Abstract
The aim of this dissertation is to develop a suitable bidding strategy for an
internet bidding agent that allows the agent to obtain the lowest possible
price for a desired product at an internet auction. The bidding strategy is
obtained under the constraint of limited information available about the
strategies of the opponents.
The agent will operate in an internet auction environment. Therefore
classic auction theory is researched and explained. Auctions are widely
used to bring buyers and sellers of products together and to create a
market to buy and sell goods. The buyer wants to pay the lowest possible
price and the seller wants to receive the highest possible price. However,
the seller has no influence on the final selling price of the product. Instead
the price is determined by the buyers. The agent will place bids on the
auction site on behalf of the human instructor. The bidding agent will
make use of the theory behind auctions to influence the other bidders on
the auction to make the lowest possible bids.
The model suggested in this dissertation, the regressive bidding agent
model (RBA model), will incorporate auction theory to create a suitable
agent. The agent will predict the future bids of opponents on the auction,
basing its predictions on a regressive function. The agent will base its
own bids placed at the auction on the bidding time remaining at the
auction together with the bids placed by other bidders on the auction.
Prof. E.M. Ehlers