Bibliografie tematiche / Game theory

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Letteratura scientifica selezionata sul tema "Game theory"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Game theory"

M. Ferreira, Manuel Alberto, and Maria Cristina Peixoto Matos. "Game theory and coopetition." Journal of Economics and Engineering 5, no. 1 (April 30, 2014): 5–8. http://dx.doi.org/10.7813/jee.2014/5-1/1.

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Hanley, John T. "GAMES, game theory and artificial intelligence." Journal of Defense Analytics and Logistics 5, no. 2 (December 7, 2021): 114–30. http://dx.doi.org/10.1108/jdal-10-2021-0011.

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PurposeThe purpose of this paper is to illustrate how game theoretic solution concepts inform what classes of problems will be amenable to artificial intelligence and machine learning (AI/ML), and how to evolve the interaction between human and artificial intelligence.Design/methodology/approachThe approach addresses the development of operational gaming to support planning and decision making. It then provides a succinct summary of game theory for those designing and using games, with an emphasis on information conditions and solution concepts. It addresses how experimentation demonstrates where human decisions differ from game theoretic solution concepts and how games have been used to develop AI/ML. It concludes by suggesting what classes of problems will be amenable to AI/ML, and which will not. It goes on to propose a method for evolving human/artificial intelligence.FindingsGame theoretic solution concepts inform classes of problems where AI/ML 'solutions' will be suspect. The complexity of the subject requires a campaign of learning.Originality/valueThough games have been essential to the development of AI/ML, practitioners have yet to employ game theory to understand its limitations.

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Goodwin, Paul. "Forecasting games: can game theory win?" International Journal of Forecasting 18, no. 3 (July 2002): 369–74. http://dx.doi.org/10.1016/s0169-2070(02)00022-5.

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Olsder, Geert Jan. "Foundations of game theory: Noncooperative games." Automatica 32, no. 9 (September 1996): 1341–42. http://dx.doi.org/10.1016/0005-1098(96)88873-x.

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Taylor, Mark, Mike Baskett, Denis Reilly, and Somasundaram Ravindran. "Game Theory for Computer Games Design." Games and Culture 14, no. 7-8 (November 12, 2017): 843–55. http://dx.doi.org/10.1177/1555412017740497.

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Designing and developing computer games can be a complex activity that may involve professionals from a variety of disciplines. In this article, we examine the use of game theory for supporting the design of gameplay within the different sections of a computer game and demonstrate its application in practice via adapted high-level decision trees for modeling the flow in gameplay and payoff matrices for modeling skill or challenge levels.

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Fudenberg, Drew, and David K. Levine. "Whither Game Theory? Towards a Theory of Learning in Games." Journal of Economic Perspectives 30, no. 4 (November 1, 2016): 151–70. http://dx.doi.org/10.1257/jep.30.4.151.

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Game theory has been a huge success in economics. Many important questions have been answered, and game theoretic methods are now central to much economic investigation. We suggest areas where further advances are important, and argue that models of learning are a promising route for improving and widening game theory's predictive power while preserving the successes of game theory where it already works well. We emphasize in particular the need for better understanding of the speed with which learning takes place.

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Coffey, Aodhan L., Tomas E. Ward, and Richard H. Middleton. "Game Theory." International Journal of Ambient Computing and Intelligence 3, no. 3 (July 2011): 43–51. http://dx.doi.org/10.4018/jaci.2011070106.

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Designing suitable robotic controllers for automating movement-based rehabilitation therapy requires an understanding of the interaction between patient and therapist. Current approaches do not take into account the highly dynamic and interdependent nature of this relationship. A better understanding can be accomplished through framing the interaction as a problem in game theory. The main strength behind this approach is the potential to develop robotic control systems which automatically adapt to patient interaction behavior. Agents learn from experiences, and adapt their behaviors so they are better suited to their environment. As the models evolve, structures, patterns and behaviors emerge that were not explicitly programmed into the original models, but which instead surface through the agent interactions with each other and their environment. This paper advocates the use of such agent based models for analysing patient-therapist interactions with a view to designing more efficient and effective robotic controllers for automated therapeutic intervention in motor rehabilitation. The authors demonstrate in a simplified implementation the effectiveness of this approach through simulating known behavioral patterns observed in real patient-therapist interactions, such as learned dependency.

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Rubinstein, Ariel, Drew Fudenberg, and Jean Tirole. "Game Theory." Economica 60, no. 238 (May 1993): 245. http://dx.doi.org/10.2307/2554596.

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Eichberger, Jurgen, Drew Fudenberg, Jean Tirole, and Roger B. Myerson. "Game Theory." Economic Journal 103, no. 419 (July 1993): 1065. http://dx.doi.org/10.2307/2234726.

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Paturel, Amy. "Game Theory." Neurology Now 10, no. 3 (2014): 32–36. http://dx.doi.org/10.1097/01.nnn.0000451325.82915.1d.

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Tesi sul tema "Game theory"

Novak, K. "Game theory." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46882.

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Game theory is a section of applied mathematics that studies various mathematical models of optimal decision making in conflict situations. J. Von Neumann and O. Monhenshternom in 1944 wrote the work "Theory of Games and Economic Behavior." From the very beginning of its development, it was aimed at solving economic problems. Later it began to be applied in other areas related to the conflict. Theoretical and playing methods of optimal solutions are widely used in medicine, in economic and social planning and forecasting, and other matters of science and technology.Today, the game theory is widely used in various sciences such as economic, political, computer, social, etc. Game theory attempts to identify strategic behavior in different situations mathematically in which success is the subject of the decision-making and depends on the moves of other players.

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Mehta, Aranyak. "Algorithmic Game Theory." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7220.

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The interaction of theoretical computer science with game theory and economics has resulted in the emergence of two very interesting research directions. First, it has provided a new model for algorithm design, which is to optimize in the presence of strategic behavior. Second, it has prompted us to consider the computational aspects of various solution concepts from game theory, economics and auction design which have traditionally been considered mainly in a non-constructive manner. In this thesis we present progress along both these directions. We first consider optimization problems that arise in the design of combinatorial auctions. We provide an online algorithm in the important case of budget-bounded utilities. This model is motivated by the recent development of the business of online auctions of search engine advertisements. Our algorithm achieves a factor of $1-1/e$, via a new linear programming based technique to determine optimal tradeoffs between bids and budgets. We also provide lower bounds in terms of hardness of approximation in more general submodular settings, via a PCP-based reduction. Second, we consider truth-revelation in auctions, and provide an equivalence theorem between two notions of strategy-proofness in randomized auctions of digital goods. Last, we consider the problem of computing an approximate Nash equilibrium in multi-player general-sum games, for which we provide the first subexponential time algorithm.

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Majure, William Robert. "Disequilibrium game theory." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11660.

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Blankinship, Erik Jackson 1974. "Who's got game (theory)?" Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33877.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005. Includes bibliographical references (leaves 85-88). Many players enjoy the challenge of outwitting computer opponents in strategy games. Devising strategies to defeat a computer opponent may enhance certain cognitive skills (e.g., analysis, evaluation, planning). This thesis takes a constructionist approach to gaming, hypothesizing that players may learn more about strategic planning by building their own computer opponents and then playing them to understand how their strategic theories play out in real experiments. I have developed a graphic toolkit for designing strategy games and computer opponents. The goal is to help students learn the underlying mathematical and computer science theories used to win these games. The tools have been designed to eliminate the overhead of using conventional programming languages to build games and focus students on the pedagogical issues of designing and understanding game theory algorithms. I describe the tools as well as initial evaluations of their effectiveness with populations of teenage students. Teenagers in this study posed their own problems, in the form of games they designed, and then hypothesized about winning strategies. Of their own volition, most teenagers iterated on their strategic designs, reformulated problems and hypotheses, isolated variables, and informed next generation versions of this tool with astute suggestions. (cont.) The toolkit designed for this thesis has a low floor, making it easy for people to quickly start playing with mathematical concepts, and a high ceiling for sophisticated exploration. by Erik Jackson Blankinship. Ph.D.

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Rigos, Alexandros. "Essays in game theory." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/37520.

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This thesis explores interactions among agents whose rationality is bounded in distinct ways. It consists of three self-contained chapters/papers. Chapters 2 and 3 consider myopic and hard-wired strategy revisions based on evolutionary game dynamics, while Chapter 4 deals with rationally inattentive agents who acquire costly information in a flexible manner. The thesis, thus, spans two extremes of the range of models with boundedly rational agents. The first paper proposes a novel way to formalize matching mechanisms in evolutionary games. The proposed formalization nests group selection models such as the haystack (Maynard Smith, 1964) and trait-group models (Wilson, 1975). It is shown that evolutionary optima can be obtained as Nash equilibria under appropriately defined matching rules. In the second paper matching rules are endogenized and the co-evolution of cooperation and matching is studied in social dilemma situations. It turns out that only full-or-null assortativity levels are evolutionarily stable. The extent to which efficient outcomes are achieved by this endogenization process is evaluated, which crucially depends on the structure of the particular interaction considered. The third paper extends recent models of flexible information acquisition to an uncountable-action-space setting: a beauty contest coordination game. Necessary conditions for the existence of equilibria with well-behaved strategies are derived. It is established that affine equilibria exist only if the fundamental is normally distributed. A higher coordination motive, a more concentrated prior distribution of the fundamental and higher information costs lead to less attention being paid to the fundamental. Moreover, flexible information acquisition technology is shown to result in equilibrium multiplicity under certain parameter combinations.

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CAPPELLETTI, GIUSEPPE. "Essays in game theory." Doctoral thesis, Università Bocconi, 2005. http://hdl.handle.net/11565/4050398.

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Leclerc, Philip. "Prospect Theory Preferences in Noncooperative Game Theory." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3522.

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The present work seeks to incorporate a popular descriptive, empirically grounded model of human preference under risk, prospect theory, into the equilibrium theory of noncooperative games. Three primary, candidate definitions are systematically identified on the basis of classical characterizations of Nash Equilibrium; in addition, three equilibrium subtypes are defined for each primary definition, in order to enable modeling of players' reference points as exogenous and fixed, slowly and myopically adaptive, highly flexible and non-myopically adaptive. Each primary equilibrium concept was analyzed both theoretically and empirically; for the theoretical analyses, prospect theory, game theory, and computational complexity theory were all summoned to analysis. In chapter 1, the reader is provided with background on each of these theoretical underpinnings of the current work, the scope of the project is described, and its conclusions briefly summarized. In chapters 2 and 3, each of the three equilibrium concepts is analyzed theoretically, with emphasis placed on issues of classical interest (e.g. existence, dominance, rationalizability) and computational complexity (i.e, assessing how difficult each concept is to apply in algorithmic practice, with particular focus on comparison to classical Nash Equilibrium). This theoretical analysis leads us to discard the first of our three equilibrium concepts as unacceptable. In chapter 4, our remaining two equilibrium concepts are compared empirically, using average-level data originally aggregated from a number of studies by Camerer and Selten and Chmura; the results suggest that PT preferences may improve on the descriptive validity of NE, and pose some interesting questions about the nature of the PT weighting function (2003, Ch. 3). Chapter 5 concludes, systematically summarizes theoretical and empirical differences and similarities between the three equilibrium concepts, and offers some thoughts on future work.

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Ramström, Ola. "Visual attention using game theory." Licentiate thesis, KTH, Numerical Analysis and Computer Science, NADA, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-349.

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Jiang, Ge. "Essays in evolutionary game theory." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16917/.

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This thesis contains three essays in evolutionary game theory. In the first chapter, we study the impact of switching costs on the long run outcome in 2X2 coordination games played in the circular city model of local interactions. We find that for low levels of switching costs, the risk dominant convention is the unique long run equilibrium. For intermediate levels of switching costs the set of long run equilibria contains the risk dominant convention but may also contain conventions that are not risk dominant. For high levels of switching costs also nonmonomorphic states will be included in the set of LRE. We study the impact of location heterogeneity on neighborhood segregation in the one-dimensional Schelling residential model in the second chapter. We model location heterogeneity by introducing an advantageous node, in which a player’s utility is impartial to the composition of her neighborhood. We find that when every player interacts with two neighbors, one advantageous node in the circular city will lead to a result that segregation is no longer the unique LRE. When players interact with more neighbors, more advantageous nodes are necessary to obtain the same result. In the third chapter, we consider a model of social coordination and network formation, where players of two groups play a 2X2 coordination game when connected. Players in one group actively decide on whom they play with and on the action in the game, while players in the other group decide on the action in the game only. We find that if either group’s population size is small in comparison to the linking restriction, all players will choose the risk dominant equilibrium, while when both groups are sufficiently large in population, the players of two groups will coordinate on the payoff dominant action.

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Dal, Borgo Maria Manuela Wagner. "Thucydides : father of game theory." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1507820/.

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In this thesis, I interpret Thucydides’ History of the Peloponnesian War utilizing models of game theory to distil the abstract strategic structures that Thucydides illuminates. It is possible by close analysis of the narrative to extract an implicit descriptive theory embedded in the narrative, never made explicit but a consistent presence wherever characters, groups and nations interact. Game theory in its informal structure (i.e. without deploying the full formal apparatus of mathematics) offers a valuable extension to narratology, a narrative theory already successfully introduced into Classical studies. The thesis studies Thucydides’ conception of the agon (contest/competition) in its basic framework from simple strategic and dynamic games to games with boundedly rational players. I argue that Thucydides describes a tropology of interaction by inferring motivations from observed actions. Chapter 1 and 2 discuss Thucydides’ method of reading the minds of historical agents to explore historical causation in simultaneous move and sequential move environments, respectively. Chapter 3 discusses agents with incomplete information and also agents who take irrational decisions. Thucydides allows room in his narrative for players to miscalculate or make conjectures when faced with an interactive environment. He writes history as a description of similar types of potentially recurrent events and sequences linked by a causal chain, whose outcomes are only probabilistically predictable. Whilst analysing different types of interactions, the study aims to explore different game theoretic models based on Thucydides’ tropology of interaction, in order to identify in the final chapter new research directions for rational actor models as well as stochastic environments for the benefit of political science.

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Libri sul tema "Game theory"

Gillman, Richard Alan, and David Housman. Game Theory. Boca Raton : CRC Press, Taylor & Francis Group, 2019. | Series: Textbooks in mathematics: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315156880.

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McEachern, Andrew. Game Theory. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-02118-3.

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Eatwell, John, Murray Milgate, and Peter Newman, eds. Game Theory. London: Palgrave Macmillan UK, 1989. http://dx.doi.org/10.1007/978-1-349-20181-5.

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Rosenmüller, Joachim. Game Theory. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3212-2.

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Peters, Hans. Game Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46950-7.

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Durlauf, Steven N., and Lawrence E. Blume, eds. Game Theory. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280847.

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Barron, E. N. Game Theory. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9781118032398.

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Barron, E. N. Game Theory. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118547168.

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Peters, Hans. Game Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69291-1.

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Li, Deng-Feng, ed. Game Theory. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0657-4.

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Capitoli di libri sul tema "Game theory"

Fujiwara-Greve, Takako. "Games in Game Theory." In Non-Cooperative Game Theory, 1–5. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55645-9_1.

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Marwala, Tshilidzi, and Evan Hurwitz. "Game Theory." In Artificial Intelligence and Economic Theory: Skynet in the Market, 75–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66104-9_7.

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Aradhye, Chinmay. "Game Theory." In Encyclopedia of Personality and Individual Differences, 1735–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_2277.

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Fox, William P., and Robert Burks. "Game Theory." In Applications of Operations Research and Management Science for Military Decision Making, 251–329. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20569-0_6.

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Papageorgiou, Nikolaos S., and Sophia Th Kyritsi-Yiallourou. "Game Theory." In Advances in Mechanics and Mathematics, 609–50. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b120946_8.

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Vanderbei, Robert J. "Game Theory." In International Series in Operations Research & Management Science, 173–87. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74388-2_11.

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Eiselt, H. A., and C. L. Sandblom. "Game Theory." In Decision Analysis, Location Models, and Scheduling Problems, 111–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24722-7_4.

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Carl, Siegfried, and Seppo Heikkilä. "Game Theory." In Fixed Point Theory in Ordered Sets and Applications, 317–99. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7585-0_8.

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Hurlbert, Glenn H. "Game Theory." In Undergraduate Texts in Mathematics, 145–62. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-79148-7_9.

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Hwang, Ching-Lai, and Ming-Jeng Lin. "Game Theory." In Lecture Notes in Economics and Mathematical Systems, 306–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-61580-1_4.

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Atti di convegni sul tema "Game theory"

Rathore, Rachna, Pranay Sharma, Umesh Chandra Sharma, Abhinash Jenasamanta, S. Shilpa, and Praveen Kumar Sahu. "Game Theory Applications in AI." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE), 255–60. IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911263.

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Gautam, Pooja. "Game theory." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1742240.

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Mascareñas-Pazos, A., S. Lorenzo-Freire, and J. M. Alonso-Meijide. "Cooperative Game Theory in Machine Learning." In VII Congreso XoveTIC: impulsando el talento científico, 177–82. Servizo de Publicacións. Universidade da Coruña, 2024. https://doi.org/10.17979/spudc.9788497498913.25.

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One of the key challenges in constructing a machine learning model is to select the most relevant features for optimal performance, as too many features can diminish model's effectiveness. This article explores the application of Cooperative Game Theory to facilitate such selection. Specifically, we utilize the Shapley value, a well-known solution in cooperative games. The machine learning model is represented as a cooperative game, where the Shapley value assesses the contribution of individual features to the model's overall performance.

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Ghani, Neil, Jules Hedges, Viktor Winschel, and Philipp Zahn. "Compositional Game Theory." In LICS '18: 33rd Annual ACM/IEEE Symposium on Logic in Computer Science. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209108.3209165.

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Smith III, James F., and Robert D. Rhyne. "Knowledge discovery through games and game theory." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Belur V. Dasarathy. SPIE, 2001. http://dx.doi.org/10.1117/12.421063.

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Alessio, Danielle, D. Marc Kilgour, Ilias Kotsireas, Roderick Melnik, and Brian West. "Game Theory and Social Psychology: Conformity Games." In ADVANCES IN MATHEMATICAL AND COMPUTATIONAL METHODS: ADDRESSING MODERN CHALLENGES OF SCIENCE, TECHNOLOGY, AND SOCIETY. AIP, 2011. http://dx.doi.org/10.1063/1.3663496.

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Fang, Fei. "Integrating Learning with Game Theory for Societal Challenges." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/894.

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Real-world problems often involve more than one decision makers, each with their own goals or preferences. While game theory is an established paradigm for reasoning strategic interactions between multiple decision-makers, its applicability in practice is often limited by the intractability of computing equilibria in large games, and the fact that the game parameters are sometimes unknown and the players are often not perfectly rational. On the other hand, machine learning and reinforcement learning have led to huge successes in various domains and can be leveraged to overcome the limitations of the game-theoretic analysis. In this paper, we introduce our work on integrating learning with computational game theory for addressing societal challenges such as security and sustainability.

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Kovařík, Vojtěch, Caspar Oesterheld, and Vincent Conitzer. "Game Theory with Simulation of Other Players." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/312.

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Game-theoretic interactions with AI agents could differ from traditional human-human interactions in various ways. One such difference is that it may be possible to simulate an AI agent (for example because its source code is known), which allows others to accurately predict the agent's actions. This could lower the bar for trust and cooperation. In this paper, we first formally define games in which one player can simulate another at a cost, and derive some basic properties of such games. Then, we prove a number of results for such games, including: (1) introducing simulation into generic-payoff normal-form games makes them easier to solve; (2) if the only obstacle to cooperation is a lack of trust in the possibly-simulated agent, simulation enables equilibria that improve the outcome for both agents; and (3) however, there are settings where introducing simulation results in strictly worse outcomes for both players.

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Li, Yibo, Meixiang Yu, and Xianzhuo Liu. "Event Game Theory and Air Combat Game." In 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 2009. http://dx.doi.org/10.1109/ihmsc.2009.116.

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Zhang, Shengyu. "Quantum strategic game theory." In the 3rd Innovations in Theoretical Computer Science Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2090236.2090241.

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Rapporti di organizzazioni sul tema "Game theory"

Washburn, Alan R. Notes on Game Theory. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada383137.

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Dimitriou, Panagiotis. CARTEL: a board game for teaching game theory. Bristol, UK: The Economics Network, July 2018. http://dx.doi.org/10.53593/n3132a.

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Ianulardo, Giancarlo. Using Panopto for Game Theory. Bristol, UK: The Economics Network, December 2009. http://dx.doi.org/10.53593/n978a.

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Güner, Serdar. Game Theory and International Politics. Instats Inc., 2024. http://dx.doi.org/10.61700/t65wxs3sl7qn1805.

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This one-day workshop offers a comprehensive understanding of the application of game theory in the complex world of international politics. Ideal for PhD students, academics, and professional researchers, the seminar provides invaluable insights, knowledge, and skills to enhance their analytical capabilities and understanding of game theory in their research.

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Gibbons, Robert. An Introduction to Applicable Game Theory. Cambridge, MA: National Bureau of Economic Research, July 1997. http://dx.doi.org/10.3386/t0199.

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Elliott, Caroline. A Lecture on Experimental Game Theory. Bristol, UK: The Economics Network, October 2001. http://dx.doi.org/10.53593/n214a.

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Isavnin, A. G. Digital educational resource on the discipline «Game Theory». Ailamazyan Program Systems Institute of Russian Academy of Sciences, February 2025. https://doi.org/10.12731/ofernio.2025.25459.

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Güner, Serdar. Game Theory and International Politics (Free 2-Hour Introduction). Instats Inc., 2024. http://dx.doi.org/10.61700/hnujsicvw309c643.

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This free workshop, led by professor Serdar Güner from Bilkent University, explores the application of game theory in international politics, targeting PhD students, professors, and researchers in related fields. Participants will gain comprehensive insights into the strategic interactions within international politics through game theory, learning to apply these concepts to conflict resolution, negotiation, and policy-making. The workshop will be conducted via Zoom and all recordings and materials (including slides) will be availble for 30 days after the conclusion of the workshop.

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Outkin, Alexander V. Teaching Game Theory to Kids and Limits of Prediction. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1459605.

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Won, Chang-Hee. Characteristics, Nonlinearity of Statistical Control and Relations with Dynamic Game Theory. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada440472.

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