Society's Nervous System: Building Effective Government, Energy, and Public Health Systems

Drawing on a unique, multi-year collaboration with the heads of major IT, wireless, hardware, health, and financial firms, as well as the heads of American, EU, and other regulatory organizations, and a variety of NGOs [1,2],I describe the potential for pervasive and mobile sensing and computing over the next decade, and the challenges that will have to be faced in order to realize this potential.United States. Army Research Laboratory (Cooperative Agreement Number W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award Number FA9550-10-1-0122

Eigenbehaviors: Identifying Structure in Routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research

Understanding Effects of Feedback on Group Collaboration

http://www.aaai.org/Press/Reports/Symposia/Spring/ss-09-04.phpSmall group collaboration is vital for any type of organization to function successfully. Feedback on group dynamics has been proven to help with the performance of collaboration. We use sociometric sensors to detect group dynamics and use the data to give real-time feedback to people. We are especially interested in the effect of feedback on distributed collaboration. The goal is to bridge the gap in distributed groups by detecting and communicating social signals. We conducted an initial experiment to test the effects of feedback on brainstorming and problem solving tasks. The results show that real-time feedback changes speaking time and interactivity level of groups. Also in groups with one or more dominant people, the feedback effectively reduced the dynamical difference between co-located and distributed collaboration as well as the behavioral difference between dominant and non-dominant people. Interestingly, feedback had a different effect depending on the type of meeting and types of personality. We intend to continue this direction of research by personalizing the visualization by automatically detecting type of meeting and personality. Moreover we propose to demonstrate the correlation of group dynamics with higher level characteristics such as performance, interest and creativity

Inducing Peer Pressure to Promote Cooperation

Cooperation in a large society of self-interested individuals is notoriously difficult to achieve when the externality of one individual's action is spread thin and wide on the whole society. This leads to the ‘tragedy of the commons’ in which rational action will ultimately make everyone worse-off. Traditional policies to promote cooperation involve Pigouvian taxation or subsidies that make individuals internalize the externality they incur. We introduce a new approach to achieving global cooperation by localizing externalities to one's peers in a social network, thus leveraging the power of peer-pressure to regulate behavior. The mechanism relies on a joint model of externalities and peer-pressure. Surprisingly, this mechanism can require a lower budget to operate than the Pigouvian mechanism, even when accounting for the social cost of peer pressure. Even when the available budget is very low, the social mechanisms achieve greater improvement in the outcome.Martin Family Fellowship for SustainabilityU.S. Army Research Laboratory (Cooperative Agreement W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award FA9550-10-1-0122

An interpretable approach for social network formation among heterogeneous agents

Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an “endowment vector” that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology.King Abdulaziz City of Science and Technology (Saudia Arabia)MIT Trust Data Consortiu

Decoding Social Influence and the Wisdom of the Crowd in Financial Trading Network

In this paper, we study roles of social mechanisms in a financial system. Our data come from a novel on-line foreign exchange trading brokerage for individual investors, which also allows investors to form social network ties between each other and copy others' trades. From the dataset, we analyze the dynamics of this connected social influence systems in decision making processes. We discover that generally social trades outperform individual trades, but the social reputation of the top traders is not completely determined by their performance due to social feedback even when users are betting their own money. We also find that social influence plays a significant role in users' trades, especially decisions during periods of uncertainty. We report evidences suggesting that the dynamics of social influence contribute to market overreaction

A quantitative analysis of the collective creativity in playing 20-questions games

Creativity is an important ingredient in problem solving, and problem solving is an important activity for both individuals and societies. This paper discusses our novel approach of discovering the structure of problem-solving creativity with statistical methods, and mapping the interaction patterns of group processes to their performances through the discovered creativity structure. Our discussion is based on a lab study data set using the meeting mediator system through which we collected objective quantitative data. We hope our findings and quantitative approach could be applied to many other real-world problem-solving processes and to helping people

Making big data work: smart, sustainable, and safe cities

The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the ‘Telecom Italia Big Data Challenge 2014’ and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness

Are You Your Friends’ Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change

Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action

Sensing, Understanding, and Shaping Social Behavior

The ability to understand social systems through the aid of computational tools is central to the emerging field of computational social systems. Such understanding can answer epistemological questions on human behavior in a data-driven manner, and provide prescriptive guidelines for persuading humans to undertake certain actions in real-world social scenarios. The growing number of works in this subfield has the potential to impact multiple walks of human life including health, wellness, productivity, mobility, transportation, education, shopping, and sustenance. The contribution of this paper is twofold. First, we provide a functional survey of recent advances in sensing, understanding, and shaping human behavior, focusing on real-world behavior of users as measured using passive sensors. Second, we present a case study on how trust, which is an important building block of computational social systems, can be quantified, sensed, and applied to shape human behavior. Our findings suggest that:1) trust can be operationalized and predicted via computational methods (passive sensing and network analysis) and 2) trust has a significant impact on social persuasion; in fact, it was found to be significantly more effective than the closeness of ties in determining the amount of behavior change.U.S. Army Research Laboratory (Cooperative Agreement W911NF-09-2-0053