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Game theory

Game theory
Game theory is the study of strategic decision making. Specifically, it is "the study of mathematical models of conflict and cooperation between intelligent rational decision-makers."[1] An alternative term suggested "as a more descriptive name for the discipline" is interactive decision theory.[2] Game theory is mainly used in economics, political science, and psychology, as well as logic, computer science, and biology. The subject first addressed zero-sum games, such that one person's gains exactly equal net losses of the other participant or participants. Today, however, game theory applies to a wide range of behavioral relations, and has developed into an umbrella term for the logical side of decision science, including both humans and non-humans (e.g. computers, animals). Modern game theory began with the idea regarding the existence of mixed-strategy equilibria in two-person zero-sum games and its proof by John von Neumann. Representation of games[edit] Extensive form[edit] [edit]

http://en.wikipedia.org/wiki/Game_theory

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To Test a Powerful Computer, Play an Ancient Game DEEP BLUE's recent trouncing of Garry Kasparov sent shock waves through the Western world. In much of the Orient, however, the news that a computer had beaten a chess champion was likely to have been met with a yawn. While there are avid chess players in Japan, China, Korea and throughout the East, far more popular is the deceptively simple game of Go, in which black and white pieces called stones are used to form intricate, interlocking patterns that sprawl across the board. So subtle and beautiful is this ancient game that, to hear aficionados describe it, Go is to chess what Asian martial arts like aikido are to a boxing match. And, Go fans proudly note, a computer has not come close to mastering what remains a uniquely human game.

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Top Ten Reasons To Play Go 1. Go is the simplest of all games. When we play go, we try to surround territory and to avoid being surrounded. No muss, no fuss, no thought-up fancy rules. Behavioral dynamics and influence in networked coloring and consensus Author Affiliations Edited by Brian Skyrms, University of California, Irvine, CA, and approved July 16, 2010 (received for review February 3, 2010) A correction has been published Go Infinitesimals at Sensei In combinatorial game theory (CGT) infinitesimals are games of temperature zero where the whole point is to get the last move. (The winner always moves to a score of 0, and so does not win on points.) The simplest infinitesimal is {0|0}, called STAR (*). Each player can play to a position worth 0.

Behavioral experiments on biased voting in networks Author Affiliations Edited by Ronald L. Graham, University of California at San Diego, La Jolla, CA, and approved December 5, 2008 (received for review August 19, 2008) Abstract Lifelong Learning Programme 2007-2013 Comenius - Action for school The Comenius programme focuses on the first phase of education, from pre-school and primary to secondary schools. It is relevant for all members of the education community: pupils, teachers, local authorities, parents associations, non-government organisations, teacher training institutes, universities and all other educational staff. Learn more about Comenius Erasmus - Higher education

Nash equilibrium In game theory, the Nash equilibrium is a solution concept of a non-cooperative game involving two or more players, in which each player is assumed to know the equilibrium strategies of the other players, and no player has anything to gain by changing only their own strategy.[1] If each player has chosen a strategy and no player can benefit by changing strategies while the other players keep theirs unchanged, then the current set of strategy choices and the corresponding payoffs constitutes a Nash equilibrium. The reality of the Nash equilibrium of a game can be tested using experimental economics method. Stated simply, Amy and Will are in Nash equilibrium if Amy is making the best decision she can, taking into account Will's decision while Will's decision remains unchanged, and Will is making the best decision he can, taking into account Amy's decision while Amy's decision remains unchanged. Applications[edit] History[edit] The Nash equilibrium was named after John Forbes Nash, Jr.

Teaching Expert Learners Harrisburg Presentation Resources Here are some resources from my presentation in Harrisburg. Defining and Exploring Gamification from Karl Kapp Here is some additional information. Articles and Blog Entries of Interest 8 Types of Stories to Effect Change Storytelling and Instructional Design Eight Game Elements to Make Learning More Intriguing Games, Gamification and the Quest for Learner Engagement Gamification, […] Continue Reading → 2014 DOE Symposium Conference Resources

PageRank Mathematical PageRanks for a simple network, expressed as percentages. (Google uses a logarithmic scale.) Page C has a higher PageRank than Page E, even though there are fewer links to C; the one link to C comes from an important page and hence is of high value. If web surfers who start on a random page have an 85% likelihood of choosing a random link from the page they are currently visiting, and a 15% likelihood of jumping to a page chosen at random from the entire web, they will reach Page E 8.1% of the time. (The 15% likelihood of jumping to an arbitrary page corresponds to a damping factor of 85%.) Without damping, all web surfers would eventually end up on Pages A, B, or C, and all other pages would have PageRank zero.

Some Differences Between Experts and Novices Harrisburg Presentation Resources Here are some resources from my presentation in Harrisburg. Defining and Exploring Gamification from Karl Kapp Here is some additional information. Articles and Blog Entries of Interest 8 Types of Stories to Effect Change Storytelling and Instructional Design Eight Game Elements to Make Learning More Intriguing Games, Gamification and the Quest for Learner Engagement Gamification, […] Continue Reading → 2014 DOE Symposium Conference Resources

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