Decline Now: A Combinatorial Model for Algorithmic Collective Action

TL;DR

A first combinatorial model is proposed to study the strategic interaction between workers and the platform and shows that the collective benefit of the strategy is always positive, while the benefit of participation is positive only for small degrees of labor oversupply.

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 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.

Figures (9)

• Figure 1: Assignment of orders in our model. In each level of the tree the order is assigned with probability $q_t$ to a participant and with probability $1-q_t$ to a non-participant. Every time a participant declines the order, the pay increases by an extra $\delta$. An order can be declined maximally $Z= \frac{\tau - r}{\delta}$ times, before the threshold $\tau$ is reached and it is accepted by both groups. The probability $q_t$ can vary in different time steps $t$. The figure illustrates a time step with $I_t=1$.
• Figure 2: Utility and tradeoffs. Base utility (dashed black), utility for participants (red) and non-participants (blue) for different levels of participators, $\alpha$. deg $< 1$ represents an undersupply and the dotted vertical line marks the transition from under- to oversupply.
• Figure 3: Gain of collective action for varying supply level $\mathrm{deg}$ and collective size $\alpha$. The dashed black line indicated $\mathrm{deg}\leq 1$ and the colored lines the different supply levels. We fix $b=30$ and vary $N \in [30, 150]$.
• Figure 4: Benefit of freeriding for varying supply level $\mathrm{deg}$ and collective size $\alpha$. We fix $b$ and vary $N \in [30, 150]$. The black line shows the maximum $F$ for each $\alpha$.
• Figure 5: Benefit of participation for varying supply level $\mathrm{deg}$ and collective size $\alpha$. We fix $b=30$ and vary $N \in [30, 150]$. The black line shows the boundary of the condition from Theorem \ref{['th:oversupplygroupsizethreshold']} under which $B>0$. The dashed black line shows the ground-truth boundary obtained from the simulation.

Theorems & Definitions (12)

• Definition 1: Supply-Conditions
• Definition 2: Degree of Oversupply
• Definition 3: Gain of Collective Action
• Theorem 1: Overall Gain of Collective Action
• Definition 4: Benefit of Freeriding
• Theorem 2
• Definition 5: Benefit of Participation
• Theorem 3
• Theorem 4: Threshold of Participation
• Lemma 1