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

  • Data-Pop Alliance

    Alex 'Sandy' Pentland, Harvard Humanitarian Initiative and Overseas Development Institute

    Data-Pop Alliance is a joint initiative on Big Data and development with a goal of helping to craft and leverage the new ecosystem of Big Data—new personal data, new tools, new actors—to improve decisions and empower people in a way that avoids the pitfalls of a new digital divide, de-humanization, and de-democratization. Data-Pop Alliance aims to serve as a designer, broker, and implementer of ideas and activities, bringing together institutions and individuals around common principles and objectives through collaborative research, training and capacity building, technical assistance, convening, knowledge curation, and advocacy. Our thematic areas of focus include official statistics, socio-economic and demographic methods, conflict and crime, climate change and environment, literacy, and ethics.

  • Inducing Peer Pressure to Promote Cooperation

    Alex 'Sandy' Pentland, erez, Dhaval Adjodah and David Shrier

    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.

  • Mobile Territorial Lab

    Alex 'Sandy' Pentland, Bruno Lepri and David Shrier

    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.

  • On the Reidentifiability of Credit Card Metadata

    Yves-Alexandre de Montjoye, Laura Radaelli, Vivek Kumar Singh, Alex "Sandy" Pentland

    Even when real names and other personal information are stripped from metadata datasets, it is often possible to use just a few pieces of the information to identify a specific person. Here, we study three months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.

  • 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.

  • 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.


    which one is this?? TGREENE-TEST-BRIEF-ABS

  • 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.