Parameterising the dynamics of inter-group conflict from real world data

Turner, Liam ORCID: https://orcid.org/0000-0003-4877-5289, Colombo, Gualtiero, Whitaker, Roger ORCID: https://orcid.org/0000-0002-8473-1913 and Felmlee, Diane 2017. Parameterising the dynamics of inter-group conflict from real world data. Presented at: DAIS 2017 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations, San Francisco, CA, USA, 4-8 August 2017. IEEE,

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Abstract

Generative modelling of inter-group relations enables probabilistic forecasting of possible conflict for scenarios where real-world data is sparse. In order for such models to have relevance and integrity, it is important to ensure that realworld data is used to parameterise the model and verify its characteristics. In this paper we investigate how real-world datasets can be mapped into generative model parameters concerning group structures and behaviours. We highlight the issues involved and present a framework for classifying potential data based on three attributes: (i) inter-group structure, (ii) inter-group actions and (iii) impact of actions. We argue that these attributes are fundamental for benchmarking and developing generative models in the context of limited existing data on inter-group interaction.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	In Press
Schools:	Computer Science & Informatics Crime and Security Research Institute (CSURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Publisher:	IEEE
Related URLs:	Organisation
Date of First Compliant Deposit:	15 June 2017
Last Modified:	02 Nov 2022 11:17
URI:	https://orca.cardiff.ac.uk/id/eprint/101467

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