Alex P Pentland

All Publications

• Evaluating Amazon effects and the limited impact of COVID-19 with purchases crowdsourced from US consumers. PloS one Berke, A., Calacci, D., Pentland, A., Larson, K. 2025; 20 (11): e0336571

  Abstract

  We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study changes in online purchasing behaviors over time, how changes vary by demographics, the impact of COVID-19, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing trends beyond those available from aggregate metrics. For example, in addition to analyzing spending behavior, we develop new metrics to quantify changes in consumers' online purchase frequency and the diversity of products purchased, to better reflect the growing ubiquity of online retail. Between 2018 and 2022 these consumer-level metrics grew on average by more than 85%, peaking in 2021. We find a steady upward trend in individuals' online purchasing prior to COVID-19, with a significant increase in the first year of COVID, but without a lasting effect. Purchasing behaviors in 2022 were no greater than the result of the pre-pandemic trend. We also find changes in purchasing significantly differ by demographics, with different responses to the pandemic. We further use the consumer-level data to show substitution effects between online and offline retail in sectors where Amazon heavily invested: books, shoes, and grocery. Prior to COVID we find year-to-year changes in the number of consumers making online purchases for books and shoes negatively correlated with changes in employment at local bookstores and shoe stores. During COVID we find online grocery purchasing negatively correlated with in-store grocery visits. This work demonstrates how crowdsourced, open purchases data can enable economic insights that may otherwise only be available to private firms.

  View details for DOI 10.1371/journal.pone.0336571

  View details for PubMedID 41202084

  View details for PubMedCentralID PMC12594372

• Competition between AI foundation models: dynamics and policy recommendations INDUSTRIAL AND CORPORATE CHANGE Schrepel, T., Pentland, A. 2024

  View details for DOI 10.1093/icc/dtae042

  View details for Web of Science ID 001388809900001

• Behaviour-based dependency networks between places shape urban economic resilience. Nature human behaviour Yabe, T., García Bulle Bueno, B., Frank, M. R., Pentland, A., Moro, E. 2024

  Abstract

  Disruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks.

  View details for DOI 10.1038/s41562-024-02072-7

  View details for PubMedID 39715878

  View details for PubMedCentralID 8009945

• Insights from an Experiment Crowdsourcing Data from Thousands of US Amazon Users: The importance of transparency, money, and data use PROCEEDINGS OF THE ACM ON HUMAN COMPUTER INTERACTION Berke, A., Mahari, R., Pentland, S., Larson, K., Calacci, D. 2024; 8 (CSCW2)

  View details for DOI 10.1145/3687005

  View details for Web of Science ID 001549442700010

• Megastudy testing 25 treatments to reduce antidemocratic attitudes and partisan animosity. Science (New York, N.Y.) Voelkel, J. G., Stagnaro, M. N., Chu, J. Y., Pink, S. L., Mernyk, J. S., Redekopp, C., Ghezae, I., Cashman, M., Adjodah, D., Allen, L. G., Allis, L. V., Baleria, G., Ballantyne, N., Van Bavel, J. J., Blunden, H., Braley, A., Bryan, C. J., Celniker, J. B., Cikara, M., Clapper, M. V., Clayton, K., Collins, H., DeFilippis, E., Dieffenbach, M., Doell, K. C., Dorison, C., Duong, M., Felsman, P., Fiorella, M., Francis, D., Franz, M., Gallardo, R. A., Gifford, S., Goya-Tocchetto, D., Gray, K., Green, J., Greene, J., Güngör, M., Hall, M., Hecht, C. A., Javeed, A., Jost, J. T., Kay, A. C., Kay, N. R., Keating, B., Kelly, J. M., Kirk, J. R., Kopell, M., Kteily, N., Kubin, E., Lees, J., Lenz, G., Levendusky, M., Littman, R., Luo, K., Lyles, A., Lyons, B., Marsh, W., Martherus, J., Maurer, L. A., Mehl, C., Minson, J., Moore, M., Moore-Berg, S. L., Pasek, M. H., Pentland, A., Puryear, C., Rahnama, H., Rathje, S., Rosato, J., Saar-Tsechansky, M., Almeida Santos, L., Seifert, C. M., Shariff, A., Simonsson, O., Spitz Siddiqi, S., Stone, D. F., Strand, P., Tomz, M., Yeager, D. S., Yoeli, E., Zaki, J., Druckman, J. N., Rand, D. G., Willer, R. 2024; 386 (6719): eadh4764

  Abstract

  Scholars warn that partisan divisions in the mass public threaten the health of American democracy. We conducted a megastudy (n = 32,059 participants) testing 25 treatments designed by academics and practitioners to reduce Americans' partisan animosity and antidemocratic attitudes. We find that many treatments reduced partisan animosity, most strongly by highlighting relatable sympathetic individuals with different political beliefs or by emphasizing common identities shared by rival partisans. We also identify several treatments that reduced support for undemocratic practices-most strongly by correcting misperceptions of rival partisans' views or highlighting the threat of democratic collapse-which shows that antidemocratic attitudes are not intractable. Taken together, the study's findings identify promising general strategies for reducing partisan division and improving democratic attitudes, shedding theoretical light on challenges facing American democracy.

  View details for DOI 10.1126/science.adh4764

  View details for PubMedID 39418366

• Toward building deliberative digital media: from subversion to consensus. PNAS nexus Pentland, A., Tsai, L. 2024; 3 (10): pgae407

  Abstract

  Evidence-based and human-centric design of digital media platforms could reduce many of the problems of misinformation, polarization, and misaligned incentives that plague both society and individual organizations. With these sorts of design changes, it may become possible to build deliberative digital media that are useful both for discussions of contentious issues and for achieving successful collective action. In this Perspective paper, we discuss several issues in which current-day social science indicates the origin of these problems and suggests methods for improvement. Finally, we analyze a popular deliberative democracy platform to illustrate how social science might enable design of next-generation digital media suitable for democratic deliberation, and in which generative artificial intelligence might be useful.

  View details for DOI 10.1093/pnasnexus/pgae407

  View details for PubMedID 39411091

  View details for PubMedCentralID PMC11475399

• A large-scale audit of dataset licensing and attribution in AI NATURE MACHINE INTELLIGENCE Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., Wu, X., Shippole, E., Bollacker, K., Wu, T., Villa, L., Pentland, S., Hooker, S. 2024; 6 (8)

  View details for DOI 10.1038/s42256-024-00878-8

  View details for Web of Science ID 001303646000003

• Interpretable Stochastic Block Influence Model: Measuring Social Influence Among Homophilous Communities IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING Leng, Y., Sowrirajan, T., Zhai, Y., Pentland, A. 2024; 36 (2): 708-714

  View details for DOI 10.1109/TKDE.2023.3289848

  View details for Web of Science ID 001140611700033

• Unstoppable Wallets: Chain-assisted Threshold ECDSA and its Applications Zyskind, G., Yanai, A., Pentland, A., ASSOC COMPUTING MACHINERY ASSOC COMPUTING MACHINERY. 2024: 1844-1860

  View details for DOI 10.1145/3634737.3637657

  View details for Web of Science ID 001283918100128

• Network constraints on worker mobility NATURE CITIES Frank, M. R., Moro, E., South, T., Rutherford, A., Pentland, A., Taska, B., Rahwan, I. 2024; 1 (1)

  View details for DOI 10.1038/s44284-023-00009-1

  View details for Web of Science ID 001630911000009

• Gift Contagion in Online Groups: Evidence from Virtual Red Packets MANAGEMENT SCIENCE Yuan, Y., Liu, T., Tan, C., Chen, Q., Pentland, A., Tang, J. 2024; 70 (7)

  View details for DOI 10.1287/mnsc.2023.4906

  View details for Web of Science ID 001122939900001

• Satellite imagery and machine learning for channel member selection INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT Brei, V., Rech, N., Bozkaya, B., Balcisoy, S., Pentland, A., Silveira Netto, C. 2023; 51 (11): 1552-1568

  View details for DOI 10.1108/IJRDM-02-2023-0073

  View details for Web of Science ID 001050867800001

• Flexible social inference facilitates targeted social learning when rewards are not observable. Nature human behaviour Hawkins, R. D., Berdahl, A. M., Pentland, A. '., Tenenbaum, J. B., Goodman, N. D., Krafft, P. M. 2023

  Abstract

  Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behaviour. We compared our social inference model against simpler heuristics in three studies of human behaviour in a collective-sensing task. Experiment 1 demonstrated that average performance improved as a function of group size at a rate greater than predicted by heuristic models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behaviour.

  View details for DOI 10.1038/s41562-023-01682-x

  View details for PubMedID 37591983

• Implications of COVID-19 vaccination heterogeneity in mobility networks COMMUNICATIONS PHYSICS Yuan, Y., Jahani, E., Zhao, S., Ahn, Y., Pentland, A. 2023; 6 (1)

  View details for DOI 10.1038/s42005-023-01325-7

  View details for Web of Science ID 001043526700001

• Why voters who value democracy participate in democratic backsliding NATURE HUMAN BEHAVIOUR Braley, A., Lenz, G. S., Adjodah, D., Rahnama, H., Pentland, A. 2023; 7 (8): 1282-+

  Abstract

  Around the world, citizens are voting away the democracies they claim to cherish. Here we present evidence that this behaviour is driven in part by the belief that their opponents will undermine democracy first. In an observational study (N = 1,973), we find that US partisans are willing to subvert democratic norms to the extent that they believe opposing partisans are willing to do the same. In experimental studies (N = 2,543, N = 1,848), we revealed to partisans that their opponents are more committed to democratic norms than they think. As a result, the partisans became more committed to upholding democratic norms themselves and less willing to vote for candidates who break these norms. These findings suggest that aspiring autocrats may instigate democratic backsliding by accusing their opponents of subverting democracy and that we can foster democratic stability by informing partisans about the other side's commitment to democracy.

  View details for DOI 10.1038/s41562-023-01594-w

  View details for Web of Science ID 000992368300003

  View details for PubMedID 37217740

  View details for PubMedCentralID 7935088

• Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters NATURE COMMUNICATIONS Yabe, T., Bueno, B., Dong, X., Pentland, A., Moro, E. 2023; 14 (1): 2310

  Abstract

  Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

  View details for DOI 10.1038/s41467-023-37913-y

  View details for Web of Science ID 001061881500015

  View details for PubMedID 37085499

  View details for PubMedCentralID PMC10120472

• An experimental study of tie transparency and individual perception in social networks PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES Lyu, D., Teng, Y., Wang, L., Wang, X., Pentland, A. 2022; 478 (2258)

  View details for DOI 10.1098/rspa.2021.0744

  View details for Web of Science ID 000759390900004

• Detection of Coordination Between State-Linked Actors Erhardt, K., Pentland, A. edited by Thomson, R., Dancy, C., Pyke, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 144-154

  View details for DOI 10.1007/978-3-031-17114-7_14

  View details for Web of Science ID 000869621500014

• Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial JMIR FORMATIVE RESEARCH Noriega, A., Meizner, D., Camacho, D., Enciso, J., Quiroz-Mercado, H., Morales-Canton, V., Almaatouq, A., Pentland, A. 2021; 5 (8): e25290

  Abstract

  The automated screening of patients at risk of developing diabetic retinopathy represents an opportunity to improve their midterm outcome and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes.This study aimed to develop and evaluate the performance of an automated deep learning-based system to classify retinal fundus images as referable and nonreferable diabetic retinopathy cases, from international and Mexican patients. In particular, we aimed to evaluate the performance of the automated retina image analysis (ARIA) system under an independent scheme (ie, only ARIA screening) and 2 assistive schemes (ie, hybrid ARIA plus ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the 3 schemes.A randomized controlled experiment was performed where 17 ophthalmologists were asked to classify a series of retinal fundus images under 3 different conditions. The conditions were to (1) screen the fundus image by themselves (solo); (2) screen the fundus image after exposure to the retina image classification of the ARIA system (ARIA answer); and (3) screen the fundus image after exposure to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists' classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of 3 retina specialists for each fundus image.The ARIA system was able to classify referable vs nonreferable cases with an area under the receiver operating characteristic curve of 98%, a sensitivity of 95.1%, and a specificity of 91.5% for international patient cases. There was an area under the receiver operating characteristic curve of 98.3%, a sensitivity of 95.2%, and a specificity of 90% for Mexican patient cases. The ARIA system performance was more successful than the average performance of the 17 ophthalmologists enrolled in the study. Additionally, the results suggest that the ARIA system can be useful as an assistive tool, as sensitivity was significantly higher in the experimental condition where ophthalmologists were exposed to the ARIA system's answer prior to their own classification (93.3%), compared with the sensitivity of the condition where participants assessed the images independently (87.3%; P=.05).These results demonstrate that both independent and assistive use cases of the ARIA system present, for Latin American countries such as Mexico, a substantial opportunity toward expanding the monitoring capacity for the early detection of diabetes-related blindness.

  View details for DOI 10.2196/25290

  View details for Web of Science ID 000853673200025

  View details for PubMedID 34435963

  View details for PubMedCentralID PMC8430849

• Mobility patterns are associated with experienced income segregation in large US cities NATURE COMMUNICATIONS Moro, E., Calacci, D., Dong, X., Pentland, A. 2021; 12 (1): 4633

  Abstract

  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.

  View details for DOI 10.1038/s41467-021-24899-8

  View details for Web of Science ID 000684313500001

  View details for PubMedID 34330916

  View details for PubMedCentralID PMC8324796

• Secure and secret cooperation in robot swarms SCIENCE ROBOTICS Ferrer, E., Hardjono, T., Pentland, A., Dorigo, M. 2021; 6 (56)

  Abstract

  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.

  View details for DOI 10.1126/scirobotics.abf1538

  View details for Web of Science ID 000679973600005

  View details for PubMedID 34321346

• Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions ENTROPY Adjodah, D., Leng, Y., Chong, S., Krafft, P. M., Moro, E., Pentland, A. 2021; 23 (7)

  Abstract

  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.

  View details for DOI 10.3390/e23070801

  View details for Web of Science ID 000676148700001

  View details for PubMedID 34202445

  View details for PubMedCentralID PMC8307866

• Effect of COVID-19 response policies on walking behavior in US cities NATURE COMMUNICATIONS Hunter, R. F., Garcia, L., de Sa, T., Zapata-Diomedi, B., Millett, C., Woodcock, J., Pentland, A., Moro, E. 2021; 12 (1): 3652

  Abstract

  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.

  View details for DOI 10.1038/s41467-021-23937-9

  View details for Web of Science ID 000664860100001

  View details for PubMedID 34135325

  View details for PubMedCentralID PMC8209100

• Housing Prices and the Skills Composition of Neighborhoods FRONTIERS IN BIG DATA Althobaiti, S., Alghumayjan, S., Frank, M. R., Moro, E., Alabdulkareem, A., Pentland, A. 2021; 4: 652153

  Abstract

  In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

  View details for DOI 10.3389/fdata.2021.652153

  View details for Web of Science ID 000659105800001

  View details for PubMedID 34136803

  View details for PubMedCentralID PMC8200666

• The Strength of Structural Diversity in Online Social Networks RESEARCH Zhang, Y., Wang, L., Zhu, J. J. H., Wang, X., Pentland, A. 2021; 2021: 9831621

  Abstract

  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.

  View details for DOI 10.34133/2021/9831621

  View details for Web of Science ID 000658423800001

  View details for PubMedID 34386773

  View details for PubMedCentralID PMC8328400

• COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour JOURNAL OF THE ROYAL SOCIETY INTERFACE Heroy, S., Loaiza, I., Pentland, A., O'Clery, N. 2021; 18 (176): 20201035

  Abstract

  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.

  View details for DOI 10.1098/rsif.2020.1035

  View details for Web of Science ID 000636365800001

  View details for PubMedID 33784887

  View details for PubMedCentralID PMC8098708

• Universal resilience patterns in labor markets. Nature communications Moro, E., Frank, M. R., Pentland, A., Rutherford, A., Cebrian, M., Rahwan, I. 2021; 12 (1): 1972

  Abstract

  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.

  View details for DOI 10.1038/s41467-021-22086-3

  View details for PubMedID 33785734

• Spillovers across industries and regions in China's regional economic diversification REGIONAL STUDIES Gao, J., Jun, B., Pentland, A., Zhou, T., Hidalgo, C. A. 2021; 55 (7): 1311-1326

  View details for DOI 10.1080/00343404.2021.1883191

  View details for Web of Science ID 000635413800001

• Prediction and prevention of disproportionally dominant agents in complex networks PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Lera, S., Pentland, A., Sornette, D. 2020; 117 (44): 27090-27095

  Abstract

  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.

  View details for DOI 10.1073/pnas.2003632117

  View details for Web of Science ID 000587503000011

  View details for PubMedID 33067387

  View details for PubMedCentralID PMC7959489

• Looking for a better future: modeling migrant mobility APPLIED NETWORK SCIENCE Saa, I., Novak, M., Morales, A. J., Pentland, A. 2020; 5 (1)

  View details for DOI 10.1007/s41109-020-00308-9

  View details for Web of Science ID 000680088100003

• Contextualizing Human Psychology TECHNOLOGY, MIND, AND BEHAVIOR Pentland, A. 2020; 1 (1)

  View details for DOI 10.1037/tmb0000013

  View details for Web of Science ID 001394150100001

• Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences of the United States of America Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., Morgan, A. C., Pentland, A., Polimis, K., Raes, L., Rigobon, D. E., Roberts, C. V., Stanescu, D. M., Suhara, Y., Usmani, A., Wang, E. H., Adem, M., Alhajri, A., AlShebli, B., Amin, R., Amos, R. B., Argyle, L. P., Baer-Bositis, L., Buchi, M., Chung, B., Eggert, W., Faletto, G., Fan, Z., Freese, J., Gadgil, T., Gagne, J., Gao, Y., Halpern-Manners, A., Hashim, S. P., Hausen, S., He, G., Higuera, K., Hogan, B., Horwitz, I. M., Hummel, L. M., Jain, N., Jin, K., Jurgens, D., Kaminski, P., Karapetyan, A., Kim, E. H., Leizman, B., Liu, N., Moser, M., Mack, A. E., Mahajan, M., Mandell, N., Marahrens, H., Mercado-Garcia, D., Mocz, V., Mueller-Gastell, K., Musse, A., Niu, Q., Nowak, W., Omidvar, H., Or, A., Ouyang, K., Pinto, K. M., Porter, E., Porter, K. E., Qian, C., Rauf, T., Sargsyan, A., Schaffner, T., Schnabel, L., Schonfeld, B., Sender, B., Tang, J. D., Tsurkov, E., van Loon, A., Varol, O., Wang, X., Wang, Z., Wang, J., Wang, F., Weissman, S., Whitaker, K., Wolters, M. K., Woon, W. L., Wu, J., Wu, C., Yang, K., Yin, J., Zhao, B., Zhu, C., Brooks-Gunn, J., Engelhardt, B. E., Hardt, M., Knox, D., Levy, K., Narayanan, A., Stewart, B. M., Watts, D. J., McLanahan, S. 2020

  Abstract

  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.

  View details for DOI 10.1073/pnas.1915006117

  View details for PubMedID 32229555

• An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection SCIENTIFIC REPORTS Gomes, P. A. B., Suhara, Y., Nunes-Silva, P., Costa, L., Arruda, H., Venturieri, G., Imperatriz-Fonseca, V., Pentland, A., de Souza, P., Pessin, G. 2020; 10 (1): 9

  Abstract

  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.

  View details for DOI 10.1038/s41598-019-56352-8

  View details for Web of Science ID 000511416600001

  View details for PubMedID 31913302

  View details for PubMedCentralID PMC6949272

• Computational social science: Obstacles and opportunities. Science (New York, N.Y.) Lazer, D. M., Pentland, A. n., Watts, D. J., Aral, S. n., Athey, S. n., Contractor, N. n., Freelon, D. n., Gonzalez-Bailon, S. n., King, G. n., Margetts, H. n., Nelson, A. n., Salganik, M. J., Strohmaier, M. n., Vespignani, A. n., Wagner, C. n. 2020; 369 (6507): 1060–62

  View details for DOI 10.1126/science.aaz8170

  View details for PubMedID 32855329

• Segregation and polarization in urban areas ROYAL SOCIETY OPEN SCIENCE Morales, A. J., Dong, X., Bar-Yam, Y., Pentland, A. 2019; 6 (10): 190573

  Abstract

  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.

  View details for DOI 10.1098/rsos.190573

  View details for Web of Science ID 000510431100017

  View details for PubMedID 31824692

  View details for PubMedCentralID PMC6837204

• BLOCKCHAIN TECHNOLOGIES AND APPLICATIONS CHINA COMMUNICATIONS Liu, E., Pentland, A., Adamson, G., Ramadoss, R., Yang, Y., Tsai, W., Zhao, Y., Lei, M. 2019; 16 (6): III-V

  View details for Web of Science ID 000474575900001

• Machine behaviour NATURE Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A., Roberts, M. E., Shariff, A., Tenenbaum, J. B., Wellman, M. 2019; 568 (7753): 477-486

  Abstract

  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.

  View details for DOI 10.1038/s41586-019-1138-y

  View details for Web of Science ID 000465594200038

  View details for PubMedID 31019318

  View details for PubMedCentralID 5777393

• THE RISE OF DECENTRALIZED PERSONAL DATA MARKETS TRUSTED DATA: A NEW FRAMEWORK FOR IDENTITY AND DATA SHARING Staiano, J., Zyskind, G., Lepri, B., Oliver, N., Pentland, A. edited by Hardjono, T., Shrier, D. L., Pentland, A. 2019: 155-166

  View details for Web of Science ID 000546629600007

• An interpretable approach for social network formation among heterogeneous agents NATURE COMMUNICATIONS Yuan, Y., Alabdulkareem, A., Pentland, A. 2018; 9: 4704

  Abstract

  Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an "endowment vector" that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology.

  View details for DOI 10.1038/s41467-018-07089-x

  View details for Web of Science ID 000449494700004

  View details for PubMedID 30410019

  View details for PubMedCentralID PMC6224571

• Breaking the Bank. Scientific American Lipton, A., Pentland, A. S. 2017; 318 (1): 26-31

  View details for DOI 10.1038/scientificamerican0118-26

  View details for PubMedID 29257802

• Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders JOURNAL OF MEDICAL INTERNET RESEARCH Place, S., Blanch-Hartigan, D., Rubin, C., Gorrostieta, C., Mead, C., Kane, J., Marx, B. P., Feast, J., Deckersbach, T., Pentland, A., Nierenberg, A., Azarbayejani, A. 2017; 19 (3): e75

  Abstract

  There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable.The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform.A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns.Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36).Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed.

  View details for DOI 10.2196/jmir.6678

  View details for Web of Science ID 000411949800006

  View details for PubMedID 28302595

  View details for PubMedCentralID PMC5374272

• Enigma: Decentralized Computation Platform with Guaranteed Privacy NEW SOLUTIONS FOR CYBERSECURITY Zyskind, G., Pentland, A. edited by Shrobe, H., Shrier, D., Pentland, A. 2017: 425-454

  View details for Web of Science ID 000477882400016

• bandicoot: a Python Toolbox for Mobile Phone Metadata JOURNAL OF MACHINE LEARNING RESEARCH de Montjoye, Y., Rocher, L., Pentland, A. 2016; 17

  View details for Web of Science ID 000391679800001

• Keynote: Building a Nervous System for Society: The 'New Deal on Data' and How to Make Health, Financial, Logistics, and Transportation Systems Work Pentland, A. edited by Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., BlomQvist, E. SPRINGER-VERLAG BERLIN. 2011: 390

  View details for Web of Science ID 000306458200034