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Break the Cycle of Collusion: Simulation to Influence Mechanism of Cognitive Bias on To-Collude Decision Making

Author(s):
ORCID

Medium: journal article
Language(s): English
Published in: Buildings, , n. 7, v. 12
Page(s): 997
DOI: 10.3390/buildings12070997
Abstract:

Collusion is an all-pervading illegal market behavior that can undermine the sustainable development of the construction industry. It is acknowledged that collusive bidding decision making is influenced by conspirators’ cognitive bias. Nevertheless, the understanding of such an influence mechanism remains vague in the literature. This study aims to examine the mechanism of conspirators’ to-collude decision making by establishing a system dynamic model. The model development is based on the theories of cognitive biases, collusive bidding, and complex adaptive system. Multiple scenarios were simulated in the context of the Chinese construction industry. Three most influential cognitive bias are overconfidence, the illusion of control, and cognitive dissonance. The simulation results reveal conspirators’ intrinsic mechanisms to decide whether they deserve to participate in collusive bidding. The evolution of to-collude decision making is characterized by nonlinearity, multiplier, and stimulus enhancement effects. Collusion motivation and enterprise network relationships expand conspirators’ to-collude decision making. The increase of government regulation intensity and enterprise performance inhibit conspirators’ to-collude decision making. This study provides an insight into the cycle of collusion emergence from a complex system perspective and implies that antitrust authorities can launch carrot-and-stick measures for better regulation.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10688712
  • Published on:
    13/08/2022
  • Last updated on:
    10/11/2022
 
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