A Peer-to-Peer Agent Auction

In this work we examine a peer-to-peer agent continuous double auction. We compare agents trading using peer-to-peer communications with agents using the same trading strategy in an auction that makes use of a centralized auctioneer to disseminate information. We present simulation data for these two auctions running with 2,500 to 160,000 agents. We find that the peer-to-peer auction is able to display price convergence behavior similar to that of the centralized auction. Further, the data shows that the peer-to-peer system has a constant cost in the number of message rounds needed to find the market equilibrium price as the number of traders is increased, in contrast to the linear cost incurred by the central auctioneer. Considering the above message costs, the peer-to-peer system outperformed the simple central auction by at least 100 times in our simulations. We further calculate that for a distributed hierarchical set of auctioneers, for which the message rounds cost of finding equilibrium are reduced to logarithmic in the number of traders, the peer-to-peer system will still produce better performance for systems with more than 5,000 traders

A Method for Decentralized Clustering in Large Multi-Agent Systems

This paper examines a method of clustering within a fully decentralized multi-agent system. Our goal is to group agents with similar objectives or data without first collecting their details in a central database. Instead we connect agents in a random network and have them search in a peer-to-peer fashion for other similar agents. In this way we aim to tackle the basic clustering problem on an Internet scale, and create a method by which agents themselves can be grouped, forming coalitions. This paper presents a number of simulation experiments in which each agent represents a two-dimensional point, and makes a comparison between our method's clustering ability and that of the k-means clustering algorithm. The generated data sets examined contain 2,500 to 160,000 points (agents) grouped in 25 to 1,600 clusters. Our agent method produces a better clustering than the k-means algorithm, quickly placing 95% to 99% of points correctly. The time increases with system size depends on the quality of solution required; a fairly good solution is quickly converged on, and a slower tail behavior improves the solution. Our experiments indicate that the time to find a particular quality of solution is less than linear