In search of efficient network structures: the needle in the haystack
Tóm tắt
Searching for efficient networks can prove a very difficult analytical and even computational task. In this paper, we explore the possibility of using the genetic algorithms (GA) technique to identify efficient network structures in the case of non-trivial payoff functions. The robustness of this method in predicting optimal networks is tested on the two simple stylized models introduced by Jackson and Wolinsky (1996), for which the efficient networks are known over the whole state space of the parameters’ values. This approach allows us to obtain new exploratory results in the case of the linear-spatialized connections model proposed by Johnson and Gilles (Rev Econ Des 5:273–299, 2000), for which the efficient allocation of bilateral connections is driven by contradictory forces that push either for a centralized structure around a coordinating agent, or for only locally and evenly distributed connections.
Tài liệu tham khảo
Carayol N, Roux P (2006) Knowledge flows and the geography of networks. The strategic formation of small worlds. J Econ Behav Organ (forthcoming)
Carayol N, Roux P (2004) Behavioral foundations and equilibrium notions for social network formation processes. Adv Complex Syst 7(1): 77–92
Carayol N, Roux P, Yıldızoğlu M (2007) Inefficiencies in a model of spatial networks formation with positive externalities. J Econ Behav Organ (forthcoming)
Goldberg D (1991) Genetic Algorithms. Addison Wesley, Reading
Holland JH (2001) Adaptation in natural and artificial systems, sixth printing. MIT, Cambridge
Jackson MO (2004) A Survey of models of network formation: stability and efficiency. In: Demange G, Wooders M (eds) Group formation in economics: networks, clubs and coalitions, Chap. 1. Cambridge University Press, Cambridge
Jackson MO, Wolinsky A (1996) A strategic model of social and economic networks. J Econ Theory 71: 44–74
Johnson C, Gilles RP (2000) Spatial social networks. Rev Econ Des 5: 273–299
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Berlin
Vallée T, Yıldızoğlu M (2004) Présentation des algorithmes génétiques et de leurs applications en économie. Rev Econ Polit 114(6): 711–745
Yıldızoğlu M (2002) Competing R&D strategies in an evolutionary industry model. Comput Econ 19: 52–65