Social Foundations of Computation Algorithms and Society Conference Paper 2024

Decline Now: A Combinatorial Model for Algorithmic Collective Action

Drivers on food delivery platforms often run a loss on low-paying orders. In response, workers on DoorDash started a campaign, DeclineNow, to purposefully decline orders below a certain pay threshold. For each declined order, the platform returns the request to other available drivers with slightly increased pay. While contributing to overall pay increase the implementation of the strategy comes with the risk of missing out on orders for each individual driver. In this work, we propose a first combinatorial model to study the strategic interaction between workers and the platform. Within our model, we formalize key quantities such as the average worker benefit of the strategy, the benefit of freeriding, as well as the benefit of participation. We extend our theoretical results with simulations. Our key insights show that the average worker gain of the strategy is always positive, while the benefit of participation is positive only for small degrees of labor oversupply. Beyond this point, the utility of participants decreases faster with an increasing degree of oversupply, compared to the utility of non-participants. Our work highlights the significance of labor supply levels for the effectiveness of collective action on gig platforms. We suggest organizing in shifts as a means to reduce oversupply and empower collectives

Author(s): Sigg, Dorothee and Hardt, Moritz and Mendler-Dünner, Celestine
Year: 2024
Month: October
Project(s):
Bibtex Type: Conference Paper (conference)
Event Name: CHI Conference on Human Factors in Computing Systems
State: Accepted
Links:

BibTex

@conference{sigg2024decline,
  title = {Decline Now: A Combinatorial Model for Algorithmic Collective Action},
  abstract = {Drivers on food delivery platforms often run a loss on low-paying orders. In response, workers on DoorDash started a campaign, DeclineNow, to purposefully decline orders below a certain pay threshold. For each declined order, the platform returns the request to other available drivers with slightly increased pay. While contributing to overall pay increase the implementation of the strategy comes with the risk of missing out on orders for each individual driver. In this work, we propose a first combinatorial model to study the strategic interaction between workers and the platform. Within our model, we formalize key quantities such as the average worker benefit of the strategy, the benefit of freeriding, as well as the benefit of participation. We extend our theoretical results with simulations. Our key insights show that the average worker gain of the strategy is always positive, while the benefit of participation is positive only for small degrees of labor oversupply. Beyond this point, the utility of participants decreases faster with an increasing degree of oversupply, compared to the utility of non-participants. Our work highlights the significance of labor supply levels for the effectiveness of collective action on gig platforms. We suggest organizing in shifts as a means to reduce oversupply and empower collectives},
  month = oct,
  year = {2024},
  slug = {sigg2024decline},
  author = {Sigg, Dorothee and Hardt, Moritz and Mendler-D{\"u}nner, Celestine},
  month_numeric = {10}
}