arXiv subjects

Pentland

Publications and source records attributed to Pentland.

10 recordsLinked to original sources

arXiv2025

Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers

We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time, how changes vary across demographic groups, the impact of the COVID-19 pandemic, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing behaviors and 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 and dominance 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.

cs.CY↗

arXiv2020

Quantization Games on Social Networks and Language Evolution

We consider a strategic network quantizer design setting where agents must balance fidelity in representing their local source distributions against their ability to successfully communicate with other connected agents. We study the problem as a network game and show existence of Nash equilibrium quantizers. For any agent, under Nash equilibrium, 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 show that under certain conditions, the agents need not know the source origin and yet still settle on a Nash equilibrium using only the observed sources. Further, the network may converge to equilibrium through a distributed version of the Lloyd-Max algorithm. In contrast to traditional results in the evolution of language, we find several vocabularies may coexist in the 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 argue 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 results are given.

cs.IT↗

arXiv2019

The tradeoff between the utility and risk of location data and implications for public good

High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it's been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.

cs.CY↗

arXiv2014

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.

cs.CY↗

arXiv2014

Using sociometers to quantify social interaction patterns

Research on human social interactions has traditionally relied on self-reports. Despite their widespread use, self-reported accounts of behaviour are prone to biases and necessarily reduce the range of behaviours, and the number of subjects, that may be studied simultaneously. The development of ever smaller sensors makes it possible to study group-level human behaviour in naturalistic settings outside research laboratories. We used such sensors, sociometers, to examine gender, talkativeness and interaction style in two different contexts. Here, we find that in the collaborative context, women were much more likely to be physically proximate to other women and were also significantly more talkative than men, especially in small groups. In contrast, there were no gender-based differences in the non-collaborative setting. Our results highlight the importance of objective measurement in the study of human behaviour, here enabling us to discern context specific, gender-based differences in interaction style.

physics.soc-ph↗

arXiv2013

Dynamics of Human Social Networks: People, Time, Relationships, and Places

The availability of advanced social interaction sensing technologies provides fine grained data for social network analysis. Although traditional methods of gathering social network data may be subject to human ability to recall social details, people's rating of their closeness to persons in their network is important. This study assesses the relationship amidst closeness ratings, sensed interactions and shared places of recreation. The study found that people tend to give high closeness ratings to people with whom they spend more time. Shared places are correlated to social closeness ratings but not to length of interactions. The results of this study highlight the importance of sensed interactions and closeness ratings.

cs.SI↗

arXiv2013

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining, and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level.Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network.

cs.CY↗

arXiv2010

Generalized Stable Matching in Bipartite Networks

In this paper we study the generalized version of weighted matching in bipartite networks. Consider a weighted matching in a bipartite network in which the nodes derive value from the split of the matching edge assigned to them if they are matched. The value a node derives from the split depends both on the split as well as the partner the node is matched to. We assume that the value of a split to the node is continuous and strictly increasing in the part of the split assigned to the node. A stable weighted matching is a matching and splits on the edges in the matching such that no two adjacent nodes in the network can split the edge between them so that both of them can derive a higher value than in the matching. We extend the weighted matching problem to this general case and study the existence of a stable weighted matching. We also present an algorithm that converges to a stable weighted matching. The algorithm generalizes the Hungarian algorithm for bipartite matching. Faster algorithms can be made when there is more structure on the value functions.

math.OC↗

arXiv2010

Existence of Stable Exclusive Bilateral Exchanges in Networks

In this paper we show that when individuals in a bipartite network exclusively choose partners and exchange valued goods with their partners, then there exists a set of exchanges that are pair-wise stable. Pair-wise stability implies that no individual breaks her partnership and no two neighbors in the network can form a new partnership while breaking other partnerships if any so that at least one of them improves her payoff and the other one does at least as good. We consider a general class of continuous, strictly convex and strongly monotone preferences over bundles of goods for individuals. Thus, this work extends the general equilibrium framework from markets to networks with exclusive exchanges. We present the complete existence proof using the existence of a generalized stable matching in \cite{Generalized-Stable-Matching}. The existence proof can be extended to problems in social games as in \cite{Matching-Equilibrium} and \cite{Social-Games}.

cs.GT↗

arXiv2010

Patterns of Individual Shopping Behavior

Much of economic theory is built on observations of aggregate, rather than individual, behavior. Here, we present novel findings on human shopping patterns at the resolution of a single purchase. Our results suggest that much of our seemingly elective activity is actually driven by simple routines. While the interleaving of shopping events creates randomness at the small scale, on the whole consumer behavior is largely predictable. We also examine income-dependent differences in how people shop, and find that wealthy individuals are more likely to bundle shopping trips. These results validate previous work on mobility from cell phone data, while describing the unpredictability of behavior at higher resolution.

cs.CY↗