3.3. Modelling Symbols in Use: A Closer Look at Drug-related Crime
To see all the modelling symbols in use we revisit the problem of drug-related crime from Chapter 2.[] Recall the original intention was to identify systemic factors that explain growth in drug-related crime despite the drug busting efforts of police. Figure 3.7 shows the sectors of society involved and one important feedback loop, a reinforcing crime spiral. There are four sectors: the community itself (suffering from crime), the police department (trying to control crime), the street market for drugs and the world of the drug user. A simulatable model of this situation represents the stock accumulations, causal links, information flows and operating policies that lie behind the reinforcing crime spiral. The model is presented sector by sector.
[] Another way to gain familiarity with the modelling symbols is to work through the iThink tutorial in the CD folder for Chapter 3. You will build a tiny model of population dynamics, develop equations and simulate it. Please bear in mind that you are unable to save the model because the license for the software on the book's CD does not allow newly-built models to be saved. But that doesn't matter in this exercise – the point is to gain familiarity with symbols and conventions of visual modelling in system dynamics.
Figure 3.8 shows the causal links in the community. The community is concerned about drug-related crime and raises its collective concern through a call for police action. Notice that each concept is accompanied by units of measure that help ground the model and subsequently aid quantification. The search for practical and consistent units of measure is an important modelling and thinking discipline. Drug-related crime is expressed as incidents per month. A practical measure of the community's 'call for police action' is complaints per month. The link here is the same as in the causal loop diagram, but the difference in units between the cause and effect shows the need for another concept, community sensitivity to crime, to operationalise the original link. Community sensitivity can be thought of in terms of complaints per incident. A community that is very sensitive to crime will generate more complaints per incident than a community resigned or indifferent to crime, thereby bringing to bear more pressure for police action.
Figure 3.7. Drug-related crime – sectors and causal loop
Figure 3.8. Community reaction to crime
Figure 3.9. Inside the Police Department
Figure 3.9 takes us inside the police department. Notice that the police department converts the call for police action (in complaints per month) into drug seizures (in kg per month). In the causal loop diagram, this conversion of complaints into seizures is achieved in a single causal link. The stock-and-flow diagram reveals the operating detail behind the link. In the middle of the diagram there is a stock accumulation representing the number of police allocated to drug busting. The policy controlling the allocation of police is in the top half of the diagram and is a typical goal-seeking adjustment process. Call for police action leads to an indicated allocation of police – the number of police officers deemed necessary to deal with the drug problem. This goal is implemented by reallocating police between duties. The change in allocation of police (measured in police officers per month) depends on the difference between the indicated allocation, the current number of police allocated to drug busting and the time it takes to move staff. In reality the process of reallocating police takes time and organisational effort, all of which is captured by the stock and flow network for number of police. Incidentally, the 'cloud' on the left of this network represents the total pool of police in the department, currently working on other duties, who might be called into drug busting.[] The amount of drug seizures is proportional to the number of police allocated. To operationalise this link it is necessary to introduce a new concept 'police effectiveness in drug busting' measured in kilograms per officer per month – a kind of drug busting productivity.
[] By using a cloud symbol we assume that the pool of police officers assigned to duties other than drug busting is outside the boundary of the model. If for some reason we wanted to track the number of officers in this pool, then the cloud symbol would be replaced by a stock accumulation with its own initial number of officers. It would then be apparent from the diagram that shifting more officers to drug-busting reduces the number available to work on other duties.
Figure 3.10. The street market for drugs
The street market for drugs adjusts the street price of drugs according to the supply and demand of drugs, as shown in Figure 3.10. The supply of drugs on the street is equal to the total supply of drugs less drug seizures. The drug supply gap is the difference between demand for drugs and supply on the street (all measured in kilograms per month). The existence of a supply gap generates pressure for price change, which in turn drives the change in street price that accumulates in the street price (measured in £ per kilogram). The pricing 'policy' here is informal – an invisible hand. Note there is no target price. The price level continues to change as long as there is a difference between supply and demand.
In Figure 3.11 we enter the world of drug-dependent users with an addiction and craving that must be satisfied at all costs, even if it involves crime. Addicts need funds (in £ per month) to satisfy their addiction. In a given geographical region the funds required by addicts are proportional to their collective demand for drugs (in kilograms per month) and the prevailing street price (in £ per kilogram). Drug-related crime is the amount of crime (in incidents per month) necessary to raise the funds required. This conversion of funds into crime depends also on the average yield per crime incident (measured in £ per incident), which is a measure of criminal productivity and reflects the wealth of the burgled community.
Figure 3.11. World of the drug users
