Modularity and composite diversity affect the collective gathering of information online

Many modern interactions happen in a digital space, where automated recommendations and homophily can shape the composition of groups interacting together and the knowledge that groups are able to tap into when operating online. Digital interactions are also characterized by different scales, from small interest groups to large online communities. Here, we manipulate the composition of groups based on a large multi-trait profiling space (including demographic, professional, psychological and relational variables) to explore the causal link between group composition and performance as a function of group size. We asked volunteers to search news online under time pressure and measured individual and group performance in forecasting real geo-political events. Our manipulation affected the correlation of forecasts made by people after online searches. Group composition interacted with group size so that composite diversity benefited individual and group performance proportionally to group size. Aggregating opinions of modular crowds composed of small independent groups achieved better forecasts than aggregating a similar number of forecasts from non-modular ones. Finally, we show differences existing among groups in terms of disagreement, speed of convergence to consensus forecasts and within-group variability in performance. The present work sheds light on the mechanisms underlying effective online information gathering in digital environments

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

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

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

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

Machine Impostors Can Avoid Human Detection and Interrupt the Formation of Stable Conventions by Imitating Past Interactions: A Minimal Turing Test

Supporting Information is available online at: https://onlinelibrary.wiley.com/doi/10.1111/cogs.13288#support-information-section .Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans’ ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants’ own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily.Open access funding enabled and organized by Projekt DEAL