Characterizing And Predicting Human Bidirectional Selection Patterns

Two-way selection is wide-spread in nature and society, such as the marriages be-tween men and women, the conclusion of contracts between job hunters and recruiters, and the trade between buyers and sellers. We propose a model of two-way selection system and provide its exact solution. Defining two basic relative ratios:η for the ra-tio of selection quantities from both sides, and ξ for the ratio of the total number of states to the smaller number of two sides, we find the matching rate P of two-way se-lections trends toward an inverse proportion to either η or ξ under different conditions, in agreement with the numerical simulations. Eighty-two pieces of news of real-world matchmaking fairs are collected from the Internet. We surprisingly notice that, most of the empirical data of the matchmaking fairs, the typical real-world two-way selections, are included in the range predicted by our model, implying such a simple model is help-ful for understanding dynamics mechanism of real-world two-way selection system and would be valuable to the real life.A bidirectional selection model we described above has been presented to investi-gate the factors influencing the matching rate. The model is presented on the Globally coupled network. But the real networks often are not the Globally coupled network, and the average degree are often far smaller than the number of nodes in the real net-work. In addition, many real complex networks have emerged some common charac-teristics, such as small world phenomena, scale-free properties with power-law degree distributions. Therefore some famous networks’models with real networks charac-teristics have been proposed, for instance:Nearest-neighbor coupled network model, WS small world network model, ER random network model and BA scale-free network model, etc. Therefore, we should deeply study the matching of two-way selection sys-tem on complex networks. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals’neighborhoods in social networks, regard-less of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching perfor-mance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.