Agent-Based Models (Part 5)

Agent-based models are crucial in modern epidemiology. But currently, many of these models are large monolithic computer programs—opaque to everyone but their creators. That’s no way to do science!

Our team of category theorists, computer scientists, and public health experts has come up with a cool plan to create agent-based models out of small reusable modules which can be explained, tested, compared and shared. This will make it easier to compare different models and build new ones. As a test case, we plan to apply these models to the vaping crisis—comparing them with traditional monolithic models.

If you know some philanthropists who might want to fund some potentially revolutionary research in public health and computer science, please let them know about this. I’m working for free, but some of my team members need to earn a living.

Here are the other team members:

• Kris Brown and Evan Patterson, who work on computer science and scientific modeling with category theory at the Topos Institute,

• Nathaniel Osgood, who works on computer science and epidemiological modeling at the University of Saskatchewan, and

• Patty Mabry, who works on public health and human behavior modeling at HealthPartners Institute.

After a few weeks of hard work, racing against deadlines and many obstacles, we team have just finished writing a grant proposal for this project. It was worth writing, because we came up with some really exciting ideas and nailed down a lot of technical details.

Brendan Fong, head of the Topos Institute, wrote:

Very excited for this project—it takes over a decade of work in applied category theory and uses it to make a significant difference in the science of behaviour change, by modularising and systematising modelling. The goal is to show how it addresses very concrete problems like the public health of vaping.

There’s a lot to say, but for now I’ll just give a sketchy summary of our project.

Overview

Modeling is a key to understanding the specific mechanisms that underlie human behavior. Moreover, explicit examination and experimentation to isolate the behavioral mechanisms responsible for the effectiveness of behavioral interventions are foundational in advancing the field of Science of Behavior Change (SOBC). Unfortunately, this has been held back by the difficulty of precisely comparing behavioral mechanisms that are formulated in disparate contexts using different operational definitions. This is a problem we aim to solve.

We propose to initiate a new era of epidemiological modeling, in which agent-based models (ABMs) can be flexibly created from standardized behavioral mechanism modules that can be easily combined, shared, adapted, and reused in diverse ways for different types of insight. To do this, we must transform the sprawling repertoire of ABM methodologies into a systematic science of modular ABMs. This requires developing new mathematics based on Applied Category Theory (ACT). The proposers have already used ACT to develop modular models that represent human behavior en masse. To capture human behavior at the individual level in a modular way demands significant further conceptual advances, which we propose to make here.

We will develop the mathematics of modular ABMs and implement it by creating modules that capture the behavioral mechanisms put forward by SOBC: self-regulation, interpersonal & social processes, and stress-reactivity & resilience. We will evaluate this approach in a test case—the vaping crisis—by using these modules to build proof-of-concept ABMs of this crisis and comparing these new modular ABMs to existing models. We will create open-access libraries of modules for specific behavioral mechanisms and larger ABMs built from these modules. We will also run education and training events to disseminate our work.

Intellectual Merit

This interdisciplinary project will have a longstanding and substantial impact across three fields: Applied Category Theory, Science of Behavior Change, and Incorporating Human Behavior in Epidemiological Models. Our unified approach to describing system dynamics in a modular and functorial way will be a major contribution to ACT. The SOBC has ushered in a new era in behavioral intervention design based on the study of behavioral mechanisms. Our work incorporating SOBC-studed behavioral mechanisms as standardized modules in ABMs will transform the field of epidemiological modeling and lead to more rapid progress in SOBC.

The key contribution of our work to all three fields is that it provides the ability to easily compare models with different operational definitions of behavioral mechanisms—since rather than a model being a large monolithic structure, opaque to everyone but its creators, it can now be built from standardized behavioral mechanism modules, and the effect of changing a single module can easily be studied.

Broader Impacts

Our work will be transformative to behavioral science, and the magnitude of the impact can hardly be overstated. The ability to communicate and compare behavioral mechanisms will pave the way for large-scale leveraging of evidence from across behavioral epidemiological models for collective use. A valuable impact of our work will be in providing an accessible framework for reproducible, standardized models built from transparent, mathematically well-defined modules.

Until now, interacting with epidemiological models has required knowledge of mathematics, programming skills, and access to proprietary software. Our ACT-enabled modular representation of behavioral mechanisms will open up access to health professionals and community members of all levels to participate in the construction of epidemiological models. We will also build capacity in applying our methodology through events funded by this proposal.