8 ms·

Game Theory: Open Access textbook

• tramGG 9y ago
  I'm looking to learn Game Theory. Anyone with knowledge in this space have good additional recommendations?
  • paultopia 9y ago
    Kreps, Game Theory and Economic Modeling has a nice overview and contextualization as I recall. I also really like Brian Skyrm's little books (Evolution of the Social Contract and Stag Hunt) for an introduction to the evolutionary side. Note, those aren't textbooks. They're applied/context works with introductions, and I think they're better for developing intuition before getting into a textbook. For a really accessible textbook that (as I recall) isn't as math-heavy as some others, Morrow, Game Theory for Political Scientists.
  • dombili 9y ago
    Thinking Strategically by Avinash Dixit and Barry Nalebuff is a fantastic way to start. You can pick it up without any prior knowledge of game theory. I'd also recommend Ken Binmore's Game Theory: A Very Short Introduction.
  • bsdnoob 9y ago
    Look up game theory lectures on coursera
  • rripken 9y ago
    The last two lectures in the The Georgia Tech Machine Learning course on Udacity cover some basics of Game Theory. Just skip ahead, the game theory part is mostly self-contained. https://www.udacity.com/course/machine-learning--ud262 https://www.udacity.com/course/machine-learning--ud262 The Reinforcement Learning course includes some of the same (exactly the same) game theory content and then adds an additional lecture on further topics in game theory https://www.udacity.com/course/reinforcement-learning--ud600 https://www.udacity.com/course/reinforcement-learning--ud600 Again, skip to the last couple lectures.
  • ianai 9y ago
    I had a professor once classify GT as a sub set of statistical decision theory. Might be worth picking up a SDT text. Does anyone ever read Theory of Games by von Neumann?
    • jmmcd 9y ago
      "Decision theory is 1-player game theory" -- I think I saw this on LessWrong, once, but I can't find it now.
    • VHRanger 9y ago
      Theory of games is VERY old, predating most other groundbreaking work (Nash, Shapley, Myerson, etc.)
    • madhadron 9y ago
      This is backwards. Decision theory, which is the foundation of inference in statistics today (Bayesian, minimax, etc. are all special cases of it) is formulated as a one player game. Certain things mesh nicely when you realize this. For example, we know that there is a Nash equilibrium for large classes of games if we allow random strategies. Likewise, for decision theory with nonconvex loss functions, optimal procedures are almost always random. But: game theory of two or more players is qualitatively different. For a one player game, we speak of optimal strategies. For multiplayer, noncooperative games, Nash equilibria take what would seem to be the obvious generalization of that and twist it in a whole new direction.
  • brianbreslin 9y ago
    I'm building some games for this topic for my startup [1]. But we won't have them ready until the spring. [1] http://www.simcase.io http://www.simcase.io
    • bluetwo 9y ago
      I hope you'll post something here when you launch. I like the ideas presented on the site but they are a bit vague right now.
  • jasonjmcghee 9y ago
    MIRI recommends Game Theory: An Introduction by Steven Tadelis https://intelligence.org/research-guide/ https://intelligence.org/research-guide/
  • pkrumins 9y ago
    I learned Game Theory from this Open Yale course: https://www.youtube.com/playlist?list=PL6EF60E1027E1A10B https://www.youtube.com/playlist?list=PL6EF60E1027E1A10B 24 excellent lectures by Ben Polak.
  • MichaelAO 9y ago
    This youtube channel has proven invaluable to me: https://www.youtube.com/user/JimBobJenkins https://www.youtube.com/user/JimBobJenkins His book is also great (http://gametheory101.com/textbooks/ http://gametheory101.com/textbooks/), but mostly a reiteration of the videos.
  • rasmusei 9y ago
    I'm currently a TA in a basic game theory for MSc students at a technical university. For the basic theoretical concepts we use as a course book Leyton-Brown & Shoham (2008) Essentials of Game Theory: A Concise, Multidisciplinary Introduction. This book is great because it is short and to the point, precise without diving deep into everything. Introduces most important concepts in less than 100 pages.
  • MaysonL 9y ago
    The original: Theory of Games and Economic Behavior, published in 1944 by Princeton University Press, by John von Neumann and Oskar Morgenstern the text that created the interdisciplinary research field of game theory, is still worth reading.
  • madhadron 9y ago
    I really enjoyed Gintis's 'Game Theory Evolving', which focuses on evolutionary game theory. Mind you, I was a biologist when I was working with it, so that was much more relevant to what I was thinking about.
• azdle 9y ago
  Looks like there are more format options here: https://archive.org/details/1512.06808v1 https://archive.org/details/1512.06808v1
• wenc 9y ago
  Has anyone here applied Game Theory to a real-world productionized problem? I'm curious. There are many hypothetical situations where game theory is said to be useful, but I've never seen an application of it in real life.
  • zoren 9y ago
    In The Black Swan, Nassim Nicholas Taleb argues that applying game theory to real life situations is one instance of the ludic fallacy. https://en.wikipedia.org/wiki/Ludic_fallacy https://en.wikipedia.org/wiki/Ludic_fallacy
    • VHRanger 9y ago
      Fair warning: N. Taleb is more or less an anti-intellectual. He hates on most formal models he didn't write himself and most of his books are musings on heuristics.
  • deleted 9y ago
    [deleted]
  • duality 9y ago
    Perhaps there are real world scenarios not totally unlike those from game theory. You can imagine situations like the prisoners' dilemma, where maximizing each individual's expected benefit from taking some action is suboptimal due to no means to cooperate.
  • paultopia 9y ago
    Define "applied." Researchers in economics and political science use it to understand all kinds of things, does that account? The Rand Corporation probably had an influence on real-world military policy during the cold war with it...
  • TFortunato 9y ago
    From what I understand, Game Theory is used in "mechanism design", or basically the design of auctions for things like the FCC spectrum auctions, or how facebook / google auction off ad slots. Not a mathematician so I don't have any great references, but a couple bits in the press about it: http://blogs.cornell.edu/info2040/2015/10/20/application-of-game-theory-in-spectrum-auctions/ http://blogs.cornell.edu/info2040/2015/10/20/application-of-... https://www.wired.com/2015/09/facebook-doesnt-make-much-money-couldon-purpose/ https://www.wired.com/2015/09/facebook-doesnt-make-much-mone...
• lynal 9y ago
  There are many sources to develop an understanding of game theory. To build mastery in game theory, check out Osborne and Rubinstein's text. The authors offer it as a free download (http://arielrubinstein.tau.ac.il/books.html http://arielrubinstein.tau.ac.il/books.html). This is the text used by advanced graduate students, the material is explained precisely.
  • TheGorramBatman 9y ago
    It's a pretty intro level book, but a great one. I'd say its suitable for maybe junior level undergraduates.
• jessriedel 9y ago
  It's a shame that he released it under a no-deriv's CC license. The actual cost of textbooks is small compared to the penalty we all pay from not having our textbooks iteratively refined. http://blog.jessriedel.com/all-posts/2015/04/16/beyond-papers-gitwikxiv/ http://blog.jessriedel.com/all-posts/2015/04/16/beyond-paper...
• nurettin 9y ago
  Explanation of Part V in the introduction is interesting and might be a property which sets this book apart from the fold.
• nurettin 9y ago
  Anyone know of any sources for game theory applications to ethics?
• ivan_ah 9y ago
  Aside: I misinterpreted the title as saying "applying game theory to OER textbooks" and imagined a paper discussing the incentives/payouts for creators of educational content like textbooks. Some possible moves: - Write for a mainstream publisher (payout: ~5% royalties, reach: medium) - Self publish commercially (payout: 45%+ royalties from createspace/lulu, reach: small) - Release for free as OER under public domain / CC0 (no payout, reach: broad) I'm fascinated by the interplay between the for-money publishing business and the idea of open source content, and would really love to see a paper studying this subject. Some concrete questions: 1. If an author is interested in maximizing total payout, should she write for a mainstream publisher or self-publish? 2. If an author is interested in most educational impact (maximize the number of readers), should she pursue a one of the commercial routes (publish or self-publish) or release the book as public domain? The answer to 2. is not obvious: some of the best textbooks I know are free (GFDL, CC *, or public domain because old), but somehow they don't get the respect they deserve because people have the perception of "free" as being somehow inferior quality. People think, if the book really was any good, surely you'd have to pay for it? Even more interesting is to consider the game theoretic aspects of multiple authors/contributors. Why are software projects on githun with hundreds of collaborators the norm, but textbooks project limited to one or few main authors?
• zwaps 9y ago
  I use Game Theory in pretty much everything (academic) Here are some thoughts on the literature. There are several different strands and evolutions of Game Theory. 1. Game Theory (non-cooperative): The basis was Neumann/Morgenstern Theory of Games. It has been suggested in this thread, however its focus is a bit obscure today. Still useful for repeated games, for example. Both authors are also important for Decision Theory, see below. Afterwards came Nash, defining the what the basic solution concept would be up until maybe 1990. Simple Nash equilibria are used primarily where rational agents choose in mathematically nice spaces where uncertainty is not a major factor. Following Nash, the Game Theory literature developed to produce equilibrium refinements. These, usually subsets of Nash equilibria, were created because Nash often predicts very little - the space of equilibria is often so large that nothing can be learned, or uncertainty requires the incorporation of different information sets of agents. The first developments came while incorporating uncertainty and multi-stage games (where people move in sequence). Harsanyi was able to show that most configurations of uncertainty situations can be represented as a Bayesian Game (the issue was the recursion of "he knows, that I know, that he knows that I know..."). The problem became, that these often produced unintuitive and large sets of equilibria. So we have refinements. Some target robustness, like Selten's Trembling Hand. Others target "natural behavior", empty threats and so on. Almost all of those refinements are a subset of a Nash concept. The development of refinements was en vogue prior to the 90's, when it stopped for reasons I will detail below. Basic Nash has survived, however, and is still the go-to tool to understand multi-agent decision problems (at least initially). 1.a Cooperative Game Theory: Largely in parallel, mathematicians like Shapely and later economists like Roth also tried to think about cooperative games. Here, we don't look directly at what individual people do in isolation, but rather what groups are stable and plausible and what they can achieve. If for example a smaller group can "break" a coalition, then such a large coalition can not be considered a plausible solution. Matching theory comes from here, for example, so you will find it in most problems of assignment (say, students). Much as non-cooperative Game Theory, it is applied widely. 2. Decision Theory: Decision theory developed in parallel and is a wide field. It is, however, critically important to Game Theory because it sets the stage for information, constraints and decisions that agents take. Expected utility, by Neumann and Morgenstern, was and is the basic instrument to understand how agents incorporate their knowledge. This was based on objective probabilities, so in parallel the Bayesian stream also developed. With a monumental and beautiful proof, Savage then developed Bayesian Decision Theory (based on works by de Finetti and others). This is critical to many, many fields in maths, statistics and science in general, and was then the basis for Game Theory. Aumann is associated with latter refinements of decision theory. Later on, the idea of uncertainty (Knightean uncertainy) became important. This is when you can not assign a probability to an outcome. Paradoxes by Elsberg and Allais have shown that this is actually an important decision problem in real life. Multiple approaches exist to generalize Decision Theory, such as Prospect Theory, MinMax Preferences, integration by capacities as opposed to measures. Schmeidler, Gilboa and Wakker are some names. Game Theory exists in this space as well. 3. Evolutionary Game Theory: The idea came from Biology and is important because it is a way to justify Game Theoretic outcomes without even requiring purposeful action by agents. It had a huge impact on many problems, especially dynamic ones and "top down" models, but did not surplant traditional Nash in general. Some scientists believe it should. Other's think it's just one more tool. There are those who believe the whole of social sciences should be based on it... Let's say it did not achieve that yet. 4. Economic applications: Economics was historically the discipline to apply Game Theory most. Initial concepts like Nash justified many early models of Markets. Earlier concepts of non-perfect competition were formalized with Game Theory. Things really started to take off when asymmetric information were introduced. Think Moral Hazard, Signaling Games, Contract Theory and so forth. What we know about economics, organizations, business, competition and many social phenomena today has largely been developed by applying Game Theory. There are too many great names to mention: Akerlof, Tirole, Spence, Hart, Homström, Myerson, Stiglitz. 5. Mechanism Design and Auction Theory In the 70's and 80's, from the above applications, economists like Hurwitz, Myerson and Maskin developed mechanism design. The idea is simple and genius: If agents play games, what if we can choose the game they play? Which game do we choose without them walking away, but with us getting the desireable outcome? What is, in other words, the optimal mechanism inducing the agents to play a game? Initial examples and todays shining example of econ in action is Auction design. Which sort of auction mechanism is best to sell ads, be ebay or assign broadband licences? Mechanism design leads to very complex problems, which is why until the early 90's many simplified assumptions were used. While mechanism design has been very useful, this also lead to two developments. In econ, papers started to get more and more complex to accomodate real life issues like non-monetary transfers, dynamics, complex type sets and so forth. Computer scientists trying to implement mechanisms quickly discovered that many were simply to complex, so they started Algorithmic MD. 6. Experimental and behavioral games: So earlier, I said that a whole cataloque of equilibrium refinement basically died out. Why is that? Well, with behavioral econ we were introduced to more realistic approaches to decisionmaking. Then questions arose, such as "what if I can not count on rationality of my competitor". As it turns out, this may actually break the inference of Nash equilibria pretty handily. At the same time, economists and psychologists put people in experiments to play games. In some situations, Nash worked well. In other situations, one could accomodate much by using more complex Decision Theory. But in many instances, people would just not play Nash. In other words, they couldn't even figure out the most basic solution concept. Indeed one can do all sorts of experiments in a Game Theory 101 class showing that people often choose much too heuristically. Equilibrium refinements make Nash more complex, it was clear they had to be abandoned. Currently, research joint in decision theory and game theory works on finding better ways to model behavior when Nash is not reached. Books: Osborne/Rubinstein has been mentioned. Contrary to what was said, this is an undergrad book and a solid intro. There are two classic works. The major one is by Tirole and Fudenberg, the other is by Myerson. The former is more standard, the latter is better. Now there is a new book by Maschler, Solan and Zamir with like 900 pages. It's really good, and I would definitly get it as a second book after an intro. For Mechanism Design, the best book is by Tilman Boergers. It's also free to download. Auction Theory specifically has a standard volume by Krishna. Both of those are math heavy. This is true in general, but Game Theory concepts can often be explained by intuition. For Mechanism Design, I fear that a solid math background would be required, because the space of "choosing a game" is mathematically not so nice. However solid means you should have a good grounding in analysis and optimization, perhaps dynamic systems. Basically, a math heavy undergrad education will be fine. hope this helps