Agent Based Model: Essential Tool or Unnecessary Complexity?

Rhoda, I think the key question is not “Can we use an Agent-Based Model?” but rather “What question can an ABM answer that a simpler method cannot answer adequately?”

For me, an ABM becomes justified when individual heterogeneity, interactions, adaptation, networks, feedback loops, and emergent behaviour are central to the decision problem. ABMs are particularly useful in public health when people or organisations behave differently and their interactions can change the trajectory of the system.

I would use a simple decision test:

1. Is the research question inherently individual-level?
If individuals differ meaningfully in risk, behaviour, access to services, treatment adherence, mobility, or decision-making, ABM may add value.

2. Do interactions between individuals matter?
For example, infectious disease transmission, social networks, health-seeking behaviour, peer influence, or household/community effects. If individuals can influence one another, an aggregate model may hide an important mechanism.

3. Are there feedback loops or emergent outcomes?
If changing one part of the system changes behaviour elsewhere, creating effects that cannot easily be predicted by simply adding individual effects, ABM becomes more attractive. Complex-systems guidance specifically highlights dynamics, feedback, non-linearity and interactions as reasons complexity may be warranted.

4. Is there a meaningful counterfactual that we cannot test empirically?
This is particularly important in public health. If we want to ask “What would happen if we changed this policy, behaviour or service configuration?” but a real-world experiment is impossible or unethical, simulation can help explore plausible scenarios.

But I would also apply the “complexity penalty.”

If a regression model, cohort analysis, compartmental model, microsimulation, or discrete-event simulation can answer the question adequately, I would hesitate to build an ABM simply because it is more sophisticated.

ABMs require additional assumptions, parameters, programming, calibration and validation. As complexity increases, uncertainty and the difficulty of external validation can also increase.

So my rule of thumb would be:

Use ABM when the mechanism of interaction is part of the research question—not merely because the population is large or the problem sounds complex.

For example, in TB research, I could justify ABM if the question involves interactions among patients, households, communities, health facilities, providers, diagnostic pathways and treatment behaviours, and we want to understand how these interactions collectively produce diagnostic or treatment delays.

But if my question is simply “Which factors are associated with patient delay?”, a well-designed epidemiological analysis may be much more appropriate. An ABM there could become complexity for complexity’s sake.

Ultimately, I would ask three questions before building one:

What decision are we trying to inform?
What mechanism must be represented?
What does ABM allow us to learn that a simpler model cannot?

If we cannot answer the third question convincingly, I probably wouldn't build the ABM.

And perhaps the most important principle is: the model should be as complex as necessary, but no more complex than the decision problem requires.

I’d say the key question is whether individual behaviour and interactions actually drive the outcome you’re trying to understand.

If the problem involves people making different decisions, influencing each other, adapting to their environment, or producing unexpected “bottom-up” effects, then an Agent-Based Model (ABM) can be really useful.

But if a simpler statistical model, system dynamics model, or straightforward simulation can answer the question, then ABM may just add unnecessary complexity.

A simple rule I’d use is: don’t use ABM because the problem is complex; use it when the complexity comes from interactions between individual agents.