Identifiability of the Linear Threshold Model

• 成果类型: Article
• 署名作者: Lekamalage, Anuththara; Ramazi, Pouria
• 署名单位: Brock University; University of Calgary; University of Calgary
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3647570
• 发表日期: 2026
• 关键词: NONLINEAR-SYSTEMS

摘要

In binary decision making under the linear threshold model, each individual has a time-invariant threshold and is either a conformist, who tends to adopt an action if the population proportion of adopters exceeds their threshold, or a nonconformist, who tends to adopt if it falls short. The resulting decision-making dynamics can be predicted and controlled, provided that the thresholds are known. In practice, however, the thresholds are unknown and often only the evolution of the total number of individuals who have chosen a specific action is observed. The question then is whether the thresholds are identifiable given this aggregated quantity over time, which can be considered as the output of the decision-making dynamics. We find necessary and sufficient conditions for threshold identifiability of both conformists and nonconformists under synchronous and asynchronous decision making. The results enable reliable threshold estimation and, in turn, prediction and control of the decision-making dynamics using real data.