Reverse Auction Bidding: Studying Player Behavior Academic Article uri icon

abstract

  • © 2017 American Society of Civil Engineers. Reverse auction bidding is a recent development in the auctioning of goods and services. Significant controversy exists within the construction industry as to whether reversed auction bidding is akin to bid shopping. This is a serious ethical question for the engineering and construction community and one that is worthy of research. First, in the simplest terms, the practical purpose of this paper is to outline the development of a simple web-based game that allows for a controlled study of reverse auction bidding with a standard cohort of trained construction-oriented players and to outline the statistical results of the different games played between 2004 and 2016. These studies provide the pragmatic and statistical framework within a very highly structured game to consider the truth of this paper's hypothesis, which is that an economically efficient player exists in legal game play. In this paper, the legal game play must coincide with actual construction bidding methods and law. The hypothesis is shown to be true in this study of reverse auction bidding. An economically efficient bidder is someone wanted on a good bidding team; this simple game provides a quick and simple classification system and a good training tool. Second, the utility of the study, not as a bidding method in the real world, but as a training tool for bidding specialists is outlined and the statistical data shown to provide the definition for economically efficient players and nonefficient players is summarized and explained in terms of application to the real world and game theory. The ultimate objective is to create an artificially intelligent player who passes a Turing test for the game, and who maximizes the returns within the valid monetary and utility matrix models used by game players.

altmetric score

  • 3

author list (cited authors)

  • Nichols, J.

citation count

  • 2

publication date

  • November 2017