Evolution of innovative behaviors on scale-free networks

doi:10.1007/s11467-018-0767-1

Frontiers of Physics

2018, Vol. 13

Issue (4): 130308 https://doi.org/10.1007/s11467-018-0767-1

本期目录

Evolution of innovative behaviors on scale-free networks

Ying-Ting Lin¹, Xiao-Pu Han², Bo-Kui Chen^3,⁴(

), Jun Zhou⁴, Bing-Hong Wang⁵

¹. Department of Physics and Electronic Information Engineering, Minjiang University, Fuzhou 350108, China
². Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
³. Division of Logistics and Transportation, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
⁴. Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore
⁵. Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, China

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Abstract：

Innovation, which involves technological transformation and management reorganization, brings about significant changes in modern society. In this paper, to investigate how innovations can be promoted, we propose a game-based model to study the co-evolutionary dynamics of human innovative behaviors. A simulation on scale-free networks is conducted, in which the innovative behavior of each node is determined and updated based on the feedback regarding its innovation, namely the diffusion of the innovation status. Numerical simulations of the model generate a series of patterns, which is consistent with people’s daily experiences and perceptions as regards real-world innovative behaviors. Specifically, various scaling spatiotemporal properties and rich structural impacts on dynamics can be observed. This model provides a novel approach to understand the evolution of innovative behaviors and provides insight for strategy studies of innovation promotion.

Key words： innovative behaviors innovation diffusion evolutionary game coevolution dynamics scale-free networks

收稿日期: 2017-05-21 出版日期: 2018-03-20

Corresponding Author(s): Bo-Kui Chen

引用本文:

. [J]. Frontiers of Physics, 2018, 13(4): 130308.
Ying-Ting Lin, Xiao-Pu Han, Bo-Kui Chen, Jun Zhou, Bing-Hong Wang. Evolution of innovative behaviors on scale-free networks. Front. Phys. , 2018, 13(4): 130308.

链接本文:

https://academic.hep.com.cn/fop/CN/10.1007/s11467-018-0767-1
https://academic.hep.com.cn/fop/CN/Y2018/V13/I4/130308

1	C. Castellano, S. Fortunato, and V. Loreto, Statistical physics of social dynamics, Rev. Mod. Phys. 81(2), 591 (2009) https://doi.org/10.1103/RevModPhys.81.591
2	L. F. Costa, J. O. N. Jr Oliveira, G. Travieso, F. A. Rodrigues, P. R. Villas Boas, L. Antiqueira, M. P. Viana, and L. E. Correa Rocha, Analyzing and modeling real world phenomena with complex networks: A survey of applications, Adv. Phys. 60(3), 329 (2011) https://doi.org/10.1080/00018732.2011.572452
3	S. Galam, Sociophysics: A Physicists Modeling of Psycho-Political Phenomena, Berlin: Springer, 2012 https://doi.org/10.1007/978-1-4614-2032-3
4	D. Stauffer, A biased review of sociophysics, J. Stat. Phys. 151(1–2), 9 (2013) https://doi.org/10.1007/s10955-012-0604-9
5	W. Z. Zheng, Y. Liang, and J. P. Huang, Equilibrium state and non-equilibrium steady state in an isolated human system, Front. Phys. 9(1), 128 (2014) https://doi.org/10.1007/s11467-013-0337-5
6	T. T. Chen, B. Zheng, Y. Li, and X.F. Jiang, New approaches in agent-based modeling of complex financial systems, Front. Phys. 12(6), 128905 (2017) https://doi.org/10.1007/s11467-017-0661-2
7	P. A. Geroski, Models of technology diffusion, Res. Policy 29(4–5), 603 (2000) https://doi.org/10.1016/S0048-7333(99)00092-X
8	R. Peres, E. Muller, and V. Mahajan, Innovation diffusion and new product growth models: A critical review and research directions, Intern. J. Res. Mar. 27, 91 (2010)
9	E. Kiesling, M. Günther, C. Stummer, and L. M. Wakolbinger, Agent-based simulation of innovation diffusion: A review, Cent. Eur. J. Oper. Res. 20(2), 183 (2012) https://doi.org/10.1007/s10100-011-0210-y
10	N. Meade and T. Islam, Modelling and forecasting the diffusion of innovation- A 25-year review, Int. J. Forecast. 22(3), 519 (2006) https://doi.org/10.1016/j.ijforecast.2006.01.005
11	J. Goldenberg, B. Libai, S. Solomon, N. Jan, and D. Stauffer, Marketing percolation, Physica A 284(1–4), 335 (2000) https://doi.org/10.1016/S0378-4371(00)00260-0
12	M. Hohnisch, S. Pittnauer, and D. Stauffer, A percolation-based model explaining delayed takeoff in new-product diffusion, Ind. Corp. Change 17(5), 1001 (2008) https://doi.org/10.1093/icc/dtn031
13	S. Cantono and G. Silverberg, A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies, Technol. Forecast. Soc. 76(4), 487 (2009) https://doi.org/10.1016/j.techfore.2008.04.010
14	J. Goldenberg and S. Efroni, Using cellular automata modeling of the emergence of innovations, Technol. Forecast. Soc. 68(3), 293 (2001) https://doi.org/10.1016/S0040-1625(00)00095-0
15	J. Goldenberg, B. Libai, and E. Muller, Riding the saddle: How cross-market communications can create a major slump in sales, J. Mark. 66(2), 1 (2002) https://doi.org/10.1509/jmkg.66.2.1.18472
16	S. Moldovan and J. Goldenberg, Cellular automata modeling of resistance to innovations: Effects and solutions, Technol. Forecast. Soc. 71(5), 425 (2004) https://doi.org/10.1016/S0040-1625(03)00026-X
17	C. E. Laciana and S. L. Rovere, Ising-like agent-based technology diffusion model: Adoption patterns vs. seeding strategies, Physica A 390(6), 1139 (2011) https://doi.org/10.1016/j.physa.2010.11.006
18	C. E. Laciana and N. Oteiza-Aguirre, An agent based multi-optional model for the diffusion of innovations, Physica A 394, 254 (2014) https://doi.org/10.1016/j.physa.2013.09.046
19	M. A. Janssen and W. Jager, Simulating market dynamics: Interactions between consumer psychology and social networks, Artif. Life 9(4), 343 (2003) https://doi.org/10.1162/106454603322694807
20	L. Kuandykov and M. Sokolov, Impact of social neighborhood on diffusion of innovation S-curve, Decis. Support Syst. 48(4), 531 (2010) https://doi.org/10.1016/j.dss.2009.11.003
21	H. Choi, S. H. Kim, and J. Lee, Role of network structure and network effects in diffusion of innovations, Ind. Mark. Manage. 39(1), 170 (2010) https://doi.org/10.1016/j.indmarman.2008.08.006
22	J. D. Bohlmann, R. J. Calantone, and M. Zhao, The effects of market network heterogeneity on innovation diffusion: An agent-based modeling approach, J. Prod. Innov. Manage. 27(5), 741 (2010) https://doi.org/10.1111/j.1540-5885.2010.00748.x
23	R. Peres, The impact of network characteristics on the diffusion of innovations, Physica A 402, 330 (2014) https://doi.org/10.1016/j.physa.2014.02.003
24	J. Zhang, F. Xia, Z. Ning, T. M. Bekele, X. Bai, X. Su, and J. Wang, A Hybrid Mechanism for Innovation Diffusion in Social Networks, IEEE Access 4, 5408 (2016) https://doi.org/10.1109/ACCESS.2016.2610101
25	H. Dong, W. Hou, and J. Wang, Research on high-tech industry cluster innovation diffusion based on the perspective of complex network: Taking Zhongguancun industrial park as an example, Science and Technology Management Research 36, 149 (2016)
26	G. Szabó and G. Fáth, Evolutionary games on graphs, Phys. Rep. 446(4–6), 97 (2007) https://doi.org/10.1016/j.physrep.2007.04.004
27	M. Perc and A. Szolnoki, Coevolutionary games- A mini review, Biosystems 99(2), 109 (2010) https://doi.org/10.1016/j.biosystems.2009.10.003
28	W. Liu, K. Yue, H. Wu, J. Li, D. Liu, and D. Tang, Containment of competitive influence spread in social networks, Knowl. Base. Syst. 109, 266 (2016) https://doi.org/10.1016/j.knosys.2016.07.008
29	B. Chopard, M. Droz, and S. Galam, An evolution theory in finite size systems, Eur. Phys. J. B 16(4), 575 (2000) https://doi.org/10.1007/s100510070175
30	A. Montanari and A. Saberi, The spread of innovations in social networks, Proc. Natl. Acad. Sci. USA 107(47), 20196 (2010) https://doi.org/10.1073/pnas.1004098107
31	H. P. Young, The dynamics of social innovation, Proc. Natl. Acad. Sci. USA 108(Suppl. 4), 21285 (2011) https://doi.org/10.1073/pnas.1100973108
32	A. Di Mare and V. Latora, Opinion formation models based on game theory, Int. J. Mod. Phys. C 18(09), 1377 (2007) https://doi.org/10.1142/S012918310701139X
33	N. Alon, M. Feldman, A. D. Procaccia, and M. Tennenholtz, A note on competitive diffusion through social networks, Inf. Process. Lett. 110(6), 221 (2010) https://doi.org/10.1016/j.ipl.2009.12.009
34	R. Takehara, M. Hachimori, and M. Shigeno, A comment on pure-strategy Nash equilibria in competitive diffusion games, Inf. Process. Lett. 112(3), 59 (2012) https://doi.org/10.1016/j.ipl.2011.10.015
35	G. Silverberg, The discrete charm of the bourgeoisie: quantum and continuous perspectives on innovation and growth, Res. Policy 31(8–9), 1275 (2002) https://doi.org/10.1016/S0048-7333(02)00063-X
36	G. Silverberg and B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ. Dyn. Control 29(1–2), 225 (2005) https://doi.org/10.1016/j.jedc.2003.05.005
37	G. Silverberg and B. Verspagen, Self-organization of R&D search in complex technology spaces, J. Econ. Interact. Coord. 2(2), 211 (2007) https://doi.org/10.1007/s11403-006-0008-5
38	S. Bornholdt, M. H. Jensen, and K. Sneppen, Emergence and decline of scientific paradigms, Phys. Rev. Lett. 106(5), 058701 (2011) https://doi.org/10.1103/PhysRevLett.106.058701
39	Y. T. Lin, X. P. Han, and B. H. Wang, Dynamics of human innovative behaviors, Physica A 394, 74 (2014) https://doi.org/10.1016/j.physa.2013.09.039
40	J. Juang and Y. H. Liang, The impact of vaccine success and awareness on epidemic dynamics, Chaos 26(11), 113105 (2016) https://doi.org/10.1063/1.4966945
41	T. Liu, P. Li, Y. Chen, and J. Zhang, Community size effects on epidemic spreading in multiplex social networks, PLoS One 11(3), e0152021 (2016) https://doi.org/10.1371/journal.pone.0152021
42	W. Wang, M. Chen, Y. Min, and X. Jin, Structural diversity effects of multilayer networks on the threshold of interacting epidemics, Physica A 443, 254 (2016) https://doi.org/10.1016/j.physa.2015.09.064
43	J. Zhou and Z. Liu, Epidemic spreading in complex networks, Front. Phys. China 3(3), 331 (2008) https://doi.org/10.1007/s11467-008-0027-x
44	M. Tomochi, H. Murata, and M. Kono, A consumerbased model of competitive diffusion: The multiplicative effects of global and local network externalities, J. Evol. Econ. 15(3), 273 (2005) https://doi.org/10.1007/s00191-005-0245-0
45	L. Weng, A. Flammini, A. Vespignani, and F. Menczer, Competition among memes in a world with limited attention, Sci. Rep. 2(1), 335 (2012) https://doi.org/10.1038/srep00335
46	J. P. Gleeson, J. A. Ward, K. P. O’sullivan, and W. T. Lee, Competition-induced criticality in a model of meme popularity, Phys. Rev. Lett. 112(4), 048701 (2014) https://doi.org/10.1103/PhysRevLett.112.048701
47	R. Hou, J. Wu, and H. S. Du, Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model, Physica A 469, 644 (2017) https://doi.org/10.1016/j.physa.2016.11.110

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