CONTOURA: CONtext-aware Territorial decisiOn Analysis with Utility, Resilience and Acceptability
Description
Decision-making (DM) processes in territorial contexts typically involve multiple stakeholders with heterogeneous preferences, operating in uncertain environments and facing multiple, often conflicting criteria. In such situations, decision-support approaches must be transparent, understandable to all participants, and capable of representing both the social and behavioral dimensions of DM. CONTOURA aims to design a multi-criteria, multi-actor decision-support methodology hat promotes the emergence of solutions perceived as resilient, fair, and acceptable within complex and uncertain environments, applied to territorial issues.
The project pursues four scientific objectives: i) To formalize the subjective and individual performance’s perception of each alternatives, relying on utility functions and preferences derived from Decision Theory [9 ; 10] ; ii) To analyze the resilience of decisions across different future scenarios [11]; iii) To integrate the individual behavioral dimension of DM, particularly the perception of risk and individual attitudes towards uncertainty [12 ; 13]; iv) To model collective DM processes in order to explore social acceptability, fairness, and equity in negotiation and compromise [14 ; 15; 16].
The innovative contribution of the project lies in: i) the contextualization of individual DM behavior, moving beyond purely rational models; ii) the explicit consideration of social interactions among stakeholders, including influence, mutual perception, and negotiation; iii) the simultaneous integration of multidimensional criteria specific to each stakeholder in the territory and the uncertainties inherent in the DM process iv) the commitment to developing a transparent tool, avoiding the “black box” effect often associated with advanced quantitative methods.
Classical Multi-Criteria Decision Analysis methods [17; 18; 19] (e.g. AHP, ELECTRE) rarely account for the subjective perception of performance. They only partially represent attitudes toward risk and uncertainty. They remain limited in capturing social dynamics and interactions among stakeholders. Moreover, the increasing complexity of situations creates impossible solving process.
Therefore CONTOURA combines decision support, behavioral understanding, and social dynamics modeling to facilitate a more realistic, transparent, and collectively acceptable DM process.
After an in-depth bibliographic study to identify the various scientific obstacles, the methodology to be developed will be based on the description of situational awareness, the modeling of individual perceptions and preferences, the modeling of social interactions, multi-criteria assessment and the resilience of alternatives, and the identification of a collective compromise and its acceptability. A territorial case study will serve as a demonstration for the entire approach.
Bibliography
Bibliography
[1] Fertier, A., Martin, G., Barthe-Delanoë, A.-M., Lesbegueries, J., Montarnal, A., Truptil, S., Bénaben, F., Salatgé, N., 2021. Managing events to improve situation awareness and resilience in a supply chain. Computers in Industry 132, 103488. https://doi.org/10.1016/j.compind.2021.103488
[2] Le Duff, C., Lesbegueries, J., Falco, R., Adrot, A., Montreuil, B., Benaben, F., 2025. Toward a generic information system for projects portfolio management using Physics of Decision (POD). Expert Systems with Applications 271, 126607. https://doi.org/10.1016/j.eswa.2025.126607
[3] Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D., 2014. Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials 16, 414–454. https://doi.org/10.1109/SURV.2013.042313.00197
[4] Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance), 2024.
[5] Endsley, M.R., 2016. Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition, 2nd ed. CRC Press, Boca Raton. https://doi.org/10.1201/b11371
[6] Steen-Tveit, K., Erik Munkvold, B., 2021. From common operational picture to common situational understanding: An analysis based on practitioner perspectives. Safety Science 142, 105381. https://doi.org/10.1016/j.ssci.2021.105381
[7] Artime, O., Grassia, M., De Domenico, M., Gleeson, J.P., Makse, H.A., Mangioni, G., Perc, M., Radicchi, F., 2024. Robustness and resilience of complex networks. Nat Rev Phys 6, 114–131. https://doi.org/10.1038/s42254-023-00676-y
[8] Durieux,S. 2021, Prise de décisions multi-acteurs en présence de données imparfaites : application aux processus industriels , Habilitation à diriger les recherches, Université Clermont Auvergne.
[9] Olteanu, A.-L., Meyer, P., 2022. Inferring a hierarchical majority-rule sorting model. Computers & Operations Research 146, 105888. https://doi.org/10.1016/j.cor.2022.105888
[10] Rădulescu, R., Mannion, P., Roijers, D.M., Nowé, A., 2019. Multi-objective multi-agent decision making: a utility-based analysis and survey. Auton Agent Multi-Agent Syst 34, 10. https://doi.org/10.1007/s10458-019-09433-x
[11] Moghaddasi, P., Kerachian, R., Sharghi, S., 2022. A stakeholder-based framework for improving the resilience of groundwater resources in arid regions. Journal of Hydrology 609, 127737. https://doi.org/10.1016/j.jhydrol.2022.127737
[12] Khannoussi, A., Olteanu, A.-L., Meyer, P., Benabbou, N., 2024a. A regret-based query selection strategy for the incremental elicitation of the criteria weights in an SRMP model. Oper Res Int J 24, 12. https://doi.org/10.1007/s12351-024-00823-y
[13] Khannoussi, A., Olteanu, A.-L., Meyer, P., Pasdeloup, B., 2024b. A metaheuristic for inferring a ranking model based on multiple reference profiles. Ann Math Artif Intell 92, 1467–1488. https://doi.org/10.1007/s10472-024-09926-w
[14] Carré, M., Nantier, E., Durieux, S., Piétrac, L., 2021a. Equity within Air Transportation Managementan Analysis of Inequity Index for Multi-Stakeholders Optimisation, in: Fourteenth USA/Europe Air Traffic Management Research and Development Seminar. Virtual (on line), United States.
[15] Carré, M., Nantier, E., Durieux, S., Piétrac, L., 2021b. Identification of a Consensus in a Multicriteria Decision Process involving several Decision-Makers Trade-off between global benefits and local consensus for multi-hub airlines operations, in: SESAR Innovation Days 2021. SESAR, Virtuel - Online, Belgium.
[16] Heleno, M., Sigrin, B., Popovich, N., Heeter, J., Jain Figueroa, A., Reiner, M., Reames, T., 2022. Optimizing equity in energy policy interventions: A quantitative decision-support framework for energy justice. Applied Energy 325, 119771. https://doi.org/10.1016/j.apenergy.2022.119771
[17] Roy, B., 1996. Multicriteria Methodology for Decision Aiding, Nonconvex Optimization and Its Applications. Springer US, Boston, MA. https://doi.org/10.1007/978-1-4757-2500-1
[18] Morelli, M., Casagrande, M., Forte, G., 2022. Decision Making: a Theoretical Review. Integr. psych. behav. 56, 609–629. https://doi.org/10.1007/s12124-021-09669-x
[19] Bouyssou, D., Marchant, T., Perny, P., 2009. Social Choice Theory and Multicriteria Decision Aiding, in: Decision-Making Process. John Wiley & Sons, Ltd, pp. 779–810. https://doi.org/10.1002/9780470611876.ch19
