doi: 10.3934/dcdss.2020266

Simulation analysis between enterprise and consumer group based on evolutionary game

1. 

School of Business Administration, University of Science and Technology Liaoning, Anshan 114051, China

2. 

School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China

*Corresponding author: Hua Li

Received  April 2019 Revised  May 2019 Published  February 2020

In this paper, an evolutionary game model between enterprises and consumer groups is constructed under the background of restraining the online false information of production and management of Enterprises. It innovatively incorporates the parameter of consumer group's psychological recognition with enterprises into the construction of the model. Through the analysis of the stability of the model and the data simulation using Netlogo, it is found that the choice of enterprises and consumer groups under the Online False Information is related to the following parameters: Firstly, the additional cost and additional income earned by the company in actively responding to false information. Secondly, the labor cost of the consumer group to investigate false information and the opportunity cost of finding alternatives. Finally, the real information released by the enterprise and the psychological recognition of the consumer group to the enterprise. This study provides a theoretical basis and reference for the behavior and strategy selection of enterprises and consumer groups in the context of false information.

Citation: Qiubai Sun, Bowen Li, Hua Li, Xuebo Chen. Simulation analysis between enterprise and consumer group based on evolutionary game. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020266
References:
[1]

A. Shvets and A. Makaseyev, Deterministic chaos in pendulum systems with delay, Applied Mathematics and Nonlinear Sciences, 4 (2019), 1-8.  doi: 10.2478/AMNS.2019.1.00001.  Google Scholar

[2]

A. Antoci and F. Sabatini, Online networks, social interaction and segregation: An evolutionary approach, Journal of Evolutionary Economics, 28 (2018), 859-883.  doi: 10.1007/s00191-018-0556-6.  Google Scholar

[3]

S. De, D. S. Nau and M. J. Gelfand, Understanding norm change: An evolutionary game-theoretic approach, in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2017, 1433–1441. Google Scholar

[4]

D. Friedman, On economic applications of evolutionary game theory, Journal of evolutionary economics, 8 (1998), 15-43.  doi: 10.1007/s001910050054.  Google Scholar

[5]

K. C. M. IE, Travelling waves and conservation laws of a (2+1)-dimensional coupling system with korteweg-de vries equation, Applied Mathematics and Nonlinear Sciences, 3 (2018), 241-254.   Google Scholar

[6]

R. Mahmoudi and M. Rasti-Barzoki, Sustainable supply chains under government intervention with a real-world case study: An evolutionary game theoretic approach, Computers & Industrial Engineering, 116 (2018), 130-143.  doi: 10.1016/j.cie.2017.12.028.  Google Scholar

[7]

J. Newton, Evolutionary game theory: A renaissance, Games, 9 (2018), Paper No. 31, 67 pp. doi: 10.3390/g9020031.  Google Scholar

[8]

J. M. Smith and G. R. Price, The logic of animal conflict, Nature, 246 (1973), 15-18.  doi: 10.1038/246015a0.  Google Scholar

[9]

Q. C. Song and Q. Zhang, The influence of social integration factors on the choice of floating population's future housing-an empirical analysis based on the data of 2014 national mobile population dynamic monitoring survey, Shandong Social Sciences, 2014, 70–76. Google Scholar

[10]

Y. Song, Y. D. Xu and Z. Y. Zhang, Research on false information of product green quality under asymmetric information, Jilin University Journal Social Sciences Edition, 59 (2019), 146–155+222–223. Google Scholar

[11]

Z. L. Sun, The influence of consumer psychological identity on marketing efficiency, Journal of Commercial Economics, 43–44. Google Scholar

[12]

S. UllahN. Massoud and B. Scholnick, The impact of fraudulent false information on equity values, Journal of Business Ethics, 120 (2014), 219-235.  doi: 10.1007/s10551-013-1657-7.  Google Scholar

[13]

D. Z. WeiF. J. Chen and X. X. Zheng, Simulation research on wechat false information propagation based on game theory, Information Science, 34 (2016), 146-149.   Google Scholar

[14]

S. S. WuJ. Y. WangY. P. Yan and L. Zhang, Simulation research on enterprise's optimization of false information recognition, Computer Simulation, 34 (2017), 313-316.   Google Scholar

show all references

References:
[1]

A. Shvets and A. Makaseyev, Deterministic chaos in pendulum systems with delay, Applied Mathematics and Nonlinear Sciences, 4 (2019), 1-8.  doi: 10.2478/AMNS.2019.1.00001.  Google Scholar

[2]

A. Antoci and F. Sabatini, Online networks, social interaction and segregation: An evolutionary approach, Journal of Evolutionary Economics, 28 (2018), 859-883.  doi: 10.1007/s00191-018-0556-6.  Google Scholar

[3]

S. De, D. S. Nau and M. J. Gelfand, Understanding norm change: An evolutionary game-theoretic approach, in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2017, 1433–1441. Google Scholar

[4]

D. Friedman, On economic applications of evolutionary game theory, Journal of evolutionary economics, 8 (1998), 15-43.  doi: 10.1007/s001910050054.  Google Scholar

[5]

K. C. M. IE, Travelling waves and conservation laws of a (2+1)-dimensional coupling system with korteweg-de vries equation, Applied Mathematics and Nonlinear Sciences, 3 (2018), 241-254.   Google Scholar

[6]

R. Mahmoudi and M. Rasti-Barzoki, Sustainable supply chains under government intervention with a real-world case study: An evolutionary game theoretic approach, Computers & Industrial Engineering, 116 (2018), 130-143.  doi: 10.1016/j.cie.2017.12.028.  Google Scholar

[7]

J. Newton, Evolutionary game theory: A renaissance, Games, 9 (2018), Paper No. 31, 67 pp. doi: 10.3390/g9020031.  Google Scholar

[8]

J. M. Smith and G. R. Price, The logic of animal conflict, Nature, 246 (1973), 15-18.  doi: 10.1038/246015a0.  Google Scholar

[9]

Q. C. Song and Q. Zhang, The influence of social integration factors on the choice of floating population's future housing-an empirical analysis based on the data of 2014 national mobile population dynamic monitoring survey, Shandong Social Sciences, 2014, 70–76. Google Scholar

[10]

Y. Song, Y. D. Xu and Z. Y. Zhang, Research on false information of product green quality under asymmetric information, Jilin University Journal Social Sciences Edition, 59 (2019), 146–155+222–223. Google Scholar

[11]

Z. L. Sun, The influence of consumer psychological identity on marketing efficiency, Journal of Commercial Economics, 43–44. Google Scholar

[12]

S. UllahN. Massoud and B. Scholnick, The impact of fraudulent false information on equity values, Journal of Business Ethics, 120 (2014), 219-235.  doi: 10.1007/s10551-013-1657-7.  Google Scholar

[13]

D. Z. WeiF. J. Chen and X. X. Zheng, Simulation research on wechat false information propagation based on game theory, Information Science, 34 (2016), 146-149.   Google Scholar

[14]

S. S. WuJ. Y. WangY. P. Yan and L. Zhang, Simulation research on enterprise's optimization of false information recognition, Computer Simulation, 34 (2017), 313-316.   Google Scholar

Figure 1.  Simulation of game strategy between enterprises and consumer groups
Figure 2.  Simulation of game strategy between enterprises and consumer groups
Figure 3.  Simulation of game strategy between enterprises and consumer groups
Figure 4.  Simulation of game strategy between enterprises and consumer groups
Figure 5.  Simulation of game strategy between enterprises and consumer group
Figure 6.  Simulation of game strategy between enterprises and consumer groups
Figure 7.  Simulation of game strategy between enterprises and consumer groups
Table 1.  Parameters and Symbol Explanations
Parameters Symbol Explanations
$ C $ The cost of enterprises' negative response to false information
$ \Delta $C The additional cost of enterprises' active response to false information (such as investigation, evidence collection and refuting rumors, etc.)
$ I $ Income earned when enterprises negatively respond to false information
$ \Delta I $ Additional income earned when enterprises actively respond to false information
$ L $ When consumer groups choose not to buy, the loss suffered by enterprises caused by false information in addition to the cost (such as the decline in purchase volume and the trust level of consumer groups)
$ \lambda $ The psychological recognition of consumer groups on enterprises (the higher the trust, the lower the loss caused by the false information to enterprises, and the higher the utilization degree of the statement issued by enterprises by consumer groups when investigating the false information. The range is $ 0<\lambda<1) $
$ H $ The efforts paid by consumer groups when investigating false information when enterprises negatively respond to false information
$ T $ The opportunity cost of consumer groups when abandoning the purchase of the product in order to find a new substitute
$ M $ Authentic information released by enterprises with active response
$ x $ Probability of enterprises choosing to respond actively $ (\le x\le 1) $
$ y $ Probability of consumer groups choosing to buy products $ (\le y\le 1) $
Parameters Symbol Explanations
$ C $ The cost of enterprises' negative response to false information
$ \Delta $C The additional cost of enterprises' active response to false information (such as investigation, evidence collection and refuting rumors, etc.)
$ I $ Income earned when enterprises negatively respond to false information
$ \Delta I $ Additional income earned when enterprises actively respond to false information
$ L $ When consumer groups choose not to buy, the loss suffered by enterprises caused by false information in addition to the cost (such as the decline in purchase volume and the trust level of consumer groups)
$ \lambda $ The psychological recognition of consumer groups on enterprises (the higher the trust, the lower the loss caused by the false information to enterprises, and the higher the utilization degree of the statement issued by enterprises by consumer groups when investigating the false information. The range is $ 0<\lambda<1) $
$ H $ The efforts paid by consumer groups when investigating false information when enterprises negatively respond to false information
$ T $ The opportunity cost of consumer groups when abandoning the purchase of the product in order to find a new substitute
$ M $ Authentic information released by enterprises with active response
$ x $ Probability of enterprises choosing to respond actively $ (\le x\le 1) $
$ y $ Probability of consumer groups choosing to buy products $ (\le y\le 1) $
Table 2.  The Income Matrix between Enterprises and Consumer Groups
Consumer Groups
Buying Not Buying
Enterprises Active $ (I+\Delta I-C-\Delta C-(1-\lambda )L, \lambda M-H) $ $ (-C-\Delta C-L, -T) $
Response $ (I-C-(1-\lambda )L, -H) $ $ (-C-L, -T) $
Consumer Groups
Buying Not Buying
Enterprises Active $ (I+\Delta I-C-\Delta C-(1-\lambda )L, \lambda M-H) $ $ (-C-\Delta C-L, -T) $
Response $ (I-C-(1-\lambda )L, -H) $ $ (-C-L, -T) $
Table 3.  Formulas of Determinant and Trace of Jacobian Matric for Each Equilibrium Point
Equilibrium Point $ detJ $ $ trJ $
$ G_{1}(0, 0) $ $ \Delta C(H-T) $ $ T-H-\Delta C $
$ G_{2}(0, 1) $ $ (\Delta I-\Delta C)(H-T) $ $ \Delta I-\Delta C+H-T $
$ G_{3}(1, 0) $ $ \Delta C(\lambda M-H+T) $ $ \Delta C+\lambda M+T-H $
$ G_{4}(1, 1) $ $ (\Delta I-\Delta C)(\lambda M-H+T) $ $ (\Delta I-\Delta C)(T-H) $
$ G_{5}((H-T)/\lambda M, \Delta C/\Delta I) $ $ (H-T)\Delta C(\Delta I-\Delta C)(H-T-\lambda M)/\Delta I\lambda M $ 0
Equilibrium Point $ detJ $ $ trJ $
$ G_{1}(0, 0) $ $ \Delta C(H-T) $ $ T-H-\Delta C $
$ G_{2}(0, 1) $ $ (\Delta I-\Delta C)(H-T) $ $ \Delta I-\Delta C+H-T $
$ G_{3}(1, 0) $ $ \Delta C(\lambda M-H+T) $ $ \Delta C+\lambda M+T-H $
$ G_{4}(1, 1) $ $ (\Delta I-\Delta C)(\lambda M-H+T) $ $ (\Delta I-\Delta C)(T-H) $
$ G_{5}((H-T)/\lambda M, \Delta C/\Delta I) $ $ (H-T)\Delta C(\Delta I-\Delta C)(H-T-\lambda M)/\Delta I\lambda M $ 0
Table 4.  The Evolutionary Stability Strategy Summary
$ G_{1}(0, 0) $ $ G_{2}(0, 1) $ $ G_{3}(1, 0) $ $ G_{4}(1, 1) $ $ G_{5}((H-T)/\lambda M, \Delta C/ \Delta I) $
detJ trJ Stability detJ trJ Stability detJ trJ Stability detJ trJ Stability detJ trJ Stability
(1) + - ESS + + Unstable + + Unstable + - ESS - 0 Saddle Point
(2) + - ESS + + Unstable - ? Saddle Point - - Saddle Point / / /
(3) + - ESS - ? Saddle Point + + Unstable - + Saddle Point / / /
(4) + - ESS - ? Saddle Point - ? Saddle Point + + Unstable / / /
(5) - ? Saddle Point - ? Saddle Point + + Unstable + + Unstable / / /
(6) - ? Saddle Point + - ESS + + Unstable - - Saddle Point / / /
$ G_{1}(0, 0) $ $ G_{2}(0, 1) $ $ G_{3}(1, 0) $ $ G_{4}(1, 1) $ $ G_{5}((H-T)/\lambda M, \Delta C/ \Delta I) $
detJ trJ Stability detJ trJ Stability detJ trJ Stability detJ trJ Stability detJ trJ Stability
(1) + - ESS + + Unstable + + Unstable + - ESS - 0 Saddle Point
(2) + - ESS + + Unstable - ? Saddle Point - - Saddle Point / / /
(3) + - ESS - ? Saddle Point + + Unstable - + Saddle Point / / /
(4) + - ESS - ? Saddle Point - ? Saddle Point + + Unstable / / /
(5) - ? Saddle Point - ? Saddle Point + + Unstable + + Unstable / / /
(6) - ? Saddle Point + - ESS + + Unstable - - Saddle Point / / /
[1]

William H. Sandholm. Local stability of strict equilibria under evolutionary game dynamics. Journal of Dynamics & Games, 2014, 1 (3) : 485-495. doi: 10.3934/jdg.2014.1.485

[2]

Astridh Boccabella, Roberto Natalini, Lorenzo Pareschi. On a continuous mixed strategies model for evolutionary game theory. Kinetic & Related Models, 2011, 4 (1) : 187-213. doi: 10.3934/krm.2011.4.187

[3]

Anna Lisa Amadori, Astridh Boccabella, Roberto Natalini. A hyperbolic model of spatial evolutionary game theory. Communications on Pure & Applied Analysis, 2012, 11 (3) : 981-1002. doi: 10.3934/cpaa.2012.11.981

[4]

Scott G. McCalla. Paladins as predators: Invasive waves in a spatial evolutionary adversarial game. Discrete & Continuous Dynamical Systems - B, 2014, 19 (5) : 1437-1457. doi: 10.3934/dcdsb.2014.19.1437

[5]

John Cleveland. Basic stage structure measure valued evolutionary game model. Mathematical Biosciences & Engineering, 2015, 12 (2) : 291-310. doi: 10.3934/mbe.2015.12.291

[6]

Yuanyuan Huang, Yiping Hao, Min Wang, Wen Zhou, Zhijun Wu. Optimality and stability of symmetric evolutionary games with applications in genetic selection. Mathematical Biosciences & Engineering, 2015, 12 (3) : 503-523. doi: 10.3934/mbe.2015.12.503

[7]

King-Yeung Lam. Dirac-concentrations in an integro-pde model from evolutionary game theory. Discrete & Continuous Dynamical Systems - B, 2019, 24 (2) : 737-754. doi: 10.3934/dcdsb.2018205

[8]

Stamatios Katsikas, Vassilli Kolokoltsov. Evolutionary, mean-field and pressure-resistance game modelling of networks security. Journal of Dynamics & Games, 2019, 6 (4) : 315-335. doi: 10.3934/jdg.2019021

[9]

Hassan Najafi Alishah, Pedro Duarte. Hamiltonian evolutionary games. Journal of Dynamics & Games, 2015, 2 (1) : 33-49. doi: 10.3934/jdg.2015.2.33

[10]

Shui-Nee Chow, Kening Lu, Yun-Qiu Shen. Normal forms for quasiperiodic evolutionary equations. Discrete & Continuous Dynamical Systems - A, 1996, 2 (1) : 65-94. doi: 10.3934/dcds.1996.2.65

[11]

Alexey Cheskidov, Landon Kavlie. Pullback attractors for generalized evolutionary systems. Discrete & Continuous Dynamical Systems - B, 2015, 20 (3) : 749-779. doi: 10.3934/dcdsb.2015.20.749

[12]

Andrzej Swierniak, Michal Krzeslak. Application of evolutionary games to modeling carcinogenesis. Mathematical Biosciences & Engineering, 2013, 10 (3) : 873-911. doi: 10.3934/mbe.2013.10.873

[13]

Minette Herrera, Aaron Miller, Joel Nishimura. Altruistic aging: The evolutionary dynamics balancing longevity and evolvability. Mathematical Biosciences & Engineering, 2017, 14 (2) : 455-465. doi: 10.3934/mbe.2017028

[14]

Jim M. Cushing. The evolutionary dynamics of a population model with a strong Allee effect. Mathematical Biosciences & Engineering, 2015, 12 (4) : 643-660. doi: 10.3934/mbe.2015.12.643

[15]

Jeremias Epperlein, Vladimír Švígler. On arbitrarily long periodic orbits of evolutionary games on graphs. Discrete & Continuous Dynamical Systems - B, 2018, 23 (5) : 1895-1915. doi: 10.3934/dcdsb.2018187

[16]

Amy Veprauskas, J. M. Cushing. Evolutionary dynamics of a multi-trait semelparous model. Discrete & Continuous Dynamical Systems - B, 2016, 21 (2) : 655-676. doi: 10.3934/dcdsb.2016.21.655

[17]

Jinyuan Zhang, Aimin Zhou, Guixu Zhang, Hu Zhang. A clustering based mate selection for evolutionary optimization. Big Data & Information Analytics, 2017, 2 (1) : 77-85. doi: 10.3934/bdia.2017010

[18]

Caichun Chai, Tiaojun Xiao, Eilin Francis. Is social responsibility for firms competing on quantity evolutionary stable?. Journal of Industrial & Management Optimization, 2018, 14 (1) : 325-347. doi: 10.3934/jimo.2017049

[19]

Siegfried Carl. Comparison results for a class of quasilinear evolutionary hemivariational inequalities. Conference Publications, 2007, 2007 (Special) : 221-229. doi: 10.3934/proc.2007.2007.221

[20]

Alexander Mielke. Deriving amplitude equations via evolutionary $\Gamma$-convergence. Discrete & Continuous Dynamical Systems - A, 2015, 35 (6) : 2679-2700. doi: 10.3934/dcds.2015.35.2679

2018 Impact Factor: 0.545

Article outline

Figures and Tables

[Back to Top]