D. Fudenberg and D. Levine. The Theory of Learning Games. MIT Press, 1998. Home/Search Document Not in Database Summary Related Articles Check |
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Resource Allocation Games with Changing Resource Capacities - Galstyan, Kolar, Lerman (2002) (Correct)
.... important problem that has attracted much interest recently [27, 23, 9, 13] Reinforcement learning has been shown to be a general and robust method for achieving coordination in MAS, even when agents are not directly communicating or sharing information [24] Game dynamics o#ers a rich foundation [10] for studying learning in multi agent systems. In the game theory formalism, each agent is characterized by a set of strategies and it seeks to maximize its payo# (i.e. utility or profit) Permission to make digital or hard copies of all or part of this work for personal or classroom use is ....
D. Fudenberg and D. K. Levine. The Theory of Learning in Games. MIT Press, Cambridge, MA, 1998.
Market Mechanisms for Network Resource Sharing - Semret (1999) (3 citations) (Correct)
....(i.e. than they would at the efficient equilibrium) end up cancelling out each others advantages. These are very preliminary results and further work needs to be done. The appropriate analytical tools for this context can are in the branch of game theory that deals with learning and evolution [44, 23]. In conclusion, the auction game needs to be further understood in terms of user behaviour over time. The dual question is how to price resources over time, when the players do not play repeatedly, but rather only once. This arises naturally when considering connection oriented network ....
D. Fudenberg and D. K. Levine. The Theory of Learning in Games. MIT Press, 1998. See also http://levine.sscnet.ucla.edu/Papers/Essay/ESSAY7.htm.
Convergence Time to Nash Equilibria - Even-Dar, Kesselman, Mansour (Correct)
....centralized control. In this work we are concerned with the time it takes for the system to converge to a Nash equilibrium, rather than the quality of the resulting allocation. The question of convergence to a Nash equilibrium has received signi cant attention in the Game Theory literature (see [11]) Our approach is di erent from most of that line of research in a few crucial aspects. First, we are interested in quantitative bounds, rather than showing a convergence in the limit. Second, we consider games with many players (jobs) and actions (machines) and study their asymptotic behavior. ....
....immediately translates to an n upper bound for two identical machines with general weight setting in our model. In [27] they observe that the improvement strategy that moves the maximum weight job converges in n steps. Some interesting related learning models are stochastic ctitious play [11], graphical games [14, 20] and large population games [15] Uniqueness of Nash Equilibria in communication networks with sel sh users has been investigated in [24] An analysis of the convergence to a Nash Equilibrium in the limit appears in [1, 5] Paper organization: The rest of the paper is ....
D. Fudenberg and D. Levine, \The theory of learning in games," MIT Press, 1998.
Modelling markets dynamics: Minority games and beyond - Challet (2000) (1 citation) (Correct)
....of strategies, where at time t, each strategy s of agent i is played with probability i;s (t) 2 , so that each agent i behavior at time t is characterized by the vector i of components i;s . Inductive agents as de ned in the MG use their strategies according to a reinforcing scheme [30], that is, more rewarding strategies are more likely to be used. By de nition of probabilities i;s (t) f(U i;s (t) P N 0 =1 f(U i;s 0 (t) 2.34) where f(x) is a monotonously growing function of x. The most simple choice is to consider f(x) x, but is not convenient, since it requires ....
D. Fudenberg and D. K. Levine, The theory of learning in games (MIT Press, 1998).
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics - Pelillo (2002) (Correct)
....the average population payoff is strictly increasing along the trajectories of any monotone game dynamics, provided that payoffs are symmetric. This result generalizes the celebrated fundamental theorem of natural selection [14] 23] Here, we provide a different proof adapting a technique from [10]. Theorem 4. If the payoff matrix A is symmetric, then #xx 0 Ax is strictly increasing along any nonconstant trajectory of any payoffmonotonic dynamics. In other words, ##xt 0 for all t, with equality if and only if x xt is a stationary point. Proof. For x #, let x#0g ....
D. Fudenberg and D.K. Levine, The Theory of Learning in Games. Cambridge, Mass.: MIT Press, 1998.
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