expEa

expEa is a modular modeling framework designed to analyze processes that exhibit exponential growth with feedback. It provides a structured approach to simulate, estimate, and compare growth trajectories under varying parameters, and emphasizes transparent uncertainty communication.

Origins and naming: The term expEa arose in a 2019 workshop on complex systems, where researchers proposed the name by combining 'exponential' and 'Ea' as a stand-in for evolutionary analytics.

Architecture and methods: The framework comprises a growth engine that implements an exponential growth model with adjustable carrying capacity, an adaptive parameter estimator based on Bayesian updating, and an evaluation module for scenario analysis. It supports data import, model fitting, cross-validation, and visualization, with reference implementations in Python and C++.

Applications: ExpEa has been used in theoretical ecology to model invasive species spread, in epidemiology to simulate early-stage outbreaks, and in finance to explore rapid expansion under feedback loops. It is also used in education to illustrate model calibration and uncertainty.

Reception and status: In academic discussions, expEa is noted for modularity and clarity but criticized for potential overfitting on small datasets and for reliance on simplifying assumptions about growth. The project is maintained through a public repository with ongoing community contributions.

See also: Exponential growth models; Bayesian updating; Agent-based modeling.