Design of Quality Assuring Mechanisms with Learning for Strategic Crowds
Satyanath Bhat, K
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In this thesis, we address several generic problems concerned with procurement of tasks from a crowd that consists of strategic workers with uncertainty in their qualities. These problems assume importance as the quality of services in a service marketplace is known to degrade when there is (unchecked) information asymmetry pertaining to quality. Moreover, crowdsourcing is increasingly being used for a wide variety of tasks these days since it offers high levels of flexibility to workers as well as employers. We seek to address the issue of quality uncertainty in crowdsourcing through mechanism design and machine learning. As the interactions in web-based crowdsourcing platform are logged, the data captured could be used to learn unknown parameters such as qualities of individual crowd workers. Further, many of these platforms invite bids by crowd workers for available tasks but the strategic workers may not bid truthfully. This warrants the use of mechanism design to induce truthful bidding. There ensues a complex interplay between machine learning and mechanism design, leading to interesting technical challenges. We resolve some generic challenges in the context of the following problems. Design of a quality eliciting mechanism with interdependent values We consider an expert sourcing problem, where a planner seeks opinions from a pool of experts. Execution of the task at an assured quality level in a cost effective manner turns out to be a mechanism design problem when the individual qualities are private information of the experts. Also, the task execution problem involves interdependent values, where truthfulness and efficiency cannot be achieved in an unrestricted setting due to an impossibility result. We propose a novel mechanism that exploits the special structure of the problem and guarantees allocative efficiency, ex-post incentive compatibility and strict budget balance for the mechanism, and ex-post individual rationality for the experts. Design of an optimal dimensional crowdsourcing auction We study the problem faced by an auctioneer who gains stochastic rewards by procuring multiple units of a service from a pool of heterogeneous strategic workers. The reward obtained depends on the inherent quality of the worker; the worker’s quality is fixed but unknown. The costs and capacities are private information of the workers. The auctioneer is required to elicit costs and capacities (making the mechanism design dimensional) and further, has to learn the qualities of the workers as well, to enable utility maximization. To solve this problem, we design a dimensional multi-armed bandit auction that maximizes the expected utility of the auctioneer subject to incentive compatibility and individual rationality while simultaneously learning the unknown qualities of the agents. Design of a multi-parameter learning mechanism for crowdsourcing We investigate the problem of allocating divisible jobs, arriving online, to workers in a crowd-sourcing platform. Each job is split into a certain number of tasks that are then allocated to workers. These tasks have to meet several constraints that depend on the worker performance. The performance of each worker in turn is characterized by several intrinsic stochastic parameters. In particular, we study a problem where each arriving job has to be completed within a deadline and each task has to be completed, honouring a lower bound on quality. The job completion time and quality of each worker are stochastic with fixed but unknown means. We propose a learning mechanism to elicit the costs truthfully while simultaneously learning the stochastic parameters. Our proposed mechanism is dominant strategy incentive compatible and ex-post individually rational with asymptotically optimal regret performance.