Fuzzy Cognitive Maps: Best Practices and Modern Methods

Giabbanelli, Philippe J., Nápoles, Gonzalo

  • 出版商: Springer
  • 出版日期: 2024-01-30
  • 售價: $6,210
  • 貴賓價: 9.5$5,900
  • 語言: 英文
  • 頁數: 219
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031489624
  • ISBN-13: 9783031489624
  • 海外代購書籍(需單獨結帳)

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商品描述

This book starts with the rationale for creating an FCM by contrast to other techniques for participatory modeling, as this rationale is a key element to justify the adoption of techniques in a research paper. Fuzzy cognitive mapping is an active research field with over 20,000 publications devoted to externalizing the qualitative perspectives or "mental models" of individuals and groups. Since the emergence of fuzzy cognitive maps (FCMs) back in the 80s, new algorithms have been developed to reduce bias, facilitate the externalization process, or efficiently utilize quantitative data via machine learning. It covers the development of an FCM with participants through a traditional in-person setting, drawing from the experience of practitioners and highlighting solutions to commonly encountered challenges. The book continues with introducing principles of simulations with FCMs as a tool to perform what-if scenario analysis, while extending those principles to more elaborated simulation scenarios where FCMs and agent-based modeling are combined. Once an FCM model is obtained, the book then details the analytical tools available for practitioners (e.g., to identify the most important factors) and provides examples to aid in the interpretation of results. The discussion concerning relevant extensions is equally pertinent, which are devoted to increasing the expressiveness of the FCM formalism in problems involving uncertainty. The last four chapters focus on building FCM models from historical data. These models are typically needed when facing multi-output prediction or pattern classification problems. In that regard, the book smoothly guides the reader from simple approaches to more elaborated algorithms, symbolizing the noticeable progress of this field in the last 35 years. Problems, recent references, and functional codes are included in each chapter to provide practice and support further learning from practitioners and researchers.

商品描述(中文翻譯)

本書首先解釋了相對於其他參與式建模技術,創建模糊認知圖(FCM)的理據,因為這一理據是在研究論文中采用技術的關鍵元素。模糊認知圖是一個活躍的研究領域,有超過20,000篇論文專注於外部化個人和群體的定性觀點或“心智模型”。自80年代以來,新的算法已經被開發出來,以減少偏見,促進外部化過程,或通過機器學習有效利用定量數據。本書通過傳統的面對面方式介紹了與參與者一起開發FCM的過程,借鑒了從實踐者那裡獲得的經驗,並突出了常見挑戰的解決方案。本書繼續介紹了使用FCM進行模擬的原則,作為進行假設情景分析的工具,同時將這些原則擴展到更複雜的模擬情景,其中結合了FCM和基於代理的建模。一旦獲得FCM模型,本書詳細介紹了可供實踐者使用的分析工具(例如,用於識別最重要的因素),並提供了示例以幫助解釋結果。同樣重要的是有關擴展的討論,這些擴展致力於增加FCM形式主義在涉及不確定性問題中的表達能力。最後四章專注於從歷史數據中建立FCM模型。在面對多輸出預測或模式分類問題時,通常需要這些模型。在這方面,本書將讀者從簡單的方法順利引導到更複雜的算法,象徵著這一領域在過去35年中的顯著進展。每章中都包含問題、最新參考文獻和功能代碼,以提供實踐者和研究人員進一步學習和支持。

作者簡介

​Dr. Philippe J. Giabbanelli received his B.S. from Université Côte d'Azur (France) and his M.Sc. and Ph.D. from Simon Fraser University (Canada). He worked as a researcher at the University of Cambridge (UK) and as a tenure-track faculty at several nationally ranked American universities, where he developed a variety of courses on predictive modeling and artificial intelligence. He taught fuzzy cognitive maps (FCMs) from the perspective of AI, as an object of study for network science, or as a tool in modeling and simulation. His research focuses on developing and applying AI to support population health interventions. He has published about 130 articles (mostly with his students), covering multiple aspects of FCM research from the elicitation and aggregation of causal maps to their structural validation or their combination with other techniques such as agent-based modeling.
Dr. Gonzalo Nápoles received his B.S. and M.Sc. from the Central University of Las Villas (Cuba) and his Ph.D. from Hasselt University (Belgium) and Maastricht University (the Netherlands). Currently, he is a tenured assistant professor at the Department of Cognitive Science and Artificial Intelligence, Tilburg University (the Netherlands). He has taught fuzzy cognitive maps (FCMs) in several courses, including the First Summer School on Fuzzy Cognitive Mapping held in Volos (Greece). His research focuses on developing learning algorithms for FCM models, understanding their mathematical properties, and exploiting their potentialities in pattern classification and time series forecasting settings. He was a recipient of the Cuban Academy of Science Award for his contributions to the FCM field. More recently, his research efforts have shifted toward developing fair machine learning algorithms that can intrinsically be explained (to a large extent) and methods to mitigate implicit and explicit bias.

作者簡介(中文翻譯)

Dr. Philippe J. Giabbanelli畢業於法國Université Côte d'Azur獲得學士學位,並在加拿大Simon Fraser University獲得碩士和博士學位。他曾在英國劍橋大學擔任研究員,並在多所國內排名靠前的美國大學擔任常任教職,開設了多門有關預測建模和人工智慧的課程。他以人工智慧的角度教授模糊認知圖(FCMs),將其作為網絡科學的研究對象,或者作為建模和模擬工具。他的研究重點是開發和應用人工智慧來支持人口健康干預。他已發表約130篇文章(大部分與他的學生合作),涵蓋了FCM研究的多個方面,從因果圖的引出和聚合到結構驗證或與其他技術(如基於代理的建模)的結合。

Dr. Gonzalo Nápoles畢業於古巴中央拉斯維拉斯大學獲得學士和碩士學位,並在比利時Hasselt大學和荷蘭馬斯特里赫特大學獲得博士學位。目前,他是荷蘭蒂爾堡大學認知科學與人工智慧系的常任助理教授。他曾在多門課程中教授模糊認知圖(FCMs),包括在希臘Volos舉辦的第一屆模糊認知映射夏季學校。他的研究重點是開發FCM模型的學習算法,理解其數學特性,並利用其在模式分類和時間序列預測中的潛力。他曾獲古巴科學院獎,以表彰他對FCM領域的貢獻。最近,他的研究工作轉向開發公平的機器學習算法,這些算法在很大程度上可以解釋,以及減輕隱含和明確的偏見的方法。