Dissertation (Metadaten)
Titel:Evolutionary learning in auctions
 
Autor:Konrad Richter
 
URN:NBN:urn:nbn:de:gbv:8-diss-14791
 
Fakultät:Wirtschafts- und Sozialwissenschaftliche Fakultät
DDC Sachgebiet:380 Handel, Kommunikation, Verkehr
 
Datum der mdl. Prüfung:09.06.2005
 
Referent(in):Prof. Dr. Thomas Lux
Korreferent(en) Korreferentin:Prof. Horst Raff, PhD
 
Beschreibung:Classical auction theory relies heavily on the assumption of perfectly rational bidders that play according to their Nash Equilibrium Strategies. An alternative model is developed in this Thesis. It develops a framework for analyzing evolutionary learning in repeated auctions - bidders' strategies are influenced by the outcome of the past rounds in the game. Scientifically, the Thesis takes a complementary approach: On the one hand it develops mathematical techniques to quantify the outcome of learning in auctions. On the other hand it uses computational simulation. The simulation is first checked against the mathematical results and then used to assess the outcome of more complex setups that can't be analyzed mathematically. The main result of the Thesis is the instability of the NE in first price auctions, third and higher price auctions and double auctions under best response when compared to second price auctions.
 
Schlagworte:Auctions, Evolutionary Learning, Best Respone, Simulation, Agent Based, Quantitative Economics, Econophysics, Quantal Response, Auction Theory, Double Auctions
 
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