Pricing tasks in online labor markets

In this paper we present a mechanism for determining nearoptimal prices for tasks in online labor markets, often used for crowdsourcing. In particular, the mechanisms are designed to handle the intricacies of markets like Mechanical Turk where workers arrive online and requesters have budget constraints. The mechanism is incentive compatible, budget feasible, and has competitive ratio performance and also performs well in practice. To demonstrate the mechanism’s practical effectiveness we conducted experiments on the Mechanical Turk platform.

Reputation-based Incentive Protocols in Crowdsourcing Applications

Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in order to improve the performance of the non-cooperative equilibria emerging in such applications. We first formulate the exchanges on a crowdsourcing website as a two-sided market where requesters and workers are matched and play gift-giving games repeatedly. Subsequently, we study the protocol designer's problem of finding an optimal and sustainable (equilibrium) protocol which achieves the highest social welfare for that website. We prove that the proposed incentives protocol can make the website operate close to Pareto efficiency. Moreover, we also examine an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol

Labour market effects of crowdwork in the US and EU: an empirical investigation

Is it possible to estimate the real impact of micro-task crowdwork on wages and working conditions of platform workers? Do workers involved in micro-task outsourcing differ in their characteristics from traditional salaried workers of similar ability? Are micro-task crowdworkers similar or different in the United States and in Europe? In this paper, we address these questions by comparing wages and working conditions across onlineplatform workers and traditional workers in a quasi-experimental approach which exploits caregiving as an instrument for participation in crowdwork. We find evidence that, when controlling for workers’ observed and unobserved ability, traditional workers retain a significant premium in their earnings with respect to platform workers, though this effect is not as large as descriptive statistics may hint. Moreover, labour force in crowdworking arrangements appears to suffer from high levels of under-utilisation, relegating crowdworkers into a new category of idle workers whose human capital is neither fully utilised nor adequately compensated

Platforming Equality: Policy Challenges for the Digital Economy

This is the final version. Available from Autonomy via the link in this recordWelcome to Autonomy’s ‘Platforming Equality’ document: a collection of papers on the challenges that the digital economy poses to policymakers, activists and researchers. We’ve invited a range of contributors to probe deeper into under-examined topics in the digital economy and to shed light on how they operate. Another aim of the collection is to explore policy options for alleviating a range of new challenges that have emerged within the digital economy. Contributors move beyond theoretical discussion of the problems themselves and turn towards an analysis of responses that are open to activists, municipal authorities and government policy makers. Articles suggest a range of policy recommendations and discuss the strengths and weaknesses of different approaches. Each contributor examines a specific issue based on their own research and an analysis of the existing literature. They then provide their own perspective on the policies and approaches that would be most suitable to tackling the issue

A data-driven analysis of workers' earnings on Amazon Mechanical Turk

A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~2 USD/h, and only 4% earned more than 7.25 USD/h. While the average requester pays more than 11 USD/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, e.g., time spent searching for tasks, working on tasks that are rejected, and working on tasks that are ultimately not submitted. We further explore the characteristics of tasks and working patterns that yield higher hourly wages. Our analysis informs platform design and worker tools to create a more positive future for crowd work

On the Design and Analysis of Incentive Mechanisms in Network Science

With the rapid development of communication, computing and signal processing technologies, the last decade has witnessed a proliferation of emerging networks and systems, examples of which can be found in a wide range of domains from online social networks like Facebook or Twitter to crowdsourcing sites like Amazon Mechanical Turk or Topcoder; to online question and answering (Q&A) sites like Quora or Stack Overflow; all the way to new paradigms of traditional systems like cooperative communication networks and smart grid. Different from tradition networks and systems where uses are mandated by fixed and predetermined rules, users in these emerging networks have the ability to make intelligent decisions and their interactions are self-enforcing. Therefore, to achieve better system-wide performance, it is important to design effective incentive mechanisms to stimulate desired user behaviors. This dissertation contributes to the study of incentive mechanisms by developing game-theoretic frameworks to formally analyze strategic user behaviors in a network and systematically design incentive mechanisms to achieve a wide range of system objectives. In this dissertation, we first consider cooperative communication networks and propose a reputation based incentive mechanism to enforce cooperation among self-interested users. We analyze the proposed mechanism using indirect reciprocity game and theoretically demonstrate the effectiveness of reputation in cooperation stimulation. Second, we propose a contract-based mechanism to incentivize a large group of self-interested electric vehicles that have various preferences to act coordinately to provide ancillary services to the power grid. We derive the optimal contract that maximizes the system designer's profits and propose an online learning algorithm to effectively learn the optimal contract. Third, we study the quality control problem for microtask crowdsourcing from the perspective of incentives. After analyzing two widely adopted incentive mechanisms and showing their limitations, we propose a cost-effective incentive mechanism that can be employed to obtain high quality solutions from self-interested workers and ensure the budget constraint of requesters at the same time. Finally, we consider social computing systems where the value is created by voluntary user contributions and understanding how user participate is of key importance. We develop a game-theoretic framework to formally analyze the sequential decision makings of strategic users under the presence of complex externality. It is shown that our analysis is consistent with observations made from real-word user behavior data and can be applied to guide the design of incentive mechanisms in practice