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An Algorithm for Modelling the Impact of the Judicial Conflict-Resolution Process on Construction Investment

Author

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  • Andrej Bugajev

    (Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Sauletekio ave. 11, LT-10223 Vilnius, Lithuania
    These authors contributed equally to this work.)

  • Olga R. Šostak

    (Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Sauletekio ave. 11, LT-10223 Vilnius, Lithuania
    These authors contributed equally to this work.)

Abstract

In this article, the modelling of the judicial conflict-resolution process is considered from a construction investor’s point of view. Such modelling is important for improving the risk management for construction investors and supporting sustainable city development by supporting the development of rules regulating the construction process. Thus, this raises the problem of evaluation of different decisions and selection of the optimal one followed by distribution extraction. First, the example of such a process is analysed and schematically represented. Then, it is formalised as a graph, which is described in the form of a decision graph with cycles. We use some natural problem properties and provide the algorithm to convert this graph into a tree. Then, we propose the algorithm to evaluate profits for different scenarios with estimation of time, which is done by integration of an average daily costs function. Afterwards, the optimisation problem is solved and the optimal investor strategy is obtained—this allows one to extract the construction project profit distribution, which can be used for further analysis by standard risk (and other important information)-evaluation techniques. The overall algorithm complexity is analysed, the computational experiment is performed and conclusions are formulated.

Suggested Citation

  • Andrej Bugajev & Olga R. Šostak, 2018. "An Algorithm for Modelling the Impact of the Judicial Conflict-Resolution Process on Construction Investment," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:182-:d:126740
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    References listed on IDEAS

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    1. Jennifer Martínez-Ferrero & Isabel Gallego-Álvarez & Isabel María García-Sánchez, 2015. "A Bidirectional Analysis of Earnings Management and Corporate Social Responsibility: The Moderating Effect of Stakeholder and Investor Protection," Australian Accounting Review, CPA Australia, vol. 25(4), pages 359-371, December.
    2. Tanja Schwarzmüller & Prisca Brosi & Vera Stelkens & Matthias Spörrle & Isabell M. Welpe, 2017. "Investors’ reactions to companies’ stakeholder management: the crucial role of assumed costs and perceived sustainability," Business Research, Springer;German Academic Association for Business Research, vol. 10(1), pages 79-96, June.
    3. Paolo Manasse & Roberto Savona & Marika Vezzoli, 2016. "Danger Zones for Banking Crises in Emerging Markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 360-381, October.
    4. Davidov, Sreten & Pantoš, Miloš, 2017. "Stochastic assessment of investment efficiency in a power system," Energy, Elsevier, vol. 119(C), pages 1047-1056.
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    Citations

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    Cited by:

    1. Edmundas Kazimieras Zavadskas & Jonas Šaparauskas & Jurgita Antucheviciene, 2018. "Sustainability in Construction Engineering," Sustainability, MDPI, vol. 10(7), pages 1-7, June.
    2. Augustinas Maceika & Andrej Bugajev & Olga R. Šostak, 2019. "The Modelling of Roof Installation Projects Using Decision Trees and the AHP Method," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    3. Augustinas Maceika & Andrej Bugajev & Olga Regina Šostak & Tatjana Vilutienė, 2021. "Decision Tree and AHP Methods Application for Projects Assessment: A Case Study," Sustainability, MDPI, vol. 13(10), pages 1-33, May.

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