Human Dynamics
Exploring how social networks can influence our lives in business, health, governance, and 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

  • Ethos

    Amir Lazarovich, Guy Zyskind, Oz Nathan, Alex 'Sandy' Pentland, Andy Lippman

    Ethos is a decentralized, Bitcoin-like network for storing and sharing valuable information. We provide transparency, control, and ownership over personal data and its distribution. Validation and maintenance is distributed throughout the data community and automatically maintained without needing a safe deposit box or a commercial site. What Bitcoin has done for currency and BitTorrent for media, Ethos does for personal data. Nodes in the network are incentivized by collecting transaction fees, coinbase transactions ("finding blocks"), and proof-of-storage fees to sustain the distribution of personal data. Fees are paid with the underlying crypto currency represented by the network, also known as "PrivacyCoin." The role of nodes besides the usual proof-of-work, which protects against "double spending," is to maintain shredded pieces of information and present them to the network on-demand.

  • 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. This leads to the ‘tragedy of the commons,’ with rational action ultimately making 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.

  • Measuring Political Polarization

    Alex 'Sandy' Pentland, Peter Krafft, Ali Nahm and Harvard

    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. Is this polarization in Congress a reflection of the polarization of the country as a whole? We are using PAC funding data and precinct-level voting data to help answer this question.

  • 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 MTL 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); 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. This project is a collaboration with Telecom Italia SKIL Lab, Foundation Bruno Kessler, and Telefonica I+D.

  • openPDS/SaferAnswers: Protecting the Privacy of Metadata

    Alex 'Sandy' Pentland, Brian Sweatt, Erez Shmueli, and Yves-Alexandre de Montjoye

    In a world where sensors, data storage, and processing power are too cheap to meter, how do you ensure that users can realize the full value of their data while protecting their privacy? openPDS is a field-tested, personal metadata management framework which allows individuals to collect, store, and give fine-grained access to their metadata to third parties. SafeAnswers is a new and practical way of protecting the privacy of metadata at an individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals' metadata. Together, openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata.

  • Predicting Spending Behavior Using Social Behavior

    Alex 'Sandy' Pentland, Bruno Lepri, Vivek K. Singh and Laura Freeman

    Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone-based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, and call and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone-based social interaction patterns can provide more predictive power on spending behavior than personality-based features. Interestingly, we find that more social couples also tend to overspend. Obtaining such insights about couple-level spending behavior via novel social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.

  • 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.
  • 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.

  • Using Big Data for Effective Marketing

    Pål Sundsøy, Johannes Bjelland, Asif Iqbal, Sandy Pentland, and Yves-Alexandre de Montjoye

    Using big data for effective marketing is hard. As a consequence, 80% of marketing decisions are still based on gut feeling. This work shows how a principled approach to big data can improve customer segmentation. We run a large-scale text-based experiment in an Asian country, comparing our data-driven approach to the company marketer's best practice. Our approach outperforms marketing's 13 times in click-through rate for a data plan. It also shows significantly better retention rate.

  • What Can Your Phone Metadata Tell about You?

    Yves-Alexandre de Montjoye, Jordi Quoidbach, Florent Robic, and Sandy Pentland

    How much can others learn about your personality just by looking at the way you use your phone? We provide the first evidence that personality types (for example, neurotism, extraversion, openness) can be predicted from standard mobile phone metadata. We have developed a set of novel psychology-informed indicators that can be computed from any set of mobile phone metadata. These fall into five categories, and range from the time it took you to answer a text, the entropy of your contacts, your daily distance traveled, or the percentage of text conversations you started. Using these 36 indicators, we were able to predict people's personalities correctely up to 63%, 1.7 times better than random using only metadata.