generate good results. They collect training data by scraping the Internet. You can see the issue here, can’t you? 

The Grey Goo scenario is managed through containment and quarantine in science fiction. For example, in The Expanse series (see image), containing the “Proto-Molecule” is a crucial plot element. 

The need to contain and quarantine Generative AI will result in more paywalls, subscriptions, and gated content. Crypto may even find its calling in guaranteeing the authenticity of online content. 

I fear that the Open Internet that made ChatGPT possible will be crippled by the actions of ChatGPT and its cousins.