Economic Evaluations with Agent-Based Modelling: An Introduction
Agent-Based Modelling (ABM) is a method that simulates how individual people or groups (called agents) make decisions and interact with each other and their environment. These interactions help predict how health interventions might work in real-world settings, especially when traditional models fall short. This method is useful for studying complex systems like disease spread and screening programs.
At a glance
Use when
Evaluating interventions in complex, dynamic systems where individual behaviors and interactions drive outcomes, such as infectious disease control, public health policies, or organizational change.
Avoid when
Simple, linear problems with homogeneous populations where traditional models (e.g., decision trees, Markov models) are sufficient and computationally cheaper.
Inputs
Individual-level data on behavior, disease transmission dynamics, intervention parameters, cost inputs, and environmental rules for agent interaction.
Outputs
System-level outcomes such as disease prevalence, cost-effectiveness ratios, intervention impact over time, and emergent patterns from agent interactions.
How it works
Agent-Based Modelling (ABM) is a computational, bottom-up simulation technique that models autonomous agents—representing individuals, organizations, or other entities—whose behaviors and interactions generate system-level outcomes. ABMs overcome limitations of conventional models by allowing non-linearity, heterogeneity, and non-stationarity. They are particularly suited for capturing emergent phenomena in complex health systems. This method was demonstrated in a 2015 tutorial using NetLogo to evaluate the cost-effectiveness of infectious disease screening, highlighting its application in health technology assessment.
- HTA domains
- Clinical Effectiveness, Costs & Economic Evaluation, Organisational aspects
- Assumptions
- Agent behaviors and rules are representative of real-world decision-making; interactions are accurately modeled; initial conditions and environment reflect the target population.
- Strengths
- Captures heterogeneity, non-linear dynamics, and emergent phenomena; allows for realistic representation of complex systems; supports policy testing in virtual environments.
- Limitations
- Computationally intensive; requires detailed input data; model validation can be challenging; results may be sensitive to initial conditions and rule specifications.
- Also known as
- ABM, Agent-Based Simulation, Individual-Based Modelling
Questions this answers
- › How can we model complex health interventions where individual behaviors and interactions matter?
- › What is the cost-effectiveness of screening programs for infectious diseases in dynamic populations?
- › How do emergent system-level patterns arise from individual-level decisions?
- › How can heterogeneity and non-linear dynamics be incorporated into economic evaluations?
- › What are the advantages of bottom-up modelling over traditional top-down approaches?
- › How can we simulate real-world complexity in health policy evaluation?
References & sources
Similar by meaning
- A Framework for Developing the Structure of Public Health Economic Models
- Whole Disease Modeling
- Approaches to Aggregation and Decision Making—A Health Economics Approach
- BCEA (Bayesian Cost-Effectiveness Analysis) R package
- Guidance for good health economic modelling practices in personalised medicines
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