As researchers delve deeper into the behavior of decentralized collective systems, they’re beginning to question some of their initial assumptions. Research has found that adding intelligence to the individual agents in decentralized systems doesn’t always make their collective behaviors more complex. Collective systems are more the rule than the exception.
Whether biological, technological, economic or social, collective systems are often considered to be “decentralized,” meaning that they lack a main control hub for coordinating their individual components. Instead, control is distributed among the components, which make their own decisions based on local information; complex behaviors arise through their interactions. That kind of setup can be advantageous, in part because it is resilient: If one part isn’t working properly, the system can continue to function — in stark contrast to when a central brain or leader stops doing its job.
Decentralization has ridden a wave of hype, particularly among those hoping to revolutionize marketplaces with blockchain technology and societies with more dispersed governments. “Some of this stems from political ideology having to do with a preference for bottom-up governing styles and systems with natural checks on the emergence of inequality,” Jessica Flack, an evolutionary biologist and complexity scientist at the Santa Fe Institute, wrote in an email. “And some of it stems from engineering biases … that are based on the assumption these types of structures are more robust, less exploitable.”
But “most of this discussion,” she added, “is naive.” The line between centralization and decentralization is often blurry, and deep questions about the flow and aggregation of information in these networks persist. Even the most basic and intuitive assumptions about them need more scrutiny, because emerging evidence suggests that making networks bigger and making their parts more sophisticated doesn’t always translate to better overall performance.
In a paper published earlier this month in Science Advances, for instance, a team led by Neil Johnson, now a physicist at George Washington University, demonstrated that a decentralized model performed best under Goldilocks conditions, when its parts were neither too simple nor too capable. That finding echoes other results from complexity research about the optimal use of information and the tradeoff between independence and correlation. The new insights could help to point out the strengths and limitations of decentralized designs for robots, self-driving vehicles, medical treatments and corporate structures — and might even help to explain aspects of natural evolution.
Over the past few years, he and others have found that medium-size groups of animals or humans are optimal for decision-making. That conclusion runs contrary to the standard beliefs about the “wisdom of crowds,” Kao said, “where the larger the group, the better the collective performance.” Success lies in achieving the right balance between coordination and independence among the system’s components.
Further research is needed on how the sophistication of components, their interconnectivity and other parameters affect the overall robustness and limitations of a network. Johnson and others plan to study how the availability of information affects phenomena as diverse as the formation of opinions among voters, the behavior of better robots, and potential mechanisms of recovery from neurological diseases. They also hope the work could help explain why natural evolution has made organisms a mix of centralized and decentralized systems (these kinds of results, Johnson said, could help to justify “why we aren’t just fantastic larvae”).
Siehe auch den aktuellen Beitrag von Conny Dethloff: Hierarchie und Selbstorganisation passt sehr gut zusammen. Wie so oft geht es um ein sowohl-als-auch. Bzw. unterstreichen die Ergebnisse auch unseren Energiezellenansatz: Auf der einen Seite autonome funktionale Einheiten, die sich mit den Nachbarn koordinieren, aber auch eine übergeordnete „Orchestrierung“ für das Gesamtsystem-