发表机构
Artificial Intelligence Research Institute (IIIA-CSIC); United Nations Economic and Social Commission for Western Asia (UN-ESCWA); United Nations University Institute in Macau (UNU Macau)（人工智能研究所（IIIA-CSIC）; 联合国西亚经济社会委员会（UN-ESCWA）; 联合国大学澳门研究所（UNU Macau））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个与能力方法对齐的通用建模框架，用于构建可信的政策模拟，并以埃及金融包容性为例，通过区分机会与偏好参数，分析制度和社会障碍对金融包容性差距的影响，提升政策模拟的可信度和有用性。
AI 中文摘要
可信度是旨在支持政策制定的基于智能体模型的核心议题。模拟不仅必须代表目标场景及其核心动态，还必须证明其假设、参数和输出在经验上是有根据的，并且对其预期用途足够准确。本文通过提出一个与能力方法对齐的通用建模框架来应对这一挑战，该框架用于构建依赖数据和领域专家知识的可信政策模拟。随后，本文展示了如何将该框架情境化并实施，以研究埃及金融包容性的社会挑战，基于一个代表异质性个体和企业的智能体模型，这些主体根据其财务状况、障碍、机会和偏好行事。该模型分两个阶段拟合真实世界数据：初始化和校准，分别构建具有代表性的合成总体并估计行为参数。通过固定决定主体机会的可行性参数，并校准不同人群的偏好参数，我们能够区分并分析制度和社会障碍在系统中的作用，以及主体动机和优先级的作用。这一校准阶段提供了关于观察到的金融包容性差距驱动因素的透明且针对群体的假设，这些假设可以进一步分析为主体机会与实际结果之间的差距，这对政策制定是一个非常重要的见解。因此，本文是朝着提高政策模拟的可信度和有用性迈出的一步，加强了模型、真实目标系统以及将使用它的利益相关者之间的关系。代码可在以下网址获取：\url{ this https URL }。
英文摘要
Credibility is a central topic for agent-based models intended to support policy-making. Simulations must not only represent the target scenarios and their core dynamics but also demonstrate that their assumptions, parameters, and outputs are empirically grounded and sufficiently accurate for their intended use. This paper addresses this challenge by presenting a general modelling framework, aligned with the Capability Approach, for building credible policy simulations that rely on data and domain-expert knowledge. It then demonstrates how it can be contextualised and implemented to study the social challenge of financial inclusion in Egypt, building on an agent-based model that represents heterogeneous individuals and firms behaving according to their financial states, barriers, opportunities, and preferences. The model is fitted to real-world data in two stages, initialisation and calibration, which respectively build representative synthetic populations and estimate behavioural parameters. By fixing the feasibility parameters, which determine agents' opportunities, and calibrating preference parameters across different population groups, we are able to distinguish and analyse the role of institutional and social barriers in the system, as well as the role of agents' motivations and priorities. This calibration stage provides transparent and group-specific hypotheses about the drivers of observed financial-inclusion gaps, which can further be analysed as gaps between agents' opportunities and realised outcomes, a very relevant insight for policy-making. This paper is thus a step towards improving the credibility and usefulness of policy simulations, strengthening the relationship between the model, the real target system, and the stakeholders who will use it. The code is available at: \url{https://www.comses.net/codebase-release/df8383cb-f49b-4f09-8cce-73603b59adcc/}.
