期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2017
卷号：20
期号：4
页码：1-19
DOI：10.18564/jasss.3485
出版社：University of Surrey, Department of Sociology
摘要：Microsimulations and agent-based models across various disciplines need to match agents into relationships. Some of these models need to repeatedly match dierent pairs of agents, for example microsimulations of sexually transmitted infection epidemics. We describe the requirements for pair-matching in these types of microsimulations, and present several pair-matching algorithms: Brute force (BFPM), Random (RPM), Random k (RKPM), Weighted shule (WSPM), Cluster shule (CSPM), and Distribution counting (DCPM). Using two microsimulations, we empirically compare the speeds, and pairing quality of these six algorithms. For models which execute pair-matching many thousands or millions of times, BFPM is not usually a practical option because it is slow. On the other hand RPM is fast but chooses poor quality pairs. Nevertheless both algorithms are used, sometimes implicitly, in many models. Here we use them as yardsticks for upper and lower bounds for speed and quality. In these tests CSPM oers the best trade-o of speed and eectiveness. In general, CSPM is fast and produces stochastic, high quality pair-matches, which are o en desirable characteristics for pairmatching in discrete time step microsimulations. Moreover it is a simple algorithm that can be easily adapted for the specific needs of a particular domain. However, for some models, RKPM or DCPM would be as fast as CSPM with matches of similar quality. We discuss the circumstances under which this would happen.
关键词：Agent-Based Modelling; Pair-Matching; Partner Matching; Sexually Transmitted Infections; HIV
