Scarce water resources should have sufficient mobility among different industries and enterprises. The structure of the SD model running in AnyLogic 7.1.1 is as shown in Figure 7.Įssentially, water shortages and pollution are resource misallocation issues. The model consists of four stocks, two flows and seven parameters. The mechanism involved in our model is derived from the aforementioned results in Section 3. Additionally, the AnyLogic 7.1.1 platform provides us a compact way to observe the causation. There is underlying issues of system structure and behavior which ask for continuous monitoring where events and decisions are blurred.
#Anylogic 7 crack series#
The process of encouraging market participation is a series of non-linear behaviors and the influences between variables are not surface phenomena. applied SD simulation in water resources management as well. Stave studied the case of Las Vegas that SD model facilitates public understanding, and expands his understanding using four cases in another paper seven years later. The model system is a causally closed structure that itself defines its behavior while taking an endogenous point of view. System dynamics is widely used in such complex situations, according to behavior or project in a system simulation, even in compartmental-spatial occasions. We build a system dynamic (SD) model to study the dynamic behavior system of two enterprise populations when deciding on participation in WET. The destination (ESS point E1 or E3) of the dynamic process is relevant to p, q and the dynamic differential equations’ PM (plus or minus) is organized in corresponding intervals, as shown in Table 1. Furthermore, ESS point E3 is equipped with stability to cope with external interference and reinstates the result. Then, the contradiction aforementioned could be solved by a positive cycle. The more participation, the larger the market scale is. From the perspective of government, the more active WET is, the more attention will be attracted to water environment sustainable development. Specifically, E3 ( 1, 1 ) is the dreaming point, which represents all the potential participants choosing bidding no matter which enterprise population they belong to. The vertical arrow shows the increase or decrease direction of q with time. The horizontal arrow shows the increase or decrease direction of p with time. On the basis of Table 1, we draw the phase diagram as shown in Figure 6. Then, we get the equilibrium points and their local stability as per Table 1. The findings benefit both water management practice and further research. We build a system dynamics model on AnyLogic 7.1.1 to simulate the aforementioned game and draw four conclusions: (1) to reach ESS more quickly, we need to minimize the bidding related cost r H and price G, but regulate the heavy penalty F (2) an ESS can be significantly transformed, such as from ( D,D) to ( H,H) by regulating r H, G and F accordingly (3) the initial choice of strategy is essential to the final result (4) if participation seems stable but unsatisfying, it is important to check whether it is a saddle point and adjust external factors accordingly. Participation increase equals reaching point ( H,H) in the model and is treated as an evolutionarily stable strategy (ESS). External factors are simplified according to three categories: r H-bidding related cost, G-price and F-penalty. Due to the different perceived value of certain permits, enterprises choose H strategy (bidding for permit) or D strategy (not bidding). We develop an evolutionary game model of two enterprise populations’ dynamics and stability in the decision-making behavior process. However, low participation impedes its development. Water emission trading (WET) is promising in sustainable development strategy.
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