The Crime & Punishment Model shares some characteristics of Schelling’s Segregation Model and J.M. Dalle’s Model in terms of local interactions and heterogeneity but also has different characteristics, such as learning/ individual memory, two-way interaction, individuality, and transition of agents. The aim of the model is to show the different outcomes of the different policies in a given time period (i.e. in turn.) and explore the possibilities of reaching a “criminal-free” world.
Those four groups of agents, namely “Policemen,” “Criminals,” “Warners,” and “Ignorers,” have some common properties, such as recording/knowing with how many agents from each group they interact through the simulation and thus behave accordingly. And, if certain conditions are met, some agents from one group (i.e., a “Criminal”) transform into another group (i.e., an “Ignorer” or vice versa). The Policemen do not transform into another type unless the Policemen/Criminals ratio is set to a specific percentage as a policy option.
At the initial step, n number of agents from those four groups are randomly placed onto the matrix. The user sets the number of agents from each group (or default by the program).
Four policy options are provided for the user, and the user must choose one at the initial step. She cannot change those options after the simulation is started:
(a) Fix POL/CRM Rate – (STRATEGY 1) Normally, Policemen (POL) do not transform into other agents throughout the simulation process. However, if this option is chosen, the program adjusts the number of policemen to the ratio set; first by converting the required number of Warners (WRN) into Policemen, and if there are no Warners, left Ignorers(IGN) are converted into Policemen before the next turn.
(b) Increase Efficiency – (STRATEGY 2) by reducing the CRMPOL parameter, the frequency of correction of criminals is increased: Because being merely punished once is not enough for a Criminal to give up and become an Ignorer. Therefore, the effectiveness ratio sets the ratio or the number of times (out of 100) to be punished to be “corrected” (=to become an Ignorer). Increasing effectiveness has some costs. (For example, if we assume that in real life, deterrence of the punishment is directly related to the number of years served in jail, and as those numbers are increased, the costs of the government increase…). This is a kind of variable cost and changes according to the number of Criminals punished. So, it increases in line with the number of Criminals.
(c) Increase Public Awareness – (STRATEGY 3): Increase public awareness – This option aims to create public awareness; thus, Ignorers are transformed into Warners throughout the simulation. This is done by decreasing the parameter IGNPOL (which will be explained below). To raise public awareness, the executives must also bear some costs, comparatively less than the costs of applying policies (a) and (b). This cost occurs each turn, as long as there are criminals, but it is independent of the number of agents of any type.
(d) Do Nothing – The interactions will solely be conducted according to the interaction table.
Main Features of the Model
- Heterogeneity: There are 4 groups of agents: Policemen, Criminals, Warners and Ignorers.
- Learning: Each group has some common characteristics, such as memory/ability to learn.
- Transition: An agent from one type can transform into another one.
- Local Interaction: There are rule-based and two-way local interactions for each type of agent.
- Global Interaction: If the POL/CRM ratio is fixed, the number of criminals affects the transition of Policemen, and in addition, costs are calculated globally.
- Individuality: Individuality matters since agents learn, interact, and transform individually.
- Countervailing Forces: Theoretically, a policeman may transform into a warner, then to an ignore, and then to a criminal, or vice versa. And in a certain policy option, an increase in the number of criminals is balanced with an increase in the number of policemen.
For a detailed documentation on the model, the link is as follows: