Automotive traffic is a classical example of a complex system, being the simplest case the homogeneous traffic where all vehicles are of the same kind, and using different means of transportation increases complexity due to different driving rules and interactions between each vehicle type. In particular, when motorcyclists drive in between the lanes of stopped or slow-moving vehicles. This later driving mode is a Venezuelan pervasive practice of mobilization that clearly jeopardizes road safety. We developed a minimalist agent-based model to analyze the interaction of road users with and without motorcyclists on the way. The presence of motorcyclists dwindles significantly the frequency of lane changes of motorists while increasing their frequency of acceleration-deceleration maneuvers, without significantly affecting their average speed. That is, motorcyclist "corralled" motorists in their lanes limiting their ability to maneuver and increasing their acceleration noise. Comparison of the simulations with real traffic videos shows good agreement between model and observation. The implications of these results regarding road safety concerns about the interaction between motorists and motorcyclists are discussed.
Simulating the interaction of road users: A glance to complexity of Venezuelan traffic Juan C. Correa, Mario I. Caicedo, Ana L. C. Bazzan, Klaus Jaffe
Earth has become an urban planet. More than half of the world's people now live in cities, and the proportion is growing. And urban areas are sprawling even faster than they are adding people, swallowing up both farmland and wildlands. The implications are sobering. The land area needed to provide city residents with food, energy, and materials is expanding; this ecological footprint is often 200 times greater than the area of a city itself. The resulting carbon emissions, added to those from cities themselves, mean that urbanization is now the main driver of climate change.
Policy directives in several nations are focusing on the development of smart cities, linking innovations in the data sciences with the goal of advancing human well-being and sustainability on a highly urbanized planet. To achieve this goal, smart initiatives must move beyond city-level data to a higher-order understanding of cities as transboundary, multisectoral, multiscalar, social-ecological-infrastructural systems with diverse actors, priorities, and solutions. We identify five key dimensions of cities and present eight principles to focus attention on the systems-level decisions that society faces to transition toward a smart, sustainable, and healthy urban future.
Meta-principles for developing smart, sustainable, and healthy cities Anu Ramaswami, Armistead G. Russell, Patricia J. Culligan, Karnamadakala Rahul Sharma, Emani Kumar
Many programs being implemented by US employers, insurers, and health care providers use incentives to encourage patients to take better care of themselves. We critically review a range of these efforts and show that many programs, although well-meaning, are unlikely to have much impact because they require information, expertise, and self-control that few patients possess. As a result, benefits are likely to accrue disproportionately to patients who already are taking adequate care of their health. We show how these programs could be made more effective through the use of insights from behavioral economics. For example, incentive programs that offer patients small and frequent payments for behavior that would benefit the patients, such as medication adherence, can be more effective than programs with incentives that are far less visible because they are folded into a paycheck or used to reduce a monthly premium. Deploying more-nuanced insights from behavioral economics can lead to policies with the potential to increase patient engagement and deliver dividends for patients and favorable cost-effectiveness ratios for insurers, employers, and other relevant commercial entities.
Behavioral Economics Holds Potential To Deliver Better Results For Patients, Insurers, And Employers George Loewenstein, David A. Asch and Kevin G. Volpp
One of the main challenges for aerial robots is the high-energy consumption of powered flight, which limits flight times to typically only tens of minutes for systems below 2 kg in weight (1). This limitation greatly reduces their utility for sensing and inspection tasks, where longer hovering times would be beneficial. Perching onto structures can save energy and maintain a high, stable observation or resting position, but it requires a coordination of flight dynamics and some means of attaching to the structure. Birds and insects have mastered the ability to perch successfully and have inspired perching robots at various sizes. On page 978 of this issue, Graule et al. (2) describe a perching robotic insect that represents the smallest flying robot platform that can autonomously attach to surfaces. At a mass of only 100 mg, it combines advanced flight control with adaptive mechanical dampers and electro-adhesion to perch on a variety of natural and artificial structures.
Learning from nature how to land aerial robots Mirko Kovac
A complete solution is given for a symmetric case of the problem of the planar central configurations of four bodies, when two bodies are on an axis of symmetry, and the other two bodies have equal masses and are situated symmetrically with respect to the axis of symmetry. The positions of the bodies on the axis of symmetry are described by angle coordinates with respect to the outside bodies. The solution is such, that giving the angle coordinates, the masses for which the given configuration is a central configuration, can be computed from simple analytical expressions of the angles. The central configurations can be described as one-parameter families, and these are discussed in detail in one convex and two concave cases. The derived formulae represent exact analytical solutions of the four-body problem.
Central configurations of four bodies with an axis of symmetry Bálint Érdi, Zalán Czirják
Celestial Mechanics and Dynamical Astronomy May 2016, Volume 125, Issue 1, pp 33-70
Complexity Labs is an online resource dedicated to the area of complex systems providing a wide variety of users with information, research, learning and media content relating to this exciting new area. Our mission statement is to assist in the development of a coherent, robust and accessible framework for modelling, designing and managing complex systems.
Mutualistic behaviours wherein several individuals act together for a common benefit, such as a collective hunt, are often deemed of minor interest by theoreticians in evolutionary biology. These behaviours benefit all the individuals involved, and therefore, so the argument goes, their evolution is straightforward. However, mutualistic behaviours do pose a specific kind of evolutionary problem: they require the coordination of several partners. Indeed, a single individual expressing a preference for cooperation cannot benefit if others wish to remain solitary. Here we use simulations in evolutionary robotics to study the consequences of this problem. We show that it constitutes a far more serious obstacle for the evolution of cooperation than was previously thought on the basis of game theoretical analyses. We find that the transition from solitary to cooperative strategies is very unlikely, and we also observe that successful cooperation requires the evolution of a specific and rather complex behaviour, necessary for individuals to coordinate with each other. This reveals the critical role of the practical mechanics of behaviour in evolution.
Fame, popularity and celebrity status, frequently used tokens of success, are often loosely related to, or even divorced from professional performance. This dichotomy is partly rooted in the difficulty to distinguish performance, an individual measure that captures the actions of a performer, from success, a collective measure that captures a community’s reactions to these actions. Yet, finding the relationship between the two measures is essential for all areas that aim to objectively reward excellence, from science to business. Here we quantify the relationship between performance and success by focusing on tennis, an individual sport where the two quantities can be independently measured. We show that a predictive model, relying only on a tennis player’s performance in tournaments, can accurately predict an athlete’s popularity, both during a player’s active years and after retirement. Hence the model establishes a direct link between performance and momentary popularity. The agreement between the performance-driven and observed popularity suggests that in most areas of human achievement exceptional visibility may be rooted in detectable performance measures.
Although no one can quite agree how to define it, the general idea is to find datasets so enormous that they can reveal patterns invisible to conventional inquiry. The data are often generated by millions of real-world user actions, such as tweets or credit-card purchases, and they can take thousands of computers to collect, store, and analyze. To many companies and researchers, though, the investment is worth it because the patterns can unlock information about anything from genetic disorders to tomorrow’s stock prices.
But there’s a problem: It’s tempting to think that with such an incredible volume of data behind them, studies relying on big data couldn’t be wrong. But the bigness of the data can imbue the results with a false sense of certainty. Many of them are probably bogus—and the reasons why should give us pause about any research that blindly trusts big data.
The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have be used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex networks metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.
Combining complex networks and data mining: why and how M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, S. Boccaletti
Socioeconomic inequalities in cities are embedded in space and result in neighborhood effects, whose harmful consequences have proved very hard to counterbalance efficiently by planning policies alone. Considering redistribution of money flows as a first step toward improved spatial equity, we study a bottom-up approach that would rely on a slight evolution of shopping mobility practices. Building on a database of anonymized credit card transactions in Madrid and Barcelona, we quantify the mobility effort required to reach a reference situation where commercial income is evenly shared among neighborhoods. The redirections of shopping trips preserve key properties of human mobility, including travel distances. Surprisingly, for both cities only a small fraction (∼5%) of trips need to be altered to reach equity situations, improving even other sustainability indicators. The method could be implemented in mobile applications that would assist individuals in reshaping their shopping practices, to promote the spatial redistribution of opportunities in the city.
Crowdsourcing the Robin Hood effect in cities Thomas Louail, Maxime Lenormand, Juan Murillo Arias, José J. Ramasco
Macroscopic behavior of scientific and societal systems results from the aggregation of microscopic behaviors of their constituent elements, but connecting the macroscopic with the microscopic in human behavior has traditionally been difficult. Manifestations of homophily, the notion that individuals tend to interact with others who resemble them, have been observed in many small and intermediate size settings. However, whether this behavior translates to truly macroscopic levels, and what its consequences may be, remains unknown. Here, we use call detail records (CDRs) to examine the population dynamics and manifestations of social and spatial homophily at a macroscopic level among the residents of 23 states of India at the Kumbh Mela, a 3-month-long Hindu festival. We estimate that the festival was attended by 61 million people, making it the largest gathering in the history of humanity. While we find strong overall evidence for both types of homophily for residents of different states, participants from low-representation states show considerably stronger propensity for both social and spatial homophily than those from high-representation states. These manifestations of homophily are amplified on crowded days, such as the peak day of the festival, which we estimate was attended by 25 million people. Our findings confirm that homophily, which here likely arises from social influence, permeates all scales of human behavior.
Social and Spatial Clustering of People at Humanity's Largest Gathering Ian Barnett, Tarun Khanna, Jukka-Pekka Onnela
A new mode of innovation is emerging that blurs the lines between universities, industry, governments and communities. It exploits disruptive technologies — such as cloud computing, the Internet of Things and big data — to solve societal challenges sustainably and profitably, and more quickly and ably than before. It is called open innovation 2.0.
The promise is sustainable, intelligent living: innovations drive economic growth and improve quality of life while reducing environmental impact and resource use. For example, a dynamic congestion-charging system can adjust traffic flow and offer incentives to use park-and-ride schemes, guided by real-time traffic levels and air quality. Car-to-car communication could manage traffic to minimize transit times and emissions and eliminate road deaths from collisions. Smart electricity grids lower costs, integrate renewable energies and balance loads. Health-care monitoring enables early interventions, improving life quality and reducing care costs.
Twelve principles for open innovation 2.0 Martin Curley
In recent years researchers have gravitated to social media platforms, especially Twitter, as fertile ground for empirical analysis of social phenomena. Social media provides researchers access to trace data of interactions and discourse that once went unrecorded in the offline world. Researchers have sought to use these data to explain social phenomena both particular to social media and applicable to the broader social world. This paper offers a minireview of Twitter-based research on political crowd behavior. This literature offers insight into particular social phenomena on Twitter, but often fails to use standardized methods that permit interpretation beyond individual studies. Moreover, the literature fails to ground methodologies and results in social or political theory, divorcing empirical research from the theory needed to interpret it. Rather, papers focus primarily on methodological innovations for social media analyses, but these too often fail to sufficiently demonstrate the validity of such methodologies. This minireview considers a small number of selected papers; we analyze their (often lack of) theoretical approaches, review their methodological innovations, and offer suggestions as to the relevance of their results for political scientists and sociologists.
A Biased Review of Biases in Twitter Studies on Political Collective Action Peter Cihon, Taha Yasseri
The literature views many African cities as dysfunctional with a hodgepodge of land uses and poor “connectivity.” One driver of inefficient land uses is construction decisions for highly durable buildings made under weak institutions. In a novel approach, we model the dynamics of urban land use with both formal and slum dwellings and ongoing urban redevelopment to higher building heights in the formal sector as a city grows. We analyze the evolution of Nairobi using a unique high–spatial resolution data set. The analysis suggests insufficient building volume through most of the city and large slum areas with low housing volumes near the center, where corrupted institutions deter conversion to formal sector usage.
Building functional cities J. Vernon Henderson, Anthony J. Venables, Tanner Regan, Ilia Samsonov
Evolution is often conceived as changes in the properties of a population over generations. Does this notion exhaust the possible dynamics of evolution? Life is hierarchically organized, and evolution can operate at multiple levels with conflicting tendencies. Using a minimal model of such conflicting multilevel evolution, we demonstrate the possibility of a novel mode of evolution that challenges the above notion: individuals ceaselessly modify their genetically inherited phenotype and fitness along their lines of descent, without involving apparent changes in the properties of the population. The model assumes a population of primitive cells (protocells, for short), each containing a population of replicating catalytic molecules. Protocells are selected towards maximizing the catalytic activity of internal molecules, whereas molecules tend to evolve towards minimizing it in order to maximize their relative fitness within a protocell. These conflicting evolutionary tendencies at different levels and genetic drift drive the lineages of protocells to oscillate endlessly between high and low intracellular catalytic activity, i.e. high and low fitness, along their lines of descent. This oscillation, however, occurs independently in different lineages, so that the population as a whole appears stationary. Therefore, ongoing evolution can be hidden behind an apparently stationary population owing to conflicting multilevel evolution.
Evolutionarily stable disequilibrium: endless dynamics of evolution in a stationary population Nobuto Takeuchi, Kunihiko Kaneko, Paulien Hogeweg
Little is known about cooperative behaviour among the gut microbiota; here, limited cooperation is demonstrated for Bacteroides thetaiotaomicron, but Bacteroides ovatus is found to extracellularly digest a polysaccharide not for its own use, but to cooperatively feed other species such as Bacteroides vulgatus from which it receives return benefits.
The evolution of cooperation within the gut microbiota Seth Rakoff-Nahoum, Kevin R. Foster & Laurie E. Comstock
There is enormous interest in inferring features of human behavior in the real world from potential digital footprints created online - particularly at the collective level, where the sheer volume of online activity may indicate some changing mood within the population regarding a particular topic. Civil unrest is a prime example, involving the spontaneous appearance of large crowds of otherwise unrelated people on the street on a certain day. While indicators of brewing protests might be gleaned from individual online communications or account content (e.g. Twitter, Facebook) societal concerns regarding privacy can make such probing a politically delicate issue. Here we show that instead, a simple low-level indicator of civil unrest can be obtained from online data at the aggregate level through Google Trends or similar tools. Our study covers countries across Latin America during 2011-2014 in which diverse civil unrest events took place. In each case, we find that the combination of the volume and momentum of searches from Google Trends surrounding pairs of simple keywords, tailored for the specific cultural setting, provide good indicators of periods of civil unrest. This proof-of-concept study motivates the search for more geographically specific indicators based on geo-located searches at the urban level.
Open source data reveals connection between online and on-street protest activity Hong Qi, Pedro Manrique, Daniela Johnson, Elvira Restrepo and Neil F Johnson
Community detection, the division of a network into dense subnetworks with only sparse connections between them, has been a topic of vigorous study in recent years. However, while there exist a range of powerful and flexible methods for dividing a network into a specified number of communities, it is an open question how to determine exactly how many communities one should use. Here we describe a mathematically principled approach for finding the number of communities in a network using a maximum-likelihood method. We demonstrate this approach on a range of real-world examples with known community structure, finding that it is able to determine the number of communities correctly in every case.
Estimating the number of communities in a network M. E. J. Newman, Gesine Reinert
Instead of continuing to develop new materials the old-fashioned way — stumbling across them by luck, then painstakingly measuring their properties in the laboratory — Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands. Even data from failed experiments can provide useful input1. Many of these candidates are completely hypothetical, but engineers are already beginning to shortlist those that are worth synthesizing and testing for specific applications by searching through their predicted properties — for example, how well they will work as a conductor or an insulator, whether they will act as a magnet, and how much heat and pressure they can withstand.
Large floods should seemingly influence the depth and width of rivers. Phillips and Jerolmack, however, suggest that the self-organization of bedrock river channels blunts the impact of extreme rainfall events. River channel geometries from a wide range of course-grained rivers across the United States show that larger floods have very limited additional impact on channel geometry. River channel sculpting does increase as flood size increases, but the effect is most pronounced for moderate floods. This relationship may explain the long-term stability of rivers across shifts in climate.
Coordination games provide ubiquitous interaction paradigms to frame human behavioral features, such as information transmission, conventions and languages as well as socio-economic processes and institutions. By using a dynamical approach, such as Evolutionary Game Theory (EGT), one is able to follow, in detail, the self-organization process by which a population of individuals coordinates into a given behavior. Real socio-economic scenarios, however, often involve the interaction between multiple co-evolving sectors, with specific options of their own, that call for generalized and more sophisticated mathematical frameworks. In this paper, we explore a general EGT approach to deal with coordination dynamics in which individuals from multiple sectors interact. Starting from a two-sector, consumer/producer scenario, we investigate the effects of including a third co-evolving sector that we call public. We explore the changes in the self-organization process of all sectors, given the feedback that this new sector imparts on the other two.
An Evolutionary Game Theoretic Approach to Multi-Sector Coordination and Self-Organization Fernando P. Santos, Sara Encarnação, Francisco C. Santos, Juval Portugali and Jorge M. Pacheco
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