Analytical modelling and UK Government policy

Oldfield, Marie; Haig, Ella

doi:10.1007/s43681-021-00078-9

“…It provides a comprehensive representation of policies, actors, resources, and relationships between them. Policy Grid enables the modeling of policy scenarios and the evaluation of their impact on different stakeholders [15].…”

Section: Policy Gridmentioning

confidence: 99%

Untitled

2023

J Curr Trends Comp Sci Res

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This literature review focuses on the development of an ontology for public policy problems in Ethiopia. The study aims to provide a structured representation of concepts, relationships, and knowledge specific to the Ethiopian policy context. The research follows a systematic ontology development process that incorporates domain-specific knowledge and stakeholder involvement.The review begins by describing the steps involved in the ontology development process, including problem identification and scoping, requirement analysis, ontology modeling, and stakeholder involvement. It emphasizes the importance of engaging policymakers, subject matter experts, and researchers throughout the process to validate the ontology and ensure its relevance and effectiveness in addressing Ethiopian policy problems.

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“…It provides a comprehensive representation of policies, actors, resources, and relationships between them. PolicyGrid enables the modeling of policy scenarios and the evaluation of their impact on different stakeholders [15] 2.PolicyCore: PolicyCore is an ontology designed to capture and represent policies in the healthcare domain. It focuses on representing policies related to patient consent, confidentiality, and access control.…”

Section: A Review Of Relevant Ontologies Developed For Public Policy ...mentioning

confidence: 99%

Developing an Ontology for Public Policy Problems in Ethiopia: A Comprehensive Review, Evaluation, And Future Directions

Kero,

Demissie,

Tune

2023

Preprint

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This literature review focuses on the development of an ontology for public policy problems in Ethiopia. The study aims to provide a structured representation of concepts, relationships, and knowledge specific to the Ethiopian policy context. The research follows a systematic ontology development process that incorporates domain-specific knowledge and stakeholder involvement.The review begins by describing the steps involved in the ontology development process, including problem identification and scoping, requirement analysis, ontology modeling, and stakeholder involvement. It emphasizes the importance of engaging policymakers, subject matter experts, and researchers throughout the process to validate the ontology and ensure its relevance and effectiveness in addressing Ethiopian policy problems.The application of the developed ontology to specific policy problems is then discussed. Examples include agricultural policy analysis, healthcare policy evaluation, and education policy planning. The review highlights how the ontology can support policy analysis, decision-making, and evaluation by capturing relevant concepts, simulating policy scenarios, and assessing policy impacts.Furthermore, the review identifies potential challenges and limitations in the ontology development process, such as data availability, expert knowledge engagement, and the evolving policy landscape. These challenges need to be addressed to ensure the ontology's accuracy and usability in addressing Ethiopian policy problems.Finally, the review provides suggestions for further research and improvement, such as integrating the ontology with data sources, fostering collaborative ontology development, exploring semantic reasoning and inferencing techniques, and promoting interoperability and standardization. These suggestions aim to enhance the ontology's capabilities and its contribution to the field of ontology development for public policy problems.In conclusion, the development of an ontology for public policy problems in Ethiopia offers a valuable tool for policymakers, researchers, and stakeholders to understand, analyze, and address complex policy challenges. The ontology's relevance and applicability to the Ethiopian context, stakeholder involvement, and support for policy analysis make it a significant contribution to the field of ontology development for public policy.

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“…Such systems have played a pivotal role in improving our scientific understanding across a wide range of phenomena and disciplines, and are increasingly used outside of academia. Governments, for example, make extensive use of scientific modelling software to simulate and evaluate various policies and interventions [75]. Perhaps most notably, this has included the use of epidemiological models to predict the impact of a number of COVID-19 mitigation measures [56,102].…”

Section: Introductionmentioning

confidence: 99%

Testing Causality in Scientific Modelling Software

Clark

¹

,

Foster

²

,

Prifling

³

et al. 2023

ACM Trans. Softw. Eng. Methodol.

6

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From simulating galaxy formation to viral transmission in a pandemic, scientific models play a pivotal role in developing scientific theories and supporting government policy decisions that affect us all. Given these critical applications, a poor modelling assumption or bug could have far-reaching consequences. However, scientific models possess several properties that make them notoriously difficult to test, including a complex input space, long execution times, and non-determinism, rendering existing testing techniques impractical. In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal Inference has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse data instead of costly experiments. This paper introduces the Causal Testing Framework: a framework that uses Causal Inference techniques to establish causal effects from existing data, enabling users to conduct software testing activities concerning the effect of a change, such as Metamorphic Testing, a posteriori . We present three case studies covering real-world scientific models, demonstrating how the Causal Testing Framework can infer metamorphic test outcomes from reused, confounded test data to provide an efficient solution for testing scientific modelling software.

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Analytical modelling and UK Government policy

Cited by 6 publications

References 27 publications

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Developing an Ontology for Public Policy Problems in Ethiopia: A Comprehensive Review, Evaluation, And Future Directions

Testing Causality in Scientific Modelling Software

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