arXiv subjects

Alex 'Sandy' Pentland

Publications and source records attributed to Alex 'Sandy' Pentland.

At least 19 recordsLinked to original sources

arXiv2026

Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents

LLM agents in social simulation revise their opinions implicitly, in context: how open an agent is to persuasion can neither be specified nor verified, and collective outcomes inherit the model's training prior. We introduce Bayesian Chronicle Agents (BCA), a minimal belief layer separating what an agent believes from how it speaks. Each stance is a probability, updated by one Bayesian step per utterance heard. A single prior-strength parameter $κ$ encodes stubbornness, modeled after its role in Friedkin--Johnsen (FJ) opinion dynamics. We then sweep this parameter to yield three canonical regimes of opinion dynamics on demand (consensus, persistent disagreement, committed-minority influence), with persistent disagreement matching the FJ closed-form fixed points at $R^2\!=\!0.93$--$0.99$. We further show that prescribed $κ$ remains recoverable after the language round-trip, with perfect rank-order recovery across all four models. Explicit belief also makes simulation auditable: the layer surfaces systematic per-model stance biases that end-to-end simulation would silently absorb.

cs.MA↗

arXiv2024

Temporal fingerprints: Identity matching across fully encrypted domain

Technological advancements have significantly transformed communication patterns, introducing a diverse array of online platforms, thereby prompting individuals to use multiple profiles for different domains and objectives. Enhancing the understanding of cross domain identity matching capabilities is essential, not only for practical applications such as commercial strategies and cybersecurity measures, but also for theoretical insights into the privacy implications of data disclosure. In this study, we demonstrate that individual temporal data, in the form of inter-event times distribution, constitutes an individual temporal fingerprint, allowing for matching profiles across different domains back to their associated real-world entity. We evaluate our methodology on encrypted digital trading platforms within the Ethereum Blockchain and present impressing results in matching identities across these privacy-preserving domains, while outperforming previously suggested models. Our findings indicate that simply knowing when an individual is active, even if information about who they talk to and what they discuss is lacking, poses risks to users' privacy, highlighting the inherent challenges in preserving privacy in today's digital landscape.

cs.CR↗

arXiv2024

Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LLMs) in legal education to help non-experts learn intricate legal concepts through storytelling, an effective pedagogical tool in conveying complex and abstract concepts. We also introduce a new dataset LegalStories, which consists of 294 complex legal doctrines, each accompanied by a story and a set of multiple-choice questions generated by LLMs. To construct the dataset, we experiment with various LLMs to generate legal stories explaining these concepts. Furthermore, we use an expert-in-the-loop approach to iteratively design multiple-choice questions. Then, we evaluate the effectiveness of storytelling with LLMs through randomized controlled trials (RCTs) with legal novices on 10 samples from the dataset. We find that LLM-generated stories enhance comprehension of legal concepts and interest in law among non-native speakers compared to only definitions. Moreover, stories consistently help participants relate legal concepts to their lives. Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment. Our work has strong implications for using LLMs in promoting teaching and learning in the legal field and beyond.

cs.CL↗

arXiv2024

Verifiable evaluations of machine learning models using zkSNARKs

In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results-whether over task accuracy, bias evaluations, or safety checks-are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. We present a flexible proving system that enables verifiable attestations to be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.

cs.LG↗

arXiv2023

The Law and NLP: Bridging Disciplinary Disconnects

Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. In this position paper, we argue that the slow uptake of NLP in legal practice is exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers. In a review of recent trends in the legal NLP literature, we find limited overlap between the legal NLP community and legal academia. Our interpretation is that some of the most popular legal NLP tasks fail to address the needs of legal practitioners. We discuss examples of legal NLP tasks that promise to bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored.

cs.CL↗

arXiv2022

The Network Structure of Unequal Diffusion

Social networks affect the diffusion of information, and thus have the potential to reduce or amplify inequality in access to opportunity. We show empirically that social networks often exhibit a much larger potential for unequal diffusion across groups along paths of length 2 and 3 than expected by our random graph models. We argue that homophily alone cannot not fully explain the extent of unequal diffusion and attribute this mismatch to unequal distribution of cross-group links among the nodes. Based on this insight, we develop a variant of the stochastic block model that incorporates the heterogeneity in cross-group linking. The model provides an unbiased and consistent estimate of assortativity or homophily on paths of length 2 and provide a more accurate estimate along paths of length 3 than existing models. We characterize the null distribution of its log-likelihood ratio test and argue that the goodness of fit test is valid only when the network is dense. Based on our empirical observations and modeling results, we conclude that the impact of any departure from equal distribution of links to source nodes in the diffusion process is not limited to its first order effects as some nodes will have fewer direct links to the sources. More importantly, this unequal distribution will also lead to second order effects as the whole group will have fewer diffusion paths to the sources.

stat.AP↗

arXiv2022

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.

cs.SI↗

arXiv2021

Secure and secret cooperation in robotic swarms

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 novel method to encapsulate cooperative robotic missions in an authenticated data structure known as 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 towards 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 operations 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 such as environmental monitoring, infrastructure surveillance, and disaster management.

cs.RO↗

arXiv2020

Prediction and Prevention of Disproportionally Dominant Agents in Complex Networks

We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (`winner-takes-all') 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 4-dimensional phase diagram that separates the growing system into two regimes: one where the `fit-get-richer' (FGR) and one where, eventually, the `winner-takes-all' (WTA). By calibrating the system's parameters with maximum likelihood, its distance from the 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 others 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.

physics.soc-ph↗

arXiv2020

A Study of Compositional Generalization in Neural Models

Compositional and relational learning is a hallmark of human intelligence, but one which presents challenges for neural models. One difficulty in the development of such models is the lack of benchmarks with clear compositional and relational task structure on which to systematically evaluate them. In this paper, we introduce an environment called ConceptWorld, which enables the generation of images from compositional and relational concepts, defined using a logical domain specific language. We use it to generate images for a variety of compositional structures: 2x2 squares, pentominoes, sequences, scenes involving these objects, and other more complex concepts. We perform experiments to test the ability of standard neural architectures to generalize on relations with compositional arguments as the compositional depth of those arguments increases and under substitution. We compare standard neural networks such as MLP, CNN and ResNet, as well as state-of-the-art relational networks including WReN and PrediNet in a multi-class image classification setting. For simple problems, all models generalize well to close concepts but struggle with longer compositional chains. For more complex tests involving substitutivity, all models struggle, even with short chains. In highlighting these difficulties and providing an environment for further experimentation, we hope to encourage the development of models which are able to generalize effectively in compositional, relational domains.

cs.LG↗

arXiv2020

User behavior and token adoption on ERC20

Cryptocurrencies and Blockchain-based technologies are disrupting all markets. While the potential of such technologies remains to be seen, there is a current need to understand emergent patterns of user behavior and token adoption in order to design future products. In this paper we analyze the social dynamics taking place during one arbitrary day on the ERC20 platform. We characterize the network of token transactions among agents. We show heterogeneous profiles of user behavior, portfolio diversity, and token adoption. While most users are specialized in transacting with a few tokens, those that have diverse portfolios are bridging across large parts of the network and may jeopardize the system stability. We believe this work to be a foundation for unveiling the usage dynamics of crypto-currencies networks.

cs.SI↗

arXiv2020

Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break past the false dichotomy of effective versus privacy-preserving contact tracing. We offer an alternative approach to assess and communicate users' risk of exposure to an infectious disease while preserving individual privacy. Our proposal uses recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol to interface with a semi-trusted authority. There have been other recent proposals for privacy-preserving contact tracing, based on Bluetooth and decentralization, that could further eliminate the need for trust in authority. However, solutions with Bluetooth are currently limited to certain devices and contexts while decentralization adds complexity. The goal of this work is two-fold: we aim to propose a location-based system that is more privacy-preserving than what is currently being adopted by governments around the world, and that is also practical to implement with the immediacy needed to stem a viral outbreak.

cs.CR↗

arXiv2019

Assessing Refugees' Integration via Spatio-temporal Similarities of Mobility and Calling Behaviors

In Turkey the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing a real-world Call Details Records (CDRs) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatiotemporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample to a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allows computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: (i) correlated with the amount of interaction with locals; (ii) an effective proxy for refugee's economic capacity, thus refugee's potential employment; and (iii) able to capture events that may disrupt the integration phenomena, such as social tensions.

cs.CY↗

arXiv2018

Detecting Bot Activity in the Ethereum Blockchain Network

The Ethereum blockchain network is a decentralized platform enabling smart contract execution and transactions of Ether (ETH) [1], its designated cryptocurrency. Ethereum is the second most popular cryptocurrency with a market cap of more than 100 billion USD, with hundreds of thousands of transactions executed daily by hundreds of thousands of unique wallets. Tens of thousands of those wallets are newly generated each day. The Ethereum platform enables anyone to freely open multiple new wallets [2] free of charge (resulting in a large number of wallets that are controlled by the same entities). This attribute makes the Ethereum network a breeding space for activity by software robots (bots). The existence of bots is widespread in different digital technologies and there are various approaches to detect their activity such as rule-base, clustering, machine learning and more [3,4]. In this work we demonstrate how bot detection can be implemented using a network theory approach.

cs.SI↗

arXiv2017

Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.

cs.LG↗

arXiv2017

Open Badges: A Low-Cost Toolkit for Measuring Team Communication and Dynamics

We present Open Badges, an open-source framework an toolkit for measuring and shaping face-to-face social interactions using either custom hardware devices or smart phones, and real-time web-based visualizations. Open Badges is a modular system that allows researchers to monitor and collect interaction data from people engaged in real-life social settings. In this paper we describe the technical aspects of the Open Badges project and the motivation for its creation.

cs.HC↗

arXiv2016

Analysis of Tourism Dynamics and Special Events through Mobile Phone Metadata

Tourism has been an increasingly important factor in global economy, society and environment, accounting for a significant share of GDP and labor force. Policy and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at low spatial and temporal granularity. Anonymous call detail records (CDRs) are a novel source of data, showing enormous potential in areas of high societal value: such as epidemics, poverty, and urban development. This study demonstrates the added value of using CDRs for the formulation, analysis and evaluation of tourism strategies, at the national and local levels. In the context of the European country of Andorra, we use CDRs to evaluate marketing strategies in tourism, understand tourists' experiences, and evaluate revenues and externalities generated by touristic events. We do this by extracting novel indicators in high spatial and temporal resolutions, such as tourist flows per country of origin, flows of new tourists, tourist revisits, tourist externalities on transportation congestion, spatial distribution, economic impact, and profiling of tourist interests. We exemplify the use of these indicators for the planning and evaluation of high impact touristic events, such as cultural festivals and sports competitions.

cs.CY↗

arXiv2016

Managing travel demand: Location recommendation for system efficiency based on mobile phone data

Growth in leisure travel has become increasingly significant economically, socially, and environmentally. However, flexible but uncoordinated travel behaviors exacerbate traffic congestion. Mobile phone records not only reveal human mobility patterns, but also enable us to manage travel demand for system efficiency. In this paper, we propose a location recommendation system that infers personal preferences while accounting for constraints imposed by road capacity in order to manage travel demand. We first infer unobserved preferences using a machine learning technique from phone records. We then formulate an optimization method to improve system efficiency. Coupling mobile phone data with traffic counts and road network infrastructures collected in Andorra, this study shows that uncoordinated travel behaviors lead to longer average travel delay, implying the opportunities in managing travel demand by collective decisions. The interplay between congestion relief and overall satisfied location preferences observed in extensive simulations indicate that moderate sacrifices of individual utility lead to significant travel time savings. Specifically, the results show that under full compliance rate, travel delay fell by 52% at a cost of 31% less satisfaction. Under 60% compliance rate, 41% travel delay is saved with a 17% reduction in satisfaction. This paper highlights the effectiveness of the synergy among collective behaviors in increasing system efficiency.

cs.CY↗