39 papers
This paper demonstrates that Large Language Model-based pricing agents in oligopoly and auction settings can autonomously achieve supracompetitive prices and profits, a behavior significantly influenced by prompt variations and driven by price-war concerns, thereby posing unique challenges for future AI regulation.
This paper develops a dynamic equilibrium model for Automated Market Makers that reveals how the strategic interaction between informed arbitrageurs, noise traders, and liquidity providers—shaped by intrinsic buy-sell asymmetry and execution costs—leads to a non-monotonic, hump-shaped relationship between volatility and optimal liquidity provision.
This paper presents a stochastic macro-financial model integrating the Keen framework with a jump-diffusion asset market to formalize the feedback loops between credit expansion and crash risk, demonstrating how endogenous financial fragility can generate regimes ranging from stable growth to recurrent boom-bust cycles.
While theoretical models suggest Condorcet voting methods produce more moderate winners than Instant Runoff Voting, this study demonstrates that incorporating realistic voter behaviors, such as partial ballots, significantly diminishes the moderating advantage of Condorcet methods.
This paper extends the restrictiveness measure to functional and structural econometric settings using Gaussian process priors, demonstrating that models appear more restrictive over continuum domains than finite observations, establishing restrictiveness as the limit of noise-free learning curves, and revealing how endogeneity and discrepancy function choices fundamentally alter model rankings.
This paper presents a unified equilibrium model combining Arrow-Debreu and Kyle frameworks to explain how informed agents with arbitrary private information about state probabilities trade state-contingent claims, thereby accounting for key option market phenomena such as common trading strategies and the volatility smile.
This paper analyzes Italian manufacturing firms to demonstrate that while external recruitment is consistently necessary for all types of innovation, internal training strategies are particularly effective in driving the adoption of circular innovations.
This paper analyzes AI and robotics patenting trends from 1980 to 2019, revealing that while AI-enhanced robotics experienced a sharp post-2010 acceleration, the long-run integration of these technologies and the role of innovation systems vary significantly across countries, with China showing strong public-sector-driven integration and the US exhibiting a more market-oriented, less integrated structure.
This paper proposes a model of "agent capital" where AI reduces coordination costs to endogenously reshape organizational structures, revealing a regime fork where the distributional outcome—ranging from broad-based gains to superstar concentration—depends on who controls the resulting organizational elasticity, even as overall inequality declines through employment expansion.
Using a hybrid DEA and machine learning approach on PISA 2022 data from nine Latin American countries, this study reveals that private schools outperform public schools in both cognitive and non-cognitive efficiency, identifying distinct determinants such as home resources and autonomy for private schools versus climate and student engagement challenges for public schools.
Using explainable machine learning on PISA 2022 data from nine Latin American countries, this study identifies distinct household and school-level determinants of student academic resilience post-pandemic, revealing how factors like digital access, teaching quality, and pandemic disruptions differentially influence disadvantaged students' ability to achieve high performance.
Across 13 studies, researchers found that people consistently reduce compensation for workers who use AI due to perceived decreases in effort and agency, though this "AI penalty" can be mitigated when workers retain creative control or when contracts prohibit such reductions.
This study analyzes voting records of over 14,000 U.S. state legislators from 2011 to 2022 and finds that mass shootings occurring within a legislator's district do not, on average, lead to measurable changes in their voting behavior on firearm policy, regardless of partisanship or geographic proximity.
This research paper examines the sustainability challenges facing luxury resorts in the Maldives and proposes a framework based on the Triple Bottom Line approach and 12 Sustainable Tourism Principles to guide the implementation of effective practices, regulatory reforms, and collaborative strategies for long-term environmental and economic viability.
This paper argues that the rise of agentic AI necessitates a reconceptualization of Team Situation Awareness to address the structural uncertainty of open-ended agency, distinguishing between continuous alignment mechanisms and emerging tensions to guide future human-AI teaming research.
This paper extends Froeseth's (2026) wealth tax neutrality framework by demonstrating that while neutrality holds under stochastic volatility and Epstein-Zin preferences, it breaks under non-homothetic preferences and four specific implementation channels—such as progressive thresholds and inelastic markets—which are formalized and calibrated to the Norwegian system to evaluate global minimum tax proposals.
This paper demonstrates that a proportional wealth tax levied on market value is economically equivalent to the government acquiring a proportional stake in investor portfolios, thereby leaving optimal portfolio weights, asset pricing, and the Sharpe ratio unchanged, provided that taxation is universal, markets are frictionless, and dividends are consumed.
This paper presents a weighted-sum mixed-integer linear programming model to optimize the UK apple supply chain by balancing economic and sustainability objectives—specifically price, quantity, freshness, and distance—to enhance localisation and circularity while reducing waste and emissions.
Based on a conjoint experiment with 1,725 recruiters across three countries, this study provides causal evidence that possessing AI skills significantly increases interview invitation rates and can partially or fully offset labor market disadvantages related to age and lower education, though the magnitude of these benefits varies by occupation and recruiter background.