Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach
Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based app...
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MDPI AG
2025-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/4/179 |
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| author | Aitor Salas-Peña Juan Carlos García-Palomares |
| author_facet | Aitor Salas-Peña Juan Carlos García-Palomares |
| author_sort | Aitor Salas-Peña |
| collection | DOAJ |
| description | Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents. |
| format | Article |
| id | doaj-art-64b494cf0ec24197bed2739868ca8cba |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-64b494cf0ec24197bed2739868ca8cba2025-08-20T02:28:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114417910.3390/ijgi14040179Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based ApproachAitor Salas-Peña0Juan Carlos García-Palomares1Transport, Infrastructure and Territory Research Group (tGIS), Geography Department, Complutense University of Madrid (UCM), Profesor Aranguren S/N, 28040 Madrid, SpainTransport, Infrastructure and Territory Research Group (tGIS), Geography Department, Complutense University of Madrid (UCM), Profesor Aranguren S/N, 28040 Madrid, SpainSome complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents.https://www.mdpi.com/2220-9964/14/4/179complex networksagent-based modelsco-evolutionknowledge transfercommunity detectionlogistic clusters |
| spellingShingle | Aitor Salas-Peña Juan Carlos García-Palomares Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach ISPRS International Journal of Geo-Information complex networks agent-based models co-evolution knowledge transfer community detection logistic clusters |
| title | Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach |
| title_full | Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach |
| title_fullStr | Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach |
| title_full_unstemmed | Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach |
| title_short | Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach |
| title_sort | simulating co evolution and knowledge transfer in logistic clusters using a multi agent based approach |
| topic | complex networks agent-based models co-evolution knowledge transfer community detection logistic clusters |
| url | https://www.mdpi.com/2220-9964/14/4/179 |
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