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Detecting abnormal and collusive bids in capped tendering

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posted on 2018-05-01, 07:47 authored by Pablo Ballesteros-Perez, M. Carmen Gonzalez-Cruz, Antonio Canavate-Grimal, Eugenio Pellicer
Recent developments in the area of Bid Tender Forecasting have enabled bidders to implement new types of easy-to-use tools for increasing their chances of winning contracts. Although these new tools (such as iso-Score Curve Graphs, Scoring Probability Graphs, and Position Probability Graphs) are designed for bidders in capped tendering (tenders with an upper price limit), some of their principles can also be applied by a Contracting Authority to detect which bidders do not follow a standard pattern, that is, their bids are extremely high or low. Since a collusive bid generally needs to be sufficiently high or low to make an impact on the bid distribution, any person in charge of supervising capped tenders can be alerted to any bidder that might be involved in a cartel after identifying the same abnormal behavior in a series of tenders through simple calculations and a new type of graph. © 2012 Elsevier B.V.

Funding

The translation of this paper was funded by the Universitat Politècnica de València.

History

School

  • Architecture, Building and Civil Engineering

Published in

Automation in Construction

Volume

31

Pages

215 - 229

Citation

BALLESTEROS-PEREZ, P. ...et al., 2013. Detecting abnormal and collusive bids in capped tendering. Automation in Construction, 31, pp. 215-229.

Publisher

© Elsevier

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2013

Notes

This paper was accepted for publication in the journal Automation in Construction and the definitive published version is available at https://doi.org/10.1016/j.autcon.2012.11.036

ISSN

0926-5805

Language

  • en

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