Moore Douglas G, Valentini Gabriele, Walker Sara I, Levin Michael
BEYOND: Center for Fundamental Concepts in Science, Arizona Sate University, Tempe, AZ, United States.
Department of Biology, Allen Discovery Center, Tufts University, Medford, MA, United States.
Front Robot AI. 2018 Jun 11;5:60. doi: 10.3389/frobt.2018.00060. eCollection 2018.
The study of collective behavior has traditionally relied on a variety of different methodological tools ranging from more theoretical methods such as population or game-theoretic models to empirical ones like Monte Carlo or multi-agent simulations. An approach that is increasingly being explored is the use of information theory as a methodological framework to study the flow of information and the statistical properties of collectives of interacting agents. While a few general purpose toolkits exist, most of the existing software for information theoretic analysis of collective systems is limited in scope. We introduce Inform, an open-source framework for efficient information theoretic analysis that exploits the computational power of a C library while simplifying its use through a variety of wrappers for common higher-level scripting languages. We focus on two such wrappers here: PyInform (Python) and rinform (R). Inform and its wrappers are cross-platform and general-purpose. They include classical information-theoretic measures, measures of information dynamics and information-based methods to study the statistical behavior of collective systems, and expose a lower-level API that allow users to construct measures of their own. We describe the architecture of the Inform framework, study its computational efficiency and use it to analyze three different case studies of collective behavior: biochemical information storage in regenerating planaria, nest-site selection in the ant , and collective decision making in multi-agent simulations.
集体行为的研究传统上依赖于各种不同的方法工具，范围从理论性更强的方法（如种群或博弈论模型）到实证方法（如蒙特卡洛或多智能体模拟）。一种越来越受到探索的方法是使用信息论作为方法框架来研究信息流动以及相互作用智能体群体的统计特性。虽然存在一些通用工具包，但现有的用于集体系统信息论分析的大多数软件在范围上是有限的。我们介绍Inform，这是一个用于高效信息论分析的开源框架，它利用C库的计算能力，同时通过针对常见高级脚本语言的各种包装器简化其使用。我们在此重点介绍两个这样的包装器：PyInform（Python）和rinform（R）。Inform及其包装器是跨平台且通用的。它们包括经典的信息论度量、信息动态度量以及用于研究集体系统统计行为的基于信息的方法，并公开了一个底层API，允许用户构建自己的度量。我们描述了Inform框架的架构，研究了其计算效率，并使用它来分析集体行为的三个不同案例研究：再生涡虫中的生化信息存储、蚂蚁的巢穴选址以及多智能体模拟中的集体决策。
