Markus Schläpfer, Michael Szell, Hadrien Salat, Carlo Ratti, Geoffrey B. West
The interaction of all mobile species with their environment hinges on their movement patterns: the places they visit and how frequently they go there. In human society, where the prevalent form of cohabitation is in cities, the highly dynamic and diverse movement of people is fundamental to almost every aspect of socio-economic life, including social interactions or disease spreading, and ultimately is key to the evolution of urban infrastructure, productivity, innovation and technology. However, despite the crucial role of the spatio-temporal structure of movement in cities, the laws that govern the variation of population flows to specific locations have remained elusive. Here we show that behind the apparent complexity of movement a surprisingly simple universal scaling relation drives the flow of individuals to any specific location based on both frequency of visitation and distance travelled. We derive a first principles argument stating that the number of visiting individuals should decrease as an inverse square of the product of visitation frequency and travel distance; or, equivalently, as a power law with exponent ≈−2. Using large-scale data analyses, we demonstrate that population flows obey this theoretical prediction in virtually all tested areas across the globe, ranging from Europe and America to Asia and Africa, regardless of the detailed geographies, cultures or levels of development. The revealed regularity offers unprecedented possibilities for the modelling of mobility fluxes at high spatial and temporal resolution, and it places an important constraint on any theory of movement, spatial organisation and social interaction in cities.
We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.
Jonathan Grizou, Laurie J. Points, Abhishek Sharma and Leroy Cronin
Science Advances 31 Jan 2020:
Vol. 6, no. 5, eaay4237
It has been called the third great revolution of 20th-century physics, after relativity and quantum theory. But how can something called chaos theory help you understand an orderly world? What practical things might it be good for? What, in fact, is chaos theory? "Chaos theory," according to Dr. Steven Strogatz, Director of the Center for Applied Mathematics at Cornell University, "is the science of how things change." It describes the behavior of any system whose state evolves over time and whose behavior is sensitive to small changes in its initial conditions.
Juan C. Correa
Front. Phys., 21 February 2020
In a recent round table organized by the Santa Fe Institute, the complexity of commerce captured the attention of those interested in understanding how complex systems science can be applicable for settings where consumers and providers interact. Despite the usefulness of applied complexity for commerce-related phenomena, few works have attempted to provide insightful ideas. This mini-review aims at providing a succinct discussion of how the metrics of emergence, self-organization, and complexity might benefit the research agenda of applied complexity and commerce/consumer studies. In particular, the paper argues possible pragmatic ways to understanding the valuable information present in word-of-mouth data found on electronic commerce platforms.