Paper Info
Title
A Quantitative Method for Comparing Multi-Agent-Based Simulations in Feature Space
Abstract
Comparisons of simulation results (model-to-model approach) are important for examining the validity of simulation models. One of the factors preventing the widespread application of this approach is the lack of methods for comparing multi-agent-based simulation results. In order to expand the application area of the model-to-model approach, this paper introduces a quantitative method for comparing multi-agent-based simulation models that have the following properties: (1) time series data is regarded as a simulation result and (2) simulation results are different each time the model is used due to the effect of randomness, even though the parameter setups are all the same. To evaluate the effectiveness of the proposed method, we used it for the comparison of artificial stock market simulations using two different learning algorithms. We concluded that our method is useful for (1) investigating the difference in the trends of simulation results obtained from models using different learning algorithms; and (2) identifying reliable simulation results that are minimally influenced by the learning algorithms used.
Year
DOI
Venue
2008
10.1007/978-3-642-01991-3_12
mAbs
Keywords
DocType
Volume
multi-agent-based simulations,feature space,simulation model,quantitative method,model-to-model approach,simulation result,multi-agent-based simulation result,different learning algorithm,artificial stock market simulation,multi-agent-based simulation model,reliable simulation result,time series data,reinforcement learning,time series
Conference
5269
ISSN
Citations 
PageRank 
0302-9743
2
0.39
References 
Authors
10
2
Name
Order
Citations
PageRank
Ryota Arai120.39
Shigeyoshi Watanabe2157.42