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An Agent-Based Model to Study Competitive Construction Bidding and the Winner's Curse

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An Agent-Based Model to Study Competitive Construction Bidding and the Winner's Curse Missouri University of Science and Technology Scholars' Mine Engineering Management and Systems Engineering Management and Systems Engineering Faculty Research & Creative Works Engineering 13 May 2020 An Agent-Based Model to Study Competitive Construction Bidding and the Winner's Curse Amr Elsayegh Cihan H. Dagli Missouri University of Science and Technology, [email protected] Islam H. El-Adaway Missouri University of Science and Technology, [email protected] Follow this and additional works at: https://scholarsmine.mst.edu/engman_syseng_facwork Part of the Civil Engineering Commons, and the Operations Research, Systems Engineering and Industrial Engineering Commons Recommended Citation A. Elsayegh et al., "An Agent-Based Model to Study Competitive Construction Bidding and the Winner's Curse," Procedia Computer Science, vol. 168, pp. 147-153, Elsevier B.V., May 2020. The definitive version is available at https://doi.org/10.1016/j.procs.2020.02.278 This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. This Article - Conference proceedings is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Engineering Management and Systems Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available onlineScienceDirect at www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science 00 (2019) 000–000 www.elsevier.com/locate/procedia ScienceDirect www.elsevier.com/locate/procedia Procedia Computer Science 168 (2020) 147–153 Complex Adaptive Systems Conference with Theme: LeveragingComplex AI and Machine Adaptive Learning Systems for Conference Societal Challenges, with Theme: CAS 2019 Leveraging AI and Machine Learning for Societal Challenges, CAS 2019 An Agent-based Model to Study Competitive Construction Bidding and the Winner’s Curse a* a a Amr Elsayegha*, Cihan H. Daglia, Islam H. El-adawaya 0F Amr Elsayegh , Cihan H. Dagli , Islam H. El-adaway 0F aMissouri University of Science and Technology, Rolla, MO, USA, 65401 aMissouri University of Science and Technology, Rolla, MO, USA, 65401 Abstract Abstract Reverse auction theory is the basis for competitive construction bidding process. The lowest bid method is utilized for selecting contractors in publicReverse projects. auction The theory winning is the c ontractorbasis for havingcompetitive the lowest construction bid value bidding could be process. cursed Thewhen lowest the submitted bid method bid valueis utilized results for in selectingnegative profits.contractors This inis causedpublic projects.by many The factors winning such casontractor the contractor’s having the estimation lowest bid accuracy value could and markup.be cursed This when is theaddressed submitted in thisbid papervalue resultsby providing in negative a model profits. sim ulatingThis is thecaused construction by many competitivefactors such bidding as the contractor’s and the occurrence estimation of theaccuracy winner’s and curse. markup. To thisThis end, is addressed the authors in showthis paper the extent by providing to which a the model winner’s simulating curse affectsthe construction the status competitiveof contracting bidding companies. and the The occurrence objectives of are the to winner’s understand curse. the To characteristics this end, the ofauthors the competitive show the extent bidding to phasewhich in the construction winner’s curse and toaffects study the the status behavior of contracting of contractor companies.s subject The to competitiveobjectives are bidding to understand and the occurrencethe characteristics of the winner’sof the competitive curse. As bidding such, the phase authors in construction implemented and a twoto study-step themethodology behavior of that contractor incorporatess subject (1) developingto competitive a gene biddingral simulation and the occurrencemodel involving of the awinner’s population curse. of contractorsAs such, the and authors projects implemented using agent a- basedtwo-step modeling methodology for the that competitive incorporates bidding (1) developing process and a gene (2) ralanalyzing simulation the modelresults involvingof the simulation a population model. of contractorsThis model and should projects provide using a agentbetter- understandingbased modeling to forthe theconstructi competitiveon profession bidding as process in contractors, and (2) projectanalyzing owners the results and Departments of the simulation of Transportation model. This of modelhow decisions should parerovide made a inbetter this biddingunderstanding environment to the .constructi on profession as in contractors, project owners and Departments of Transportation of how decisions are made in this bidding environment. © 2019 The Authors. Published by Elsevier B.V. © 20192020 The Authors. Published byby ElsevierElsevier B.V. B.V. This is an open access articlearticle underunder the the CC CC BY BY-NC-ND-NC-ND license license ( http://creativecommons.org/licenses/by(http://creativecommons.org/licenses/by-nc-nd/4.0/-nc-nd/4.0/) ) PeerThis -isreview an open under access responsibility article under of the the CC scientific BY-NC -NDcommittee license (ofhttp://creativecommons.org/licenses/by the Complex Adaptive Systems Conference-nc-nd/4.0/ with) Theme: Leveraging AI and Peer-reviewPeer-review underunder responsibilityresponsibility of of the the scientific scientific committee committee of theof theComplex Complex Adaptive Adaptive Systems Systems Conference Conference with Theme:with Theme: Leveraging Leveraging AI and AIMachine and LearningMachine Learnifor Societalng for Challenges Societal Challenges Machine Learning for Societal Challenges Keywords: Construction bidding; Contracting; Winner’s curse; Agent-based modeling; Simulation. Keywords: Construction bidding; Contracting; Winner’s curse; Agent-based modeling; Simulation. 1. Introduction 1. Introduction Winning projects bids is the blood that gives life to contractors in the construction industry. Contractors bid on projects to ensure Winning projects bids is the blood that gives life to contractors in the construction industry. Contractors bid on projects to ensure that more cash flow keeps coming towards their companies. Most public projects are bid by choosing the lowest bidder which is that more cash flow keeps coming towards their companies. Most public projects are bid by choosing the lowest bidder which is * Corresponding author. Tel.: +1-573-466-1630. * CorrespondingE-mail address: author. [email protected] Tel.: +1-573-466- 1630. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This1877 -is0509 an open © 2019 access The article Authors. under Published the CC BYby Elsevier-NC-ND B.V. license (http://creativecommons.org/licenses/by-nc-nd/4.0/) PeerThis- isreview an open under access responsibility article under of the scientificCC BY-NC committee-ND license of the (http://creative Complex Adaptivecommons.org/licenses/by Systems Conference-nc -withnd/4.0/ Theme:) Leveraging AI and Machine Learning for SocietalPeer-review Challenges under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for Societal Challenges 1877-0509 © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for Societal Challenges 10.1016/j.procs.2020.02.278 10.1016/j.procs.2020.02.278 1877-0509 148 Amr Elsayegh et al. / Procedia Computer Science 168 (2020) 147–153 A. Elsayegh; CH Dagli; IH El-adaway / Procedia Computer Science 00 (2019) 000–000 commonly known as Reverse Auction theory. As the construction industry grows, the competition between contractors increases and becomes more complex [1]. Bidding for a project is a major decision that contractors make after receiving relevant information about such project [2]. The major challenge in competitive bidding is that the contractor needs to bid low enough to win the contract but still high enough to generate positive profits [3]. Contractors try to be as accurate as possible in their estimates and include a minimum acceptable markup to maximize their chances of winning a bid. The estimation accuracy is subjective and depends on multiple factors such as the estimation staff, their experience and previous knowledge of similar projects [4]. Determining the markup is a strategic decision made by the upper management in each company which depends upon many factors such as size of the company, size of the project and how desperate they are willing to win. Desperate or unexperienced contractors could end up submitting low bids to secure winning a certain project. This could also be another strategy implemented by claims-conscious contractors that plan to compensate their low bids through future change orders by filing claims during construction which will only harm the project objectives [5]. This is one of the major drawbacks of the lowest bid
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