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view/download model file: criminality_prog_18.nlogo
Criminal behavior has been explained by rational or normative arguments. This is a game theoretical framework of criminal behavior integrating both concepts. Specifically the modeling includes three factors which are the gain from criminality, norm abiding behavior and social pressure from peers.
Basically each period, a sub-population of agents (slider = acting-agent) choose an action $red = taking a criminal action$ or $green = staying honest$. An agent i choose the action having the maximum average utility. Utility is composed of the relative gain from criminality (G) and an emotional part depending of the actions of other players in agent’s i neighborhood.
Other player Stay honest Take an illegal action Stay honest (green) 0 -X agent i Take an illegal action (red) G - Y G
X is a idiosyncratic random variable for agent i with a Bernouilli distribution with probability of succes (X=1) = p. If not X=0. X= 0 corresponds thus to an individual not sensitive to criminal pressure whereas X=1 to sensitive individuals. The higher p the more individual sensitive to criminals’ pressure. p could thus mimick the impact of population concentration on parental control. The higher population concentration the less control. Think on residential areas versus suburbs. Y has a uniform distribution on [0,1] and corresponds to the guilt an agent i has by taking a illegal action if he faces a honest neighbor.
Average utility is computed by taking the effective proportion of honest agents in i’s neighborhood. The optimal action is the one maximizing average utility. Each period, a number of acting-agents choose their action given the rate of criminality in their neighborhood. They are happy if criminality in their neighborhood is less than their tolerance rate. If agents are allowed to move, then an unhappy agent moves to a new place with a lower criminality rate if available. A new period starts.
Honest agents are happy if the rate of criminality in their neighborhood is less than their tolerance for criminality. They are unhappy otherwise. Criminals are happy if the rate of criminality in their neighborhood is between a minimal and maximal tolerance rate for criminality. The minimal and maximal tolerance rate for criminals is set to mimick the fact that criminals may be interested in having some criminals around them (to create a ghetto for criminality or to escape police attention for example) but not to much (because it may end up in rivalry about competitive groups for example). In a first step we fix the minimal tolerance rate for criminals to zero and the maximal.
Only unhappy agents move if move is allowed. Honests agents move to a nex place if criminality there is lower than criminality of the current place. Criminals move to a new place if criminality there is within their minimum and maximum tolerance rate of criminals as given by sliders Crm-tol-min and Crim-tol-max.
The general procedure for one period is the following:
You can choose to let criminal move or not (button move on or off). Fix the relative gain from criminality: if positive then criminality pays more than honesty. Choose p which is the proportion of agent sensitive to social pressure from criminals. p may be thought as a good proxy for the degree of parental control relative to the quality of the living environments. For example, in a suburb with high rises it may be more difficult to exert effective parental control and p may then be higher than in residential areas.
Polymorphic equilibria, i.e. coexistence of criminals and honest people, may exist with the result that higher gain from criminality, lower adherence to the legal norm or higher social pressure from peers never lowers criminality. In addition, the spatial structure of interaction is one factor of segregative criminality. Interactions within a fixed small number of neighbors leads to higher spatial inequality than within larger groups.
Change the neighborhood size of agents:
- look at the effect of neighborhood size on the level of honests compared to the expected equilibrium
- look at the segregation level as a function of neighborhood size.
- allow agents to move and look at segregation and criminality levels
- test different levels of social pressure p or gain from criminality G.
This section could point out any especially interesting or unusual features of NetLogo that the model makes use of, particularly in the Procedures tab. It might also point out places where workarounds were needed because of missing features. Acting agents are the number of agents plying the game each period.
Segregated cooperation in a prisoner’s dilemma.
The segregation and interaction procedure has been inspired from Sebastian Grauwin
http://ccl.northwestern.edu/netlogo/models/community/Segreg-vs-coord . All errors my own.
seed-procedure by Uri Wilensky.