Catch Laffer If You Can: Tax Take in an Evasion‐Detection Game
Journal of Public Economic Theory, Volume 27, Issue 6, December 2025.ABSTRACT In a simple taxation framework, we analyze a taxpayer's decision of whether to report income truthfully or engage in an evasion game with the tax agency. Specifically, taxpayer and tax agency can expend efforts, respectively, to conceal income and detect evasion. These activities are costly, and the final outcome—whether evasion is detected or
Rosaria Distefano, Francesco Reito
wiley +1 more source
Biased Dueling Bandits with Stochastic Delayed Feedback
CoRRThe dueling bandit problem, an essential variation of the traditional multi-armed bandit problem, has become significantly prominent recently due to its broad applications in online advertising, recommendation systems, information retrieval, and more.
Bongsoo Yi, Yue Kang, Yao Li
openaire +3 more sources
Concurrency and Computation: Practice and Experience, Volume 37, Issue 21-22, 25 September 2025.ABSTRACT Autonomous vehicles (AVs) are one of the building blocks of modern intelligent transportation systems and have the potential to change some aspects related to mobility, safety, and operational efficiency. In this paper, we analyze recent progress in AV algorithms and simulation frameworks, emphasizing their roles in decision‐making processes ...
Majd Alkorabi +2 more
wiley +1 more source
FinRL Contests: Data‐Driven Financial Reinforcement Learning Agents for Stock and Crypto Trading
Artificial Intelligence for Engineering, Volume 1, Issue 1, Page 44-68, 29 September 2025.FinRL Contests 2023–2025 explore the application of reinforcement learning in financial tasks, which are modelled as the Markov decision process (MDP). Participants specify state, reward and action to train the FinRL agents in stable market environments, advancing the development of RL‐based trading strategies in real‐world financial markets.
Keyi Wang +7 more
wiley +1 more source
Fusing Reward and Dueling Feedback in Stochastic Bandits
CoRRThis paper investigates the fusion of absolute (reward) and relative (dueling) feedback in stochastic bandits, where both feedback types are gathered in each decision round. We derive a regret lower bound, demonstrating that an efficient algorithm may incur only the smaller among the reward and dueling-based regret for each individual arm.
Xuchuang Wang +6 more
openaire +3 more sources
Evolution in fluctuating environments: A generic modular approach. [PDF]
Evolution, 2022 Steinmetz B, Meyer I, Shnerb NM.
europepmc +1 more source
How the storage effect and the number of temporal niches affect biodiversity in stochastic and seasonal environments. [PDF]
PLoS Comput Biol, 2022 Meyer I, Steinmetz B, Shnerb NM.
europepmc +1 more source
Game Theory-Based Authentication Framework to Secure Internet of Vehicles with Blockchain. [PDF]
Sensors (Basel), 2022 Gupta M +7 more
europepmc +1 more source
Negative feedback may suppress variation to improve collective foraging performance. [PDF]
PLoS Comput Biol, 2022 Reina A, Marshall JAR.
europepmc +1 more source
Conflict-free collective stochastic decision making by orbital angular momentum of photons through quantum interference. [PDF]
Sci Rep, 2021 Amakasu T +5 more
europepmc +1 more source