Month: November 2020

Recovery Coupling in Multilayer Networks

Michael M. Danziger, Albert-László Barabási

The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks—communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming that a component failure in one network causes failures in the other network, a hard interdependence that results in a cascade of failures across multiple systems. While empirical evidence of such hard coupling is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in well documented interdependencies induced by the recovery process. If the support networks are not functional, recovery will be slowed. Here we collected data on the recovery time of millions of power grid failures, finding evidence of universal nonlinear behavior in recovery following large perturbations. We develop a theoretical framework to address recovery coupling, predicting quantitative signatures different from the multilayer cascading failures. We then rely on controlled natural experiments to separate the role of recovery coupling from other effects like resource limitations, offering direct evidence of how recovery coupling affects a system’s functionality. The resulting insights have implications beyond infrastructure systems, offering insights on the fragility and senescence of biological systems.

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To Regulate or Not: A Social Dynamics Analysis of an Idealised AI Race

The Anh Han, Luis Moniz Pereira, Francisco C. Santos, Tom Lenaerts

Journal of Artificial Intelligence Research

Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the time-scale of the race is thus required, as this may induce important non-trivial effects.

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NERCCS 2021: Fourth Northeast Regional Conference on Complex Systems

NERCCS 2021: The Fourth Northeast Regional Conference on Complex Systems will follow the success of the previous NERCCS conferences to promote the emerging venue of interdisciplinary scholarly exchange for complex systems researchers in the Northeast U.S. region (and beyond) to share their research outcomes through presentations and online publications, network with their peers, and promote interdisciplinary collaboration and the growth of the research community.

NERCCS will particularly focus on facilitating the professional growth of early career faculty, postdocs, and students in the region who will likely play a leading role in the field of complex systems science and engineering in the coming years.

The 2021 conference will be held fully online via Zoom.

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