Research map / Learning theory and algorithms / Submodular and game algorithms
When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use myopic routing based on current expected latency rather than socially optimal exploration | Selfish users in the congestion game · Parallel routing network, worst case | PoA: PoA^(m)≥ 1/(1−ρ) | Hurt |
| Hide all traffic information | Information-hiding mechanism · Parallel routing network, worst case | PoA: PoA^∅=∞ | Hurt |
| Selectively disclose expected path latencies when users would over-explore, and hide them otherwise | Selfish users in the congestion game · Parallel routing network, worst case | PoA: PoA^(SID)≤ 1/(1−ρ/2) | Helped |
| Use SID instead of myopic routing | Congestion game · Simulation, N=2 | average inefficiency ratio: γ^(m)>10 to γ^(SID)<2 | Helped |
| Increase the number of risky paths | Congestion game under selfish routing · Simulation, varying risky path number N | average inefficiency ratio | Helped |
| Increase the high-hazard coefficient α_H | Congestion game under selfish routing · Simulation, varying α_H | average inefficiency ratio | Helped |
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- Trade-Offs Between Information and Crowding in Sequential Decisions (Student Abstract) (AAAI 2025 · similarity 0.82)
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