Human Dynamics
How social networks can influence our lives in business, health, and governance, as well as technology adoption and diffusion.
Today people leave digital breadcrumbs wherever they go, through smart phones, RFIDs, and more. The Human Dynamics group uses Reality Mining to ask how we can use this data to better organize companies, public health, and governance, by better understanding how social networks influence people when they make decisions, transmit information, adopt new technologies, or change behaviors. Our projects have already demonstrated the potential to dramatically improve the competitiveness of companies, and hint at the ability to revolutionize social environments.

Research Projects

  • Belief Dynamics

    Alex 'Sandy' Pentland, Peter Krafft and Ankur Mani

    The current political system in the United States is paralyzed by polarization. On numerous occasions in recent years, we have come to the edge of a fiscal cliff because of our Senate's inability to reach compromise. This polarization in Congress is a reflection of the polarization of the country as a whole. We are interested in understanding situations in which this type of polarization can occur or is likely to occur. In this work, we apply ideas from game theory and social network analysis to address this question.

  • Bilateral Exchanges in Social Networks

    Ankur Mani and Alex 'Sandy' Pentland

    How different are the characteristics of societies that are constrained to local interactions in networks, as compared to societies where all interactions happen in organized markets? Among most species and even in modern human societies, exchange, whether of food, information, or labor, naturally tends to occur locally, as encounters happen between nearby individuals in networks. We study how these local exchanges govern the large scale properties of networked societies--stability, welfare, dynamics, and fairness, and how we can use peer-pressure to improve social welfare. Today we have easy availability of "big data" about social and economic interactions. To use this new resource for the betterment of society, we identify the properties of stable exchanges in networked societies, build tools for computing the structure of stable and fair networked societies, and predict how they may respond to policy changes.

  • Economic Decision-Making in the Wild

    Coco Krumme
    How predictable are people? We use financial transaction data to look at how patterns of human behavior change over time and space.

  • Funf: Open Sensing Framework

    Alex (Sandy) Pentland, Nadav Aharony, Wei Pan, Cody Sumter and Alan Gardner

    The Funf open sensing framework is an Android-based extensible framework for phone-based mobile sensing. The core concept is to provide a reusable set of functionalities enabling collection, uploading, and configuration for a wide range of data types. Funf Journal is an Android application for researchers, self-trackers, and anyone interested in collecting and exploring information related to the mobile device, its environment, and its user's behavior. It is built using the Funf framework and makes use of many of its built-in features.

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  • Inducing Peer Pressure to Promote Cooperation

    Ankur Mani, Iyad Rahwan, and Alex "Sandy" Pentland

    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.

  • Mobile Territorial Lab

    Alex 'Sandy' Pentland and Bruno Lepri

    The Mobile Territorial Lab (MTL) aims at creating a “living” laboratory integrated in the real life of the Trento territory in Italy, open to manifold kinds of experimentations. In particular, the Lab is focused on exploiting the sensing capabilities of mobile phones to track and understand human behaviors (e.g., families spending behaviors, lifestyles, mood and stress patterns, etc.), on designing and testing social strategies aimed at empowering individual and collective lifestyles through attitude and behavior change, and on investigating new paradigms in personal data management and sharing. MTL has been created by Human Dynamics group, Telecom Italia SKIL Lab, Foundation Bruno Kessler and Telefonica I+D.

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  • openPDS: A Privacy-Preserving Personal Data Store

    Alex (Sandy) Pentland, Brian Sweatt, Henrik Sandell, Jeffrey Schmitz, John Clippinger and Yves-Alexandre de Montjoye

    With their built-in sensors, smartphones are at the forefront of personal data collection. However, personal data currently tends to be monopolized and siloed, preventing companies from building innovative data-driven services. While there is substantial work on privacy and fair use of personal data, a pragmatic technical solution has yet to be realized. openPDS is a privacy-preserving implementation of an information repository which allows the user to collect, store, and give access to his data. Via an innovative framework for third-party applications, the system ensures that the sensitive data processing takes place within the user's PDS, as opposed to a third-party server. The framework allows for PDSs to engage in privacy-preserving group computation, which is used as a replacement for centralized aggregation.

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  • Predicting Individual Behavior Using Network Interaction Data

    Alex 'Sandy' Pentland, Dhaval Adjodah, Erez Shmueli, Vivek Singh

    If we are to enact better policy, fight crime, and decrease poverty, we will need better computational models of how society works. In order to make computational social science a useful reality, we will need models and theories of how social influence sprouts at the individual level and how it leads to emergent social behavior. In this project, we take steps toward understanding the motivators and conduits of social influence by analyzing real-life data, and we use our findings to create a high-accuracy prediction model of individuals' future behavior. In addition to explaining and demonstrating the causes of social influence with unprecedented detail using network analysis and machine learning, this project also makes the contribution of investigating the policy ramifications of providing the social and algorithmic capabilities of changing behavior at the individual level.

  • Sensible Organizations

    Alex (Sandy) Pentland, Benjamin Waber and Daniel Olguin Olguin
    Data mining of email has provided important insights into how organizations function and what management practices lead to greater productivity. But important communications are almost always face-to-face, so we are missing the greater part of the picture. Today, however, people carry cell phones and wear RFID badges. These body-worn sensor networks mean that we can potentially know who talks to whom, and even how they talk to each other. Sensible Organizations investigates how these new technologies for sensing human interaction can be used to reinvent organizations and management.
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  • The Privacy Bounds of Human Mobility

    Cesar A. Hidalgo and Yves-Alexandre DeMontjoye

    We used 15 months of data from 1.5 million people to show that four points–approximate places and times–are enough to identify 95 percent of individuals in a mobility database. Our work shows that human behavior puts fundamental natural constraints on the privacy of individuals, and these constraints hold even when the resolution of the dataset is low. These results demonstrate that even coarse datasets provide little anonymity. We further developed a formula to estimate the uniqueness of human mobility traces. These findings have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.

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