Automated Fuzzy Bidding Strategy for Continuous Double Auctions Using Trading Agent’s Attitude and Market Competition

2010 ◽  
Vol 2 (4) ◽  
pp. 56-74 ◽  
Author(s):  
Madhu Goyal ◽  
Saroj Kaushik ◽  
Preetinder Kaur
Keyword(s):  
Direct Method ◽  
Market Competition ◽  
Bidding Strategy ◽  
Double Auctions ◽  
Continuous Double Auctions ◽  
Two Factors ◽  
Bidding Agents ◽  
Continuous Double Auction ◽  
Transaction Price ◽  
Bidding Process

This paper designs a novel fuzzy competition and attitude based bidding strategy (FCA-Bid) for continuous double auction in which the best transaction price is calculated on account of the attitude of the agents and the competition for the goods in the market. The estimation of attitude is based on the bidding item’s attribute assessment, which adapts the fuzzy sets technique to handle uncertainty of the bidding process. Additionally, it uses heuristic rules to determine the attitude of bidding agents. The bidding strategy also uses and determines competition in the market (based on the two factors, number of the bidders participating and the total time elapsed for an auction) using Mamdani’s Direct Method. Then the range for the trading price will be determined based on the assessed attitude and the competition in the market using the fuzzy reasoning technique. The final transaction price is calculated after considering the conflicting attitudes of the seller and the bidder toward selecting the transaction price.

scholarly journals Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions

2004 ◽  
Vol 22 ◽  
pp. 175-214 ◽  
Author(s):  
S. Park ◽  
E. H. Durfee ◽  
W. P. Birmingham
Keyword(s):  
Time Horizon ◽  
Markov Chain Model ◽  
The Other ◽  
Bidding Strategy ◽  
Chain Model ◽  
Double Auctions ◽  
Continuous Double Auctions ◽  
Heuristic Strategies ◽  
Bid Price ◽  
New Strategy

As computational agents are developed for increasingly complicated e-commerce applications, the complexity of the decisions they face demands advances in artificial intelligence techniques. For example, an agent representing a seller in an auction should try to maximize the seller?s profit by reasoning about a variety of possibly uncertain pieces of information, such as the maximum prices various buyers might be willing to pay, the possible prices being offered by competing sellers, the rules by which the auction operates, the dynamic arrival and matching of offers to buy and sell, and so on. A naive application of multiagent reasoning techniques would require the seller?s agent to explicitly model all of the other agents through an extended time horizon, rendering the problem intractable for many realistically-sized problems. We have instead devised a new strategy that an agent can use to determine its bid price based on a more tractable Markov chain model of the auction process. We have experimentally identified the conditions under which our new strategy works well, as well as how well it works in comparison to the optimal performance the agent could have achieved had it known the future. Our results show that our new strategy in general performs well, outperforming other tractable heuristic strategies in a majority of experiments, and is particularly effective in a 'seller?s market', where many buy offers are available.

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