Improving the Future of Work
Park Sinchaisri, Professor
Business, Haas School
Applications for Spring 2024 are closed for this project.
[ To be considered, please also complete this form: https://bit.ly/45sjUZw ]
We are interested in exploring and understanding worker behavior in the changing nature of work, from gig economy to freelancing to crowdsourcing to remote work. Recent technologies create and accelerate new work arrangements that provide workers with flexibility in their work schedules and choice of service. At the same time, the decisions a worker faces have become more complex. Platforms dynamically offer competing incentives, and the independent nature of gig work means that workers do not experience the benefits of learning from colleagues. See my research papers as an example: https://parksinchaisri.github.io/files/paper-gigdrivers.pdf and https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502968
Active projects for the 2023-2024 academic year:
2A: Improving Worker Learning in the Gig Economy
Workers spend a significant amount of time learning how to make good decisions on the job. While most traditional workers can learn by sharing best practices with their co-workers, gig workers face challenges in learning due to the independent nature of their work. For example, an Uber driver has to independently rediscover good strategies to get more trips that might already be known to other drivers. We are interested in how workers learn when they work independently and how platforms can improve such learning. Can the platform improve task assignments in a way that helps speed up worker learning and improve performance even during disruption?
2B: Algorithm as a Boss
This project explores the implications of platforms where human workers interact with an algorithm. For example, Uber drivers or DoorDash delivery people get assigned tasks by the app’s algorithm. How would they behave/make labor decisions differently compared to a traditional work setting? Recently, there are reports of on-demand workers colluding or intentionally adjusting their behavior in order to trick or nudge the algorithm to assign a better task to them. We are interested in understanding the mechanism behind such behavior and how platforms can mitigate it.
2C: Managing Crowdsourcing and Remote Workers
Crowdsourcing platforms (such as Amazon Mechanical Turk) make it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce. This project explores how to improve the crowdsourcing labor platform by first understanding crowdsourcing worker behavior such as task selection or task delegation in distributed work. How to improve performance and productivity of these workers? How to improve learning and collaboration among these independent crowdsourcing or remote workers? We are also interested in how these workers set goals and motivate themselves.
2D: Building Tools for the Future of Work
This project does not directly study the future of work; rather we aim to use tools from computer science and machine learning to develop new algorithms, tools, and methodologies that can be used to study the future of work. For example, we are currently using Generative Adversarial Networks (GANs) to estimate how on-demand workers decide among multiple apps they can work for. We have also started building a virtual experimental environment in which human participants can make decisions similar to what they could do in the real-world on-demand economy. We particularly recruit EECS majors and/or those who have experience building the front end/games or implementing various machine learning algorithms.
2E: Open Topics
These are all in super early stages. Potential research questions include how to nudge workers to take a break, on-demand healthcare/telemedicine, the impact of having a side gig job on full-time jobs, on-demand temporary teams, and disintermediation in on-demand platforms. Open to new research questions that the students are interested in.
Role: You will gain exposure to timely and impactful research on the future of work. Depending on your interests and the projects, you could be involved with (a) literature reviews, (b) design of behavioral experiments/user studies, (c) data collection and analysis, (d) analytical modeling (using game theory and discrete choice methods). Our lab meets weekly and you will get to collaborate and connect with other members in person and via our lab Discord.
Qualifications: Applicants majoring in Economics, IEOR, Data Science, Business Administration, Psychology (EECS, Computer Science for some projects) with an intention of pursuing graduate studies (especially PhD) and/or continuing in the Spring/Summer of 2024 will be given priority.
Useful courses/skills: Econometrics, Game Theory, Industrial Organization, Python, R, and data science. For some projects, Javascript, machine learning, and front-end development.
Our most successful lab members and/or those who have had the best experience tend to be ones who are genuinely excited about the research questions, highly attentive to detail, enjoy thinking outside the box, and can be independent and self-initiative. Our lab alumni have gone to graduate programs at Harvard, MIT, Berkeley, UPenn, CMU, and UMichigan.
Hours: 9-11 hrs
Off-Campus Research Site: Open to remote as well.
Related website: https://parksinchaisri.github.io
Related website: https://parksinchaisri.github.io/files/paper-gigdrivers.pdf
Digital Humanities and Data Science, Social Sciences, Mathematical and Physical Sciences, Engineering, Design & Technologies