Flexible social inference facilitates targeted social learning when rewards are not observable.

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.

Art and the science of generative AI.

Understanding shifts in creative work will help guide AI's impact on the media ecosystem.

Diversity beyond density: Experienced social mixing of urban streets.

Urban density, in the form of residents' and visitors' concentration, is long considered to foster diverse exchanges of interpersonal knowledge and skills, which are intrinsic to sustainable human settlements. However, with current urban studies primarily devoted to city- and district-level analyses, we cannot unveil the elemental connection between urban density and diversity. Here we use an anonymized and privacy-enhanced mobile dataset of 0.5 million opted-in users from three metropolitan areas in the United States to show that at the scale of urban streets, density is not the only path to diversity. We represent the diversity of each street with the experienced social mixing (ESM), which describes the chances of people meeting diverse income groups throughout their daily experience. We conduct multiple experiments and show that the concentration of visitors only explains 26% of street-level ESM. However, adjacent amenities, residential diversity, and income level account for 44% of the ESM. Moreover, using longitudinal business data, we show that streets with an increased number of food businesses have seen an increased ESM from 2016 to 2018. Lastly, although streets with more visitors are more likely to have crime, diverse streets tend to have fewer crimes. These findings suggest that cities can leverage many tools beyond density to curate a diverse and safe street experience for people.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

The Strength of Structural Diversity in Online Social Networks.

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.

Effect of COVID-19 response policies on walking behavior in US cities.

The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy.

Validating Gravity-Based Market Share Models Using Large-Scale Transactional Data.

Customer patronage behavior has been widely studied in market share modeling contexts, which is an essential step toward estimating retail sales and finding new store locations in a competitive setting. Existing studies have conducted surveys to estimate merchants' market share and factors of attractiveness to use in various proposed mathematical models. Recent trends in Big Data analysis allow us to better understand human behavior and decision making, potentially leading to location models with more realistic assumptions. In this article, we propose a novel approach for validating the Huff gravity market share model, using a large-scale transactional dataset that describes customer patronage behavior at a regional level. Although the Huff model has been well studied and widely used in the context of sales estimation, competitive facility location, and demand allocation, this article is the first in validating the Huff model with a real dataset. Our approach helps to easily apply the model in different regions and with different merchant categories. Experimental results show that the Huff model fits well when modeling customer shopping behavior for a number of shopping categories, including grocery stores, clothing stores, gas stations, and restaurants. We also conduct regression analysis to show that certain features such as gender diversity and marital status diversity lead to stronger validation of the Huff model. We believe we provide strong evidence, with the help of real-world data, that gravity-based market share models are viable assumptions for retail sales estimation and competitive facility location models.

Bayesian collective learning emerges from heuristic social learning.

Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon 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 population 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.

Network Dynamics of a Financial Ecosystem.

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.

Machine behaviour.

Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.

How Physical Proximity Shapes Complex Social Networks.

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 [Formula: see text] 1 meter, and the long-range which includes all interactions d [Formula: see text] 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.

An interpretable approach for social network formation among heterogeneous agents.

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.

Optimizing targeted vaccination across cyber-physical networks: an empirically based mathematical simulation study.

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.

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Response to Comment on "Unique in the shopping mall: On the reidentifiability of credit card metadata".

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.

Gender Differences in Real-Home Sleep of Young and Older Couples.

STUDY 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.

Identity and privacy. Unique in the shopping mall: on the reidentifiability of credit card metadata.

Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.