Decentralized Collective Learning for Self-managed Sharing Economies

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

ДЕЦЕНТРАЛИЗАЦИЯ В ЦИФРОВОМ ОБЩЕСТВЕ: ПАРАДОКС ДИЗАЙНА

Digital societies come with a design paradox: On the one hand, technologies, such as Internet of Things, pervasive and ubiquitous systems, allow a distributed local intelligence in interconnected devices of our everyday life such as smart phones, smart thermostats, self-driving cars, etc. On the other hand, Big Data collection and storage is managed in a highly centralized fashion, resulting in privacy-intrusion, surveillance actions, discriminatory and segregation social phenomena. What is the difference between a distributed and a decentralized system design? How “decentralized” is the processing of our data nowadays? Does centralized design undermine autonomy? Can the level of decentralization in the implemented technologies influence ethical and social dimensions, such as social justice? Can decentralization convey sustainability? Are there parallelisms between the decentralization of digital technology and the decentralization of urban development?Цифровая трансформация основывается на автоматизированных процессах и инвестициях в новые технологии: искусственный интеллект, блокчейн, анализ данных и интернет вещей. Но в центре успешной стратегии цифровой трансформации все равно находится человек. Цифровая трансформация порождает парадоксы новых моделей: с одной стороны, распространяются повсеместно технологии, такие, как интернет вещей, большие данные позволяют улучшить продукты и услуги для потребителей, предложить им новую ценность и т. д. Но, с другой стороны, аналитика данных и их хранение управляются высокоцентрализованным способом, приводящим к вторжению в частную жизнь людей, контролю за их действиями, к дискриминационным и сегрегационным социальным явлениям. В статье рассматриваются вопросы: каково различие между распределенным и децентрализованным системным проектированием? Как возможна организация «децентрализованной» обработки персональных  данных в наше время? Подрывают ли централизованный сбор и обработка данных автономию? Может ли децентрализация во внедренных технологиях влиять на этические и социальные параметры, такие, как социальная справедливость? Ведет ли децентрализация к  устойчивости функционирования систем? Есть ли взаимосвязь между децентрализацией цифровых технологий и децентрализацией городского развития?В статье делается вывод о том, что децентрализаванные системы имеют гораздо большую эффективность в современных условиях и являются альтернативой или естественной адаптацией к сложившимся условиям. Например, децентрализованное производство электроэнергии делает людей одновременно производителями и потребителями, что приводит к повышению энергоэффективности. Точно так же аналитика данных не является монополией систем больших данных. Анализ может также быть выполнен полностью децентрализованным способом как общественное благо с использованием коллективного разума

DECENTRALIZATION IN DIGITAL SOCIETIES.A DESIGN PARADOX

Digital societies come with a design paradox: On the one hand, technologies, such as Internet of Things, pervasive and ubiquitous systems, allow a distributed local intelligence in interconnected devices of our everyday life such as smart phones, smart thermostats, self-driving cars, etc. On the other hand, Big Data collection and storage is managed in a highly centralized fashion, resulting in privacy-intrusion, surveillance actions, discriminatory and segregation social phenomena. What is the difference between a distributed and a decentralized system design? How “decentralized” is the processing of our data nowadays? Does centralized design undermine autonomy? Can the level of decentralization in the implemented technologies influence ethical and social dimensions, such as social justice? Can decentralization convey sustainability? Are there parallelisms between the decentralization of digital technology and the decentralization of urban development

PROJETOS DE DESENVOLVIMENTO SOCIAL COMO ESPAÇO PARA A RESILIÊNCIA EM EMPRESAS DE BASE COMUNITÁRIA VOLTADAS À RECICLAGEM

By looking at the driving and conflicting forces that permeate the self-adaptive and self-organizing actions of heterogeneous agents in social development projects (PDS), this study asks the question “How do PDS promote resilience in community-based companies focused on recycling through collective learning (CA)?". This is a collective case study, with qualitative and descriptive methods. It seeks to understand how different PDS promote resilience in Community Based Companies (EBC) aimed at recycling through CA. Data were collected using ethnographic techniques, and analyzed by interpretative textual analysis. The results indicate that CA allows recyclers to modify relationships established in the PDS, and to conceive EBC as a business, developing technical management skills and turning recycling into a cause by giving new meaning to social and economic relationships. In the cases studied, learning and resilience are never independent, but are linked to broader issues, such as the capacity for collective action. This study contributes to the understanding of the PDS as spaces for CA, enabling an analysis of the work of recyclers as an opportunity for resilience.Al observar las fuerzas impulsoras y conflictivas que impregnan las acciones autoadaptativas y autoorganizadoras de los agentes heterogéneos en los proyectos de desarrollo social (PDS), este estudio cuestiona cómo PDS promueve la resiliencia en las empresas comunitarias centradas en el reciclaje del aprendizaje colectivo (CA). Este es un estudio de caso colectivo, cualitativo y descriptivo, cuyo objetivo es comprender cómo los diferentes PDS promueven la resiliencia en las empresas basadas en la comunidad (EBC) destinadas a reciclar desde el AC. Los datos indican que CA permite a los recicladores modificar las relaciones establecidas en el PDS y concebir EBC como un negocio, desarrollar habilidades de gestión técnica y convertir el reciclaje en una causa, re-significando las relaciones sociales y económicas. En los casos estudiados, el aprendizaje y la resiliencia nunca son independientes, están vinculados a cuestiones más amplias como, por ejemplo, la capacidad de acción colectiva. El estudio contribuye a la comprensión del PDS como espacios de CA, lo que permite analizar el trabajo de los recicladores como una oportunidad para la resiliencia.Ao atentar para as forças motrizes e conflitantes que permeiam as ações autoadaptativas e auto-organizativas dos agentes heterogêneos em projetos de desenvolvimento social (PDS), este estudo problematiza sobre como, por meio do aprendizado coletivo (AC), os PDS promovem a resiliência em empresas de base comunitária voltadas à reciclagem. Trata-se de um estudo de caso coletivo, qualitativo e descritivo, cujo objetivo é compreender como distintos PDS promovem, por meio do AC, a resiliência em empresas de base comunitária (EBC) voltadas à reciclagem. Os dados foram coletados com uso de técnicas etnográficas e analisados por análise textual interpretativa. Os resultados indicam que a AC permite aos recicladores modificar relações estabelecidas nos PDS e conceber a EBC como negócio, desenvolvendo capacidades técnicas de gestão e transformando a reciclagem em uma causa, ao ressignificar relações sociais e econômicas. Nos casos estudados, a aprendizagem e a resiliência nunca são independentes, pois estão atreladas a questões mais amplas, por exemplo, à capacidade de ação coletiva. O estudo contribui para a compreensão dos PDS como espaços de AC, possibilitando analisar o trabalho dos recicladores como oportunidade de resiliência

Coordination of drones at scale: Decentralized energy-aware swarm intelligence for spatio-temporal sensing

Smart City applications, such as traffic monitoring and disaster response, often use swarms of intelligent and cooperative drones to efficiently collect sensor data over different areas of interest and time spans. However, when the required sensing becomes spatio-temporally large and varying, a collective arrangement of sensing tasks to a large number of battery-restricted and distributed drones is challenging. To address this problem, this paper introduces a scalable and energy-aware model for planning and coordination of spatio-temporal sensing. The coordination model is built upon a decentralized multi-agent collective learning algorithm (EPOS) to ensure scalability, resilience, and flexibility that existing approaches lack of. Experimental results illustrate the outstanding performance of the proposed method compared to state-of-the-art methods. Analytical results contribute a deeper understanding of how coordinated mobility of drones influences sensing performance. This novel coordination solution is applied to traffic monitoring using real-world data to demonstrate a 46.45% more accurate and 2.88% more efficient detection of vehicles as the number of drones become a scarce resource