Adaptive Bidding in Single-Sided Auctions Under Uncertainty: by Clemens van Dinther

By Clemens van Dinther

In the final years digital markets, particularly on-line auctions, became very hot and got increasingly more cognizance in either, enterprise (B2B) in addition to in public perform (B2C and C2C). technological know-how, although, remains to be faraway from having studied all phenomena and results which might be saw on digital markets. This publication exhibits that and the way software program brokers can be utilized to simulate bidding behaviour in digital auctions. the most emphasis of this booklet is to use computational economics to industry idea. It summarizes the most typical and updated agent-based simulation tools and instruments and develops the simulation software program AMASE. On foundation of the brought tools a version is validated to simulate bidding behaviour lower than uncertainty.

The publication addresses researchers, desktop scientists, economists and scholars who're attracted to utilising agent-based computational the way to digital markets. It is helping to benefit extra approximately simulations in economics regularly and customary agent-based tools and instruments particularly.

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In particular the Trading Floor is the heart of the market consisting of a component called Order Verifier determining which orders to accept, the Market Maker component, solving the allocation problem and determining the price, and the Schedule component, managing the timing of the orders to be executed. Additionally, a dynamic configuration of the auction is facilitated by a meta-component called Builder for initializing and replacing components during run-time. MARI, the Multi-Attribute Resource Intermediary, is yet another example for a generalized platform involving the buying and selling of goods and services where price is just one of a multitude of possible factors influencing the decision to trade [Tewari and Maes, 2000].

The authors develop some propositions on the effects of the number of bidders and changes in the distribution of information costs and valuations, which they are able to proof with comparative statics. g. that an increase in the number of bidders implies an increase in the expected value of the winning bid and a greater number of informed bidders, but no larger proportion of bidders that buy information. g. positive amount of expected gross profit) and the costs of information acquisition. Milgrom and Weber [1982] analyse strategic bidding in ascending auctions in general.

Ockenfels and Roth [2005] have studied experimentally an English auction with a fixed closing time versus an English auction with soft end. Their experimental results show differences in the bidding behaviour: sniping is applied much more frequently in auctions with hard end. Seifert and Ehrhart [2005] analysed the design of the third generation spectrum auction in the UK and in Germany. Results show that the bidder surplus is lower in the German auction but revenue is higher compared to the UK design.

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