NetLogo User Community Models
## WHAT IS IT?
This model is an attempt at modelling the behaviour of extorters, their victims and
This model is mainly devoted to analyse the systemic effects of the behaviour of the agents, whose decision making processes are only modelled rather crudely.
## HOW IT WORKS
The world contains several villages (shape: church) in which several enterprises (shape: house) exist which can fall victim to extortion. Extorters (shape: person) can approach
**Shops** serve for providing the population with goods and services of any type. To keep things simple, these goods and services are not detailed. Shops receive payment from their customers and bear the fixed and variable costs of their business by paying into a funds which is evenly distributed to the population which is considered to provide the shops with the necessary supply (both in goods and sevrices) --- to keep things simple here, too, the households of the population receive an equal share of the overall period income of the shops, as if all of them served as suppliers and workers for the shops equally.
Shops are often approached by criminals for "pizzo" which they can refuse at the risk of being severely punished. They can avoid this with a certain propensity to call the police (`denunciation-propensity`), denouncing the extorters and having them prosecuted. When a shop decides to denounce an extorter it joins an "addio-pizzo" movement and makes this fact known to everybody (and to the modeller by changing its colour from blue to red).
In case they are approached by more than one extorter at the same time --- this happens mainly in the initial phase and represents what might have happened when mafia-like organisations first came into being --- the shops decide whom to pay to, and the successful extorter will then protect the shop against the rivalling extorters. When due to extortion and punishment the asset of a shop falls below zero, it is closed and does not participate in the trading process until it is compensated from a funds filled by the confiscated wealth of the extorters.
**Consumers** choose a shop for purchasing their goods. They have a certain propensity `addio-pizzo-threshold` to choose shops belonging to the "addio-pizzo" movement. If their current shop is closed, they choose another, preferably belonging to the "addio-pizzo" movement and preferably having only a small number of customers (the reason for this is two-fold: they want to avoid crowded shops, and shops with only few customers should get a chance to prosper).
**Extorters** start their career as individual criminals and approach the nearest reachable shop, asking for a "pizzo" which is a certain proportion (either `extortion-level-LOW` or `extortion-level-HIGH` depending on the type the extorter belongs to) of its revenue per period. When the shop refuses and the police fails to successfully prosecute the extorter, the latter punishes the shop, taking away a certain proportion of all its assets (either `punishment-severity-LOW` or `punishment-severity-HIGH`, again depending on the type the extorter belongs to). It is understood that all assets of a shop are easily convertible into money, there are no physical assets which could be destroyed. If the police hinders the extorter from punishing, the extorter is brought to jail for a certain number of periods, and all its assets are confiscated and transferred into a funds from which in turn punished shops can be compensated (following a first come--first served principle). If several extorters approach the same shop at the same time, one of them is selected by the shop to protect the shop against rivalling extorters, and the latter subordinate to the former, forming a family and eventually a hierarchy, for instance in case the successful extorter is already subordinate to someone else; if any rivalling extorters already belong to families, the family hierarchy is not changed.
**Police** try to prosecute a denounced extorter and are successful with a certain probability (`prosecution-propensity`) --- which is their only role within the model.
## HOW TO USE IT
Set the `initial-...` sliders before pressing `setup`. This determines the numbers of the different breeds in the world. There is currently no guarantee that arbitrary combinations of the `initial-...` sliders yield reasonable results. The same applies to the sliders which determine wealth and income.
Press `setup` to populate the world with all these agents. As usual, `go` will run the simulation continuously, while `go once` will run one tick.
The `random-seed?` chooser allows to run the model with the same random seed for different parameter combinations. If it is set OFF, every run starts with a new random seed.
If the `debug?` choser is on, output will be written to the file logfile.txt. Each new run is preceded with the date and time of the start of the run unless the file is deleted. NOT TO BE USED in the applet version!
The red and blue extortion links can be switched off with the `show-extortion-links?` chooser.
The six plots show what their headlines suggest.
If the `batch-version?` choser is on, the sliders for `extortion-level-...`, `punishment-severity-...`, `prosecution-propensity`, `denunciation-propensity` and `addio-pizzo-threshold` do not work. Instead random values are assigned to these variables. This choser should only be set on in the BehaviorSpace mode.
## THINGS TO NOTICE
Target enterprises have an initial wealth uniformly distributed between the numbers on the respective min and max sliders, the income of a shop depends on the number of customers currently choosing it.
Extorters have two different extortion levels and two different severity levels (as in Nardin et al.) and a consumption per period which is currently fixed for all extorters at the same level.
Extortion is a proportion (`extortion-level`) of the income per period, punishment for not paying is a proportion (`punishment-severity`) of the total wealth of the target.
Often the local hierarchies agglomerate to one unique hierarchy.
## THINGS TO TRY
The model can be used to find out which of the input parameters guiding the behaviour of the various kinds of agents have the greatest impact on the various output parameters (see Troitzsch 2014). It goes without saying that the model, although inspired by results of empirical research into individual behaviour done within the GLODERS project, has not yet been validated with respect to its output variables, thus it cannot be used to guide policy making, but it gives hints at which empirical macro variables to analyse.
## EXTENDING THE MODEL
The model could be extended by endowing the agents with learning capabilities. Particularly shops and extorters could then optimise their behaviour as a reaction on punishment and sanctions. Consumers could punish and sanction shops by letting shops which pay extortion know explicitly that they left them just because they did not refuse to pay pizzo. And certainly the relations between the public and the shops could be modelled in more detail, e.g. by having individual consumers work for one shop and purchase different kinds of goods from several different shops.
## NETLOGO FEATURES
Nothing special. The code uses recursive functions to determine to which family an extorter belongs and to write up the hierarchy in the output window.
## RELATED MODELS
There is one mafia model in the community (http://ccl.northwestern.edu/netlogo/models/community/Siste%20Mafia33,%20edited%202) which I have not consulted so far.
## CREDITS AND REFERENCES
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007--2013) under grant agreement no. 315874 ([Global dynamics of extortion racket systems, GLODERS](http://www.gloders.eu)). The author thanks his colleagues in this project for fruitful discussions over many years.
The model owes a lot to the Nardin et al. discussion paper and to the discussions within the GLODERS project.
## HOW TO CITE
The model and its results are documented in
Klaus G. Troitzsch (2014) Distribution Effects of Extortion Racket Systems. In: Proceedings of Artificial Economics 2014. Cham etc.: Springer 2014 (to appear in September 2014)
## COPYRIGHT NOTICE
Written by Klaus G. Troitzsch 2013-2014. The model may be used and extended if the source is quoted.
(back to the NetLogo User Community Models)