Abstract
We study the excludable public project model where the decision is binary (build or not build). In a classic excludable and binary public project model, an agent either consumes the project in its whole or is completely excluded. We study a setting where the mechanism can set different project release time for different agents, in the sense that high-paying agents can consume the project earlier than the low-paying agents. The mechanism design objective is to minimize the expected maximum release delay and the expected total release delay. We propose the single deadline mechanisms. We show that the optimal single deadline mechanism is asymptotically optimal for both objectives, regardless of the prior distributions. For small number of agents, we propose the sequential unanimous mechanisms by extending the largest unanimous mechanisms from Ohseto [8]. We propose an automated mechanism design approach via evolutionary computation to optimize within the sequential unanimous mechanisms.
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Notes
- 1.
- 2.
Because the concept of release time does not exist in the classic binary excludable public project model.
- 3.
For example, an agent may over-report to turn an unsuccessful cost share into a successful cost share, in order to claim the free part.
- 4.
When o approaches 0, r(o)’s numerator is approaching of(0) while the denominator is approaching o.
- 5.
If \(o^*=0\), then we replace it with an infinitesimally small \(\gamma >0\). The achieved sum-delay is then approaching \(r(\gamma )(1+\epsilon )\) asymptotically. When \(\gamma \) approaches 0, \(r(\gamma )\) approaches \(r^*\).
- 6.
Let M be a strategy-proof mechanism. There exists a sequential unanimous mechanism \(M'\) (with exponential sequence length). \(M'\) has an approximate equilibrium where the equilibrium outcome is arbitrarily close to M’s outcome. To prove this, we only need to discretize an individual agent’s type space [0, 1] into a finite number of grid points. The number of type profiles is exponential. We place M’s outcomes for all these type profiles in a sequence.
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Wang, G., Guo, M. (2021). Public Project with Minimum Expected Release Delay. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_8
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