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(Demo) Systematic Experimentation Using Scenarios in Agent Simulation: Going Beyond Parameter Space

Vivek Nallur, Pedram Aghaei, Graham Finlay

TL;DR

This paper addresses the challenge of incorporating qualitative expert knowledge into agent-based simulations by proposing a disconnected architecture that separates design-time decision-making from run-time execution. It introduces Scenario inputs, Policy rules, and BehaviourFlow graphs to allow domain experts and policymakers to shape and compare complex simulations without programming, using a Python/Mesa foundation with a web-based UI and standard graph tools such as yEd. The key contributions are the Scenario abstraction, the JSON input format, and an integrated UI that supports scenario creation, visualization, and result comparison, enabling multi-expert collaboration beyond traditional tools like NetLogo and Mesa. This approach advances practical ABM usage for policy analysis by enabling systematic, graph-based, scenario-level experimentation with qualitative interventions and starting conditions, while keeping the implementation open source for broader adoption.

Abstract

This paper demonstrates a disconnected ABM architecture that enables domain experts, and non-programmers to add qualitative insights into the ABM model without the intervention of the programmer. This role separation within the architecture allows policy-makers to systematically experiment with multiple policy interventions, different starting conditions, and visualizations to interrogate their ABM

(Demo) Systematic Experimentation Using Scenarios in Agent Simulation: Going Beyond Parameter Space

TL;DR

This paper addresses the challenge of incorporating qualitative expert knowledge into agent-based simulations by proposing a disconnected architecture that separates design-time decision-making from run-time execution. It introduces Scenario inputs, Policy rules, and BehaviourFlow graphs to allow domain experts and policymakers to shape and compare complex simulations without programming, using a Python/Mesa foundation with a web-based UI and standard graph tools such as yEd. The key contributions are the Scenario abstraction, the JSON input format, and an integrated UI that supports scenario creation, visualization, and result comparison, enabling multi-expert collaboration beyond traditional tools like NetLogo and Mesa. This approach advances practical ABM usage for policy analysis by enabling systematic, graph-based, scenario-level experimentation with qualitative interventions and starting conditions, while keeping the implementation open source for broader adoption.

Abstract

This paper demonstrates a disconnected ABM architecture that enables domain experts, and non-programmers to add qualitative insights into the ABM model without the intervention of the programmer. This role separation within the architecture allows policy-makers to systematically experiment with multiple policy interventions, different starting conditions, and visualizations to interrogate their ABM
Paper Structure (9 sections, 4 figures)

This paper contains 9 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Architecture
  3. Conventional Approaches
  4. Current Architecture Implementation
  5. Domain Expert Designing Scenarios
  6. Defining Policy
  7. Different Types of Graphs
  8. Running Multiple Models and Saving Results
  9. Conclusion

Figures (4)

  • Figure 1: Two Sample Policies
  • Figure 2: Three Samples of BehaviourFlow
  • Figure 3: simulation settings
  • Figure 4: A table to visually compare different scenarios before starting simulation