Paper Info
Title
Using Information Gain to Analyze and Fine Tune the Performance of Supply Chain Trading Agents
Abstract
The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. During the course of each year's competition historical data is logged describing more than 800 games played by different agents from around the world. In this paper, we present analysis that is focused on determining which features of agent behavior, such as the average lead time requested for supplies or the average selling price offered on finished products, tend to differentiate agents that win from those that do not. We present a visual inspection of data from 16 games played in one bracket of the 2006 TAC SCM semi-final rounds. Plots of data from these games help isolate behavioral features that distinguish top performing agents in this bracket. We then introduce a metric based on information gain to provide a more complete analysis of the 80 games played in the 2006 TAC SCM quarter-final, semi-final and final rounds. The metric captures the amount of information that is gained about an agent's performance by knowing its value for each of 20 different behavioral features. Using this metric we find that, in the final rounds of the 2006 competition, winning agents distinguished themselves by their procurement decisions, rather than their customer bidding decisions. We also discuss how we used the analysis presented in this paper to improve our entry for the 2007 competition, which was one of the six finalists that year.
Year
DOI
Venue
2007
10.1007/978-3-540-88713-3_13
AGENT-MEDIATED ELECTRONIC COMMERCE AND TRADING AGENT DESIGN AND ANALYSIS
Keywords
Field
DocType
Automated trading,electronic commerce,supply chain management,agent performance analysis,TAC SCM
Economics,Visual inspection,Microeconomics,Operations research,Lead time,Supply chain management,Supply chain,Procurement,Bidding,Algorithmic trading,Average selling price
Conference
Volume
ISSN
Citations 
13
1865-1348
2
PageRank 
References 
Authors
0.38
13
4
Name
Order
Citations
PageRank
James Andrews1111.25
Michael Benisch226420.52
Alberto Sardinha3368.27
Norman M. Sadeh43472253.13