Fundamental structures of dynamic social networks

Social systems are in a constant state of flux with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding spreading of influence or diseases, formation of friendships, and the productivity of teams. While there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the micro-dynamics of social networks. Here we explore the dynamic social network of a densely-connected population of approximately 1000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geo-location, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-minute time slices we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores are preceded by coordination behavior in the communication networks, and demonstrating that social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39 pages, 34 figure

Characteristics of resources and the provision of biodiversity and ecosystem services in Germany: the cases of fruit tree meadows and wolf protection

Work on common pool resources has paid scant attention to the role of properties of natural resources for the way their provision is governed. This paper scrutinizes determinants of institutions that regulate the provision of biodiversity and ecosystem services. Two cases of maintaining ecosystem services are compared (protection of wolves and management of scattered fruit tree meadows). Distinct characteristics of resources (mobility) and differences in the overarching European regulatory framework explain their different institutional embeddedness. Cost-effectiveness considerations seem to be paramount in the design of institutions. In the case of wolf protection, the state uses its power to modify property rights in order to increase acceptance of wolf management. This is essential for political reasons as well as to prevent EU sanctions. On the other hand, scattered fruit tree maintenance is subject to voluntary, long-term agreements, justified by medium-term irreversibility and asset specific investments.Institutions, Governance, Wolf Management, Scattered fruit trees, Resource /Energy Economics and Policy,

USMC VERTICAL TAKEOFF AND LANDING AIRCRAFT: HUMAN–MACHINE TEAMING FOR CONTROLLING UNMANNED AERIAL SYSTEMS

The United States Marine Corps (USMC) is investing in aviation technologies through its Vertical Takeoff and Landing (VTOL) aircraft program that will enhance mission superiority and warfare dominance against both conventional and asymmetric threats. One of the USMC program initiatives is to launch unmanned aerial systems (UAS) from future human-piloted VTOL aircraft for collaborative hybrid (manned and unmanned) missions. This hybrid VTOL-UAS capability will support USMC intelligence, surveillance, and reconnaissance (ISR), electronic warfare (EW), communications relay, and kinetic strike air to ground missions. This capstone project studied the complex human-machine interactions involved in the future hybrid VTOL-UAS capability through model-based systems engineering analysis, coactive design interdependence analysis, and modeling and simulation experimentation. The capstone focused on a strike coordination and reconnaissance (SCAR) mission involving a manned VTOL platform, a VTOL-launched UAS, and a ground control station (GCS). The project produced system requirements, a system architecture, a conceptual design, and insights into the human-machine teaming aspects of this future VTOL capability.Major, United States ArmyMajor, United States ArmyMajor, United States ArmyMajor, United States ArmyMajor, United States ArmyApproved for public release. Distribution is unlimited

Patterns, Entropy, and Predictability of Human Mobility and Life

Cellular phones are now offering an ubiquitous means for scientists to observe life: how people act, move and respond to external influences. They can be utilized as measurement devices of individual persons and for groups of people of the social context and the related interactions. The picture of human life that emerges shows complexity, which is manifested in such data in properties of the spatiotemporal tracks of individuals. We extract from smartphone-based data for a set of persons important locations such as “home”, “work” and so forth over fixed length time-slots covering the days in the data-set (see also [1], [2]). This set of typical places is heavy-tailed, a power-law distribution with an exponent close to −1.7. To analyze the regularities and stochastic features present, the days are classified for each person into regular, personal patterns. To this are superimposed fluctuations for each day. This randomness is measured by “life” entropy, computed both before and after finding the clustering so as to subtract the contribution of a number of patterns. The main issue that we then address is how predictable individuals are in their mobility. The patterns and entropy are reflected in the predictability of the mobility of the life both individually and on average. We explore the simple approaches to guess the location from the typical behavior, and of exploiting the transition probabilities with time from location or activity A to B. The patterns allow an enhanced predictability, at least up to a few hours into the future from the current location. Such fixed habits are most clearly visible in the working-day length.Peer reviewe

The Relational Reconnection Function of Social Network Sites

Relational reconnection is a prominent yet under-explored function of social network sites (SNS) that encompasses both the activation and subsequent maintenance of dormant social ties. The present investigation used two data collections (Study 1, six university samples; Study 2, national United States sample) to explore the characteristics of friends who reconnect using SNS, and attempt to predict whether reconnected relationships persisted beyond the initial reconnection. Results indicated that relational reconnection is extremely common, especially among same-sex friends and individuals who identify as heavy SNS users. Predicted outcome value emerged as the best predictor of persistence beyond initial reconnection, in addition to engaging in modality expansion, being female, and reactivating a relationship with greater perceived development pre-loss-of-contact

A diagnostic procedure for applying the social-ecological systems framework in diverse cases

The framework for analyzing sustainability of social-ecological systems (SES) framework of Elinor Ostrom is a multitier collection of concepts and variables that have proven to be relevant for understanding outcomes in diverse SES. The first tier of this framework includes the concepts resource system (RS) and resource units (RU), which are then further characterized through lower tier variables such as clarity of system boundaries and mobility. The long-term goal of framework development is to derive conclusions about which combinations of variables explain outcomes across diverse types of SES. This will only be possible if the concepts and variables of the framework can be made operational unambiguously for the different types of SES, which, however, remains a challenge. Reasons for this are that case studies examine other types of RS than those for which the framework has been developed or consider RS for which different actors obtain different kinds of RU. We explore these difficulties and relate them to antecedent work on common-pool resources and public goods. We propose a diagnostic procedure which resolves some of these difficulties by establishing a sequence of questions that facilitate the step-wise and unambiguous application of the SES framework to a given case. The questions relate to the actors benefiting from the SES, the collective goods involved in the generation of those benefits, and the action situations in which the collective goods are provided and appropriated. We illustrate the diagnostic procedure for four case studies in the context of irrigated agriculture in New Mexico, common property meadows in the Swiss Alps, recreational fishery in Germany, and energy regions in Austria. We conclude that the current SES framework has limitations when applied to complex, multiuse SES, because it does not sufficiently capture the actor interdependencies introduced through RS and RU characteristics and dynamics

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars