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Linear Contracts for Supermodular Functions Based on Graphs
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AI
DOI：10.1007/978-3-032-03639-1_23.png)
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Abstract
En 中文
We study linear contracts for combinatorial problems in multi-agent settings. In this problem, a principal designs a linear contract with several agents, each of whom can decide to take a costly action or not. The principal observes only the outcome of the agents' collective actions, not the actions themselves, and obtains a reward from this outcome. Agents that take an action incur a cost, and so naturally agents require a fraction of the principal's reward as an incentive for taking their action. The principal needs to decide what fraction of their reward to give to each agent so that the principal's expected utility is maximized. Our focus is on the case when the agents are vertices in a graph and the principal's reward corresponds to the number of edges between agents who take their costly action. This case represents the natural scenario when an action of each agent complements actions of other agents through collaborations. Recently, [17] showed that for this problem it is impossible to provide any finite multiplicative approximation or additive FPTAS unless P = NP. On a positive note, the authors provided an additive PTAS for the case when all agents have the same cost. They asked whether an additive PTAS can be obtained for the general case, i.e. when agents potentially have different costs. We answer this open question in positive.
Keywords:
Linear contracts
Multi-agent systems
Combinatorial optimization
Supermodular functions
Additive PTAS
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