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1. You Get What You Share: Incentives for a Sharing Economy

   https://doi.org/10.1609/aaai.v33i01.33012004  

   Gollapudi, Sreenivas; Kollias, Kostas; Panigrahi, Debmalya (July 2019, Proceedings of the AAAI Conference on Artificial Intelligence)

   In recent years, a range of online applications have facilitated resource sharing among users, resulting in a significant increase in resource utilization. In all such applications, sharing one’s resources or skills with other agents increases social welfare. In general, each agent will look for other agents whose available resources complement hers, thereby forming natural sharing groups. In this paper, we study settings where a large population self-organizes into sharing groups. In many cases, centralized optimization approaches for creating an optimal partition of the user population are infeasible because either the central authority does not have the necessary information to compute an optimal partition, or it does not have the power to enforce a partition. Instead, the central authority puts in place an incentive structure in the form of a utility sharing method, before letting the participants form the sharing groups by themselves. We first analyze a simple equal-sharing method, which is the one most typically encountered in practice and show that it can lead to highly inefficient equilibria. We then propose a Shapley-sharing method and show that it significantly improves overall social welfare. 

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2. Are You a HAB Warrior?

   https://doi.org/10.3389/frym.2021.611282  

   Lehman, Peggy W.; Kurobe, Tomofumi; Otten, Timothy G.; Peacock, Melissa B. (August 2021, Frontiers for Young Minds)

   Microalgae and cyanobacteria are tiny, microscopic plant-like organisms that float in the water and grow using nutrients from the water, energy from the sun and carbon dioxide gas from the air. Most microalgae and cyanobacteria are helpful because, like grass for cows on land, they provide food for aquatic animals. However, some microalgae and cyanobacteria are poisonous and when large numbers of them occur, they are called harmful algal blooms, or HABs for short. HABs can poison both humans and animals through the food they eat, the water they drink, and even the air they breathe. HABs are increasing within lakes, rivers, oceans, and estuaries worldwide because of pollution and climate change. This article will tell you about HABs in San Francisco Estuary, USA: who they are, what they look like, why they occur, how they affect plants, animals and people, and things you can do as a HAB warrior to stay safe and prevent their spread. 

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3. A Bandit you can Trust.

   Prihar, E. (June 2023, Proceedings of The 31st ACM Conference On User Modeling, Adaptation And Personalization)

   This work proposes Dynamic Linear Epsilon-Greedy, a novel con- textual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning ap- proaches have trade-offs between empirical investigation and max- imal impact on users. Our algorithm seeks to balance these objec- tives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSIST- ments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students’ learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to bal- ance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/. 

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4. A Bandit You Can Trust

   https://doi.org/10.1145/3565472.3592955  

   Prihar, Ethan; Sales, Adam; Heffernan, Neil (July 2023, UMAP 2023)

   This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our algorithm seeks to balance these objectives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSISTments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students’ learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to balance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/. 

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5. So you think you can track?

   https://doi.org/10.1109/WACV57701.2024.00447  

   Gloudemans, Derek; Zachár, Gergely; Wang, Yanbing; Ji, Junyi; Nice, Matt; Bunting, Matt; Barbour, William W; Sprinkle, Jonathan; Piccoli, Benedetto; Delle_Monache, Maria Laura; et al (January 2024, IEEE)