Share:


Participant trustworthiness analysis in the game-based urban planning processes by PROMETHEE-mGqNN approach

    Romualdas Baušys Affiliation
    ; Ingrida Leščauskienė   Affiliation
    ; Rokas Semėnas Affiliation

Abstract

Serious games together with the gamified and the game-based surveys (GBS), offer an engaging way to increase citizens’ participation in urban planning projects. However, there is always the risk of untrustworthy participants, which can decrease the overall reliability of the game-based research. Trustworthiness analysis is a highly challenging task since the neuropsychology of the GBS respondents and the infinite amount of their possible in-game actions causes many uncertainties in the data analysis. The novel MCDM approach PROMETHEE-mGqNN (PROMETHEE under m-generalised q-neutrosophic numbers) is proposed in this paper as the solution to the described problem. Five criteria that might be automatically calculated from the in-game data are proposed to construct the decision matrix to identify the untrustworthy respondents. The game-based survey “Parkis” developed to assess the safety and attractiveness of the urban public park “Missionary Garden” (Vilnius, Lithuania) is proposed as the case study of this research. By applying the proposed methodology, we calculated the trustworthy index value and noticed that it is capable of detecting the behavioural tendencies of the GBS players.

Keyword : MCDM, PROMETHEE, mGqNN, urban planning, game-based research, gamification, public participation, data mining

How to Cite
Baušys, R., Leščauskienė, I., & Semėnas, R. (2021). Participant trustworthiness analysis in the game-based urban planning processes by PROMETHEE-mGqNN approach. Journal of Civil Engineering and Management, 27(6), 427-440. https://doi.org/10.3846/jcem.2021.15263
Published in Issue
Jul 15, 2021
Abstract Views
112
PDF Downloads
59
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Alonso, L., Zhang, Y. R., Grignard, A., Noyman, A., Sakai, Y., ElKatsha, M., Doorley, R., & Larson, K. (2018). Cityscope: a data-driven interactive simulation tool for urban design. use case volpe. In International Conference on Complex Systems (pp. 253–261). https://doi.org/10.1007/978-3-319-96661-8_27

Alonso-Fernández, C., Calvo-Morata, A., Freire, M., MartínezOrtiz, I., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: a systematic literature review. Computers & Education, 141, 103612. https://doi.org/10.1016/j.compedu.2019.103612

Ampatzidou, C., Gugerell, K., Constantinescu, T., Devisch, O., Jauschneg, M., & Berger, M. (2018) All work and no play? Facilitating serious games and gamified applications in participatory urban planning and governance. Urban Planning, 3, 34–46. https://doi.org/10.17645/up.v3i1.1261

Aubert, A. H., & Lienert, J. (2019). Gamified online survey to elicit citizens’ preferences and enhance learning for environmental decisions. Environmental Modelling & Software, 111, 1–12. https://doi.org/10.1016/j.envsoft.2018.09.013

Dupuis, J., & Knoepfel, P. (2015). Concluding discussion: Institutional regime and actors’ modes of participation and interaction in environmental decision-making. In The politics of contaminated sites management (pp. 147–158). Springer International Publishing. https://doi.org/10.1007/978-3-319-11307-4_9

Battista, A. (2017). An activity theory perspective of how scenario-based simulations support learning: A descriptive analysis. Advances in Simulation, 2(1), 23. https://doi.org/10.1186/s41077-017-0055-0

Bausys, R., Kazakeviciute-Januskeviciene, G., Cavallaro, F., & Usovaite, A. (2020). Algorithm selection for edge detection in satellite images by neutrosophic WASPAS method. Sustainability, 12, 548. https://doi.org/10.3390/su12020548

Berta, R., & Moreno-Ger, P. (2018). Introduction: Intelligent learning assessment in serious games. International Journal of Serious Games, 5(1), 3–4. https://doi.org/10.17083/ijsg.v5i1.237

Beullens, K., Loosveldt, G., Vandenplas, C., & Stoop, I. (2018). Response rates in the European social survey: Increasing, decreasing, or a matter of fieldwork efforts? Survey methods: Insights from the field. https://surveyinsights.org/?p=9673

Brown, G. (2017). A review of sampling effects and response bias in internet participatory mapping (PPGIS/PGIS/VGI). Transactions in GIS, 21, 39–56. https://doi.org/10.1111/tgis.12207

Chen, L., Xu, Z., Wang, H., & Liu, S. (2018). An ordered clustering algorithm based on K-means and the PROMETHEE method. International Journal of Machine Learning and Cybernetics, 9, 917–926. https://doi.org/10.1007/s13042-016-0617-9

Chen, C. T., & Hung, W. Z. (2020). A two-phase model for personnel selection based on multi-type fuzzy information. Mathematics, 8, 1703. https://doi.org/10.3390/math8101703

Christopherson, K. M. (2007). The positive and negative implications of anonymity in Internet social interactions: “On the Internet, nobody knows you’re a dog”. Computers in Human Behavior, 23, 3038–3056. https://doi.org/10.1016/j.chb.2006.09.001

Czepkiewicz, M., Jankowski, P., & Młodkowski, M. (2017). Geoquestionnaires in urban planning: Recruitment methods, participant engagement, and data quality. Cartography and Geographic Information Science, 44(6), 551–567.

Devisch, O., Poplin, A., & Sofronie, S. (2016). The gamification of civic participation: Two experiments in improving the skills of citizens to reflect collectively on spatial issues. Journal of Urban Technology, 23(2), 81–102. https://doi.org/10.1080/10630732.2015.1102419

Irvin, R. A., & Stansbury, J. (2004). Citizen participation in decision making: is it worth the effort?. Public Administration Review, 64(1), 55–65. https://doi.org/10.1111/j.1540-6210.2004.00346.x

Falco, E., & Kleinhans, R. (2018). Digital participatory platforms for co-production in urban development: A systematic review. International Journal of E-Planning Research, 7(3), 52–79. https://doi.org/10.4018/IJEPR.2018070105

Følstad, A. (2009). Co-creation through user feedback in an online living lab: A case example. Open design spaces supporting user innovation. In Proceedings of the International Workshop on Open Design Spaces (pp. 43–55). International Institute for SocioInformatics.

Foth, M., Bajracharya, B., Brown, R., & Hearn, G. (2009). The second life of urban planning? Using neogeography tools for community engagement. Journal of Location Based Services, 3(2: NeoGeography), 97–117. https://doi.org/10.1080/17489720903150016

Hassan, L., & Hamari, J. (2020). Gameful civic engagement: A review of the literature on gamification of e-participation. Government Information Quarterly, 37(3), 101461. https://doi.org/10.1016/j.giq.2020.101461

Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process and challenges. In A. Kerren, J. T. Stasko, J.-D. Fekete, & C. North (Eds.), Lecture notes in computer science: Vol. 4950. Information visualization (pp. 154–175). Springer. https://doi.org/10.1007/978-3-540-70956-5_7

Keusch, F., & Zhang, C. (2017). A review of issues in gamified surveys. Social Science Computer Review, 35(2), 147–166. https://doi.org/10.1177/0894439315608451

Lescauskiene, I., Bausys, R., Zavadskas, E. K., & Juodagalviene, B. (2020). VASMA weighting: Survey-based criteria weighting methodology that combines ENTROPY and WASPAS-SVNS to reflect the psychometric features of the VAS scales. Symmetry, 12, 1641. https://doi.org/10.3390/sym12101641

Loh, C. S., Sheng, Y., & Ifenthaler, D. (2015). Serious games analytics: Theoretical framework. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics. methodologies for performance measurement, assessment, and improvement (pp. 3–29). Springer. https://doi.org/10.1007/978-3-319-05834-4_1

Mohandes, S. R., Sadeghi, H., Mahdiyar, A., Durdyev, S., Banaitis, A., Yahya, K., & Ismail, S. (2020). Assessing construction labours’ safety level: a fuzzy MCDM approach. Journal of Civil Engineering and Management, 26(2), 175–188. https://doi.org/10.3846/jcem.2020.11926

Munster, S., Georgi, C., Heijne, K., Klamert, K., Noennig, J. R., Pump, M., Stelzle, B., & van der Meer, H. (2017). How to involve inhabitants in urban design planning by using digital tools? An overview on a state of the art, key challenges and promising approaches. Procedia Computer Science, 112, 2391–2405. https://doi.org/10.1016/j.procs.2017.08.102

Owen, V. E., & Baker, R. S. (2019). Learning analytics for games. In J. L. Plass, R. Meyer, & B. D. Homer (Eds.), Handbook of game-based learning. MIT Press.

Papadopoulos, Y., & Warin, P. (2007), Are innovative, participatory and deliberative procedures in policy making democratic and effective?. European Journal of Political Research, 46(4), 445–472. https://doi.org/10.1111/j.1475-6765.2007.00696.x

Poplin, A. (2012). Playful public participation in urban planning: A case study for online serious games. Computers, Environment and Urban Systems, 36(3), 195–206. https://doi.org/10.1016/j.compenvurbsys.2011.10.003

Pradhan, S., & Abdourazakou, Y. (2020). “Power ranking” professional circuit eSports teams using multi-criteria decisionmaking (MCDM). Journal of Sports Analytics, 6, 61–73. https://doi.org/10.3233/JSA-190420

Semenas, R., & Bausys, R. (2020). Modelling of autonomous search and rescue missions by interval-valued neutrosophic WASPAS framework. Symmetry, 12, 162. https://doi.org/10.3390/sym12010162

Senapati, T., & Yager, R.R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11, 663–674. https://doi.org/10.1007/s12652-019-01377-0

Smarandache, F. (2019). Neutrosophic set is a generalization of intuitionistic fuzzy set, inconsistent intuitionistic fuzzy set, pythagorean fuzzy set, q-rung orthopair fuzzy set, spherical fuzzy set and n-hyperbolic fuzzy set while neutrosophication is a generalization of regret theory, grey system theory and three ways decision. Journal of New Theory, 29, 1–35. https://doi.org/10.1002/9781119544203.ch1

Stauskis, G. (2014). Development of methods and practices of virtual reality as a tool for participatory urban planning: a case study of Vilnius City as an example for improving environmental, social and energy sustainability. Energy, Sustainability and Society, 4, 7. https://doi.org/10.1186/2192-0567-4-7

Urbaniak, K., Wątróbski, J., & Sałabun, W. (2020). Identification of players ranking in E-Sport. Applied Sciences, 10, 6768. https://doi.org/10.3390/app10196768

Von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge University Press.

Zavadskas, E. K., Bausys, R., Juodagalvienė, B., & Garnytė-Sapranavičienė, I. (2017). Model for residential house element and material selection by neutrosophic MULTIMOORA method. Engineering Applications of Artificial Intelligence, 64, 315–324.

Zavadskas, E. K., Bausys, R., Kaklauskas, A., & Raslanas, S. (2019a). Hedonic shopping rent valuation by one-to-one neuromarketing and neutrosophic PROMETHEE method. Applied Soft Computing, 85, 105832. https://doi.org/10.1016/j.asoc.2019.105832

Zavadskas, E. K., Bausys, R., & Mazonaviciute, I. (2019b). Safety evaluation methodology of urban public parks by multicriteria decision making. Landscape and Urban Planning, 189, 372–381. https://doi.org/10.1016/j.landurbplan.2019.05.014

Zavadskas, E. K., Bausys, R., Lescauskiene, I., & Omran, J. (2020). M-generalised q-neutrosophic MULTIMOORA for decision making. Studies in Informatics and Control, 29(4), 389–398. https://doi.org/10.24846/v29i4y202001

Zavadskas, E. K., Bausys, R., Lescauskiene, I., & Usovaite, A. (2021). MULTIMOORA under interval-valued neutrosophic sets as the basis for the quantitative heuristic evaluation methodology HEBIN. Mathematics, 9(1), 66. https://doi.org/10.3390/math9010066

Zhang, C., & Conrad, F. (2014). Speeding in web surveys: The tendency to answer very fast and its association with straightlining. Survey Research Methods, 8(2), 127–135. https://doi.org/10.18148/srm/2014.v8i2.5453