Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
293
result(s) for
"Pentland, Alex"
Sort by:
Inferring friendship network structure by using mobile phone data
Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.
Journal Article
Unique in the shopping mall: On the reidentifiability of credit card metadata
Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 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.
Journal Article
Sociometric badges: Using sensor technology to capture new forms of collaboration
This article introduces sociometrie badges as a research tool that captures with great accuracy fine-scale speech patterns and body movements among a group of individuals at a scale that heretofore has been impossible in groups and teams studies. Such a tool offers great potential for studying the changing ecology of team structures and new modes of collaboration. Team boundaries are blurring as members disperse across multiple cultures, convene through various media, and operate in new configurations. As the how and why of collaboration evolves, an opportunity emerges to reassess the methods used to capture these interactions and to identify new tools that might help us create synergies across existing approaches to teams research. We offer sociometrie badges as a complement to existing data collection methods—one that is well-positioned to capture real-time collaboration in new forms of teams. Used as one component in a multi-met or cross-sectional survey might miss, contributing to a more accurate understanding of group dynamics in new teams. We also revisit traditional teams research to suggest how we might use these badges to tackle long-standing challenges. We conclude with three case studies demonstrating potential applications of these sociometrie badges.
Journal Article
Machine behaviour
Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.
Journal Article
Reality mining: sensing complex social systems
We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms. [PUBLICATION ABSTRACT]
Journal Article
Effect of COVID-19 response policies on walking behavior in US cities
The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy.
Journal Article
Developing Public Policy To Advance The Use Of Big Data In Health Care
The vast amount of health data generated and stored around the world each day offers significant opportunities for advances such as the real-time tracking of diseases, predicting disease outbreaks, and developing health care that is truly personalized. However, capturing, analyzing, and sharing health data is difficult, expensive, and controversial. This article explores four central questions that policy makers should consider when developing public policy for the use of \"big data\" in health care. We discuss what aspects of big data are most relevant for health care and present a taxonomy of data types and levels of access. We suggest that successful policies require clear objectives and provide examples, discuss barriers to achieving policy objectives based on a recent policy experiment in the United Kingdom, and propose levers that policy makers should consider using to advance data sharing. We argue that the case for data sharing can be won only by providing real- life examples of the ways in which it can improve health care.
Journal Article