1,147 research outputs found

    Quantifying human mobility resilience to extreme events using geo-located social media data

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    Real-time internet control of situated human agents

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    A time-critical crowdsourced computational search for the origins of COVID-19

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    The past, present and future of digital contact tracing

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    Bots influence opinion dynamics without direct human-bot interaction: The mediating role of recommender systems

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    Bots' ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may indirectly affect people's views via a platform's content recommendation system's mediating role, thus influencing opinions without direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot-representing 1% of nodes in a network-on the opinion of rational Bayesian agents when a simple recommendation system mediates the agents' content consumption. We compare this experimental condition with an identical baseline condition where such a bot is absent. Across conditions, we use the same random seed and a psychologically realistic Bayesian opinion update rule so that conditions remain identical except for the bot presence. Results show that, even with limited direct interactions, the mere presence of the bot is sufficient to shift the average population's opinion. Virtually all nodes -not only nodes directly interacting with the bot- shifted towards more extreme opinions. Furthermore, the mere bot's presence significantly affected the internal representation of the recommender system. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms recommendation engines internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.The study was funded by the Max Planck Institute for Human Development. D.B. was partly funded by a research grant from the Institute of Psychology at the Chinese Academy of Sciences

    Social mobilization and polarization can create volatility in COVID‑19 pandemic control

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    Efficiency of football teams from an organisation management perspective

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    The aim of this paper is to assess the efficiency of football. Our contributions to the literature are the use of the resource-based theory and the strategy-structure-performance perspective as the study''s framework, efficiency has been considered as the result of the qualities of the productive resources in football teams and efficiency is calculated in every area in the field of play. The results show that the entire team''s efficiency is positively related to sporting success and the efficiency of attacking and ball recovery areas is directly related to the sport result and the efficiency of the team as a whole

    Time-critical decentralised situational awareness in emergencies: An adversarial biosecurity scenario

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    Abstract Crises in a global setting of interdependencies call for time-critical coordinated responses. However, it is often the case that the mechanisms responsible for these actions do not agree across all their hierarchies. This can be roughly attributed to personal estimations of the situation and to social influence. An ensuing lack of consensus against crises can be dire and echo across entire populations. One such instance is the case of biosecurity threats. A particularly interesting class of threats lie within urban environments, which tend to fall within the scope of bad actors. With this work we aim to computationally contribute to the understanding of the dynamics of perceived danger formation among agents responsible for responding to ongoing biological attacks in urban settings. We assume this perception is a function of a personal estimation of local information about the danger and of social influence stemming from the agents in question framed in an agent-based model. The simulations point towards a high dependence of perceived dangers on the personal estimations of the agents. The conditions under which the perceived dangers deviate from the real ones are explored over a range of assumptions on personal measurements and several dispositions towards the influencing environment. The insight provided by these results at the individual and collective level set the tone for further investigation on such behavioural phenomena, providing a flexible computational framework addressing generic threats (true dangers) in a time-critical context
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