Mô hình giảm dựa trên bộ nhớ và ước lượng dữ liệu về sự lan tỏa ý kiến

Journal of Nonlinear Science - Tập 31 - Trang 1-42 - 2021
Niklas Wulkow1, Péter Koltai1, Christof Schütte1,2
1Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
2Zuse Institute Berlin, Berlin, Germany

Tóm tắt

Chúng tôi nghiên cứu động lực ý kiến dựa trên mô hình agent-based và quan tâm đến việc dự đoán sự phát triển của tỷ lệ phần trăm toàn bộ dân số agent có chung một ý kiến. Do những tỷ lệ phần trăm này có thể được coi là một quan sát tổng hợp của trạng thái toàn hệ thống, tức là ý kiến cá nhân của từng agent, chúng tôi xem xét vấn đề này trong khuôn khổ của hình thức chiếu Mori–Zwanzig. Cụ thể hơn, chúng tôi chỉ ra cách ước lượng một mô hình hồi quy tự động phi tuyến tính (NAR) có bộ nhớ từ dữ liệu được cung cấp bởi một chuỗi thời gian của các tỷ lệ phần trăm ý kiến, và thảo luận về khả năng dự đoán của nó cho nhiều topo cụ thể của mạng lưới tương tác của các agent. Chúng tôi chứng minh rằng việc đưa vào các điều khoản bộ nhớ cải thiện đáng kể chất lượng dự đoán trên các ví dụ với các topo mạng khác nhau.

Từ khóa

#động lực ý kiến #mô hình agent-based #hồi quy tự động phi tuyến tính #bộ nhớ #mạng lưới tương tác #dự đoán

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