Alex Pentland – författare

How understanding the signaling within social networks can change the way we make decisions, work with others, and manage organizations.

How can you know when someone is bluffing? Paying attention? Genuinely interested? The answer, writes Alex Pentland in Honest Signals, is that subtle patterns in how we interact with other people reveal our attitudes toward them. These unconscious social signals are not just a back channel or a complement to our conscious language; they form a separate communication network. Biologically based “honest signaling,” evolved from ancient primate signaling mechanisms, offers an unmatched window into our intentions, goals, and values. If we understand this ancient channel of communication, Pentland claims, we can accurately predict the outcomes of situations ranging from job interviews to first dates.

Pentland, an MIT professor, has used a specially designed digital sensor worn like an ID badge—a “sociometer”—to monitor and analyze the back-and-forth patterns of signaling among groups of people. He and his researchers found that this second channel of communication, revolving not around words but around social relations, profoundly influences major decisions in our lives—even though we are largely unaware of it. Pentland presents the scientific background necessary for understanding this form of communication, applies it to examples of group behavior in real organizations, and shows how by “reading” our social networks we can become more successful at pitching an idea, getting a job, or closing a deal. Using this “network intelligence” theory of social signaling, Pentland describes how we can harness the intelligence of our social network to become better managers, workers, and communicators.

How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data.

Trusted Data describes a data architecture that places humans and their societal values at the center of the discussion. By involving people from all parts of the ecosystem of information, this new approach allows us to realize the benefits of data-driven algorithmic decision making while minimizing the risks and unintended consequences. It proposes a software architecture and legal framework for an Internet of Trusted Data that provides safe, secure access for everyone and protects against bias, unfairness, and other unintended effects. This approach addresses issues of data privacy, security, ownership, and trust by allowing insights to be extracted from data held by different people, companies, or governments without collecting, holding, or revealing the underlying data. The software architecture, called Open Algorithms, or OPAL, sends algorithms to databases rather than copying or sharing data. The data is protected by existing firewalls; only encrypted results are shared. Data never leaves its repository. A higher security architecture, ENIGMA, built on OPAL, is fully encrypted.

ContributorsMichiel Bakker, Yves-Alexandre de Montjoye, Daniel Greenwood, Thomas Hardjoni, Jake Kendall, Cameron Kerry, Bruno Lepri, Alexander Lipton, Takeo Nishikata, Alejandro Noriega-Campero, Nuria Oliver, Alex Pentland, David L. Shrier, Jacopo Staiano, Guy Zyskind

An MIT Connection Science and Engineering Book

After the start of the Syrian Civil War in 2011–12, increasing numbers of civilians sought refuge in neighboring countries. By May 2017, Turkey had received over 3 million refugees — the largest refugee population in the world. Some lived in government-run camps near the Syrian border, but many have moved to cities looking for work and better living conditions. They faced problems of integration, income, welfare, employment, health, education, language, social tension, and discrimination. In order to develop sound policies to solve these interlinked problems, a good understanding of refugee dynamics isnecessary.

The book will serve as a guideline for creating innovative data-centered collaborations between industry, academia, government, and non-profit humanitarian agencies to deal with complex problems in refugee scenarios. It illustrates the possibilities of big data analytics in coping with refugee crises and humanitarian responses, by showcasing innovative approaches drawing on multiple data sources, information visualization, pattern analysis, and statistical analysis.It will also provide researchers and students working with mobility data with an excellent coverage across data science, economics, sociology, urban computing, education, migration studies, and more.