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Systems mapping: How to build and use causal models of systems / Peter Barbrook-Johnson (2022)  

Public Titre : Systems mapping: How to build and use causal models of systems Type de document : Monographie Auteurs : Peter Barbrook-Johnson, Auteur ; Alexandra S. Penn, Auteur Editeur : Springer Nature Année de publication : 2022 Autre Editeur : Palgrave Macmillan (Londres, New York, ...) Importance : 186 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-031-01919-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] approche participative [Termes IGN] carte cognitive [Termes IGN] carte heuristique [Termes IGN] cartographie dynamique [Termes IGN] diagramme [Termes IGN] représentation cartographique [Termes IGN] représentation mentale [Termes IGN] réseau bayesien Résumé : (éditeur) This open access book explores a range of new and older systems mapping methods focused on representing causal relationships in systems. In a practical manner, it describes the methods and considers the differences between them; describes how to use them yourself; describes how to choose between and combine them; considers the role of data, evidence, and stakeholder opinion; and describes how they can be useful in a range of policy and research settings. This book provides a key starting point and general-purpose resource for understanding complex adaptive systems in practical, actionable, and participatory ways. The book successfully meets the growing need in a range of social, environmental, and policy challenges for a richer more nuanced, yet actionable and participatory understanding of the world. The authors provide a clear framework to alleviate any confusion about the use of appropriate terms and methods, enhance the appreciation of the value they can bring, and clearly explain the differences between approaches and the resulting outputs of mapping processes and analysis. Note de contenu : Introduction 1- Rich pictures 2- Theory of change diagrams 3- Causal loop diagrams 4- Participatory systems mapping 5- Fuzzy cognitive mapping 6- Bayesian belief networks 7- System dynamics 8- What data and evidence can you build system maps from? 9- Running systems mapping workshops 10- Comparing, choosing, and combining systems mapping methods Conclusion Numéro de notice : 24095 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie DOI : 10.1007/978-3-031-01919-7 En ligne : https://doi.org/10.1007/978-3-031-01919-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102569

On the path to AI / Thomas D. Grant (2020)  

Public Titre : On the path to AI : Law’s prophecies and the conceptual foundations of the machine learning age Type de document : Monographie Auteurs : Thomas D. Grant, Auteur ; Damon J. Wischik, Auteur Editeur : Palgrave Macmillan (Londres, New York, ...) Année de publication : 2020 Importance : 147 p. Format : 15 x 22 cm ISBN/ISSN/EAN : 978-3-030-43582-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] données massives [Termes IGN] droit [Termes IGN] statut juridique [Termes IGN] théorie des probabilités Résumé : (éditeur) This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data. Note de contenu : 1- Two revolutions 2- Getting past logic 3- Experience and data as input 4- Finding patterns as the path from input to output 5- Output as prophecy 6- Explanations of machine learning 7- Juries and other reliable predictors 8- Poisonous datasets, poisonous trees 9- From holmes to alphaGo Conclusion Numéro de notice : 25945 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-43582-0 En ligne : https://doi.org/10.1007/978-3-030-43582-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96338