Network Intervention by Polling Strategic Agents
We show that the optimal prices admit a centrality-based decomposition of the welfare kernel: each agent's contribution scales with its squared centrality in a network reweighted by agents' preferences across activities.
ProofPaper ↗
Key points
- A planner in a network of strategic agents faces three entangled challenges: the optimum depends on agents' private information, queried agents may misreport to steer the outcome, and exact computation does not scale.
- We study these challenges in multi-activity network games with heterogeneous private technologies, in which the planner sets non-discriminatory prices.
- This decomposition motivates Poll, a polling algorithm in which the planner samples one agent per round, walks briefly through the agent's neighborhood, and updates the price from a local report.
Sources (1)
- [1]Network Intervention by Polling Strategic AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 01:40 PM
We show that the optimal prices admit a centrality-based decomposition of the welfare kernel: each agent's contribution scales with its squared centrality in a network reweighted by agents' preferences across activities.
A planner in a network of strategic agents faces three entangled challenges: the optimum depends on agents' private information, queried agents may misreport to steer the outcome, and exact computation does not scale.
Extractive summary: sentences quoted from the sources.
