Ludéric Van Calck, Alexandre Pacheco, Volker Strobel, Marco Dorigo & Andreagiovanni Reina
Scientific Reports volume 13, Article number: 20417 (2023)

Robot swarms are generally considered to be composed of cooperative agents that, despite their limited individual capabilities, can perform difficult tasks by working together. However, in open swarms, where different robots can be added to the swarm by different parties with potentially competing interests, cooperation is but one of many strategies. We envision an information market where robots can buy and sell information through transactions stored on a distributed blockchain, and where cooperation is encouraged by the economy itself. As a proof of concept, we study a classical foraging task, where exchanging information with other robots is paramount to accomplish the task efficiently. We illustrate that even a single robot that lies to others—a so-called Byzantine robot—can heavily disrupt the swarm. Hence, we devise two protection mechanisms. Through an individual-level protection mechanism, robots are more sceptical about others’ information and can detect and discard Byzantine information, at the cost of lower efficiency. Through a systemic protection mechanism based on economic rules regulating robot interactions, robots that sell honest information acquire over time more wealth than Byzantines selling false information. Our simulations show that a well-designed robot economy penalises misinformation spreading and protects the swarm from Byzantine behaviour. We believe economics-inspired swarm robotics is a promising research direction that exploits the timely opportunity for decentralised economies offered by blockchain technology.

Read the full article at: www.nature.com

Takahiro Yabe, Bernardo Garcia Bulle Bueno, Morgan Frank, Alex Pentland, Esteban Moro

Urban economic resilience is intricately linked to how disruptions caused by pandemics, disasters, and technological shifts ripple through businesses and urban amenities. Disruptions, such as closures of non-essential businesses during the COVID-19 pandemic, not only affect those places directly but also influence how people live and move, spreading the impact on other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other in these situations. Leveraging large-scale human mobility data and millions of same-day visits in New York, Boston, Los Angeles, Seattle, and Dallas, we quantify dependencies between points-of-interest (POIs) encompassing businesses, stores, and amenities. Compared to places’ physical proximity, dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of POI categories. We show that using behavior-based dependency relationships improves the predictability of business resilience during shocks, such as the COVID-19 pandemic, by around 40% compared to distance-based models. Simulating hypothetical urban shocks reveals that neglecting behavior-based dependencies can lead to a substantial underestimation of the spatial cascades of disruptions on businesses and urban amenities. Our findings underscore the importance of measuring the complex relationships woven through behavioral patterns in human mobility to foster urban economic resilience to shocks.

Read the full article at: arxiv.org