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Beyond Private or Public: Large Language Models as Quasi-Public Goods in the AI Economy
Yukun Zhang, TianYang Zhang
https://arxiv.org/abs/2509.13265 https://

Beyond Private or Public: Large Language Models as Quasi-Public Goods in the AI Economy
This paper conceptualizes Large Language Models (LLMs) as a form of mixed public goods within digital infrastructure, analyzing their economic properties through a comprehensive theoretical framework. We develop mathematical models to quantify the non-rivalry characteristics, partial excludability, and positive externalities of LLMs. Through comparative analysis of open-source and closed-source development paths, we identify systematic differences in resource allocation efficiency, innovation t…

"Assessing the academic and societal impact of Open Access: bibliometric and altmetric analyses" https://doi.org/10.1007/s11192-025-05436-6
"The accessibility of scholarly research plays a crucial role in shaping academic impact and societal engagement. This study examine…

Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation
Philipp Bach, Sven Klaassen, Jannis Kueck, Mara Mattes, Martin Spindler
https://arxiv.org/abs/2510.09064

Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation
Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and untreated group are assumed to evolve in parallel over time, conditional on pre-treatment covariates. We introduce a novel approach to sensitivity analysis for DiD models that assesses the robustness of DiD…

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
Yuqi Bai, Tianyu Huang, Kun Sun, Yuting Chen
https://arxiv.org/abs/2510.11734

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
This research focuses on using large language models (LLMs) to simulate social experiments, exploring their ability to emulate human personality in virtual persona role-playing. The research develops an end-to-end evaluation framework, including individual-level analysis of stability and identifiability, as well as population-level analysis called progressive personality curves to examine the veracity and consistency of LLMs in simulating human personality. Methodologically, this research propo…

CrisisNews: A Dataset Mapping Two Decades of News Articles on Online Problematic Behavior at Scale
Jeanne Choi, DongJae Kang, Yubin Choi, Juhoon Lee, Joseph Seering
https://arxiv.org/abs/2510.12243

CrisisNews: A Dataset Mapping Two Decades of News Articles on Online Problematic Behavior at Scale
As social media adoption grows globally, online problematic behaviors increasingly escalate into large-scale crises, requiring an evolving set of mitigation strategies. While HCI research often analyzes problematic behaviors with pieces of user-generated content as the unit of analysis, less attention has been given to event-focused perspectives that track how discrete events evolve. In this paper, we examine 'social media crises': discrete patterns of problematic behaviors originating and evol…

SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management
Nan Lu, Yurong Hu, Jiaquan Fang, Yan Liu, Rui Dong, Yiming Wang, Rui Lin, Shaoyi Xu
https://arxiv.org/abs/2510.08948

SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management
The growth of the e-commerce industry has intensified the adversarial dynamics between shadow economy actors and risk management teams. Companies often conduct risk investigations into suspicious cases to identify emerging fraud patterns, thereby enhancing both preemptive risk prevention and post-hoc governance. However, the sheer volume of case analyses imposes a substantial workload on risk management analysts, as each case requires the integration of long-term expert experience and meticulou…

Trends in New Mexico School Districts Serving Low-Income Communities
Uloma E. Nelson, Onyedikachi J. Okeke
https://arxiv.org/abs/2510.09993 https://arxiv.o…

Trends in New Mexico School Districts Serving Low-Income Communities
This study examines recent enrollment trends and their socioeconomic drivers within New Mexico's public school districts, with a specific focus on those serving low-income communities. Utilizing a mixed-methods geospatial framework, the research integrates longitudinal enrollment data, district poverty metrics from the Small Area Income and Poverty Estimates (SAIPE), and neighborhood context from the American Community Survey (ACS) from 2019 to 2023. The analysis reveals that while statewide K-…

Selling Privacy in Blockchain Transactions
Georgios Chionas, Olga Gorelkina, Piotr Krysta, Rida Laraki
https://arxiv.org/abs/2512.08096 https://arxiv.org/pdf/2512.08096 https://arxiv.org/html/2512.08096
arXiv:2512.08096v1 Announce Type: new
Abstract: We study methods to enhance privacy in blockchain transactions from an economic angle. We consider mechanisms for privacy-aware users whose utility depends not only on the outcome of the mechanism but also negatively on the exposure of their economic preferences. Specifically, we study two auction-theoretic settings with privacy-aware users. First, we analyze an order flow auction, where a user auctions off to specialized agents, called searchers, the right to execute her transaction while maintaining a degree of privacy. We examine how the degree of privacy affects the revenue of the auction and, broadly, the net utility of the privacy-aware user. In this new setting, we describe the optimal auction, which is a sealed-bid auction. Subsequently, we analyze a variant of a Dutch auction in which the user gradually decreases the price and the degree of privacy until the transaction is sold. We compare the revenue of this auction to that of the optimal one as a function of the number of communication rounds. Then, we introduce a two-sided market - a privacy marketplace - with multiple users selling their transactions under their privacy preferences to multiple searchers. We propose a posted-price mechanism for the two-sided market that guarantees constant approximation of the optimal social welfare while maintaining incentive compatibility (from both sides of the market) and budget balance. This work builds on the emerging line of research that attempts to improve the performance of economic mechanisms by appending cryptographic primitives to them.
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Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia, Sven Klaassen, Nils Skotara, Martin Spindler
https://arxiv.org/abs/2510.09109

Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal effect estimates may become biased, undermining the validity of research findings. In these contexts, sensitivity analysis plays a crucial ro…

Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Jinho Cha, Justin Yoo, Eunchan Daniel Cha, Emily Yoo, Caedon Geoffrey, Hyoshin Song
https://arxiv.org/abs/2510.05504

Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending c…