RAINO: Anchoring Agents in Reality, A Systematic Review and Conceptual Framework for Realism in Agent-Based Modelling

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A systematic literature review of 73 Agent-Based Modelling (ABM) publications reveals that "realism" is often poorly defined and lacks a consistent conceptual framework, despite its central role. The review, which analyzed documents explicitly mentioning realism in their titles, found a wide variety of methods used to achieve and demonstrate realism, but with limited theoretical grounding. To address this gap, the paper introduces the Reality Anchor, Input, Output (RAINO) framework. RAINO systematizes arguments for "realism" by identifying "Reality Anchors" such as empirical data, formal theory, expert knowledge, or common-sense expectations, and categorizing their application as either model Input or Output. This framework broadens existing perspectives, explaining why different assessors may evaluate model realism differently and how this influences ABM development strategies like KISS and KIDS.

Key takeaway

For Agent-Based Model Developers designing or validating models, you should explicitly define your model's "Reality Anchors" and their Input/Output application using the RAINO framework. This clarifies how your model relates to reality and anticipates varied stakeholder perceptions of "realism", preventing misinterpretations. By considering the strength and specificity of anchors, you can guide effective validation strategies and address potential ethical implications of aligning with specific stakeholder realities.

Key insights

The RAINO framework provides a structured approach to conceptualize and assess "realism" in Agent-Based Models by categorizing "reality anchors" and their application.

Principles

• "Realism" in ABMs is a multi-dimensional, context-sensitive construct.
• "Reality Anchors" (data, theory, expert knowledge) define model-reality proximity.
• Assessor perspective significantly influences "realism" evaluation.

Method

The RAINO framework systematizes "realism" arguments by classifying "Reality Anchors" (e.g., data, theories) and their use as model "Input" (design) or "Output" (behavior comparison).

In practice

• Use RAINO to identify diverse "reality anchors" for ABMs.
• Consider assessor perspectives when demonstrating model "realism".
• Align modelling strategies (KISS/KIDS) with intended "realism" goals.

Topics

• Agent-Based Modeling
• Model Realism
• RAINO Framework
• Systematic Literature Review
• Model Validation
• Social Simulation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.