A Cognitive Process Model of Trust Change

This paper describes a new process theory model of how trust in a technology changes over time. It proposes that trust change occurs after people pay attention to an event, make sense of it, and pass a threshold for changing their trust level. We call these the cognitive gears of trust. We extend the model with two variance theory factors—perceived technology risk and loyalty to the technology vendor— which should also affect how trust changes over time. Using hierarchical linear modeling we analyze data from 1799 respondents who report their trust after seeing eight successive news briefs about the technology. We find the effects on trust change of three cognitive processes—attention, sensemaking, and threshold as well as two factors—tech risk and loyalty

Stochastic parareal: an application of probabilistic methods to time-parallelisation

Parareal is a well-studied algorithm for numerically integrating systems of time-dependent differential equations by parallelising the temporal domain. Given approximate initial values at each temporal sub-interval, the algorithm locates a solution in a fixed number of iterations using a predictor-corrector, stopping once a tolerance is met. This iterative process combines solutions located by inexpensive (coarse resolution) and expensive (fine resolution) numerical integrators. In this paper, we introduce a stochastic parareal algorithm with the aim of accelerating the convergence of the deterministic parareal algorithm. Instead of providing the predictor-corrector with a deterministically located set of initial values, the stochastic algorithm samples initial values from dynamically varying probability distributions in each temporal sub-interval. All samples are then propagated by the numerical method in parallel. The initial values yielding the most continuous (smoothest) trajectory across consecutive sub-intervals are chosen as the new, more accurate, set of initial values. These values are fed into the predictor-corrector, converging in fewer iterations than the deterministic algorithm with a given probability. The performance of the stochastic algorithm, implemented using various probability distributions, is illustrated on systems of ordinary differential equations. When the number of sampled initial values is large enough, we show that stochastic parareal converges almost certainly in fewer iterations than the deterministic algorithm while maintaining solution accuracy. Additionally, it is shown that the expected value of the convergence rate decreases with increasing numbers of samples

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

Analytical reasoning task reveals limits of social learning in networks

Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias,' which limits their social learning to the output, rather than the process, of their peers' reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning

High resolution dynamical mapping of social interactions with active RFID

In this paper we present an experimental framework to gather data on face-to-face social interactions between individuals, with a high spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess contacts with one another by exchanging low-power radio packets. When individuals wear the beacons as a badge, a persistent radio contact between the RFID devices can be used as a proxy for a social interaction between individuals. We present the results of a pilot study recently performed during a conference, and a subsequent preliminary data analysis, that provides an assessment of our method and highlights its versatility and applicability in many areas concerned with human dynamics

Antigen-Presenting Cells in Essential Fatty Acid—Deficient Murine Epidermis: Keratinocytes Bearing Class II (Ia) Antigens May Potentiate the Accessory Cell Function of Langerhans Cells

Essential fatty acid deficiency (EFAD) is a useful model for studying the role of (n-6) fatty acid metabolism in normal physiology. Because cutaneous manifestations are among the earliest signs of EFAD and because abnormalities in the distribution and function of tissue macrophages have been documented in EFAD rodents, we studied the distribution and function of Class II MHC (Ia) antigen-bearing cells in EFAD CS7B1/6 mouse epidermis. Immunofluorescence studies revealed 1.9–9.6 (mean ± SEM = 5.2 ± 2.6) times more class II MHC (Ia) antigen-bearing epidermal cells in suspensions prepared from EFAD as compared to normal skin. Analysis of epidermal sheets demonstrated similar numbers of dendritic Ia+ and NLDC145+ cells in EFAD and normal epidermis, however. This discrepancy occurred because some keratinocytes in EFAD epidermal sheets expressed class II MHC (Ia) antigens, whereas keratinocytes in normal mouse epidermis did not. Two-color flow cytometry confirmed that all Ia+ cells in normal epidermis are Langerhans (Ia+ NLDC145+) cells, whereas Ia+ cells in EFAD epidermis are comprised of Langerhans cells and a subpopulation of keratinocytes (Ia+ NLDC145-. Similar levels of Ia antigens were expressed on EFAD and normal Langerhans cells. EFAD and normal epidermal cells were also compared in several in vitro assays of accessory cell function. Epidermal cells prepared from EFAD C57B1/6 mice present the protein antigen DNP-Ova to primed helper T cells more effectively than epidermal cells prepared from normal animals. EFAD epidermal cells are also more potent stimulators of T cells in primary and secondary allogeneic mixed lymphocyte-epidermal cell reactions than normal epidermal cells. The functional differences between EFAD and normal epidermal cells do not appear to result from increased cytokine release or decreased prostaglandin production by EFAD epidermal cells. In view of these findings and the observation that the antigen-presenting cell activity of EFAD epidermal cells correlates with the number of Ia+ keratinocytes in epidermal cell preparations, Ia+ keratinocytes (in the presenceof Langerhans cells) may potentiate cutaneous immune responses in vitro and perhaps in vivo as well. these results also suggest that (n-6) fatty acids or metabolites of (n-6) fatty acids are involved in regulating the expression of class II MHC (Ia) antigens by keratinocytes in vivo

Mobile Communication Signatures of Unemployment

The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution < 2.7km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future