Tootfinder

Negotiating the Shared Agency between Humans & AI in the Recommender System
Mengke Wu (Mia), Weizi Liu (Mia), Yanyun (Mia), Wang, Mike Zhengyu Yao
https://arxiv.org/abs/2403.15919

Negotiating the Shared Agency between Humans & AI in the Recommender System
Smart recommendation algorithms have revolutionized information dissemination, enhancing efficiency and reshaping content delivery across various domains. However, concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. While both issues have been criticized by scholars via advocating explainable AI (XAI) and human-AI collaborative decision-making (HACD), few research evaluates their integrated …

This https://arxiv.org/abs/2402.18781 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
Stochastic games arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures, where information asymmetry presents challenges for decision-making entities (players). Existing computational methods for asymmetric information stochastic games (AISG) are primarily offline, targeting special classes of AISGs to avoid belief hierarchies, and lack online adaptability to deviations from equilibrium. To address this limitation, we propose a conjectural online le…

This https://arxiv.org/abs/2403.15919 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Negotiating the Shared Agency between Humans & AI in the Recommender System
Smart recommendation algorithms have revolutionized information dissemination, enhancing efficiency and reshaping content delivery across various domains. However, concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. While both issues have been criticized by scholars via advocating explainable AI (XAI) and human-AI collaborative decision-making (HACD), few research evaluates their integrated …

This https://arxiv.org/abs/2312.05561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Nonreciprocal Photon-Phonon Entanglement in Kerr-Modified Spinning Cavity Magnomechanics
Cavity magnomechanics has shown great potential in studying macroscopic quantum effects, especially for quantum entanglement, which is a key resource for quantum information science. Here we propose to realize magnon mediated nonreciprocal photon-phonon entanglement, which exhibits asymmetry when opposite magnetic or driving fields are respectively applied to the magnons with the Kerr effect or the photons with the Sagnac effect. We find that the mean magnon number can selectively exhibit nonre…

Capital Structure Adjustment Speed and Expected Returns: Examination of Information Asymmetry as a Moderating Role
Masoud Taherinia, Mehrdad Matin, Jamal Valipour, Kavian Abdolahi, Peyman Shouryabi, Mohammad Mahdi Barzegar
https://arxiv.org/abs/2403.06035

Capital Structure Adjustment Speed and Expected Returns: Examination of Information Asymmetry as a Moderating Role
Shareholders' expectations of stock returns and fluctuations are constantly changing due to restrictions in financial status and undesirable capital structure, which constrain managers to limit the changes in price trends in order to cover the risk instigated and infused by the unfavorable situation. The present research examines the moderating impact of information asymmetry on the relationship between capital structure adjustment and expected returns. The data from 120 companies approved in t…

This https://arxiv.org/abs/2212.01551 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure
Beneficial to advanced computing devices, models with massive parameters are increasingly employed to extract more information to enhance the precision in describing and predicting the patterns of objective systems. This phenomenon is particularly pronounced in research domains associated with deep learning. However, investigations of causal relationships based on statistical and informational theories have posed an interesting and valuable challenge to large-scale models in the recent decade. …

Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
Tao Li, Kim Hammar, Rolf Stadler, Quanyan Zhu
https://arxiv.org/abs/2402.18781

Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
Stochastic games arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures, where information asymmetry presents challenges for decision-making entities (players). Existing computational methods for asymmetric information stochastic games (AISG) are primarily offline, targeting special classes of AISGs to avoid belief hierarchies, and lack online adaptability to deviations from equilibrium. To address this limitation, we propose a conjectural online le…

Unitary quantum gravitational physics and the CMB parity asymmetry
Enrique Gazta\~naga, K. Sravan Kumar
https://arxiv.org/abs/2403.05587 https://

Unitary quantum gravitational physics and the CMB parity asymmetry
The proposal of Direct-Sum Quantum Field Theory (DQFT) offers a new perspective for quantum fields by combining parity and time reversal operations, blurring the distinction between quantum past and future while preserving causality. This approach provides a unitary QFT in curved spacetime, resolving the information-loss paradox. When applied to inflationary quantum fluctuations, DQFT predicts variations in CMB measurements, explaining longstanding anomalies as a result of parity asymmetry. The…

Capital Structure Adjustment Speed and Expected Returns: Examination of Information Asymmetry as a Moderating Role
Masoud Taherinia, Mehrdad Matin, Jamal Valipour, Kavian Abdolahi, Peyman Shouryabi, Mohammad Mahdi Barzegar
https://arxiv.org/abs/2403.06035

Capital Structure Adjustment Speed and Expected Returns: Examination of Information Asymmetry as a Moderating Role
Shareholders' expectations of stock returns and fluctuations are constantly changing due to restrictions in financial status and undesirable capital structure, which constrain managers to limit the changes in price trends in order to cover the risk instigated and infused by the unfavorable situation. The present research examines the moderating impact of information asymmetry on the relationship between capital structure adjustment and expected returns. The data from 120 companies approved in t…

Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending
Mario Sanz-Guerrero, Javier Arroyo
https://arXiv.org/abs/2401.16458

Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending
Peer-to-peer (P2P) lending has emerged as a distinctive financing mechanism, linking borrowers with lenders through online platforms. However, P2P lending faces the challenge of information asymmetry, as lenders often lack sufficient data to assess the creditworthiness of borrowers. This paper proposes a novel approach to address this issue by leveraging the textual descriptions provided by borrowers during the loan application process. Our methodology involves processing these textual descript…