Problem Statement

Mesa provides core agent-based modeling functionality, including agent and model abstractions, data collection via DataCollector, and browser-based visualization through SolaraViz. However, the analysis of simulation output remains largely external to the Mesa ecosystem. Users must export collected data to external tools such as pandas and matplotlib to perform even basic exploratory analysis.

This separation between simulation execution and data analysis introduces friction during exploratory modeling, parameter tuning, and teaching. Mesa currently lacks an integrated, interactive analysis layer that allows users to inspect agent-level and model-level data, compute descriptive statistics, and explore distributions or aggregated metrics live within the browser interface. Additionally, there is no standardized mechanism for analyzing ensemble-level results across multiple simulation runs within Mesa.

Proposed Solution

This project proposes the development of an interactive analysis dashboard module for Mesa, built on top of Solara and integrated with SolaraViz. The module will bridge the gap between data collection and insight extraction by enabling live, in-browser analysis of simulation output.

Key components include:

Data Integration: Seamless integration with Mesa’s DataCollector, supporting both agent-level and model-level reporters with reactive updates during simulation.

Descriptive Statistical Primitives: Built-in support for common descriptive statistics such as mean, median, variance, quantiles, and confidence intervals, designed as composable analysis primitives rather than standalone features.

Interactive Exploration: Dashboard components for time-series plots, distributions, and aggregated views, with interactive filtering and grouping based on agent attributes or model state.

Extensibility: A declarative API that allows users to define analysis views without modifying simulation logic, designed to support future extensions and community contributions.

Ensemble Awareness (Optional Extension): Support for visualizing aggregated statistics across multiple runs, enabling comparison and variability analysis in stochastic models.