Enhancing Diffusion Models by Embedding Cognitive Reasoning

Bulumulla, C, Chan, J and Padgham, L 2018, 'Enhancing Diffusion Models by Embedding Cognitive Reasoning', in Advances in Social Network Analysis and Mining, Barcelona, 28-31 August 2018, pp. 744-749.


Document type: Conference Paper
Collection: Conference Papers

Title Enhancing Diffusion Models by Embedding Cognitive Reasoning
Author(s) Bulumulla, C
Chan, J
Padgham, L
Year 2018
Conference name EEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Conference location Barcelona
Conference dates 28-31 August 2018
Proceedings title Advances in Social Network Analysis and Mining
Publisher Elsevier
Place of publication Netherlands
Start page 744
End page 749
Total pages 6
Abstract Diffusion models are powerful tools for understanding the spread of diverse content such as information, opinions and ideas through social networks. Although these models have been successfully used to study the spreading dynamics such as viral marketing, there are many real scenarios (e.g. vaccination, evacuation) that require a more complex model. Hence, we propose a new hybrid framework that combines diffusion modelling with cognitive agent modelling. The hybrid, generic framework is grounded on BDI (Belief-Desire-Intention), an advanced, efficient cognitive agent framework. We demonstrate our framework to a wildfire evacuation case study consisting of 5,000 agents. We then compare and analyse the diffusion outcomes of our model against two baseline models, the standard Linear Threshold (LT) model and a slightly modified version of the LT model, across 17 different input configurations. The results show (statistically) significant differences with the baselines for the majority of the configurations, highlighting the need for cognitive agents in diffusion modelling. The framework presented here provides the basis for modelling complex reasoning to capture diffusion phenomena in complex and dynamic social systems.
Subjects Adaptive Agents and Intelligent Robotics
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ISBN 9781538660515
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