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Inform: Efficient Information-Theoretic Analysis of Collective Behaviors
Douglas G Moore1, Gabriele Valentini1, Sara I Walker1
1BEYOND: Center for Fundamental Concepts in Science, Arizona Sate University, Tempe, AZ, United States.
Frontiers in Robotics and AI
|March 15, 2021
Summary
We introduce Inform, an open-source framework for information theoretic analysis of collective behavior. It offers efficient tools for studying information flow and statistical properties in interacting agents, with Python and R wrappers.
Area of Science:
- Computational Biology
- Theoretical Ecology
- Complex Systems
Background:
- Collective behavior studies traditionally use theoretical models or simulations.
- Information theory offers a novel framework for analyzing information flow in interacting agents.
- Existing software for information-theoretic analysis of collective systems is often limited in scope.
Purpose of the Study:
- To introduce Inform, an open-source framework for efficient information-theoretic analysis of collective systems.
- To provide wrappers for high-level scripting languages (Python and R) for broader accessibility.
- To enable the study of information dynamics and statistical properties in collective behavior.
Main Methods:
- Development of the Inform C library for computational efficiency.
- Creation of PyInform (Python) and rinform (R) wrappers for user-friendly access.
- Implementation of classical and advanced information-theoretic measures.
- Analysis of computational efficiency and application to case studies.
Main Results:
- Inform provides an efficient, cross-platform framework for information-theoretic analysis.
- PyInform and rinform wrappers simplify the application of complex measures.
- The framework successfully analyzed information dynamics in planaria regeneration, ant nest selection, and multi-agent simulations.
Conclusions:
- Inform offers a powerful and accessible tool for advancing the study of collective behavior using information theory.
- The framework's flexibility allows for the development of novel information-based analytical methods.
- Inform facilitates interdisciplinary research by bridging computational and empirical approaches to collective systems.
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