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NetLogo User Community Models

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[screen shot]

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If clicking does not initiate a download, try right clicking or control clicking and choosing "Save" or "Download".(The run link is disabled for this model because it was made in a version prior to NetLogo 6.0, which NetLogo Web requires.)

## WHAT IS IT?

This work simulates a market influenced by Mafia. The white agents are the storeowners; the black are the mafious and the blue represents the police ones. The patch is a simplification of the market, where the storeowners can obtain profits or not. When a mafious finds a storeowner, he tries to get money from this. When the police find a mafious, this one must pay a bribe for the police to don't be arrested. This simulation intends to find a way in wich the power of Mafia can be erased by social norms and new values in a community.

## HOW IT WORKS

The endowment-rate slider goes from 0 to 100, and intends to simulate the market context, where 0 is an optimist prediction to the market, in wich is easy to get profits, and 100 represents a market in crisis, where is difficult to get money. This simplification is usefull to understand what happens in a market controlled by Mafia in different economic contexts.
The storeowners-thrust-in-govs-ability-to-fight-mafia slider represent the social norms included in the model; it means that the more a population believe in their government, the more it can refuse to pay mafious and live under a parallel power.
The police-power slider represent the government intervention by itself and goes from -10 to 10. In the case that the government is engaged in help the people, the slider get positive non-null values, and the police fight against Mafia. Otherwise, the police can be corrupted and act with Mafia to increase its power in the market.
The Black Wealth graph shows the monetary power of Mafia in the market during the interactions.
The Money Flow graph represents the flow of money of each agent in the model: the Mafia money, the storeowners' profits and the police's bribes.
When the storeowners pay the mafious, they lost a part of their profits, but they get the advantadge of new profits patches growing around them. When they don't pay the mafious, they lost a part of the profits, and during one tick of interaction the green patch of profits don't grow arond them. This intends to represent the motivation in wich a storeowner pays a mafious, to get advantages in the market. When a storeowner have sufficient profit, it 'reproduces', what means that new people join in the market.
The new generations are born with a endogenous probability of refusal to pay the mafious, linked in the belief that the government protection is effective.

## HOW TO USE IT

The model way of use is simple, with easy commands in the interface.

## THINGS TO NOTICE

The user will see that in a corrupted market, initially the mafia's money increase quickly, but starts to decrease by the exit of storeowners from the market (death of the turtle by loosing profits till less than 0). It means that the effect of Mafia in a market is potentially nocive, in the way that does not spur the join of new agents in this althought the advantage of preferential profits, what suggests that a market cannot develope under this kind of control.

## THINGS TO TRY

Trying to change the signal of the police-power slider and the intensity of the storeowners-thrust-in-govs-ability-to-fight-mafia slider can show the impact of social norms in a population, and represent the importance of the joint work of government, social norms and shifts of values to fight against a parallel power.

## EXTENDING THE MODEL

Maybe introducing concurrence and cooperation among the storeowners could let the maket less simple and more real.

## NETLOGO FEATURES

The model was constructed with simple commands, but as concatenated in a non-trivial way to represent market interactions, that can be considered relatively complex.

## RELATED MODELS

Wolf and Sheep Predation model; Artificial Neural Net model.

## CREDITS AND REFERENCES

Adrian Ordemann; Benedito Faustinoni.
Università degli Studi di Torino. 2013.

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