Decline Now: A Combinatorial Model for Algorithmic Collective Action
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909352030371840 |
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| author | Sigg, Dorothee Hardt, Moritz Mendler-Dünner, Celestine |
| author_facet | Sigg, Dorothee Hardt, Moritz Mendler-Dünner, Celestine |
| contents | 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decline Now: A Combinatorial Model for Algorithmic Collective Action Sigg, Dorothee Hardt, Moritz Mendler-Dünner, Celestine Computer Science and Game Theory Human-Computer Interaction 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 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. |
| title | Decline Now: A Combinatorial Model for Algorithmic Collective Action |
| topic | Computer Science and Game Theory Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.12633 |