Why is system dynamics a “policy laboratory”?

Go to hell. . . . .

The correct answer is as follows:

Why is system dynamics a "policy laboratory"

With the rapid changes in economy, technology, society and environment, we The various systems in which they operate are also becoming more and more complex. In fact, many of the problems we face today stem from the unanticipated side effects of our past actions. Too often, the measures we take to solve a problem often make the situation worse or create new problems.

In this dynamic world full of complexity, if we want to make effective decisions, we must first become systems thinkers, thereby expanding the boundaries of our mental models and developing tools to understand complexity. How a system's structure determines its behavior.

System dynamics is just such a perspective, which can help decision-makers understand the structure and dynamic behavior of complex systems. At the same time, through system dynamics we can establish rigorous mathematical models and use computers to simulate complex systems to ultimately formulate effective policies. Taken together, these tools can help us build a manager’s “flight simulator” (a microscopic world where space can be compressed and time can be slowed down) in which we can feel the long-term side effects of decision-makers and improve learning. efficiency, build understanding of complex systems, and develop sound structures and strategies for greater success. Over the past 10 years, many of America's best-performing businesses, consulting firms, and governments have applied system dynamics to solve crises. Next, based on improving the economic benefits of urban tourism, we use the theory and methods of system dynamics to establish a dynamic model of the tourism economic income-increasing system, explore the internal causal relationship, system response and process of the system, and analyze each link of the system. analysis, and predicted the future dynamics of the system through simulation.

1. Introduction

System dynamics is developed by integrating feedback control theory, information theory, system theory, decision theory, computer simulation and system analysis experimental methods. The causal feedback relationship describes the dynamic complexity of the system, and computer simulation methods are used to simulate the behavior patterns of real systems under different strategies. The main task facing the development of the tourism industry today is to seize the opportunity of the expected increase in the number of total tourists, improve the efficiency and competitiveness of the tourism industry, and achieve sustainable and efficient development of the tourism industry. The income change activities of the tourism industry are systemic, so , by applying the system to the field of analyzing tourism revenue issues, it provides assistance and reference for finding appropriate development strategies for increasing tourism revenue. This article uses VENSIM software to model and analyze the tourism revenue-increasing system based on the assumptions of the actual situation.

2 System Modeling

In the system, the specific settings are as follows:

The total tourism revenue is affected by 2 flows, 1 increment (tourist benefit increase, specifically including ticket income, travel agency income, food and accommodation income, transportation income and other various income) and 1 decrease (loss caused by tourists to scenic spots, such as environmental losses). The increment is affected by 3 factors : The capacity of the tourism market. The expansion of the capacity of the tourism market will increase the number of tourists, but when this variable reaches saturation, the number of tourists will reach a stable state. The attraction's own ability to attract people (depends on the cost of capacity building); advertising attraction ( Depends on the amount of publicity and promotion expenses, advertising effectiveness and the current total number of people); the number of people attracted by word of mouth (depends on the total population, contact rate and word-of-mouth acceptance). There is a direct proportional relationship between the above factors. Tourism investment costs are affected by total tourism revenue, assuming that its proportion in total tourism revenue is constant.

The model parameters are described as follows: (01) FINAL TIME = 10 Units:Year; (02) INITIAL TIME = 0 Units: Year; (03) SAVEPER= TIME STEP Units: Year [0,?]; (04) TIME STEP =1 Units: Year [0,?]; (05) Economic benefit generated by unit tourist = 1,000 yuan; (06) Loss caused by unit tourist = 40; (07) Number of people attracted by word-of-mouth = Advertising effectiveness × total population + tourism promotion, Publicity, promotion expenses × 500; (11) Advertising effectiveness = 0. 015; (12) Total population = 1e+ 009; (13) Contact rate = 100; (14) Revenue reduction = losses caused by tourists; (15) Increase in income = economic benefits generated by tourists; (16) Tourism promotion and publicity, promotion cost = 0. 5 × tourism investment cost; (17) Tourism market capacity = INTEG [Tourism market capacity + Time × (1 + market capacity change rate ), 1. 1e + 007] (18) Total number of tourists = MIN (number of people attracted by advertising + number of people attracted by word-of-mouth + number of people attracted by the scenic spot’s own ability, tourism market volume); (19) Total tourism revenue = INTEG (increase in revenue – revenue Reduction, 1e + 010); (20) Tourism investment cost = 0. 2 × total tourism revenue; (21) Tourism competitiveness = total tourism revenue; (22) Tourism capacity building costs (environment, facilities, informatization, etc.) = 0. 5 × tourism investment cost; (23) The number of people attracted by the scenic spot’s own ability = 0. 001 × tourism capacity building cost (environment, facilities, informatization, etc.); (24) Economic benefits generated by tourists = Economic benefits generated by unit tourists Benefit × total number of tourists; (25) Losses caused by tourists = losses caused by unit tourists × total number of tourists.

Figure 1 System Causality Diagram

Figure 2 System Flow Diagram

Some model simulation results are as follows: (1) The total number of tourists from years 0 to 10 Changes Figure 3: Changes in the total number of tourists

According to the simulation results, it can be seen that the growth of the total number of tourists began to moderate in the eighth year, that is, the market gradually began to become saturated, and the system began to gradually stabilize.

(2) Changes in the economic benefits generated by tourists from years 0 to 10:

Figure 4 Changes in the economic benefits generated by tourists

(3) Changes in losses caused by tourists to scenic spots from years 0 to 10:

Figure 5 Changes in losses caused by tourists

(4) Total tourism revenue from years 0 to 10 Changes:

Figure 6 Changes in total tourism revenue

Through the above, some basic situations in the tourism revenue increase system, such as the total number of tourists and total tourism revenue, were simulated and analyzed. This study provides a more realistic simulation environment for tourism analysis and economic benefit investment.

Name: Huang Wufeng

Student ID: 2008101172

Department: Department of Science

Major: 08 Information and Computing Science