agent-based models

An August issue of Nature I recently browsed [on my NUS trip] contained a news feature on agent- based models applied to understanding the opioid crisis in US. (With a rather sordid picture of a drug injection in Philadelphia, hence my own picture.)

To create an agent-based model, researchers first ‘build’ a virtual town or region, sometimes based on a real place, including buildings such as schools and food shops. They then populate it with agents, using census data to give each one its own characteristics, such as age, race and income, and to distribute the agents throughout the virtual town. The agents are autonomous but operate within pre-programmed routines — going to work five times a week, for instance. Some behaviours may be more random, such as a 5% chance per day of skipping work, or a 50% chance of meeting a certain person in the agent’s network. Once the system is as realistic as possible, the researchers introduce a variable such as a flu virus, with a rate and pattern of spread based on its real-life characteristics. They then run the simulation to test how the agents’ behaviour shifts when a school is closed or a vaccination campaign is started, repeating it thousands of times to determine the likelihood of different outcomes.

While I am obviously supportive of simulation based solutions, I cannot but express some reservation at the outcome, given that it is the product of the assumptions in the model. In Bayesian terms, this is purely prior predictive rather than posterior predictive. There is no hard data to create “realism”, apart from the census data. (The article also mixes the outcome of the simulation with real data. Or epidemiological data, not yet available according to the authors.)

In response to the opioid epidemic, Bobashev’s group has constructed Pain Town — a generic city complete with 10,000 people suffering from chronic pain, 70 drug dealers, 30 doctors, 10 emergency rooms and 10 pharmacies. The researchers run the model over five simulated years, recording how the situation changes each virtual day.

This is not to criticise the use of such tools to experiment with social, medical or political interventions, which practically and ethically cannot be tested in real life and working with such targeted versions of the Sims game can paradoxically be more convincing when dealing with policy makers. If they do not object at the artificiality of the outcome, as they often do for climate change models. Just from reading this general public article, I thus wonder at whether model selection and validation tools are implemented in conjunction with agent-based models…

One Response to “agent-based models”

  1. Pierre Jacob Says:

    Aha, I’ve been very interested in these models lately! There are papers on inference for these models, e.g. Calibration and evaluation of individual-based models using Approximate Bayesian Computation, by Elske Van der Vaart et al,
    or also
    Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method, by Kattwinkel & Reichert.
    My impression is that overall they remain very challenging to calibrate, but there seems to be a real drive towards principled approaches in the recent years; at least more principled than “manual tuning”. From a modeling standpoint, these models seem to be very natural, so it’d be cool to provide efficient inference tools for them. Or at least for some of them.

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