Traffic planners, securities traders and military strategists all use it. Simulating the behavior of millions of idiosyncratic individuals also may be the best way to understand complex phenomena like pandemics.
(15 Dec 2020, feed from Knowable Magazine) It’s been a long year, but a vaccine against Covid-19 has started to roll out across the United States. There won’t be enough to vaccinate everyone right away, so public health officials will need to figure out how to manage the slow ramp-up of immunity. Do we make faster progress by focusing on locked-down regions first, or on those that have tried to stay open? Dense urban areas or suburbs full of commuters? Should we skip over pockets with high levels of vaccine skepticism or push extra-hard to vaccinate there? And, ultimately, when will it be safe to relax, unmask and start hugging people again?
Those are difficult questions to answer, because the outcome depends on so many interconnected factors, including the unpredictability of individual humans’ minds and behavior. To make things even more challenging, human behavior is a moving target: What our leaders and friends say and do today influences what we ourselves do tomorrow. Effects can loop back to alter causes in a complex tangle that’s almost impossible for analysts to think through.
The real world is full of complex problems like this, from epidemiology to economics to foreign policy. But an emerging approach within the field of complexity science promises a better way to understand how the idiosyncratic, often irrational actions of many individuals generate complex collective outcomes. Instead of trying to analyze how a society functions from the top down, the new technique known as agent-based modeling tackles the problem from the other end, by focusing on the individuals themselves.
Like SimCity or the Matrix, agent-based modeling involves the creation of a computer-simulated world replete with thousands or even millions of individual people — the “agents” the name refers to — complete with homes, jobs, friends and realistic individual foibles. “Agent-based models are artificial societies of software people,” says Joshua Epstein, a computational social scientist at New York University who is one of the pioneers of the field. “These people behave like humans behave. They’re not perfectly rational or perfectly informed.”
When enough of these agents have been created, modelers turn them loose to move, act and interact. Just as the simple behavioral choices of individual fish cause them to collectively coalesce into a school, the cumulative effect of the agents’ individual decisions and actions leads to collective patterns such as epidemics, recessions or revolutions. Such models give analysts a way to explore the sometimes surprising outcomes that result from different policy options.
Already, agent-based models have taken over fields as disparate as traffic planning, securities trading and war gaming, and their use is growing in economics and political science. And within epidemiology, such models are yielding fresh insights into the way epidemics spread and evolve, what public health officials can do to control the outcome and the likely economic consequences of the pandemic. “It’s fundamentally changed the way people do infectious disease modeling,” says Epstein.
The conventional approach to disease forecasting doesn’t look at the behavior of individuals. Instead, epidemiologists typically sort the population into groups depending on, say, their virus status. These SEIR models (for susceptible, exposed, infectious and recovered, though many models subdivide further by age or other factors) assume that people encounter one another at random — the equivalent, as one researcher puts it, of pretending that people go home to a different family every night.
The real world has more spatial structure than that, of course. “It’s silly to have a model where I’m equally likely to have contact with a meatpacker in Kansas as [with] my neighbor across the street,” says Ian Lustick, a political scientist at the University of Pennsylvania who uses agent-based models extensively.
But until the advent of GPS-enabled cell phones and social media, researchers didn’t have enough data on how individuals move and interact during their daily lives to look at such granular issues. That has changed, and at the same time, computers have grown powerful enough to keep track of the location, interactions and attributes of millions of virtual people simultaneously.
Setting up an agent-based model is simple in concept but daunting in practice. The first step is to create a population of software people that resembles the real population being studied. For several research groups, this means agent-based models that span the entire country.
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