The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning . This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections , accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.

中文翻译：

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自我管理共享经济的分散式集体学习

物联网为公民提供了一种全新的在线参与共享经济的方式。当代理自行决定他们从中选择的选项时，例如，他们的资源消耗和生产，虽然这些选择具有集体系统范围的影响，但最优决策变成了一个组合优化问题，称为 NP-hard。在此类具有挑战性的计算问题中，集中管理（深度）学习系统通常需要涉及隐私和公民自主权的个人数据。本文设想了一种替代的无监督和分散的集体学习方法，该方法保留了自组织成分层树结构的多智能体系统的隐私、自治和参与。远程交互协调了一个高效的流程分散的集体学习. 这一颠覆性概念由 I-EPOS 实现，迭代经济规划和优化选择，伴随着一个典型的软件工件。引人注目的是，I-EPOS 优于涉及非本地暴力操作或交换完整信息的相关算法。本文提供了关于网络拓扑和规划对学习效率的影响以及技术-社会-经济权衡和全局最优性的新实验发现。使用来自能源和自行车共享试点的真实数据进行的实验评估展示了集体学习在设计具有道德和社会责任感的参与式共享经济方面的巨大潜力。