Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of 'bonus' payments. ,  study the requester's problem of dynamically adjusting quality-contingent payments for tasks.  consider a multi-round version of the ll-known  principal-agent  model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work.    treat this problem as a multi-armed bandit problem, with each 'arm' representing a potential contract. To cope with the large (and in fact, infinite) number of arms,  propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely.  provide a full analysis of this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature).
['Chien-Ju Ho', 'Aleksandrs Slivkins', 'Jennifer Wortman Vaughan']
I am writing a research paper on a idea Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of 'bonus' payments. ,  study the requester's problem of dynamically adjusting quality-contingent payments for tasks.  consider a multi-round version of the ll-known  principal-agent  model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work.    treat this problem as a multi-armed bandit problem, with each 'arm' representing a potential contract. To cope with the large (and in fact, infinite) number of arms,  propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely.  provide a full analysis of this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature)."}]