https://hdl.handle.net/1721.1/130239 2026-04-03T21:24:01Z https://hdl.handle.net/1721.1/157133 Comment to Federal Trade Commission on Commercial Surveillance Berke, Alex; Calacci, Dana; Mahari, Robert In this comment, we urge the FTC to consider rulemaking that empowers consumers to pool and share their data responsibly to help researchers uncover harms such as anti-competitive practices, privacy violations, and algorithmic bias. A large aggregate dataset combined with audit tools would enable the identification of systemic issues otherwise hidden by current corporate practices. 2022-12-01T00:00:00Z https://hdl.handle.net/1721.1/154171 Comment to U.S Copyright Office on Data Provenance and Copyright Mahari, Robert; Shayne, Longpre; Donewald, Lisette; Polozov, Alan; Pentland, Alex 'Sandy'; Lipsitz, Ari Scholars have paid much attention to the copying of raw data to train and develop machine learning models. Many have argued that such use of raw data, derived either directly from the internet or from a dataset, is protected under fair use such that the owners of the original work may not be successful in a claim for copyright infringement. We refer to such compilations of data derived from another source, and repurposed for machine learning, as unsupervised datasets. Less attention, however, has been paid to supervised datasets, which we define as datasets containing data created for the sole purpose of training machine learning models (mainly for finetuning and alignment). Supervised datasets may likely contain copyrightable contributions from the dataset creators in the form of annotations. To the extent that dataset creators likely have copyright interests in their supervised datasets, model developers must either rely on fair use or a license in order to avoid infringing the work of dataset creators. However, we argue that the unauthorized use of supervised datasets is unlikely to be protected by fair use. Whereas the use of unsupervised data for training machine learning is distinct from the original purpose of the unsupervised data, the unauthorized use of supervised datasets for training machine learning is identical to its original purpose. Fair use would therefore likely not apply to the annotations, labels, and curated comments in supervised datasets. For this reason, having a valid license to a supervised dataset is perhaps particularly critical. Unfortunately, our recent research has found that the licenses attached to publicly available supervised datasets are often imprecise, inaccurate, or missing altogether. Model developers may be exposing themselves to unknown amounts of liability. We argue that this is a problem that needs to be addressed and propose a tool that might serve as a launching point for ensuring license transparency. 2023-11-01T00:00:00Z https://hdl.handle.net/1721.1/140225 What Managers Need to Know About Data Exchanges Parra-Moyano, Jose; Schmedders, Karl; Pentland, Alex 2020-06-09T00:00:00Z https://hdl.handle.net/1721.1/139619 Which factors affect the performance of technology business incubators in China? An entrepreneurial ecosystem perspective Yuan, Xiangfei; Hao, Haijing; Guan, Chenghua; Pentland, Alex To examine which factors affect the performance of technology business incubators in China, the present study proposes an entrepreneurial ecosystem framework with four key areas, i.e., people, technology, capital, and infrastructure. We then assess this framework using a three-year panel data set of 857 national-level technology business incubators in 33 major cities from 28 provinces in China, from 2015 to 2017. We utilize factor analysis to downsize dozens of characteristics of these technology business incubators into seven factors related to the four proposed areas. Panel regression model results show that four of the seven factors related to three areas of the entrepreneurial ecosystem, namely people, technology, and capital areas, have statistically significant associations with an incubator’s performance when applied to the overall national data set. Further, seven factors related to all four areas have various statistically significant associations with an incubator’s performance in five major regional data set. In particular, a technology related factor has a consistently statistically significant association with the performance of the incubator in both national model and the five regional models, as we expected. 2022-01-11T00:00:00Z https://hdl.handle.net/1721.1/138890 Tourism Event Analytics with Mobile Phone Data Leng, Yan; Noriega, Alejandro; Pentland, Alex Tourism has been an increasingly significant contributor to the economy, society, and environment. Policy-making and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at a low granularity. Anonymous call detail record (CDR) is a novel source of data with enormous potential in areas of high societal value: epidemics, poverty, and urban development. This study demonstrates the added value of CDR in event tourism, especially for the analysis and evaluation of marketing strategies, event operations, and the externalities at the local and national levels. To achieve this aim, we formalize 14 indicators in high spatial and temporal resolutions to measure both the positive and the negative impacts of the touristic events. We exemplify the use of these indicators in a tourism country, Andorra, on 22 high-impact events including sports competitions, cultural performances, and music festivals. We analyze these touristic events using the large-scale CDR data across 2 years. Our approach serves as a prescriptive and a diagnostic tool with mobile phone data and opens up future directions for tourism analytics. 2021-08-01T00:00:00Z https://hdl.handle.net/1721.1/138884 How data governance technologies can democratize data sharing for community well-being – Corrigendum Wu, Dan; Verhulst, Stefaan G.; Pentland, Alex; Avila, Thiago; Finch, Kelsey; Gupta, Abhishek 2021-09-24T00:00:00Z https://hdl.handle.net/1721.1/138876 How data governance technologies can democratize data sharing for community well-being Wu, Dan; Verhulst, Stefaan G.; Pentland, Alex; Avila, Thiago; Finch, Kelsey; Gupta, Abhishek Data sharing efforts to allow underserved groups and organizations to overcome the concentration of power in our data landscape. A few special organizations, due to their data monopolies and resources, are able to decide which problems to solve and how to solve them. But even though data sharing creates a counterbalancing democratizing force, it must nevertheless be approached cautiously. Underserved organizations and groups must navigate difficult barriers related to technological complexity and legal risk. To examine what those common barriers are, one type of data sharing effort—data trusts—are examined, specifically the reports commenting on that effort. To address these practical issues, data governance technologies have a large role to play in democratizing data trusts safely and in a trustworthy manner. Yet technology is far from a silver bullet. It is dangerous to rely upon it. But technology that is no-code, flexible, and secure can help more responsibly operate data trusts. This type of technology helps innovators put relationships at the center of their efforts. 2021-07-13T00:00:00Z https://hdl.handle.net/1721.1/138869 A partial knowledge of friends of friends speeds social search Elsisy, Amr; Szymanski, Boleslaw K.; Plum, Jasmine A.; Qi, Miao; Pentland, Alex Milgram empirically showed that people knowing only connections to their friends could locate any person in the U.S. in a few steps. Later research showed that social network topology enables a node aware of its full routing to find an arbitrary target in even fewer steps. Yet, the success of people in forwarding efficiently knowing only personal connections is still not fully explained. To study this problem, we emulate it on a real location-based social network, Gowalla. It provides explicit information about friends and temporal locations of each user useful for studies of human mobility. Here, we use it to conduct a massive computational experiment to establish new necessary and sufficient conditions for achieving social search efficiency. The results demonstrate that only the distribution of friendship edges and the partial knowledge of friends of friends are essential and sufficient for the efficiency of social search. Surprisingly, the efficiency of the search using the original distribution of friendship edges is not dependent on how the nodes are distributed into space. Moreover, the effect of using a limited knowledge that each node possesses about friends of its friends is strongly nonlinear. We show that gains of such use grow statistically significantly only when this knowledge is limited to a small fraction of friends of friends. 2021-08-19T00:00:00Z https://hdl.handle.net/1721.1/138862 Understanding collective regularity in human mobility as a familiar stranger phenomenon Leng, Yan; Santistevan, Dominiquo; Pentland, Alex Beyond the physical structures that contain daily routines, urban city dwellers repeatedly encounter strangers that similarly shape their environments. Familiar strangers are neither formal acquaintances nor completely anonymous faces in daily urban life. Due to data limitations, there is a lack of research focused on uncovering the structure of the “Familiar Stranger” phenomenon at a large scale while simultaneously investigating the social relationships between such strangers. Using countrywide mobile phone records from Andorra, we empirically show the existence of such a phenomenon as well as details concerning these strangers’ relative social relations. To understand the social and spatial components of familiar strangers more deeply, we study the temporal regularity and spatial structure of collective urban mobility to shed light on the mechanisms that guide these interactions. Furthermore, we explore the relationship between social distances and the number of encounters to show that more significant physical encounters correspond to a shorter social distance. Understanding these social and physical networks has essential implications for epidemics spreading, urban planning, and information diffusion. 2021-09-30T00:00:00Z https://hdl.handle.net/1721.1/138373 User Profiling Based on Nonlinguistic Audio Data Shen, Jiaxing; Cao, Jiannong; Lederman, Oren; Tang, Shaojie; Pentland, Alex User profiling refers to inferring people’s attributes of interest (AoIs) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively. 2021-09-07T00:00:00Z https://hdl.handle.net/1721.1/138362 Social Influence Leads to the Formation of Diverse Local Trends Epstein, Ziv; Groh, Matthew; Dubey, Abhimanyu; Pentland, Alex How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase both the inequality and unpredictability of its success, but has only been shown in the context of music listening. To further examine the effect of social influence on media popularity, we extend this research to the context of algorithmically-generated images by re-adapting Salganik et al's Music Lab experiment. On a digital platform where participants discover and curate AI-generated hybrid animals, we randomly assign both the knowledge of other participants' behavior and the visual presentation of the information. We successfully replicate the Music Lab's findings in the context of images, whereby social influence leads to an unpredictable winner-take-all market. However, we also find that social influence can lead to the emergence of local cultural trends that diverge from the status quo and are ultimately more diverse. We discuss the implications of these results for platform designers and animal conservation efforts. 2021-10-18T00:00:00Z https://hdl.handle.net/1721.1/131217 Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial Noriega, Alejandro; Meizner, Daniela; Camacho, Dalia; Enciso, Jennifer; Quiroz-Mercado, Hugo; Morales-Canton, Virgilio; Almaatouq, Abdullah; Pentland, Alex 2021-08-26T00:00:00Z https://hdl.handle.net/1721.1/131213 Quantization Games on Social Networks and Language Evolution Mani, Ankur; Varshney, Lav R.; Pentland, Alex Motivated by collaboration in human and human-robot groups, we consider designing lossy source codes for agents in networks that are in different statistical environments but also must communicate with one another along network connections. This yields a strategic network quantizer design problem where agents must balance fidelity in representing their local source distributions against their ability to successfully communicate with other connected agents. Using network game theory, we show existence of Bayes Nash equilibrium quantizers. For any agent, under Bayes Nash equilibrium, we prove that the word representing a given partition region is the conditional expectation of the mixture of local and social source probability distributions within the region. Since having knowledge of the original source of information in the network may not be realistic, we further prove that under certain conditions, the agents need not know the source origin and yet still settle on a Bayes Nash equilibrium using only the observed sources. Further, we prove the network may converge to equilibrium through a distributed version of the Lloyd-Max algorithm, rather than centralized design. In contrast to traditional results in language evolution, we demonstrate several vocabularies may coexist in Bayes Nash equilibrium, with each individual having exactly one of these vocabularies. The overlap between vocabularies is high for individuals that communicate frequently and have similar local sources. Finally, we prove that error in translation along a chain of communication does not grow if and only if the chain consists of agents with shared vocabulary. Numerical examples demonstrate our findings. 2021-06-18T00:00:00Z https://hdl.handle.net/1721.1/131212 Secure and secret cooperation in robot swarms Castelló Ferrer, Eduardo; Hardjono, Thomas; Pentland, Alex; Dorigo, Marco The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree. With this method, operators can provide the “blueprint” of the swarm’s mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate toward mission completion, have to “prove” their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission’s high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role. 2021-07-28T00:00:00Z https://hdl.handle.net/1721.1/131211 Mobility patterns are associated with experienced income segregation in large US cities Moro, Esteban; Calacci, Dan; Dong, Xiaowen; Pentland, Alex Traditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns. 2021-07-30T00:00:00Z https://hdl.handle.net/1721.1/131067 Bayesian collective learning emerges from heuristic social learning Krafft, P.M.; Shmueli, Erez; Griffiths, Thomas L.; Tenenbaum, Joshua B.; Pentland, Alex Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phe- nomenon of social learning—the use of information about other people’s decisions to make your own. Decision- making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good decisions: (1) aggregating information and (2) addressing an informational public goods problem known as the exploration-exploitation dilemma. Here, we show how a Bayesian social sampling model can in principle simultaneously optimally aggregate information and nearly optimally solve the exploration-exploitation dilemma. The key idea we explore is that Bayesian rationality at the level of a popu- lation can be implemented through a more simplistic heuristic social learning mechanism at the individual level. This simple individual-level behavioral rule in the context of a group of decision-makers functions as a distributed algorithm that tracks a Bayesian posterior in population-level statistics. We test this model using a large-scale dataset from an online financial trading platform. 2021-07-01T00:00:00Z https://hdl.handle.net/1721.1/131066 Unraveling the association between socioeconomic diversity and consumer price index in a tourism country Leng, Yan; Babwany, Nakash Ali; Pentland, Alex Diversity has tremendous value in modern society. Economic theories suggest that cultural and ethnic diversity may contribute to economic development and prosperity. To date, however, the correspondence between diversity measures and the economic indicators, such as the Consumer Price Index, has not been quantified. This is primarily due to the difficulty in obtaining data on the micro behaviors and macroeconomic indicators. In this paper, we explore the relationship between diversity measures extracted from large-scale and high-resolution mobile phone data, and the CPIs in different sectors in a tourism country. Interestingly, we show that diversity measures associate strongly with the general and sectoral CPIs, using phone records in Andorra. Based on these strong predictive relationships, we construct daily, and spatial maps to monitor CPI measures at a high resolution to complement existing CPI measures from the statistical office. The case study on Andorra used in this study contributes to two growing literature: linking diversity with economic outcomes, and macro-economic monitoring with large-scale data. Future study is required to examine the relationship between the two measures in other countries. 2021-06-28T00:00:00Z https://hdl.handle.net/1721.1/131062 Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions Adjodah, Dhaval; Leng, Yan; Chong, Shi Kai; Krafft, P. M.; Moro, Esteban; Pentland, Alex A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote. 2021-06-24T00:00:00Z https://hdl.handle.net/1721.1/131061 Association between COVID-19 outcomes and mask mandates, adherence, and attitudes Adjodah, Dhaval; Dinakar, Karthik; Chinazzi, Matteo; Fraiberger, Samuel P.; Pentland, Alex; Bates, Samantha; Staller, Kyle; Vespignani, Alessandro; Bhatt, Deepak L. We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Given the recent lifting of mandates, we estimate that the ending of mask mandates in these states is associated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13% of the highest recorded number) of daily new cases with no significant effect on hospitalizations and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in 69 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates. 2021-06-23T00:00:00Z https://hdl.handle.net/1721.1/130896 The Strength of Structural Diversity in Online Social Networks Zhang, Yafei; Wang, Lin; Zhu, Jonathan J. H.; Wang, Xiaofan; Pentland, Alex Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies. 2021-05-26T00:00:00Z https://hdl.handle.net/1721.1/130561 Gender Differences in Real-Home Sleep of Young and Older Couples Butt, Maryam; Quan, Stuart F.; Pentland, Alex; Khayal, Inas Objectives: To understand gender differences in sleep quality, architecture and duration of young healthy couples in comparison to older couples in their natural sleep environment. Design: Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of two weeks for young couples and home polysomnography for the older couples. Participants: Ten heterosexual young couples (male mean age: 28.2 1.0[SD] years /female mean age: 26.8 0.9 years) and 14 older couples (male mean age: 59.3+ 9.6 years/female mean age: 58.8+ 9.1 years). Measurements and results: In the young couples, total sleep time (395+66 vs. 367+54 min., p<0.05), sleep efficiency (97.0+3.0 vs. 91.1+7.9, p<0.001), and % REM (31.1+4.8 vs. 23.6+5.5, p<0.001) in males was higher than in females. In contrast, % light sleep (51.7+7.1 vs. 59.7+6.7, p<0.001) and number of arousals (2.9+1.9 vs. 5.3+1.9, p<0.001) were lower. These differences persisted after controlling for evening mood and various evening pre-sleep activities. In the older couples, there were no differences between genders. In addition, children in the household adversely impacted sleep. Conclusions: In couples recorded in the home, young males slept longer and had better sleep quality than young females. This difference appears to dissipate with age. In-home assessment of couples can aid in understanding of gender differences in sleep and how they are affected by age and social environment. 2015-05-19T00:00:00Z https://hdl.handle.net/1721.1/130558 Sensing Informal Networks in Organizations Orbach, Maya; Demko, Maegen; Doyle, Jeremy; Waber, Benjamin N.; Pentland, Alex We present an examination of informal network structure within the sales division of a global manufacturing organization. Sociometric Badges were used to collect data on face-to-face interactions over a total of 8 weeks, the latter half of which was spent in a redesigned workspace. These data were supplemented by employees’ e-mail and instant messaging log activity. The allocation of an individual’s communication among colleagues reflected the company’s structure as a post-bureaucratic organization. The observed interteam communication patterns differed from those expected to arise based on the various functions performed by each team throughout the sales cycle, suggesting that the communication needs of each team were not wholly provided for by the available media. A subset of workers who were encouraged to utilize flexible seating arrangements in a remodeled space had a higher proportion of face-to-face interactions with colleagues outside of their team, while employees seated far away from each other were less likely to exchange e-mail. This research has implications for companies hoping to understand the structure of informal networks within their organization as well as those considering workplace redesign as a method of stimulating communication within these networks. 2014-11-21T00:00:00Z https://hdl.handle.net/1721.1/130555 Assessing data intrusion threats—Response de Montjoye, Yves-Alexandre; Pentland, Alex 2015-04-10T00:00:00Z https://hdl.handle.net/1721.1/130554 Partnership Ties Shape Friendship Networks: A Dynamic Social Network Study Stadtfeld, Christoph; Pentland, Alex Partnership ties shape friendship networks through different social forces. First, partnership ties drive clustering in friendship networks: individuals who are in a partnership tend to have common friends and befriend other couples. Second, partnership ties influence the level of homophily in these emerging friendship clusters. Partners tend to be similar in a number of attributes (homogamy). If one partner selects friends based on preferences for homophily, then the other partner may befriend the same person regardless of whether they also have homophilic preferences. Thus, two homophilic ties emerge based on a single partner's preferences. This amplification of homophily can be observed in many attributes (e.g., ethnicity, religion, age). Gender homophily, however, may be de-amplified, as the gender of partners differs in heterosexual partnerships. In our study, we follow dynamic friendship formation among 126 individuals and their cohabiting partners in a university-related graduate housing community over a period of nine months (N = 2,250 self-reported friendship relations). We find that partnership ties strongly shape the dynamic process of friendship formation. They are a main driver of local network clustering and explain a striking amount of homophily. 2015-06-26T00:00:00Z https://hdl.handle.net/1721.1/130383 Spillovers across industries and regions in China’s regional economic diversification Gao, Jian; Jun, Bogang; Pentland, Alex; Zhou, Tao; Hidalgo, César A. Industrial diversification depends on spillovers from related industries and nearby regions, yet their interaction remains largely unclear. We study economic diversification in China during the period 1990–2015 and present supportive evidence on both spillover channels. We add to the literature by showing that these two channels behave as substitutes when explaining new entries and exits, and by using acceleration campaigns of high-speed rail to address some endogeneity concerns with regional spillovers. Our findings confirm the role of relatedness and geographical distance in the diffusion of economic capabilities and support the idea that improvements in transportation can facilitate the diffusion of productive capabilities. 2021-03-31T00:00:00Z https://hdl.handle.net/1721.1/130362 COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour Heroy, Samuel; Loaiza, Isabella; Pentland, Alex; O’Clery, Neave Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restrictiveness of cities’ mobility quotas (the share of residents allowed out daily according to policy advice) does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with more formalized and complex industrial structure experienced greater reductions in mobility. Within cities, wealthier residents are more likely to reduce mobility, and commuters are especially more likely to stay at home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities’ employment characteristics and work-from-home capabilities are the primary determinants of mobility reduction. This finding underscores the need for mitigations aimed at lower income/informal workers, and sheds light on critical dependencies between socio-economic classes in Latin American cities. 2021-03-31T00:00:00Z https://hdl.handle.net/1721.1/130361 Universal resilience patterns in labor markets Moro, Esteban; Frank, Morgan R.; Pentland, Alex; Rutherford, Alex; Cebrian, Manuel; Rahwan, Iyad Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor market resilience with an ecologically-inspired job network constructed from the similarity of occupations’ skill requirements. This framework reveals that the economic resilience of cities is universally and uniquely determined by the connectivity within a city’s job network. US cities with greater job connectivity experienced lower unemployment during the Great Recession. Further, cities that increase their job connectivity see increasing wage bills, and workers of embedded occupations enjoy higher wages than their peers elsewhere. Finally, we show how job connectivity may clarify the augmenting and deleterious impact of automation in US cities. Policies that promote labor connectivity may grow labor markets and promote economic resilience. 2021-03-30T00:00:00Z https://hdl.handle.net/1721.1/130335 Temporal fidelity in dynamic social networks Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex; Lehmann, Sune It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis. 2015-10-07T00:00:00Z https://hdl.handle.net/1721.1/130331 Simple market models fail the test Pentland, Alex 2015-09-22T00:00:00Z https://hdl.handle.net/1721.1/130329 Unique in the shopping mall: On the reidentifiability of credit card metadata de Montjoye, Yves-Alexandre; Radaelli, Laura; Singh, Vivek Kumar; Pentland, Alex 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. 2015-01-30T00:00:00Z https://hdl.handle.net/1721.1/130325 Are You Your Friends’ Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change Almaatouq, Abdullah; Radaelli, Laura; Pentland, Alex; Shmueli, Erez Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action. 2016-03-22T00:00:00Z https://hdl.handle.net/1721.1/130321 Response to Comment on “Unique in the shopping mall: On the reidentifiability of credit card metadata” de Montjoye, Yves-Alexandre; Pentland, Alex Sánchez et al.’s textbook k-anonymization example does not prove, or even suggest, that location and other big-data data sets can be anonymized and of general use. The synthetic data set that they “successfully anonymize” bears no resemblance to modern high-dimensional data sets on which their methods fail. Moving forward, deidentification should not be considered a useful basis for policy. 2016-03-18T00:00:00Z https://hdl.handle.net/1721.1/130315 Globalization and the shifting centers of gravity of world's human dynamics: Implications for sustainability Balsa-Barreiro, José; Li, Yingcheng; Morales, Alfredo; Pentland, Alex World's human dynamics can be parameterized with metrics that explain the current model of economic growth and its sustainability. Changes in the world's human dynamics are crucial for understanding the current state of the world, which is faced with increasing challenges related to globalization. In this paper, we propose to analyze the shifting locations of centers of gravity of four basic global indicators (these are Gross Domestic Product, carbon dioxide emissions, population, and urban population) for the period 1960–2016. The spatial locations of the respective centers of gravity (one per year) draw some traces that explain, at least partially, relevant changes on different world's human dynamics at a global level. These traces and dynamics are further discussed. In addition, these traces are fundamental for predicting upcoming trends for the next few years. Results shown here may help political leaders and policymakers for solving upcoming and future global challenges related to the current economic system and its impact on the environment. 2019-12-01T00:00:00Z https://hdl.handle.net/1721.1/130314 Can an Emoji a Day Keep the Doctor Away? An Explorative Mixed-Methods Feasibility Study to Develop a Self-Help App for Youth With Mental Health Problems van Dam, Levi; Rietstra, Sianne; Van Der Drift, Eva; Stams, Geert Jan J. M.; Van der Mei, Rob; Mahfoud, Maria; Popma, Arne; Schlossberg, Eric; Pentland, Alex; Reid, Todd G. 2019-08-23T00:00:00Z https://hdl.handle.net/1721.1/130313 Is social capital associated with synchronization in human communication? An analysis of Italian call records and measures of civic engagement Mamei, Marco; Pancotto, Francesca; De Nadai, Marco; Lepri, Bruno; Vescovi, Michele; Zambonelli, Franco; Pentland, Alex Social capital has been studied in economics, sociology and political science as one of the key elements that promote the development of modern societies. It can be defined as the source of capital that facilitates cooperation through shared social norms. In this work, we investigate whether and to what extent synchronization aspects of mobile communication patterns are associated with social capital metrics. Interestingly, our results show that our synchronization-based approach well correlates with existing social capital metrics (i.e., Referendum turnout, Blood donations, and Association density), being also able to characterize the different role played by high synchronization within a close proximity-based community and high synchronization among different communities. Hence, the proposed approach can provide timely, effective analysis at a limited cost over a large territory. 2018-07-18T00:00:00Z https://hdl.handle.net/1721.1/130310 Optimizing targeted vaccination across cyber–physical networks: an empirically based mathematical simulation study Mones, Enys; Stopczynski, Arkadiusz; Pentland, Alex; Hupert, Nathaniel; Lehmann, Sune Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the ‘holy grails’ of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call ‘cyber-directed vaccination’) can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission. 2018-01-03T00:00:00Z https://hdl.handle.net/1721.1/130308 Driver behavior profiling: An investigation with different smartphone sensors and machine learning Ferreira Junior, Jair; Carvalho, Eduardo; Ferreira, Bruno V.; de Souza, Cleidson; Suhara, Yoshihiko; Pentland, Alex; Pessin, Gustavo Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement. 2017-04-10T00:00:00Z https://hdl.handle.net/1721.1/130307 Social Bridges in Urban Purchase Behavior Dong, Xiaowen; Suhara, Yoshihiko; Bozkaya, Burçin; Singh, Vivek K.; Lepri, Bruno; Pentland, Alex The understanding and modeling of human purchase behavior in city environment can have important implications in the study of urban economy and in the design and organization of cities. In this article, we study human purchase behavior at the community level and argue that people who live in different communities but work at close-by locations could act as “social bridges” between the respective communities and that they are correlated with similarity in community purchase behavior. We provide empirical evidence by studying millions of credit card transaction records for tens of thousands of individuals in a city environment during a period of three months. More specifically, we show that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior than traditionally considered factors such as income and sociodemographic variables. Our findings also suggest that such an effect varies across different merchant categories, that the presence of female customers in social bridges is a stronger indicator compared to that of their male counterparts, and that there seems to be a geographical constraint for this effect, all of which may have implications in the studies of urban economy and data-driven urban planning. 2017-12-01T00:00:00Z https://hdl.handle.net/1721.1/130305 Ethical machines: The human-centric use of artificial intelligence Lepri, Bruno; Oliver, Nuria; Pentland, Alex 2021-03-19T00:00:00Z https://hdl.handle.net/1721.1/130304 Prediction and prevention of disproportionally dominant agents in complex networks Lera, Sandro Claudio; Pentland, Alex; Sornette, Didier We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (“winner takes all,” WTA) in growing complex networks. This is modeled as a system of interacting agents, whereby the rate at which an agent establishes connections to others is proportional to its already existing number of connections and its intrinsic fitness. We derive an exact four-dimensional phase diagram that separates the growing system into two regimes: one where the “fit get richer” and one where, eventually, the WTA. By calibrating the system’s parameters with maximum likelihood, its distance from the unfavorable WTA regime can be monitored in real time. This is demonstrated by applying the theory to the eToro social trading platform where users mimic each other’s trades. If the system state is within or close to the WTA regime, we show how to efficiently control the system back into a more stable state along a geodesic path in the space of fitness distributions. It turns out that the common measure of penalizing the most dominant agents does not solve sustainably the problem of drastic inequity. Instead, interventions that first create a critical mass of high-fitness individuals followed by pushing the relatively low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility. 2020-11-03T00:00:00Z https://hdl.handle.net/1721.1/130303 Looking for a better future: modeling migrant mobility Loaiza Saa, Isabella; Novak, Matej; Morales, Alfredo J.; Pentland, Alex Massive migrations have become increasingly prevalent over the last decades. A recent example is the Venezuelan migration crisis across South America, which particularly affects neighboring countries like Colombia. Creating an effective response to the crisis is a challenge for governments and international agencies, given the lack of information about migrants’ location, flows and behaviors within and across host countries. For this purpose it is crucial to map and understand geographic patterns of migration, including spatial mobility and dynamics over time. The aim of this paper is to uncover mobility and economic patterns of migrants that left Venezuela and migrated into Colombia due to the effects of the ongoing social, political and economic crisis. We analyze and compare the behavior of two types of migrants: Venezuelan refugees and Colombian nationals who used to live in Venezuela and return to their home country. We adapt the gravity model for human mobility in order to explain migrants’ dispersion across Colombia, and analyze patterns of economic integration. This study is a first attempt at analyzing and comparing two kinds of migrant populations in one destination country, providing unique insight into the processes of mobility and integration after migration. 2020-09-22T00:00:00Z https://hdl.handle.net/1721.1/130301 Diversity of Idea Flows and Economic Growth Pentland, Alex What role does access to diverse ideas play in economic growth? New forms of geo-located communications and economic data allow measurement of human interaction patterns and prediction of economic outcomes for individuals, communities, and nations at a fine granularity, with the strongest predictors of income, productivity, and growth being measures of diversity and frequency of physical interaction between communities (clusters of interaction). This finding provides both new investment opportunities and new methods of risk assessment. Access and use of these data raise privacy and security risks, and the final section of the paper describes how these challenges can be controlled. 2020-09-01T00:00:00Z https://hdl.handle.net/1721.1/130300 Contextualizing Human Psychology Pentland, Alex The study of psychology has been handicapped by the difficulty of measuring how individual traits affect interactions with the surrounding social structures and how this interaction affects both individual life outcomes and group characteristics. With the advent of continuous, fine-grain data from cell phones, credit cards, and online interactions, the field of human psychology can become better at understanding the role of social context by combining these new data sources with standard experimental methods. This article will examine how these new tools can shed light on the influence individual psychological traits have on life outcomes, as well as on social properties such as inequality. Use of these new data sources requires special care to uphold ethical standards, and so new methodologies have been developed. 2020-08-29T00:00:00Z https://hdl.handle.net/1721.1/130299 Computational social science: Obstacles and opportunities Lazer, David M. J.; Pentland, Alex; Watts, Duncan J.; Aral, Sinan; Athey, Susan; Contractor, Noshir; Freelon, Deen; Gonzalez-Bailon, Sandra; King, Gary; Margetts, Helen; Nelson, Alondra; Salganik, Matthew J.; Strohmaier, Markus; Vespignani, Alessandro; Wagner, Claudia 2020-08-28T00:00:00Z https://hdl.handle.net/1721.1/130293 Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19 Aleta, Alberto; Martín-Corral, David; Pastore y Piontti, Ana; Ajelli, Marco; Litvinova, Maria; Chinazzi, Matteo; Dean, Natalie E.; Halloran, M. Elizabeth; Longini Jr, Ira M.; Merler, Stefano; Pentland, Alex; Vespignani, Alessandro; Moro, Esteban; Moreno, Yamir While severe social-distancing measures have proven effective in slowing the coronavirus disease 2019 (COVID-19) pandemic, second-wave scenarios are likely to emerge as restrictions are lifted. Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in the Boston metropolitan area. We find that a period of strict social distancing followed by a robust level of testing, contact-tracing and household quarantine could keep the disease within the capacity of the healthcare system while enabling the reopening of economic activities. Our results show that a response system based on enhanced testing and contact tracing can have a major role in relaxing social-distancing interventions in the absence of herd immunity against SARS-CoV-2. 2020-08-05T00:00:00Z https://hdl.handle.net/1721.1/130292 Segregated interactions in urban and online space Dong, Xiaowen; Morales, Alfredo J.; Jahani, Eaman; Moro, Esteban; Lepri, Bruno; Bozkaya, Burcin; Sarraute, Carlos; Bar-Yam, Yaneer; Pentland, Alex Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications. 2020-07-10T00:00:00Z https://hdl.handle.net/1721.1/130289 Economic outcomes predicted by diversity in cities Chong, Shi Kai; Bahrami, Mohsen; Chen, Hao; Balcisoy, Selim; Pentland, Alex Much recent work has illuminated the growth, innovation, and prosperity of entire cities, but there is relatively less evidence concerning the growth and prosperity of individual neighborhoods. In this paper we show that diversity of amenities within a city neighborhood, computed from openly available points of interest on digital maps, accurately predicts human mobility (“flows”) between city neighborhoods and that these flows accurately predict neighborhood economic productivity. Additionally, the diversity of consumption behaviour or the diversity of flows together with geographic centrality and population density accurately predicts neighborhood economic growth, even after controlling for standard factors such as population, etc. We develop our models using geo-located purchase data from Istanbul, and then validate the relationships using openly available data from Beijing and several U.S. cities. Our results suggest that the diversity of goods and services within a city neighborhood is the largest single factor driving both human mobility and economic growth. 2020-06-24T00:00:00Z https://hdl.handle.net/1721.1/130288 Contextual centrality: going beyond network structure Leng, Yan; Sella, Yehonatan; Ruiz, Rodrigo; Pentland, Alex Centrality is a fundamental network property that ranks nodes by their structural importance. However, the network structure alone may not predict successful diffusion in many applications, such as viral marketing and political campaigns. We propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, relevant node characteristics. It nicely generalizes and relates to standard centrality measures. We test the effectiveness of contextual centrality in predicting the eventual outcomes in the adoption of microfinance and weather insurance. Our empirical analysis shows that the contextual centrality of first-informed individuals has higher predictive power than that of other standard centrality measures. Further simulations show that when the diffusion occurs locally, contextual centrality can identify nodes whose local neighborhoods contribute positively. When the diffusion occurs globally, contextual centrality signals whether diffusion may generate negative consequences. Contextual centrality captures more complicated dynamics on networks than traditional centrality measures and has significant implications for network-based interventions. 2020-06-10T00:00:00Z https://hdl.handle.net/1721.1/130287 Adaptive social networks promote the wisdom of crowds Almaatouq, Abdullah; Noriega-Campero, Alejandro; Alotaibi, Abdulrahman; Krafft, P. M.; Moussaid, Mehdi; Pentland, Alex Social networks continuously change as new ties are created and existing ones fade. It is widely acknowledged that our social embedding has a substantial impact on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. Therefore, little is known about how groups of individuals dynamically modify their local connections and, accordingly, the topology of the network of interactions to respond to changing environmental conditions. In this paper, we address this question through a series of behavioral experiments and supporting simulations. Our results reveal that, in the presence of plasticity and feedback, social networks can adapt to biased and changing information environments and produce collective estimates that are more accurate than their best-performing member. To explain these results, we explore two mechanisms: 1) a global-adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group (i.e., the network “edges” encode the computation); and 2) a local-adaptation mechanism where accurate individuals are more resistant to social influence (i.e., adjustments to the attributes of the “node” in the network); therefore, their initial belief is disproportionately weighted in the collective estimate. Our findings substantiate the role of social-network plasticity and feedback as key adaptive mechanisms for refining individual and collective judgments. 2020-05-11T00:00:00Z https://hdl.handle.net/1721.1/130286 Overcoming barriers to early disease intervention Caicedo, H. Hugo; Hashimoto, Daniel A.; Caicedo, Julio C.; Pentland, Alex; Pisano, Gary P. 2020-05-22T00:00:00Z https://hdl.handle.net/1721.1/130285 Measuring the predictability of life outcomes with a scientific mass collaboration Salganik, Matthew J.; Lundberg, Ian; Kindel, Alexander T.; Ahearn, Caitlin E.; Al-Ghoneim, Khaled; Almaatouq, Abdullah; Altschul, Drew M.; Brand, Jennie E.; Carnegie, Nicole Bohme; Compton, Ryan James; Datta, Debanjan; Davidson, Thomas; Filippova, Anna; Gilroy, Connor; Goode, Brian J.; Jahani, Eaman; Kashyap, Ridhi; Kirchner, Antje; McKay, Stephen; Morgan, Allison C.; Pentland, Alex; Polimis, Kivan; Raes, Louis; Rigobon, Daniel E.; Roberts, Claudia V.; Stanescu, Diana M.; Suhara, Yoshihiko; Usmani, Adaner; Wang, Erik H.; Adem, Muna; Alhajri, Abdulla; AlShebli, Bedoor; Amin, Redwane; Amos, Ryan B.; Argyle, Lisa P.; Baer-Bositis, Livia; Buchi, Moritz; Chung, Bo-Ryehn; Eggert, William; Faletto, Gregory; Fan, Zhilin; Freese, Jeremy; Gadgil, Tejomay; Gagne ́, Josh; Gao, Yue; Halpern-Manners, Andrew; Hashim, Sonia P.; Hausen, Sonia; He, Guanhua; Higuera, Kimberly; Hogan, Bernie; Horwitz, Ilana M.; Hummel, Lisa M.; Jain, Naman; Jin, Kun; Jurgens, David; Kaminski, Patrick; Karapetyan, Areg; Kim, E. H.; Leizman, Ben; Liu, Naijia; Moser, Malte; Mack, Andrew E.; Mahajan, Mayank; Mandell, Noah; Marahrens, Helge; Mercado-Garcia, Diana; Mocz, Viola; Mueller-Gastell, Katariina; Musse, Ahmed; Niu, Qiankun; Nowak, William; Omidvar, Hamidreza; Or, Andrew; Ouyang, Karen; Pinto, Katy M.; Porter, Ethan; Porter, Kristin E.; Qian, Crystal; Rauf, Tamkinat; Sargsyan, Anahit; Schaffner, Thomas; Schnabel, Landon; Schonfeld, Bryan; Sender, Ben; Tang, Jonathan D.; Tsurkov, Emma; van Loon, Austin; Varol, Onur; Wang, Xiafei; Wang, Zhi; Wang, Julia; Wang, Flora; Weissman, Samantha; Whitaker, Kirstie; Wolters, Maria K.; Woon, Wei Lee; Wu, James; Wu, Catherine; Yang, Kengran; Yin, Jingwen; Zhao, Bingyu; Zhu, Chenyun; Brooks-Gunn, Jeanne; Engelhardt, Barbara E.; Hardt, Moritz; Knox, Dean; Levy, Karen; Narayanan, Arvind; Stewart, Brandon M.; Watts, Duncan J.; McLanahan, Sara How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences. 2020-04-14T00:00:00Z https://hdl.handle.net/1721.1/130284 Network Dynamics of a Financial Ecosystem Somin, Shahar; Altshuler, Yaniv; Gordon, Goren; Pentland, Alex; Shmueli, Erez Global financial crises have led to the understanding that classical econometric models are limited in comprehending financial markets in extreme conditions, partially since they disregarded complex interactions within the system. Consequently, in recent years research efforts have been directed towards modeling the structure and dynamics of the underlying networks of financial ecosystems. However, difficulties in acquiring fine-grained empirical financial data, due to regulatory limitations, intellectual property and privacy control, still hinder the application of network analysis to financial markets. In this paper we study the trading of cryptocurrency tokens on top of the Ethereum Blockchain, which is the largest publicly available financial data source that has a granularity of individual trades and users, and which provides a rare opportunity to analyze and model financial behavior in an evolving market from its inception. This quickly developing economy is comprised of tens of thousands of different financial assets with an aggregated valuation of more than 500 Billion USD and typical daily volume of 30 Billion USD, and manifests highly volatile dynamics when viewed using classic market measures. However, by applying network theory methods we demonstrate clear structural properties and converging dynamics, indicating that this ecosystem functions as a single coherent financial market. These results suggest that a better understanding of traditional markets could become possible through the analysis of fine-grained, abundant and publicly available data of cryptomarkets. 2020-03-12T00:00:00Z https://hdl.handle.net/1721.1/130283 Turkers of the World Unite: Multilevel In-Group Bias Among Crowdworkers on Amazon Mechanical Turk Almaatouq, Abdullah; Krafft, Peter; Dunham, Yarrow; Rand, David G.; Pentland, Alex Crowdsourcing has become an indispensable tool in the behavioral sciences. Often, the “crowd” is considered a black box for gathering impersonal but generalizable data. Researchers sometimes seem to forget that crowdworkers are people with social contexts, unique personalities, and lives. To test this possibility, we measure how crowdworkers (N 1⁄4 2,337, preregistered) share a monetary endowment in a Dictator Game with another Mechanical Turk (MTurk) worker, a worker from another crowd- working platform, or a randomly selected stranger. Results indicate preferential in-group treatment for MTurk workers in particular and for crowdworkers in general. Cooperation levels from typical anonymous economic games on MTurk are not a good proxy for anonymous interactions and may generalize most readily only to the intragroup context. 2020-03-01T00:00:00Z https://hdl.handle.net/1721.1/130281 Purchase Patterns, Socioeconomic Status, and Political Inclination Dong, Xiaowen; Jahani, Eaman; Morales, Alfredo J.; Bozkaya, Burçin; Lepri, Bruno; Pentland, Alex This paper analyzes millions of credit card transaction records during several months for tens of thousands of individuals from two different countries. The study shows that, purchase patterns are strongly correlated with important societal indices such as socioeconomic status and political inclination. The results suggest the possibility of understanding and predicting the evolution of such societal indices from purchase behavioral patterns, potentially at high temporal and spatial resolutions. 2020-02-01T00:00:00Z https://hdl.handle.net/1721.1/130276 Representativity and Networked Interference in Data-Rich Field Experiments: A Large-Scale RCT in Rural Mexico Noriega, Alejandro; Pentland, Alex Modern availability of rich geospatial datasets and analysis tools can provide insight germane to the design of field experiments. Design of field experiments, and in particular the choice of sampling strategy, requires careful consideration of its consequences on the external representativity and interference (SUTVA violations) of the experimental sample. This paper presents a methodology for a) modeling the geospatial and social interaction factors that drive interference in rural field experiments; and b) eliciting a set of nondominated sample options that approximate the Pareto-optimal tradeoff between interference and external representativity, as functions of sample choice. The study develops and tests the methodology in the context of a large-scale health experiment in rural Mexico, involving more than 3,000 pregnant women and 600 health clinics across 5 states. Relevant for the practitioner, the methodology is computationally tractable and can be implemented leveraging open sourced geo-spatial data and software tools. 2020-02-01T00:00:00Z https://hdl.handle.net/1721.1/130268 An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection Gomes, Pedro A. B.; Suhara, Yoshihiko; Nunes-Silva, Patrícia; Costa, Luciano; Arruda, Helder; Venturieri, Giorgio; Imperatriz-Fonseca, Vera Lucia; Pentland, Alex; de Souza, Paulo; Pessin, Gustavo Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model. 2020-01-08T00:00:00Z https://hdl.handle.net/1721.1/130266 Globalization and the shifting centers of gravity of world's human dynamics: Implications for sustainability Balsa-Barreiro, Jose; Li, Yingcheng; Morales, Alfredo; Pentland, Alex World's human dynamics can be parameterized with metrics that explain the current model of economic growth and its sustainability. Changes in the world's human dynamics are crucial for understanding the current state of the world, which is faced with increasing challenges related to globalization. In this paper, we propose to analyze the shifting locations of centers of gravity of four basic global indicators (these are Gross Domestic Product, carbon dioxide emissions, population, and urban population) for the period 1960–2016. The spatial locations of the respective centers of gravity (one per year) draw some traces that explain, at least partially, relevant changes on different world's human dynamics at a global level. These traces and dynamics are further discussed. In addition, these traces are fundamental for predicting upcoming trends for the next few years. Results shown here may help political leaders and policymakers for solving upcoming and future global challenges related to the current economic system and its impact on the environment. 2019-12-10T00:00:00Z https://hdl.handle.net/1721.1/130264 Segregation and polarization in urban areas Morales, Alfredo J.; Dong, Xiaowen; Bar-Yam, Yaneer; Pentland, Alex Social behaviours emerge from the exchange of information among individuals—constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces. 2019-10-23T00:00:00Z https://hdl.handle.net/1721.1/130263 Winning Models for Grade Point Average, Grit, and Layoff in the Fragile Families Challenge Rigobon, Daniel E.; Jahani, Eaman; Suhara, Yoshihiko; AlGhoneim, Khaled; Alghunaim, Abdulaziz; Pentland, Alex; Almaatouq, Abdullah In this article, the authors discuss and analyze their approach to the Fragile Families Challenge. The data consisted of more than 12,000 features (covariates) about the children and their parents, schools, and overall environments from birth to age 9. The authors’ modular and collaborative approach parallelized prediction tasks and relied primarily on existing data science techniques, including (1) data preprocessing: elimination of low variance features, imputation of missing data, and construction of composite features; (2) feature selection through univariate mutual information and extraction of nonzero least absolute shrinkage and selection operator coefficients; (3) three machine learning models: random forest, elastic net, and gradient-boosted trees; and finally (4) prediction aggregation according to performance. The top-performing submissions produced winning out-of-sample predictions for three outcomes: grade point average, grit, and layoff. However, predictions were at most 20 percent better than a baseline that predicted the mean value of the training data for each outcome. 2019-09-10T00:00:00Z https://hdl.handle.net/1721.1/130260 Can an Emoji a Day Keep the Doctor Away? An Explorative Mixed- Methods Feasibility Study to Develop a Self-Help App for Youth With Mental Health Problems Van Dam, Levi; Rietstra, Sianne; Van der Drift, Eva; Jan J. M. Stams, Geert; Van der Mei, Rob; Mahfoud, Maria; Popma, Arne; Schlossberg, Eric; Pentland, Alex; G. Reid, Todd Today’s smartphones allow for a wide range of “big data” measurement, for example, ecological momentary assessment (EMA), whereby behaviours are repeatedly assessed within a person’s natural environment. With this type of data, we can better understand – and predict – risk for behavioral and health issues and opportunities for (self-monitoring) interventions. In this mixed-methods feasibility study, through convenience sampling we collected data from 32 participants (aged 16–24) over a period of three months. To gain more insight into the app experiences of youth with mental health problems, we interviewed a subsample of 10 adolescents who received psycthological treatment. The results from this feasibility study indicate that emojis) can be used to identify positive and negative feelings, and individual pattern analyses of emojis may be useful for clinical purposes. While adolescents receiving mental health care are positive about future applications, these findings also highlight some caveats, such as possible drawback of inaccurate representation and incorrect predictions of emotional states. Therefore, at this stage, the app should always be combined with professional counseling. Results from this small pilot study warrant replication with studies of substantially larger sample size. 2019-08-23T00:00:00Z https://hdl.handle.net/1721.1/130257 Is social capital associated with synchronization in human communication? An analysis of Italian call records and measures of civic engagement Mamei, Marco; Pancotto, Francesca; De Nadai, Marco; Lepri, Bruno; Vescovi, Michele; Zambonelli, Franco; Pentland, Alex Social capital has been studied in economics, sociology and political science as one of the key elements that promote the development of modern societies. It can be defined as the source of capital that facilitates cooperation through shared social norms. In this work, we investigate whether and to what extent synchronization aspects of mobile communication patterns are associated with social capital metrics. Interestingly, our results show that our synchronization-based approach well correlates with existing social capital metrics (i.e., Referendum turnout, Blood donations, and Association density), being also able to characterize the different role played by high synchronization within a close proximity-based community and high synchronization among different communities. Hence, the proposed approach can provide timely, effective analysis at a limited cost over a large territory. 2018-07-17T00:00:00Z https://hdl.handle.net/1721.1/130256 The Rippling Effect of Social Influence via Phone Communication Network Leng, Yan; Dong, Xiaowen; Moro, Esteban; Pentland, Alex 2018-06-22T00:00:00Z https://hdl.handle.net/1721.1/130255 Digital trade coin: towards a more stable digital currency Lipton, Alex; Hardjono, Thomas; Pentland, Alex We study the evolution of ideas related to creation of asset-backed currencies over the last 200 years and argue that recent developments related to distributed ledger technologies and blockchains give asset-backed currencies a new lease of life. We propose a practical mechanism combining novel technological breakthroughs with well-established hedging techniques for building an asset-backed transactional oriented cryptocurrency, which we call the digital trade coin (DTC). We show that in its mature state, the DTC can serve as a much-needed counterpoint to fiat reserve currencies of today. 2018-07-18T00:00:00Z https://hdl.handle.net/1721.1/130254 Optimizing targeted vaccination across cyber–physical networks: an empirically based mathematical simulation stud Mones, Enys; Stopczynski, Arkadiusz; Pentland, Alex; Hupert, Nathaniel; Lehmann, Sune Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the ‘holy grails’ of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call ‘cyber-directed vaccination’) can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission. 2018-01-03T00:00:00Z https://hdl.handle.net/1721.1/130252 How Physical Proximity Shapes Complex Social Networks Stopczynski, Arkadiusz; Pentland, Alex; Lehmann, Sune Social interactions among humans create complex networks and – despite a recent increase of online communication – the interactions mediated through physical proximity remain a fundamental way for people to connect. A common way to quantify the nature of the links between individuals is to consider repeated interactions: frequently occurring interactions indicate strong ties, such as friendships, while ties with low weights can indicate random encounters. Here we focus on a different dimension: rather than the strength of links, we study physical distance between individuals when a link is activated. The findings presented here are based on a dataset of proximity events in a population of approximately 500 individuals. To quantify the impact of the physical proximity on the dynamic network, we use a simulated epidemic spreading processes in two distinct networks of physical proximity. We consider the network of short-range interactions defined as d ≲ ≲  1 meter, and the long-range which includes all interactions d ≲ ≲  10 meters. Since these two networks arise from the same set of underlying behavioral data, we are able to quantitatively measure how the specific definition of the proximity network – short-range versus long-range – impacts the resulting network structure as well as spreading dynamics in epidemic simulations. We find that the short-range network – consistent with the literature – is characterized by densely-connected neighborhoods bridged by weak ties. More surprisingly, however, we show that spreading in the long-range network is quite different, mainly shaped by spurious interactions. 2018-12-07T00:00:00Z https://hdl.handle.net/1721.1/130250 Mapping Time-Varying Accessibility and Territorial Cohesion With Time-Distorted Maps Balsa-Barreiro, José; Ambuühl, Lukas; Menéndez, Mónica; Pentland, Alex Human societies have radically changed from the second half of the 20 th century. Urban areas are increasingly concentrating more people and economic activities. Connections between market economies in different regions have increased exponentially the flow of people and goods at a global level. These movements are spatially organized through a hierarchical transportation network that connects different areas. The quality and coverage of such network vary greatly across regions. Territorial cohesion and accessibility within a region could be roughly evaluated through the existing level of connectivity between urban nodes. This can be easily done by estimating travel times between different points in the territory, which would show relevant differences based on both the territory itself and the existing infrastructure. Unfortunately, this information is not typically shown in traditional maps. In this paper, we propose a novel methodology for assessing the degree of territorial accessibility within and across urban networks, by using time-distorted maps. To this end, we consider multiple scenarios related to different public transport modes and times of the day. The study area corresponds to a Spanish region, where we set up a relatively extended network by considering its most relevant cities and towns. Final maps can clearly illustrate the deficiencies in transport infrastructure and/or connections from a spatial perspective. These maps can be excellent tools for supporting technicians, politicians, public managers, and other stakeholders in the decision-making process. 2019-03-27T00:00:00Z