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the claim

Expected revenue in procurement reverse auctions can be calculated using bidding theory models.

the verdict
SUPPORTED
the evidence backs this
Recorded sources
4 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that expected revenue in procurement reverse auctions can be calculated using bidding theory models?

Yes. Multiple studies demonstrate that bidding theory models and mechanism design frameworks can successfully be constructed to analyze and calculate expected revenue, optimal pricing, and task allocation in procurement reverse auctions.

Verdict: SUPPORTED · 4 sources for, 0 against · Last checked

The analysis

The retrieved literature contains several theoretical and algorithmic papers that explicitly construct bidding models, mechanism designs, and optimization frameworks to calculate expected revenue, optimal bids, and allocations in procurement reverse auctions. All relevant papers support the claim, and none refute it.

Evidence for · 4
An Optimal Reverse Affine Maximizer Auction Mechanism for Task Allocation in Mobile Crowdsensing
2025
The paper constructs a theoretical reverse auction model (RAMA) to calculate expected revenue and task allocation optimization.
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Zhang, J., Chen, P., Yang, X., Wu, H., & Li, W. (2025). An Optimal Reverse Affine Maximizer Auction Mechanism for Task Allocation in Mobile Crowdsensing. doi.org. https://doi.org/10.1109/TMC.2025.3549504

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More for · 3
Mechanism design in project procurement auctions with cost uncertainty and failure risk
2019
The study derives closed-form solutions of optimal bid prices and expected profits using bidding models in procurement reverse auctions.
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Qian, X., Huang, M., Ching, W., Lee, L., & Wang, X. (2019). Mechanism design in project procurement auctions with cost uncertainty and failure risk. doi.org. https://doi.org/10.3934/JIMO.2018036

Revenue-Optimal Reverse Auction for Task Allocation in Mobile Crowdsensing Through Transformer Attention
2025
The article formulates a revenue maximization programming model for reverse auctions to solve pricing and allocation problems.
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Chen, P., Zhang, J., Li, W., & Wu, H. (2025). Revenue-Optimal Reverse Auction for Task Allocation in Mobile Crowdsensing Through Transformer Attention. doi.org. https://doi.org/10.1109/TCSS.2025.3547986

E-Sourcing in Procurement: Theory and Behavior in Reverse Auctions with Non-Competitive Contracts
2005
The study applies bidding theory models and behavioral assumptions regarding dominant strategies to analyze expected revenue and outcomes in reverse auctions.
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Engelbrecht-Wiggans, R. & Katok, E. (2005). E-Sourcing in Procurement: Theory and Behavior in Reverse Auctions with Non-Competitive Contracts. doi.org. https://doi.org/10.2139/ssrn.673562

What does the evidence say?

The retrieved literature contains several theoretical and algorithmic papers that explicitly construct bidding models, mechanism designs, and optimization frameworks to calculate expected revenue, optimal bids, and allocations in procurement reverse auctions. All relevant papers support the claim, and none refute it.

Receipt History
first checked05 Aug 2026
judged → SUPPORTED · 8205 Aug 2026
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