Ergun and Kane to Work on $1.2M NSF Grant for Algorithmic Workplace
Law Associate Professor Hilary Robinson, MIE Professor Ozlem Ergun, CEE Assistant Professor Michael Kane, CSSH Professor Steven Vallas, and Juliet Schor from Boston College were awarded a $1.2M NSF grant for creating an “Understanding the Algorithmic Workplace: A Multi-Method Study for Comprehensive Optimization of Platforms”.
Abstract Source: NSF
The emergence of the digital platform economy in recent years is rapidly transforming some kinds of economic activity. This project will develop new evidence about the workers, business organizations, and government institutions that are involved in the “algorithmic workplace” or “gig economy”. The research team includes experts in social science, public policy, and engineering. The team will conduct research to document the experiences, attitudes, and needs of platform workers. They will use these findings to build a mathematical model of worker behavior that will be combined with business data from an industrial partner. The result will be a model that can predict how changes in business operations will affect worker outcomes. The team will use field experiments to test the model predictions. The results of this project will be new knowledge for businesses that need to retain and motivate their workforce, new evidence for policymakers that seek to understand how neighborhoods and communities are affected by the growth of platform work, and new empirical evidence about the benefits and risks of platform work for the US workforce.
The team will undertake an interdisciplinary program of research aimed at generating theoretical and practical knowledge of issues that result from the advent of the algorithmic workplace in the United States. The project begins by conducting research on the attitudinal and behavioral characteristics of platform workers. Interviews and surveys will shed light on the occupational trajectories, experiences of risk, and regulatory preferences found among workers on several types of labor platforms. The team will analyze the qualitative data to shed new light over ongoing debates about the nature of algorithmic control of workers, the labor market dynamics which the platform economy fosters, and the policy preferences of workers themselves. A mathematical framework of worker behavior will be designed to model the fusion of these coded qualitative data with internal data from Deliv, a digital platform for package delivery. This will lead to a novel contribution of this project: agent-based modeling that can simulate the action of platform workers as they respond to shifts in the design of the algorithms on which platforms rely. This second stage of the research will be used to provide a deeper and better informed understanding of the strategies which workers employ as they respond to the technological changes programmers introduce in their efforts to refine the algorithms governing transactions made possible by platforms.