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Cette thèse est constituée de trois essais indépendants, chacun portant sur un aspect théorique ou empirique relatif à la question des connaissances provenant d'experts. Dans le premier chapitre de cette thèse, j'étudie la transmission de connaissances scientifiques entre un expert et un décideur. Un modèle scientifique est formalisé par une distribution de probabilité sur un ensemble de scénarios possibles. L'expert est supposé connaître le modèle le plus probable parmi un ensemble possible et cherche à le communiquer au décideur. Toutefois, parce que ces modèles sont trop complexes, l'expert ne peut pas certifier cette information au décideur. Je montre que s'il y a une différence d'intérêt entre les deux partis, à l'équilibre, la transmission de l'information est toujours partielle. L'expert ne pourra jamais communiquer de manière crédible quel modèle est le plus probable. Toutefois, il pourra désigner un ensemble de modèles contenant celui-ci. La taille de cet ensemble, et donc le degré d'information qu'il pourra communiquer, dépend à la fois de la différence d'intérêt entre les partis mais aussi du consensus entre les modèles scientifiques. Si la science n'est pas suffisamment consensuelle, il y a une asymétrie dans la transmission de l'information. Si le modèle le plus probable est parmi les plus optimistes, la transmission d'information dépend uniquement de la différence d'intérêt entre les partis. Mais s'il est parmi les plus pessimistes, aucune transmission d'information n'est possible. Dans le second chapitre de cette thèse, mes co-auteurs et moi mesurons expérimentalement les croyances de sujets sur des événements dont ils sont plus ou moins familiers. Pour ce faire, nous proposons une méthode novatrice d'identification des croyances des sujets qui s'appuie sur l'utilisation d'intervalles de probabilités objectives. Pour chaque événement, notre approche nous permet d'éliciter des ensembles de distribution de probabilités majoritairement non dégénérés. De plus, plus les événements sont familiers, plus les intervalles élicités sont restreints. Ainsi, plus nos sujets se sentent experts sur une question, plus leurs croyances sont précises. Notre approche nous permet également d'estimer la manière dont ces sujets agissent en correspondance avec leurs croyances. Ce faisant, nous parvenons à la première estimation du coefficient de mixture alpha dans le modèle de décision alpha-maxmin EU de Hurwicz, en contrôlant par les croyances des sujets. Dans le troisième chapitre de cette thèse, je reprends l'hypothèse que la connaissance scientifique est trop complexe pour être certifiée à des non-experts. J'étudie les conséquences de celle-ci dans un cas appliqué : celui du changement climatique. Je modélise le problème de la sur-émission de gaz à effet de serre (GES) comme un jeu de contribution à un mal public. Dans ce jeu, tous les contributeurs gagnent individuellement à émettre, car les GES sont corrélés à la consommation de biens, mais tous les contributeurs souffrent du total des émissions car celles-ci sont responsables de dommages climatiques. A l'équilibre, le niveau d'émission est toujours trop élevé, car chaque contributeur ne tient pas compte des externalités négatives dont il est responsable. Les contributeurs ne sont pas des experts du climats, et leurs connaissances sur les dommages auxquels ils s'exposent s'appuient uniquement sur un expert. Ce dernier tient compte des externalités des contributeurs, et voudrait toujours un niveau d'émission plus bas que celui obtenu à l'équilibre par les contributeurs. Il y a donc toujours une différence d'intérêt entre l'expert et les non-experts. Dans ce chapitre, je prouve qu'aucune transmission d'information ne peut avoir lieu à l'équilibre. Ce résultat montre que la seule parole de l'expert, sans pouvoir de certification, ne suffit pas lorsqu'il s'agit de communiquer sur le risque climatique.
The Handbook of Experimental Game Theory offers a comprehensive analysis of the field, discussing foundational topics that are at the core of applied game theory. It highlights the nuances that scientific experiments have delivered to our understanding of strategic interactions among decision makers. Leading experts explore methodological considerations and games of complete and incomplete information to offer new directions for research in experimental game theory. Chapters demonstrate transformative behavioral research focused on classic topics in game theory such as cooperation and coordination games. Taking a scientific approach to the study of game theory, this innovative Handbook provides an insight into laboratory and field experiments that test game theoretic propositions and suggests new ways of modeling strategic behavior. It takes a forward-thinking position, addressing the challenges inherent in innovations surrounding the measurement of strategic behavior using experimental methods. This Handbook will prove to be a valuable resource for scholars and students who are looking to gain a broader understanding of experimental game theory and how to contribute to its advancement. It will also be of particular interest to researchers in experimental and behavioral economics.
Along with the rise of digital games over the past decades came an increased interest for using games for other purposes than entertainment. Although a few successes are known, much research seems to suggest little evidence for games' advantages. Existing literature claims that more studies are needed that investigate the effective design and use of games and especially studies that are comprehensive, rigorous, and innovative. To contribute to this emerging field, the author investigated the case of Levee Patroller. The target audience of the game, levee patrollers, are considered the "eyes and ears" of the Dutch water authorities. They inspect levees and report any risks they encounter. Similarly, in the game players have to find all virtual failures in a region and report these. If they do not find the failures in time or report them incorrectly, it could result in a levee breach that floods the whole virtual region.
Comprehensive coverage of critical issues related to information science and technology.
The sustainable governance of water resources relies on processes of multi-stakeholder collaborations and interactions that facilitate knowledge co-creation and social learning. Governance systems are often fragmented, forming a barrier to adequately addressing the myriad of challenges affecting water resources, including climate change, increased urbanized populations, and pollution. Transitions towards sustainable water governance will likely require innovative learning partnerships between public, private, and civil society stakeholders. It is essential that such partnerships involve vertical and horizontal communication of ideas and knowledge, and an enabling and democratic environment characterized by informal and open discourse. There is increasing interest in learning-based transitions. Thus far, much scholarly thinking and, to a lesser degree, empirical research has gone into understanding the potential impact of social learning on multi-stakeholder settings. The question of whether such learning can be supported by forms of serious gaming has hardly been asked. This Special Issue critically explores the potential of serious games to support multi-stakeholder social learning and collaborations in the context of water governance. Serious games may involve simulations of real-world events and processes and are challenge players to solve contemporary societal problems; they, therefore, have a purpose beyond entertainment. They offer a largely untapped potential to support social learning and collaboration by facilitating access to and the exchange of knowledge and information, enhancing stakeholder interactions, empowering a wider audience to participate in decision making, and providing opportunities to test and analyze the outcomes of policies and management solutions. Little is known about how game-based approaches can be used in the context of collaborative water governance to maximize their potential for social learning. While several studies have reported examples of serious games, there is comparably less research about how to assess the impacts of serious games on social learning and transformative change.
Knowledge-Based Simulation: Methodology and Application represents a recent compilation of research material that reviews fundamental concepts of simulation methodology and knowledge-based simulation applications. Knowledge-based simulation represents a new and exciting bridge area linking the fields of computer simulation and artificial intelligence. This book will appeal to both theorists and practitioners who require simulation to solve complex problems. A primary attraction of the book is its emphasis on both methodology and applications. In this way, the reader can explore new methods for encoding knowledge-inten- sive information into a simulation model, and new applications that utilize these methods.
This book explores a new model for addressing the central issue of environmental and other collective actions. An alternative to the classical models: central authority, privatization, and self-governance, it has provisionally been named “expert and scholar-based-” or “knowledge-driven governance”. The book also identifies seven working rules (or design principles) for successful knowledge-driven governance, and argues that the more strictly these rules are abided by, the more successful this model of governance becomes. Lastly, it demonstrates that in addition to Lindblom’s observed intellectually guided society and preference-guided/volition-guided society, there may be the possibility of a knowledge-driven society in which knowledge or intellect plays a greater role. The results obtained are supplemented by numerical calculations, presented as tables and figures. This book is intended for graduate students, lecturers and researchers working in environmental management, environmental science and engineering, sustainable development, collective action, and public administration.
Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches.