Abstract
The analytical hierarchical process (AHP) is a multi-criteria, decision-making process. This work presents a method to be applied in group decisions (GAHP) using a combination of consensus process and gradient ascent to reach a joint agreement. The GAHP problem is modeled through a multilayer network, where each one of the criteria is negotiated by consensus with the direct neighbors on each layer of the network. Furthermore, each node performs a transversal gradient ascent and corrects the deviations from the personal decision locally. The process locates the optimal global decision, taking into account that this global function is never calculated nor known by any of the agents. If there is not an optimal global decision, but a set of suboptimal choices, agents are automatically divided into different groups that converges into these suboptimal decisions.
This work is supported by the PROMETEOII/2013/019 and TIN2015-65515-C4-1-R projects of the Spanish government.
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Rebollo, M., Palomares, A., Carrascosa, C. (2019). Distributed Group Analytical Hierarchical Process by Consensus. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_29
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DOI: https://doi.org/10.1007/978-3-319-94649-8_29
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