Drug Quality Co-regulation Supervision Strategy Considering Collusion Behavior With New Media Participation

Abstract

The efficiency and level of drug quality supervision are highly related to the distorted or true reporting of new media, and the collusion or non-collusion of third-party testing agencies. Therefore, based on the co-regulation information platform, considering the strategic choices of local government, drug enterprises, third-party testing agencies and new media, this article constructs a four-party evolutionary game model of co-regulation supervision. The stable equilibrium points of each participant's strategic choices are solved. The stability of the strategic combination is analyzed by Lyapunov's first method, and Matlab 2020b is used for simulation analysis to verify the influence of each decision variable on different players' strategic choices. The results show that, firstly, new media's true reporting can make up for the lack of supervision of drug enterprises by local government, and the greater the impact of new media reporting, the more active drug enterprises will be to produce high-quality drugs. Secondly, non-collusion of third-party testing agencies can improve the self-discipline ability of drug enterprises, encourage new media to report truthfully, and play the role of co-regulation supervision. Furthermore, the greater the probability of new media's true reporting, the more local government tend to be stricter, and the probability of strict supervision is positively related to the central government's accountability. Finally, increasing penalty for producing low-quality drugs and collusion will help standardize the behavior of drug enterprises and third-party testing agencies. This article enriches and expands the theoretical basis of the drug quality co-regulation supervision and proposes corresponding countermeasures and suggestions.

Keywords: collusion behavior; drug co-regulation supervision; four-party evolutionary game; new media participation; simulation analysis.

Figure 1

Four-party evolutionary game structure relationship. Figure is the structural relationship diagram that shows the relationship among local government, drug enterprises, third-party testing agencies and new media based on the co-regulation information platform under the reward and punishment of the central government.

Figure 2

Phase diagram of the local government strategic choice. Figure is the phase diagram that shows the evolutionary trend of local government's strategy obtained by calculating the response function of the probability of local government choosing the “strict supervision” strategy.

Figure 3

Phase diagram of drug enterprises strategic choice. Figure is the phase diagram that shows the evolutionary trend of drug enterprises' strategy obtained by calculating the response function of the probability of drug enterprises choosing the “producing high-quality drug” strategy.

Figure 4

Phase diagram of third-party testing agencies strategic choice. Figure is the phase diagram that shows the evolutionary trend of third-party testing agencies' strategy obtained by calculating the response function of the probability of third-party testing agencies choosing the “non-collusion” strategy.

Figure 5

Phase diagram of new media strategic choice. Figure is the phase diagram that shows the evolutionary trend of new media's strategy obtained by calculating the response function of the probability of new media choosing the “true reporting” strategy.

Figure 6

Impact of I and N on the evolution of each player's strategy. Figure is the simulation diagram that shows the influence of the reputation value brought by new media true reporting and reputation loss brought by new media distorted reporting on the strategic choices of local government, drug enterprises, third-party testing agencies, and new media. (A) When the situation I = 0.0, N = 0.0. (B) When the situation I = 2.0, N = 3.0. (C) When the situation I = 4.0, N = 6.0.

Figure 7

Impact of δ and on the evolution of each player's strategy. Figure is the simulation diagram that shows the influence of the influence effect of new media reports and additional revenue from distorted reporting on the strategic choices of local government, drug enterprises, third-party testing agencies, and new media. (A) When the situation δ = 0.0, R_mh − R_ml = 0.0, (B) When the situation δ = 0.4, R_mh − R_ml = 2.0. (C) When the situation δ = 0.8, R_mh − R_ml = 4.0.

Figure 8

Impact of additional costs on the evolution of each player's strategy. Figure is the simulation diagram that shows the influence of the additional costs on the strategic choices of local government, drug enterprises, and third-party testing agencies. (A) When the situation G_k − G_l = 0.0, C_k − C_l = 0.0. (B) When the situation G_k − G_l = 7.0, C_k − C_l = 7.0. (C) When the situation G_k − G_l = 11.0, C_k − C_l = 11.0.

Figure 9

Impact of penalties on the evolution of each player's strategy. Figure is the simulation diagram that shows the influence of the penalties on the strategic choices of local government, drug enterprises, and third-party testing agencies. (A) When the situation F_g = 0.0, F_e = 0.0. (B) When the situation F_g = 4.5, F_e = 4.5. (C) When the situation F_g = 6.0, F_e = 6.0.