Real-time internet control of situated human agents

We present an online platform, called BeeMe, designed to test the current boundaries of Internet collective action and problem solving. BeeMe allows a scalable internet crowd of online users to collectively control the actions of a human surrogate acting in physical space. BeeMe demonstrates how intelligent goal-oriented decision-making can emerge from large crowds in quasi real-time. We analyzed data collected from a global BeeMe live performance that involved thousands of individuals, collectively solving a sci-fi Internet mystery. We study simple heuristic algorithms that read in users' chat messages and output human actionable commands representing majority preferences, and compare their performance to the behavior of a human operator solving the same task. Results show that simple heuristics can achieve near-human performance in interpreting the democratic consensus. When human and machine's output differ, the discrepancy is often due to human bias favoring non-representative views. We discuss our results in light of previous work and the contemporary debate on democratic digital systems

Arguing and negotiating in the presence of social influences

Postprin

An approach for a negotiation model inspired on social networks

Supporting group decision-making in ubiquitous contexts is a complex task that needs to deal with a large amount of factors to be successful. Here we propose an approach for a negotiation model to support the group decisionmaking process specially designed for ubiquitous contexts. We propose a new look into this problematic, considering and defining strategies to deal with important points such as the type of attributes in the multi-criteria problem and agents' reasoning. Our model uses a social networking logic due to the type of communication employed by the agents as well as to the type of relationships they build as the interactions occur. Our approach intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings and is adapted to be used in a ubiquitous context.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124- FEDER-028980 (PTDC/EEISII/1386/2012) and SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio

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

Multi-trait diversity of online groups improves geo-political forecasting accuracy as a function of group size

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 online groups based on a large multi-trait profiling space to explore the causal link between group composition and performance as a function of group size. We asked volunteers to search information 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 interacts with group size so that diversity benefits individual and group performance proportionally to group size. Aggregating opinions of modular crowds composed of small independent groups achieved better results than using non-modular ones. Finally, we show differences existing among groups in terms of disagreement, speed to convergence to consensus forecasts and within-group variability in performance. The present work sheds light on the mechanisms underlying effective collaboration in digital environments