Collective Learning: A 10-Year Odyssey to Human-centered Distributed Intelligence

Abstract

This paper illustrates a 10-year research endeavor on collective learning, a paradigm for tackling tragedy of the commons problems in socio-technical systems using human-centered distributed intelligence. In contrast to mainstream centralized artificial intelligence (AI) allowing algorithmic discrimination and manipulative nudging, the decentralized approach of collective learning is by-design participatory and value-sensitive: it aligns with privacy, autonomy, fairness and democratic values. Engineering such values in a socio-technical system results in computational constraints that turn collective decision-making into complex combinatorial NP-hard problems. These are the problems that collective learning and the EPOS research project tackles. Collective learning finds striking applicability in energy, traffic, supply-chain and the self-management of sharing economies. This grand applicability and the social impact are demonstrated in this paper along with a future perspective of the collective learning paradigm.

Ethics of Smart Cities: Towards Value-Sensitive Design and Co-Evolving City Life

“…to the data management flowchart in Section 3 and gave paper recommendations on the early use of computers for cities. The authors would also like to thank all researchers and developers who have contributed code to the Nervousnet repositories (https://github.com/nervousnet, accessed on 15 August 2021) and to the Finance 4.0 project (see [116] for details) as well as to the EPOS project [145,157,159].…”

“…We would like to point out that all four steps mentioned above may be supported by properly designed digital platforms [145]. Furthermore, suitable incentive systems may improve performance beyond traditional democratic and market-based solutions [146].…”

Human-Centered Artificial Intelligence, a review

“…Collective learning benefits energy, transportation, supply chains, and sharing economy self-management. Pournaras, 2020, emphasized the paradigm's wide use and social impact [53].…”

How to Coordinate Decisions at Large Scale? A Hands-on Tutorial on Collective Learning for Smart Cities and Beyond

“…This hands-on tutorial will provide an introduction to collective learning [1], [2] and the EPOS software artifact [3], [4]], a decentralized socio-technical approach for coordinating decisions in large-scale multi-agent systems. The tutorial will be highly practical with several application scenarios of Smart Cities, for instance: energy management, coordinated traffic control of autonomous/shared vehicles, load-balancing of bikesharing stations, charging control of electric vehicles and other.…”