Computational modelling for decision-making: where, why, what, who and how.

Area of Science:

• Computational Science
• Decision Science
• Systems Engineering

Background:

• Increasingly complex world necessitates sophisticated computational models for decision-making.
• Model creation demands collaboration among commissioners, developers, users, and reviewers, beyond just data and technical skills.
• Understanding the modeling process, its purposes, and technical bases is crucial for effective use.

Purpose of the Study:

• To provide a comprehensive guide for commissioning, developing, and deploying computational models.
• To offer practical tools, including checklists, for ensuring successful model development and utilization.
• To enhance understanding and collaboration in the field of modeling across various domains.

Main Methods:

• A review of best practices in computational model development and deployment.
• Development of two checklists to guide model commissioners, developers, and users.
• Exploration of diverse application domains, including public policy, science, and engineering.

Main Results:

• Identification of key factors for successful model commissioning, development, and deployment.
• Provision of practical checklists to aid stakeholders in the modeling lifecycle.
• Highlighting the need for enhanced collaboration and understanding in modeling.

Conclusions:

• Reinforcing modeling as a distinct discipline is essential to minimize misconstruction.
• Increasing domain-wide understanding of modeling will reduce its misuse.
• Closer engagement between commissioners and modelers will lead to more impactful and useful model outputs.