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Platforms for Efficient and Incentive-Aware Collaboration
Computer Science and Game Theory · Submitted 23 Feb 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We characterize how and to what degree such platforms can leverage their information advantage to steer strategic agents toward efficient collaboration.
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The abstract, sentence by sentence
Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by a high Price of Stability (PoS) in recent literature [Blum et al., 2021]. Implicit in the traditional PoS analysis is the assumption that agents have full knowledge of how their tasks relate to one another. We offer a new perspective on bringing about efficient collaboration among strategic agents using information design. Inspired by the growing importance of collaboration in machine learning (such as platforms for collaborative federated learning and data cooperatives), we propose a framework where the platform has more information about how the agents' tasks relate to each other than the agents themselves. We characterize how and to what degree such platforms can leverage their information advantage to steer strategic agents toward efficient collaboration. Concretely, we consider collaboration networks where each node is a task type held by one agent, and each task benefits from contributions made in their inclusive neighborhood of tasks. This network structure is known to the agents and the platform, but only the platform knows each agent's real location -- from the agents' perspective, their location is determined by a random permutation. We employ private Bayesian persuasion and design two families of persuasive signaling schemes that the platform can use to ensure a small total workload when agents follow the signal. The first family aims to achieve the minmax optimal approximation ratio compared to the optimal collaboration, which is shown to be $\Theta(\sqrt{n})$ for unit-weight graphs, $\Theta(n^{2/3})$ for graphs with constant minimum edge weights, and $O(n^{3/4})$ for general weighted graphs. The second family ensures per-instance strict improvement compared to full information disclosure.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Collaboration is crucial for reaching collective goals. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by a high Price of Stability (PoS) in recent literature [Blum et al., 2021]. | background 0.98 | 0.04 | 0.03 | asserted 0.84 |
| s2 | Implicit in the traditional PoS analysis is the assumption that agents have full knowledge of how their tasks relate to one another. | gap 0.75 | 0.06 | 0.02 | asserted 0.99 |
| s3 | We offer a new perspective on bringing about efficient collaboration among strategic agents using information design. | contribution 1.00 | 0.74 | 0.11 | asserted 0.79 |
| s4 | Inspired by the growing importance of collaboration in machine learning (such as platforms for collaborative federated learning and data cooperatives), we propose a framework where the platform has more information about how the agents' tasks relate to each other than the agents themselves. | contribution 0.80 | 0.66 | 0.06 | asserted 0.94 |
| s5 | We characterize how and to what degree such platforms can leverage their information advantage to steer strategic agents toward efficient collaboration. | contribution 0.99 | 0.77 | 0.17 | asserted 0.75 |
| s6 | Concretely, we consider collaboration networks where each node is a task type held by one agent, and each task benefits from contributions made in their inclusive neighborhood of tasks. | method 0.90 | 0.31 | 0.03 | asserted 0.99 |
| s7 | This network structure is known to the agents and the platform, but only the platform knows each agent's real location -- from the agents' perspective, their location is determined by a random permutation. | method 0.90 | 0.32 | 0.03 | asserted 1.00 |
| s8 | We employ private Bayesian persuasion and design two families of persuasive signaling schemes that the platform can use to ensure a small total workload when agents follow the signal. | method 0.88 | 0.94 | 0.12 | asserted 0.99 |
| s9 | The first family aims to achieve the minmax optimal approximation ratio compared to the optimal collaboration, which is shown to be $\Theta(\sqrt{n})$ for unit-weight graphs, $\Theta(n^{2/3})$ for graphs with constant minimum edge weights, and $O(n^{3/4})$ for general weighted graphs. | result 1.00 | 0.90 | 0.33 | asserted 1.00 |
| s10 | The second family ensures per-instance strict improvement compared to full information disclosure. | result 0.86 | 0.91 | 0.17 | asserted 0.99 |
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The paper
Original abstract
Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by a high Price of Stability (PoS) in recent literature [Blum et al., 2021]. Implicit in the traditional PoS analysis is the assumption that agents have full knowledge of how their tasks relate to one another. We offer a new perspective on bringing about efficient collaboration among strategic agents using information design. Inspired by the growing importance of collaboration in machine learning (such as platforms for collaborative federated learning and data cooperatives), we propose a framework where the platform has more information about how the agents' tasks relate to each other than the agents themselves. We characterize how and to what degree such platforms can leverage their information advantage to steer strategic agents toward efficient collaboration. Concretely, we consider collaboration networks where each node is a task type held by one agent, and each task benefits from contributions made in their inclusive neighborhood of tasks. This network structure is known to the agents and the platform, but only the platform knows each agent's real location -- from the agents' perspective, their location is determined by a random permutation. We employ private Bayesian persuasion and design two families of persuasive signaling schemes that the platform can use to ensure a small total workload when agents follow the signal. The first family aims to achieve the minmax optimal approximation ratio compared to the optimal collaboration, which is shown to be $\Theta(\sqrt{n})$ for unit-weight graphs, $\Theta(n^{2/3})$ for graphs with constant minimum edge weights, and $O(n^{3/4})$ for general weighted graphs. The second family ensures per-instance strict improvement compared to full information disclosure.
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Cites as background 10 papers
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Recent studies have adopted a mechanism design approach in federated learning, targeting objectives such as incentivizing data contribution and preventing free-riding [ref] , encouraging participation [ref] , and promoting fairness [ref] .
Fair yet Asymptotically Equal Collaborative Learning · Related Works · 0.88
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Recent studies have adopted a mechanism design approach in federated learning, targeting objectives such as incentivizing data contribution and preventing free-riding [ref] , encouraging participation [ref] , and promoting fairness [ref] .
Federated Learning as a Network Effects Game · Related Works · 0.88
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Recent studies have adopted a mechanism design approach in federated learning, targeting objectives such as incentivizing data contribution and preventing free-riding [ref] , encouraging participation [ref] , and promoting fairness [ref] .
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning · Related Works · 0.88
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