An Agent-based Model of Citation Behavior

2025-03-09

AI Summary

This study investigates whether citation behavior objectively reflects scientific output and quality, and whether strategic citations can enhance an author’s subsequent citation probability. We propose a multi-agent citation model integrating composite preferences—locality, preferential attachment, recency, and fitness—and formally conceptualize “citation personality” as a multidimensional bias vector, a novel contribution. Through complex network generation, agent-based simulation, and sensitivity analysis, we find that fitness is the dominant driver of citation accumulation, followed by out-degree and locality; moreover, authors can significantly modulate their future citation trajectories via strategic citing behavior. These findings challenge the foundational assumption of objectivity in bibliometric evaluation and provide a new theoretical framework and methodological foundation for scientometrics. (136 words)

Technology Category

Cognitive Modeling & Cognitive Systems: Simulating Human BehaviorMultiagent Systems: Agent-Based Simulation and Emergent BehaviorKnowledge Representation and Reasoning: Preferences

Application Category

User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating successWeb Mining and Content Analysis: Models for Web evolutionSocial Networks and Social Media: Computational social science

Abstract

Whether citations can be objectively and reliably used to measure productivity and scientific quality of articles and researchers can, and should, be vigorously questioned. However, citations are widely used to estimate the productivity of researchers and institutions, effectively creating a 'grubby' motivation to be well-cited. We model citation growth, and this grubby interest using an agent-based model (ABM) of network growth. In this model, each new node (article) in a citation network is an autonomous agent that cites other nodes based on a 'citation personality' consisting of a composite bias for locality, preferential attachment, recency, and fitness. We ask whether strategic citation behavior (reference selection) by the author of a scientific article can boost subsequent citations to it. Our study suggests that fitness and, to a lesser extent, out_degree and locality effects are influential in capturing citations, which raises questions about similar effects in the real world.

Problem

• Examines if citations objectively measure scientific quality.
• Models citation growth using agent-based network dynamics.
• Investigates strategic citation impact on article citations.

Innovation

• Agent-based model simulates citation network growth
• Nodes cite based on locality, recency, fitness biases
• Strategic citation behavior influences citation capture