The Upland Monitor: July 27, 1916

The July 27, 1916 edition of The Upland Monitor.https://pillars.taylor.edu/monitor-1916-1917/1028/thumbnail.jp

The Trail, 1918-04

https://soundideas.pugetsound.edu/thetrail_all/1134/thumbnail.jp

Portland Daily Press: September 26,1882

https://digitalmaine.com/pdp_1882/1167/thumbnail.jp

Holland City News, Volume 30, Number 40: October 18, 1901

Newspaper published in Holland, Michigan, from 1872-1977, to serve the English-speaking people in Holland, Michigan. Purchased by local Dutch language newspaper, De Grondwet, owner in 1888.https://digitalcommons.hope.edu/hcn_1901/1041/thumbnail.jp

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

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