ACTIVITY ANALYSIS OF PRODUCTION AND ALLOCATION

By O. Brownlee, T. Koopmans. 1952

Subfields: Mathematical Optimization, Economics & Operations Research

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Citations in the corpus

Improved Techniques for Training GANs (2016, methodology): “Replacing positive classiﬁcation targets with α and negative targets with β , the optimal discriminator becomes D ( x ) = αp data ( x )+ βp model ( x ) p data ( x )+ p model ( x ) .”
Empirically Evaluating Multiagent Learning Algorithms (2014, background): “Fictitious play (Brown, 1951) is probably the earliest example of a learning algorithm for two-player games repeated games.”
Machine Theory of Mind (2018, background): “Our work thus ties in to a rich history of opponent modelling (Brown, 1951; Albrecht & Stone, 2017); within this context, we show how meta-learning could be used to furnish an agent with the ability to build flexible and sampleefficient models of others on the fly.”
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, methodology): “Fictitious play (Brown, 1951) is a popular method for learning Nash equilibria in normal-form (single-step) games.”
Evolutionary Dynamics of Multi-Agent Learning: A Survey (2015, background): “Examples of joint action learners are minimax-Q (Littman, 1994), fictitious play and AWESOME (Brown, 1951; Conitzer & Sandholm, 2007), hyper-Q (Tesauro, 2003), and Nash-Q (Hu & Wellman, 2003).”
Linear Optimization Problems with Inexact Data (2006, methodology): “The duality theorem was published by Gale, Kuhn and Tucker [40] in 1951.”
Sequential Optimality and Coordination in Multiagent Systems (1999, background): “Fictitious play (FP) is a related learning technique commonly studied in game theory [4] [6] where each agent i observes the actions played in the past by other agents and plays a best response given the empirical distributionobserved.”