提出RAINO框架，系统梳理智能体模型真实性的实现路径。
RAINO: Anchoring Agents in Reality, A Systematic Review and Conceptual Framework for Realism in Agent-Based Modelling
- 构建现实锚点-输入-输出框架，统一真实性的评估结构
- 发现现有研究对真实性的定义模糊，方法缺乏一致性
- 适合建模者与评审者参考，提升模型可信度
真实性是智能体建模中的核心概念，却常缺乏理论支撑。本文通过系统文献综述，分析了当前现实中如何操作化并展示真实性。结果显示，真实性普遍定义不清，且缺乏一致的概念框架。尽管使用了多种方法来实现和证明真实性，但对其适用性与合理性的解释普遍不足。基于此，本文提出现实锚点-输入-输出（RAINO）框架，识别出用于论证真实性的关键结构，包括现实锚点（如实证数据、形式理论、专家知识、常识预期），及其作为模型输入或输出的应用方式。RAINO拓展了对真实性的理解视角，解释了为何不同评估者可能以不同方式判断模型真实性，并揭示这种更广泛框架如何引导截然不同的建模策略。
原文摘要 · Abstract (English)
Realism is a central yet seemingly under-theorized concept in Agent-Based Modelling. This paper presents a Systematic Literature Review, aiming to identify how realism is currently operationalized and demonstrated. The results show that realism is often poorly defined and lacks a consistent conceptual framework. A wide variety of methods are used to achieve and demonstrate realism, but explanations of whether and why these methods are appropriate for their intended purposes are generally limited. Building on this review, we introduce the Reality Anchor, Input, Output (RAINO) framework. RAINO identifies the key structures used to argue for realism in Agent-Based Models, consisting of Reality Anchors (e.g., empirical data, formal theory, expert knowledge, common-sense expectations) and their application as model Input or Output. RAINO broadens existing perspectives on how realism is framed. It explains why different assessors may evaluate the realism of a model in different ways, and it shows how this broader framing can lead to significantly different approaches to model development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。