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Journal articles on the topic 'Incentive of learning'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Liu, Jiaqi, Hucheng Xu, Xiaoheng Deng, Hui Liu, and Deng Li. "A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory." Mathematics 11, no. 16 (2023): 3590. http://dx.doi.org/10.3390/math11163590.

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Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expect

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St. Clair, Bryn, Paul Putnam, Harold L. Miller, Ross Larsen, and Jamie L. Jensen. "Test-Enhanced Learning and Incentives in Biology Education." CBE—Life Sciences Education 19, no. 3 (2020): ar40. http://dx.doi.org/10.1187/cbe.19-11-0226.

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This study examined the testing effect in a postsecondary biology course with low- and high-incentive treatments and measured student learning. Although exposure to exams predicted better learning, incentive level did not moderate this effect, an outcome that contradicted recent laboratory findings that higher incentives decreased the testing effect.

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Zhu, Chun, Mengqi Sui, Haitao Zhao, Keqi Chen, Tianyu Zhang, and Chongyu Bao. "Federated Learning Incentive Mechanism Setting in UAV-Assisted Space–Terrestrial Integration Networks." Electronics 13, no. 6 (2024): 1129. http://dx.doi.org/10.3390/electronics13061129.

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The UAV-assisted space–terrestrial integrated network provides extensive coverage and high flexibility in communication services. UAVs and ground terminals collaborate to train models and provide services. In order to protect data privacy, federated learning is widely used. However, the participation of UAVs and ground terminals is not gratuitous, and reasonable incentives for federated learning need to be set up to encourage their participation. To address the above issues, this paper proposes a federated reliable incentive mechanism based on hierarchical reinforcement learning. The mechanism

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Zhang, Hanrui, and Vincent Conitzer. "Incentive-Aware PAC Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5797–804. http://dx.doi.org/10.1609/aaai.v35i6.16726.

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We study PAC learning in the presence of strategic manipulation, where data points may modify their features in certain predefined ways in order to receive a better outcome. We show that the vanilla ERM principle fails to achieve any nontrivial guarantee in this context. Instead, we propose an incentive-aware version of the ERM principle which has asymptotically optimal sample complexity. We then focus our attention on incentive-compatible classifiers, which provably prevent any kind of strategic manipulation. We give a sample complexity bound that is, curiously, independent of the hypothesis

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5

Sprinkle, Geoffrey B. "The Effect of Incentive Contracts on Learning and Performance." Accounting Review 75, no. 3 (2000): 299–326. http://dx.doi.org/10.2308/accr.2000.75.3.299.

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This paper reports the results of an experiment that examines how incentive-based compensation contracts compare to flat-wage compensation contracts in motivating individual learning and performance. I use a multiperiod cognitive task where the accounting system generates information (feedback) that has both a contracting role and a belief-revision role. The results suggest that incentives enhance performance and the rate of improvement in performance by increasing both: (1) the amount of time participants devoted to the task, and (2) participants' analysis and use of information. Further, I f

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Dekel, Ofer, Felix Fischer, and Ariel D. Procaccia. "Incentive compatible regression learning." Journal of Computer and System Sciences 76, no. 8 (2010): 759–77. http://dx.doi.org/10.1016/j.jcss.2010.03.003.

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7

Sundaresan, Shankar, and Zuopeng Justin Zhang. "Knowledge Sharing and Learning in Organizations: Role of Incentives and Information Systems." Journal of Industrial Integration and Management 01, no. 03 (2016): 1650005. http://dx.doi.org/10.1142/s2424862216500056.

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Knowledge sharing and learning play a fundamental role in the success of knowledge management (KM). While information technologies facilitate learning and sharing of knowledge in organizations, incentive systems help to align organizational and individual goals. Applying a modeling framework that captures the characteristics of both information systems and incentives in a team-based organization, we demonstrate that an output allocation rule linear in output, and sharing-learning incentive rewards linear in the amounts being shared and learned can be optimally designed. Broad conditions for im

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Pan, Chenglu, Jiarong Xu, Yue Yu, et al. "Towards Fair Graph Federated Learning via Incentive Mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14499–507. http://dx.doi.org/10.1609/aaai.v38i13.29365.

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Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This sta

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9

Thornton, Rebecca L. "The Demand for, and Impact of, Learning HIV Status." American Economic Review 98, no. 5 (2008): 1829–63. http://dx.doi.org/10.1257/aer.98.5.1829.

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This paper evaluates an experiment in which individuals in rural Malawi were randomly assigned monetary incentives to learn their HIV results after being tested. Distance to the HIV results centers was also randomly assigned. Without any incentive, 34 percent of the participants learned their HIV results. However, even the smallest incentive doubled that share. Using the randomly assigned incentives and distance from results centers as instruments for the knowledge of HIV status, sexually active HIV-positive individuals who learned their results are three times more likely to purchase condoms

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10

He, Jie, and Jianhua Zhang. "Human Capital Digital Incentive Mechanism Construction Based on Deep Learning." Mathematical Problems in Engineering 2022 (August 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/6180883.

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The introduction of human capital can be traced back to the ancient Greek period, emphasizing the important role of knowledge and skills in the production and life process. Human capital reward mechanism promotes modern social and economic development and is an important part of social and economic growth. The core management of the enterprise is the management of human capital, and the central work of human capital management is the incentive of human capital. Many enterprises are now facing difficulties in industrial operation, serious brain drain, and lack of core competitiveness in the mar

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11

Huber, Vandra L. "Comparison of Monetary Reinforcers and Goal Setting as Learning Incentives." Psychological Reports 56, no. 1 (1985): 223–35. http://dx.doi.org/10.2466/pr0.1985.56.1.223.

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This 3 × 3 study (with one cell missing) examined the effectiveness of goal setting (participative, assigned, and no goals) and financial incentives (noncontingent, piece rate, and goal contingent) as techniques to stimulate learning 88 inexperienced trainees performed a 10-hr., 5-day proofreading proficiency task in a laboratory setting. The linear trends for all groups except the control group in which noncontingent ($30 for completing the training period) pay was offered were significant. Planned comparisons indicated that learning was greatest when a performance goal was assigned and a fin

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12

Roney, Robert K., and Allen Morgan. "Renaissance Incentive Program Motivates Learning." NASSP Bulletin 75, no. 536 (1991): 131–35. http://dx.doi.org/10.1177/019263659107553620.

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13

Radanovic, Goran, and Boi Faltings. "Learning to Scale Payments in Crowdsourcing with PropeRBoost." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 179–88. http://dx.doi.org/10.1609/hcomp.v4i1.13279.

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Motivating workers to provide significant effort has been recognized as an important issue in crowdsourcing. It is important not only to compensate worker effort, but also to discourage low-quality workers from participating. Several proper incentive schemes have been proposed for this purpose; they are either based on gold tasks or on peer consistency in individual tasks. As the rewards cannot become negative, these schemes have difficulty in achieving zero expected reward for random answers. We describe a novel boosting scheme, ProperRBoost, that improves the efficiency of existing incentive

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14

Zhou, Yuzhe. "A Survey of Incentive Mechanisms for Federated Learning." Applied and Computational Engineering 99, no. 1 (2024): 121–27. http://dx.doi.org/10.54254/2755-2721/99/20251774.

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Federated learning is a cutting-edge distributed machine learning technique that lowers the possibility of data leaking from centralized uploads by enabling users to cooperatively train models. It not only protects client privacy but also better utilizes decentralized data resources, demonstrating high learning performance. To employ their models in the training process, however, a significant number of clients must be involved, which inexorably uses up the device's resourcescomputational power, communication bandwidth, and energy. Therefore, incentivizing more clients to participate in federa

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15

Davidson, T. L., Elizabeth D. Capaldi, and David H. Campbell. "Irrelevant incentive learning revisited: Associating flavors and exteroceptive cues with positive incentives." Learning and Motivation 16, no. 3 (1985): 288–300. http://dx.doi.org/10.1016/0023-9690(85)90016-5.

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16

Zhan, Yufeng, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo. "A Learning-Based Incentive Mechanism for Federated Learning." IEEE Internet of Things Journal 7, no. 7 (2020): 6360–68. http://dx.doi.org/10.1109/jiot.2020.2967772.

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17

Mintrop, Rick, Miguel Ordenes, Erin Coghlan, Laura Pryor, and Cristobal Madero. "Teacher Evaluation, Pay for Performance, and Learning Around Instruction: Between Dissonant Incentives and Resonant Procedures." Educational Administration Quarterly 54, no. 1 (2017): 3–46. http://dx.doi.org/10.1177/0013161x17696558.

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Purpose: The study examines why the logic of a performance management system, supported by the federal Teacher Incentive Fund, might be faulty. It does this by exploring the nuances of the interplay between teaching evaluations as formative and summative, the use of procedures, tools, and artifacts obligated by the local Teacher Incentive Fund system, and bonus payments as extrinsic motivators. Research Methods: The study is a qualitative longitudinal study in three public charter schools that were selected as a presumably conducive environment for incentive-driven performance management. Eigh

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18

Todd Maddox, W., and Arthur B. Markman. "The Motivation–Cognition Interface in Learning and Decision Making." Current Directions in Psychological Science 19, no. 2 (2010): 106–10. http://dx.doi.org/10.1177/0963721410364008.

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In this article we discuss how incentive motivations and task demands affect performance. We present a three-factor framework that suggests that performance is determined from the interaction of global incentives, local incentives, and the psychological processes needed to achieve optimal task performance. We review work that examines the implications of the motivation–cognition interface in classification and choice and on phenomena such as stereotype threat and performance pressure. We show that, under some conditions, stereotype threat and pressure accentuate performance. We discuss the imp

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19

Forkman, Björn, Nancy C. A. Coerse, and Marie J. Haskell. "Incentive Value learning in Domestic Hens." Ethology 118, no. 5 (2012): 489–93. http://dx.doi.org/10.1111/j.1439-0310.2012.02036.x.

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20

Balleine, Bernard, Alison Davies, and Anthony Dickinson. "Cholecystokinin attenuates incentive learning in rats." Behavioral Neuroscience 109, no. 2 (1995): 312–19. http://dx.doi.org/10.1037/0735-7044.109.2.312.

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21

Muzio, Rubén N., Virginia Pistone Creydt, Mariana Iurman, Mauro A. Rinaldi, Bruno Sirani, and Mauricio R. Papini. "Incentive or Habit Learning in Amphibians?" PLoS ONE 6, no. 11 (2011): e25798. http://dx.doi.org/10.1371/journal.pone.0025798.

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Coerse, N. C. A., M. J. Haskell, and B. Forkman. "Incentive Value Learning in Domestic Hens." Animal Welfare 10, S1 (2001): S237. http://dx.doi.org/10.1017/s0962728600023678.

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The incentive value learning theory states that the value assigned to a resource depends on the motivational state of an animal when it first encounters that resource. The present experiment investigated whether domestic hens, Gallus domesticus, assign different incentive values to a novel food depending on their deprivation state when they encounter this novel food. Hens were first trained to find a novel food type hidden under wood shavings in a large food dish. Then, after half of the animals were food-deprived and half left not food-deprived, all hens were re-exposed to the novel food in a

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23

Zhang, Shen Sheng, Wei Zhang, Xing Xing Ji, and Wen Fang Zhang. "Research of Existing Building Energy Saving Economic Incentives Policy in Liao Ning Province." Applied Mechanics and Materials 409-410 (September 2013): 570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.570.

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Analyzed the status of Liaoning Province Existing Building Energy Saving, and elaborate the role of economic incentives in the Existing Building Energy Saving. Proposed the establish principles and design ideas of Existing Building Energy Saving economic incentive policies in Liao Ning Province on the basis of learning from the developed countries economic incentives successful experience. Developed economic incentives on taxation, loans, and subsidies and so on.

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24

Glover, Jonathan C., and Haijin H. Lin. "Accounting Conservatism and Incentives: Intertemporal Considerations." Accounting Review 93, no. 6 (2018): 181–201. http://dx.doi.org/10.2308/accr-52107.

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ABSTRACT We study intertemporal incentive properties of conditional accounting conservatism. Conservatism has detrimental and beneficial properties. In our first model, conservatism introduces downward bias in the first period; any understatement of first-period performance is reversed in the second period. A conservative bias is not costly in the first period but instead is costly in the second period when a new manager may be rewarded for the performance of his predecessor. In an extension on learning, we illustrate a beneficial role of conservatism in fine-tuning incentives. In the second m

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Appa, Ayesha, Amanda P. Miller, Robin Fatch, et al. "Participant perspectives on incentives for TB preventative therapy adherence and reduced alcohol use: A qualitative study." PLOS Global Public Health 4, no. 4 (2024): e0002472. http://dx.doi.org/10.1371/journal.pgph.0002472.

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Economic incentives to promote health behavior change are highly efficacious for substance use disorders as well as increased medication adherence. Knowledge about participants’ experiences with and perceptions of incentives is needed to understand their mechanisms of action and optimize future incentive-based interventions. The Drinkers’ Intervention to Prevent Tuberculosis (DIPT) trial enrolled people with HIV (PWH) in Uganda with latent tuberculosis and unhealthy alcohol use in a 2x2 factorial trial that incentivized recent alcohol abstinence and isoniazid (INH) adherence on monthly urine t

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26

Linda Yunita. "Kontribusi Pemberian Insentif Dalam Meningkatkan Kinerja Guru Di MIS Budaya Langkat Desa Tanjung Ibus Kecamatan Secanggang." Khatulistiwa: Jurnal Pendidikan dan Sosial Humaniora 3, no. 4 (2023): 25–36. http://dx.doi.org/10.55606/khatulistiwa.v3i4.2248.

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The official notification regarding the receipt of incentive funds is based on information from the head of the madrasah, it seems that this incentive is no longer given to teachers who previously received it. According to the head of the madrasah, it seems that incentive assistance has been eliminated because all the schools that previously received it no longer receive it. It is possible that this assistance will be diverted into functional allowances for teachers. The incentives issued to teachers are usually given in the range of once every three months to four months in the amount of one

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Fahrub, Abdul Wahab, and Akhida Layinatush Shifah. "Comparative Analysis Of Performance Of Teacher Recipients And Non-Recipients Of Incentives In The LPQ Coordination Environment Agency, Kedu Sub-District, Temanggung District." AL-WIJDÃN Journal of Islamic Education Studies 9, no. 2 (2024): 275–91. http://dx.doi.org/10.58788/alwijdn.v9i2.3658.

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Teachers are an important element in implementing educational programs through learning activities in formal and nonformal education. Giving an incentive of IDR 100,000 per month is a form of government attention to teachers in the hope of improving their performance. The government distributes this incentive in collaboration with the Ministry of Religion at the regency level. With this nominal, the teachers remain enthusiastic and teach; even teachers do not receive incentives but remain enthusiastic in teaching students every day. The purpose of this study was to determine the performance of

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Kanoria, Yash, and Hamid Nazerzadeh. "Incentive-Compatible Learning of Reserve Prices for Repeated Auctions." Operations Research 69, no. 2 (2021): 509–24. http://dx.doi.org/10.1287/opre.2020.2007.

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How can an auctioneer optimize revenue by learning the reserve prices from the bids in the previous auctions? How should the long-term incentives and strategic behavior of the bidders be taken into account? Motivated in part by applications in online advertising, in “Incentive-Compatible Learning of Reserve Prices for Repeated Auctions,” Kanoria and Nazerzadeh investigate these questions. They show that if a seller attempts to dynamically update a common reserve price using the bidding history, buyers will shade their bids, which can hurt the revenue. However, when there is more than one buyer

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29

Wang, Chao, Bingze Li, and Yang Yang. "Robust Incentive Mechanism of Federated Learning for Data Quality Uncertainty." Information Technology and Control 53, no. 4 (2024): 1139–51. https://doi.org/10.5755/j01.itc.53.4.34907.

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In order to make the incentive mechanism more suitable for the actual training situation and improve the eﬃciency of the model, the robust incentive mechanism of federated leaning is proposed to deal with uncertainty of the data quality. (1) Firstly, the incentive mechanism of federated learning is constructed by the use of Stackelberg game to optimize the central server and data owner utilities, respectively. (2) Secondly, the uncertainty of data quality of the data owners is present by two robust uncertainty sets, and the corresponding incentive mechanism of the robust Stackelberg game is gi

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30

Pan, Qiufeng. "Research on the Implementation Strategy of Classroom Incentive Mechanism in Technical School." International Journal of Education and Humanities 9, no. 3 (2023): 113–18. http://dx.doi.org/10.54097/ijeh.v9i3.10321.

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As an important place to cultivate technical skills and professionalism, technical schools must establish an effective classroom incentive mechanism to stimulate students' learning motivation and active participation. The classroom reward mechanism can be used as an incentive method to help improve students' learning motivation. Learning motivation is an internal factor that promotes students' learning, which involves the individual's perception, value and expectation of the goal, as well as the interaction with other factors. The classroom reward mechanism can enhance students' learning motiv

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31

WALL, FRIEDERIKE. "LEARNING TO INCENTIVIZE IN DIFFERENT MODES OF COORDINATION." Advances in Complex Systems 20, no. 02n03 (2017): 1750003. http://dx.doi.org/10.1142/s0219525917500035.

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The paper studies which incentive systems emerge in organizations when self-interested managers collaboratively search for higher levels of organizational performance and the headquarters learn about the success of the incentive systems employed. The study uses an agent-based simulation and, in particular, controls for different levels of intra-organizational complexity and modes of coordination, i.e., the way how preferences on the departmental site are aligned with each other in respect to the overall organizational objective. The results indicate that for different levels of intra-organizat

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Wu, Ruolan, Yuling Chen, Chaoyue Tan, and Yun Luo. "MDIFL: Robust Federated Learning Based on Malicious Detection and Incentives." Applied Sciences 13, no. 5 (2023): 2793. http://dx.doi.org/10.3390/app13052793.

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Federated Learning (FL) is an emerging distributed framework that enables clients to conduct distributed learning and globally share models without requiring data to leave the local. In the FL process, participants are required to contribute data resources and computing resources for model training. However, the traditional FL lacks security guarantees and is vulnerable to attacks and damages by malicious adversaries. In addition, the existing incentive methods lack fairness to participants. Therefore, accurately identifying and preventing malicious nodes from doing evil, while effectively sel

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Owens, Mark F., and Adam D. Rennhoff. "Motion picture production incentives and filming location decisions: a discrete choice approach." Journal of Economic Geography 20, no. 3 (2018): 679–709. http://dx.doi.org/10.1093/jeg/lby054.

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Abstract We use a discrete choice model to study the impact of tax incentives on firm location choices in situations involving heterogeneous geographic characteristics, heterogeneous firm preferences and large choice sets. We apply our model to investigate the impact of movie production incentives on filming location choices for movies produced from 1999 to 2013. We gather the characteristics of filming locations and use a machine-learning technique to define choice sets. We find production incentives can attract movies to a state, but the impact depends on the type of incentive offered, studi

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Charbonneau, D., R. J. Riopelle, and R. J. Beninger. "Impaired Incentive Learning in Treated Parkinson’s Disease." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 23, no. 4 (1996): 271–78. http://dx.doi.org/10.1017/s031716710003821x.

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AbstractObjective: To quantify the performance of patients with Parkinson’s disease (PD) in incentive learning, or learning to respond to stimuli that signal the imminent presentation of a reinforcer, and in paired-associate learning, or learning of word associations. Methods: The performance of 32 patients with idiopathic PD was compared to that of 25 healthy control subjects, and 32 subjects suffering from arthritis, matched for age and education. The PD and arthritic groups were comparable on a self-report measure of physical disability. All subjects were physically capable of satisfying th

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35

Hirao, Yukiko. "Learning and Incentive Problems in Repeated Partnerships." International Economic Review 34, no. 1 (1993): 101. http://dx.doi.org/10.2307/2526952.

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Kao, Kuo-Yuan, I.-Chen Wu, Shi-Jim Yen, and Yi-Chang Shan. "Incentive Learning in Monte Carlo Tree Search." IEEE Transactions on Computational Intelligence and AI in Games 5, no. 4 (2013): 346–52. http://dx.doi.org/10.1109/tciaig.2013.2248086.

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Osmundsen, Petter. "Learning-by-doing; consequences for incentive design." Information Economics and Policy 14, no. 1 (2002): 39–49. http://dx.doi.org/10.1016/s0167-6245(01)00050-6.

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Yu, Han, Zelei Liu, Yang Liu, et al. "A Sustainable Incentive Scheme for Federated Learning." IEEE Intelligent Systems 35, no. 4 (2020): 58–69. http://dx.doi.org/10.1109/mis.2020.2987774.

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Liu, Shukun, Zhimin Liu, Xiaoyu Zhu, Xianmin Pan, and Baimu Chen. "Incentive Mechanism of Online Leaning Based on Blockchain." ITM Web of Conferences 60 (2024): 00005. http://dx.doi.org/10.1051/itmconf/20246000005.

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Incentive mechanism plays an important role in online education. In order to better play the role of the incentive mechanism, it must rely on contemporary information technology, such as blockchain technology, big data technology and so on. In this paper an incentive mechanism based on blockchain technology is proposed with which can establish a good trust relationship between multiple learning resource nodes and improve data security during the process of online education. A structure of online learning resource chain is proposed in this paper also. And a dynamic optimization framework of lea

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Sun, Hao, Xiaoli Tang, Chengyi Yang, et al. "HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 22824–32. http://dx.doi.org/10.1609/aaai.v38i21.30317.

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Gas usage estimation plays a critical role in various aspects of the power generation and delivery business, including budgeting, resource planning, and environmental preservation. Federated Learning (FL) has demonstrated its potential in enhancing the accuracy and reliability of gas usage estimation by enabling distributedly owned data to be leveraged, while ensuring privacy and confidentiality. However, to effectively motivate stakeholders to contribute their high-quality local data and computational resources for this purpose, incentive mechanism design is key. In this paper, we report our

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Andrew, R. Short, G. Orfanoudakis Τheofanis, and C. Leligou Helen. "IMPROVING SECURITY AND FAIRNESS IN FEDERATED LEARNING SYSTEMS." International Journal of Network Security & Its Applications (IJNSA) 13, no. 6 (2021): 37–53. https://doi.org/10.5281/zenodo.5778315.

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The ever-increasing use of Artificial Intelligence applications has made apparent that the quality of the training datasets affects the performance of the models. To this end, Federated Learning aims to engage multiple entities to contribute to the learning process with locally maintained data, without requiring them to share the actual datasets. Since the parameter server does not have access to the actual training datasets, it becomes challenging to offer rewards to users by directly inspecting the dataset quality. Instead, this paper focuses on ways to strengthen user engagement by offering

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Brennan, Patricia C., Poornima Madhavan, Cleotilde Gonzalez, and Frank C. Lacson. "The Impact of Performance Incentives during Training on Transfer of Learning." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 53, no. 26 (2009): 1979–83. http://dx.doi.org/10.1177/154193120905302621.

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We examined the effect of incentives on transfer of training in a visual search task. The incentives were designed to influence participants' detection of dangerous targets across two phases of airline luggage screening – training (familiar targets) and transfer (novel targets). Participants were assigned to one of five groups - hit-sensitive (points awarded for hits), miss-sensitive (points deducted for misses), equal-costs (equal points awarded and deducted for hits and misses), no-incentives (no points) and control (self-training). The goal was to examine which incentive structure was most

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Wassum, K. M., S. B. Ostlund, B. W. Balleine, and N. T. Maidment. "Differential dependence of Pavlovian incentive motivation and instrumental incentive learning processes on dopamine signaling." Learning & Memory 18, no. 7 (2011): 475–83. http://dx.doi.org/10.1101/lm.2229311.

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Wan, Tao, Tiantian Jiang, Weichuan Liao, and Nan Jiang. "Hierarchical Incentive Mechanism for Federated Learning: A Single Contract to Dual Contract Approach for Smart Industries." International Journal of Intelligent Systems 2024 (January 4, 2024): 1–15. http://dx.doi.org/10.1155/2024/6402026.

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Federated learning (FL) has shown promise in smart industries as a means of training machine-learning models while preserving privacy. However, it contradicts FL’s low communication latency requirement to rely on the cloud to transmit information with data owners in model training tasks. Furthermore, data owners may not be willing to contribute their resources for free. To address this, we propose a single contract to dual contract approach to incentivize both model owners and workers to participate in FL-based machine learning tasks. The single-contract incentivizes model owners to contribute

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Firestone, William A., and James R. Pennell. "Teacher Commitment, Working Conditions, and Differential Incentive Policies." Review of Educational Research 63, no. 4 (1993): 489–525. http://dx.doi.org/10.3102/00346543063004489.

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The push for more complex, intellectually demanding approaches to teaching suggests that teacher commitment will continue to be important for effective education. This article develops a framework for assessing how differential incentive policies affect teacher commitment. It identifies seven key workplace conditions that contribute to teacher commitment: job design characteristics, feedback, autonomy, participation, collaboration, learning opportunities, and resources. This framework is used to assess the effects of such differential incentive policies as merit pay and career ladders. The sel

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Wang, Yanfang, Shaobo Li, Kangkun Chen, Ran Guo, and Judy Li. "Privacy-Preserving Incentive Allocation for Fair and Resilient Data Sharing in Resource-Constrained Edge Computing Networks." Mathematics 13, no. 3 (2025): 422. https://doi.org/10.3390/math13030422.

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Efficient and secure data sharing is paramount for advancing modern digital ecosystems, especially within edge computing environments characterized by resource-constrained nodes and dynamic network topologies. In such settings, privacy preservation, computational efficiency, and system resilience are critical for user engagement and overall system performance. However, existing approaches face three primary challenges: (i) limited optimization of privacy protection and absence of dynamic privacy budget scheduling for resource-constrained scenarios, (ii) static incentive mechanisms that overloo

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Qi, Minfeng, Ziyuan Wang, Shiping Chen, and Yang Xiang. "A Hybrid Incentive Mechanism for Decentralized Federated Learning." Distributed Ledger Technologies: Research and Practice 1, no. 1 (2022): 1–15. http://dx.doi.org/10.1145/3538226.

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Federated Learning (FL) presents a privacy-compliant approach by sharing model parameters instead of raw data. However, how to motivate data owners to participate in and stay within an FL ecosystem by continuously contributing their data to the FL model remains a challenge. In this article, we propose a hybrid incentive mechanism based on blockchain to address the above challenge. The proposed mechanism comprises two primary smart contract-based modules, namely the reputation module and the reverse auction module. The former is used to dynamically calculate the reputation score of each FL part

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Hummel, H. G. K., D. Burgos, C. Tattersall, F. Brouns, H. Kurvers, and R. Koper. "Encouraging contributions in learning networks using incentive mechanisms." Journal of Computer Assisted Learning 21, no. 5 (2005): 355–65. http://dx.doi.org/10.1111/j.1365-2729.2005.00140.x.

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Yan, Han, and Hui Gao. "Learning-based Incentive Mechanism Design for Crowd Sensing." Journal of Physics: Conference Series 1288 (August 2019): 012008. http://dx.doi.org/10.1088/1742-6596/1288/1/012008.

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Agrell, Per J., Peter Bogetoft, and Jørgen Tind. "Incentive plans for productive efficiency, innovation and learning." International Journal of Production Economics 78, no. 1 (2002): 1–11. http://dx.doi.org/10.1016/s0925-5273(00)00097-9.

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