数字生态系统：进化主体种群的稳定性
Digital Ecosystems: Stability of Evolving Agent Populations
摘要：
考虑到在实际部署之前能够预测其对各种环境条件的响应的重要性，稳定性可能是任何工程系统最理想的特征之一。工程系统正变得越来越复杂，接近生物生态系统的同一水平，因此它们的稳定性变得越来越重要，但越来越多的微分动力学可能使稳定性变得更加难以捉摸。Chli-DeWilde的稳定性定义将多智能体系统视为具有潜在未知转移概率的离散时间马尔可夫链。当一个多智能体系统的状态（一个随机过程）收敛到一个平衡分布时，它就被认为是稳定的，因为系统的稳定性可以直观地理解为表现出有界行为。我们调查的扩展，包括多智能体系统与进化动力学，专注于我们的数字生态系统的不断发展的代理人口。然后，我们在此基础上构建了一个基于熵的不稳定度定义（极限概率的熵），后来用于执行稳定性分析。数字生态系统被认为是通过模拟来研究不断发展的智能体种群的稳定性，其结果与最初的Chli-DeWilde定义一致。
英文摘要：
Stability is perhaps one of the most desirable features of any engineered system, given the importance of being able to predict its response to various environmental conditions prior to actual deployment. Engineered systems are becoming ever more complex, approaching the same levels of biological ecosystems, and so their stability becomes ever more important, but taking on more and more differential dynamics can make stability an ever more elusive property. The Chli-DeWilde definition of stability views a Multi-Agent System as a discrete time Markov chain with potentially unknown transition probabilities. With a Multi-Agent System being considered stable when its state, a stochastic process, has converged to an equilibrium distribution, because stability of a system can be understood intuitively as exhibiting bounded behaviour. We investigate an extension to include Multi-Agent Systems with evolutionary dynamics, focusing on the evolving agent populations of our Digital Ecosystem. We then built upon this to construct an entropy-based definition for the degree of instability (entropy of the limit probabilities), which was later used to perform a stability analysis. The Digital Ecosystem is considered to investigate the stability of an evolving agent population through simulations, for which the results were consistent with the original Chli-DeWilde definition.
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