[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128437-en":3,"doc-seo-128437-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128437,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Agent-Based Modelling and Machine Learning in Children’s Social Care","Children’s Social Care is a crucial UK service for supporting children and families through early interventions, child protection, and residential care, but it faces funding shifts since 2010 and rising demand. Sensitive and low-quality data create barriers for analysis, modelling, forecasting, and predictions that must be interpretable, scrutinised, and trustworthy before informing decisions. This PhD addresses these challenges with three contributions: agent-based simulation calibrated by genetic algorithms, temporal meta-optimiser sensitivity analysis, and reinforcement-learning-driven process optimisation.","Agent-Based Modelling and Machine Learning in  \nChildren’s Social Care  \nUsing Agent-Based Modelling and Machine Learning techniques and approaches for the betterment of Children’s Social Care services and outcomes  \nLuke Andrew White  \nA thesis submitted as part of the requirements for the degree of Computing Research (PhD) at the School of Computing and Digital Technology Birmingham City University, Birmingham, UK  \nDecember 2025  \nSupervised By: Prof Shadi Basurra, Dr Faisal Saeed & Dr Adbulrahmen A  \nAlsewari  \nSponsored By: Antser LTD  \nAbstract  \nChildren’s Social Care is a critical service provided by Local Authorities within the UK, where children and their families are provided support through various means including early interventions, child protection and residential care. These services have been under considerable pressure in recent years due to funding changes since 2010 and rising demand. The importance of understanding the best policy and practice for social workers is key to ensure that these services operate, and that they can improve into the future. Data available in this context to use for conducting analysis of existing policy and outcomes is met with difficulties such as Data Sensitivity and Data Quality. Furthermore, due to the context of such data, it is critical that any analysis, modelling, forecasts and predictions conducted on this data be interpretable and scrutinised to ensure that results can be trusted before decisions are taken. Addressing these challenges is the core of this thesis, with three contributions which together allow for analysis of Children’s Social Care data that utilises novel techniques to provide new insights into the policy and practice of Children’s Services within Local Authorities.  \nFirstly, Agent-Based Simulations using Genetic Algorithm Calibration: This contribution presents the use of Agent Based Models to enable the use of existing policy to inform a model’s design, where a population of Social Workers acting within a Local Authority can be simulated, taking advantage of the limited quality data that exists to configure the model. Further, this approach is presented with a Calibration method that can optimisethe model’s parameters to the existing data to validate its design and to provide new insights from the limited data. This approach also benefits from the mitigation of possible risks of using sensitive data directly for modelling, as the model design can be indirectly informed by such data, without training, and any data produced by the model will be synthetic, removing any potential risks.  \nSecond, Temporal Meta-optimiser based Sensitivity Analysis (TMSA) for Agent-Based Models: This contribution outlines a novel approach to Sensitivity Analysis for AgentBased Models, like the one presented in the first contribution. The use of Sensitivity Analysis in the validation of model design is important in ensuring a model’s creditability and enables the interpretation of model behaviour. With existing methods being ill-suited to Agent Based Models, the TMSA method is presented that utilises novel machine learning  \napproaches to conduct this form of analysis. With TMSA, Agent-Based models can be interpreted and scrutinised more effectively by those designing them.  \nFinally, Reinforcement Learning based process optimisation for Agent-Based Models: This contribution takes the previous methods developed and utilises them to create a novel approach to optimising Agent-Based Model process design. The approach uses Reinforcement Learning to identify changes in the code of an Agent-Based Model that will lead to an improvement in the model’s ability to represent existing data, through the use of both Sensitivity Analysis and Calibration. The approach further provides better understanding and interpretation of model designs, with an ability to identify shortcomings with assumptions, thus potentially challenging the existing policies and practices of Children’s Social Care.  ","cbCaikSL3bAdMhx7","https://ap.wps.com/l/cbCaikSL3bAdMhx7","pdf",12711274,2,1,189,"English","en",105,"# Abstract\n## Agent-Based Simulations using Genetic Algorithm Calibration\n## Temporal Meta-optimiser based Sensitivity Analysis (TMSA)\n## Reinforcement Learning based process optimisation\n# Acknowledgements","[{\"question\":\"What problem does the thesis address in Children’s Social Care?\",\"answer\":\"It addresses the difficulty of using sensitive, low-quality data to perform analysis and modelling that must remain interpretable and trustworthy for decision-making in UK Children’s Social Care services.\"},{\"question\":\"How is Genetic Algorithm Calibration used with agent-based simulations?\",\"answer\":\"The thesis uses agent-based models to simulate social workers’ actions and applies a calibration method that optimises model parameters to existing policy/outcome data, validating design and producing insights while avoiding direct training on sensitive data.\"},{\"question\":\"What are the main contributions besides calibration?\",\"answer\":\"The thesis adds Temporal Meta-optimiser based Sensitivity Analysis (TMSA) for better interpretation and scrutiny of agent-based models, and reinforcement learning to optimise agent-based model process design via improvements informed by calibration and sensitivity analysis.\"}]","Agent-Based Modelling and Machine Learning in Children’s Social Care | 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problem does the thesis address in Children’s Social Care?","Question",{"text":76,"@type":77},"It addresses the difficulty of using sensitive, low-quality data to perform analysis and modelling that must remain interpretable and trustworthy for decision-making in UK Children’s Social Care services.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is Genetic Algorithm Calibration used with agent-based simulations?",{"text":81,"@type":77},"The thesis uses agent-based models to simulate social workers’ actions and applies a calibration method that optimises model parameters to existing policy/outcome data, validating design and producing insights while avoiding direct training on sensitive data.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the main contributions besides calibration?",{"text":85,"@type":77},"The thesis adds Temporal Meta-optimiser based Sensitivity Analysis (TMSA) for better interpretation and scrutiny of agent-based models, and reinforcement learning to 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