At the Fringes of Realism: Agent-Based Models Take Hold Among Quants
Waters Technology examines increasing interest in AI-powered simulation from banks, funds and institutions

Agent-based modeling breaks away from stochastic, or mathematic, varieties of modeling and forecasting. Though more commonly used for military operations, epidemiology, urban planning and other concentrations, it is gaining traction among some quant trading shops.

ABM potentially has huge implications for the way banks and asset managers of all stripes conduct stress-testing and mitigate their risk, as it seeks to simulate the rational and irrational behaviors of agents—individuals and groups—in their environments

Many things changed on September 11, 2001, and apart from the profound sense of loss, there was also stunned disbelief—a question of “How?”

In the aftermath, Justin Lyon enrolled in a graduate program at MIT, studying system dynamics, or, to put it very simply, how complex things— systems with many moving parts—act and change over time. There, he focused on simulating the growth of radical Islam—that is, predicting likelihoods of violence, insurgencies, and counterinsurgencies in tandem with potential consequences that could follow—in Afghanistan, using a technique called complex adaptive systems modeling.

Operating under the theory that the universe is composed of highly complicated, in-flux, and interconnected systems, Lyon set out to determine how to model humans and the ways in which they interact in a way that acknowledges and adjusts for all the nuance that colors their rational and irrational behaviors.

Over the better part of the next two decades, Lyon worked as a contractor for a number of organizations, including the Bank of England, ExxonMobil, Microsoft, and the US Department of Defense, for which he ran a team of analysts supporting a general based in Iraq.

Five years after 9/11, but eight years before Lyon would start and head up his own company called Simudyne, the 2008 financial crisis rocked the global financial system, and marked a clear turning point in the prevailing views economists took toward markets—that they were inherently efficient and rational. They aren’t, and increasingly, it’s dangerous to believe so.

“All models are ultimately wrong because they aren’t the real world. But you want to have an ecosystem—a zoo of models,” says Lyon, echoing a term coined by Andrew Haldane of the Bank of England. “A zoo of models: a whole bunch of different, diverse species of models so that you’re not blindsided.”

When Lyon founded Simudyne, an enterprise simulation technology company, in 2016, his thesis was that people could make radically better decisions that ultimately lead to a safer world. People could do this, in part, by using agent-based modeling, which draws upon his earlier work modeling complex adaptive systems.

The range of possible financial applications for this emerging quant technique is vast. A small universe of whitepapers and research studies pertaining to agent-based modeling and finance has exploded in the last five years…

Continue to read this article at Waters Technology here.