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Simulation of an Artificial Society with crime and punishment . José Roberto Iglesias, Instituto de Física e Faculdade de Ciências Econômicas, UFRGS, Porto Alegre, Brasil. econofis’10, são paulo, march 2010. Co-authors. Viktoriya Semeshenko (Buenos Aires) Jean-Pierre Nadal (Paris)

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Simulation of an Artificial Society with crime and punishment

José Roberto Iglesias,

Instituto de Física e Faculdade de Ciências Econômicas, UFRGS, Porto Alegre, Brasil

econofis’10, são paulo, march 2010


Co-authors

  • Viktoriya Semeshenko (Buenos Aires)

  • Jean-Pierre Nadal (Paris)

  • Mirta B. Gordon (Grenoble)

  • Gordon, Iglesias, Nadal, Semeshenko, Crime and Punishment: the economic burden of impunity, European Physical Journal B 68, 133–144 (2009)


Crime is as old as humankind

  • “Passional (non-rational) crimes”:

    • Cain and Abel

    • Don José and Carmen

  • Economic crimes

    • Jacob and Esau

    • Ronald Biggs and the Great Train Robbery (8 august 1963)

    • Bernrad Madoff and “financial pyramids”(2009)


  • “Passional (non-rational) crimes”:

    • Cain and Abel

    • Don José and Carmen

  • Economic crimes

    • Jacob and Esau

    • Ronald Biggs and the Great Train Robbery (8 august 1963)

    • Bernrad Madoff and “financial pyramids”(2009)

Crime is as old as humankind


Crime is as old as humankind


Multidisciplinary explanations and “solutions” for crime: philosophy, law, sociology, ethics, economics...


Modeling crime… and punishment


Crime and punishment: the economic burden of impunity

The main hypothesis of the model:

  • Crime, particularly economic crimes – stealing, robbery - has an economic mobile.

  • Each person is characterize by an “honesty” coefficient that, when high, has dissuasive effect of the decision of committing an offense.

  • This “honesty” label is a global characterization of education, risk-aversion, fear, moral standards, fear, etc…

  • The probability of punishment depends on the stolen amount.

  • Offenders are punished, if caught, with fines an prison, both proportional to the stolen amount.

  • The average honesty of the population changes as a function of the perception of the society of the level of control of criminality.


Becker’s Utility

We add the “honesty” factor as an additional constraint


Initial configuration

  • Each agent i is characterized by

    • a monthly wage

    • Wi [Wmin,Wmax]

    • triangular, [1,100]

    • a time-dependent honesty

    • index Hi [Hmin,Hmax]

    • triangular, [0,100]


When and how a crime is committed?

  • Criminal attempts

  • At each attempt

    • select potential criminal k and a victim v

    • success of the attempt depends on k’s honesty and the expected gain or booty* (cf. *G.Becker, P. Shikida)

    • If the crime is performed the offender gets S and the victim losses S

      So that

  • Crime: k robs a victim a random amount

    • S ≤ Kv !


Arrested offenders and punishment

  • Probability of punishment:

    • p0 – … of small offences

    • p1 – … of large offences

  • Offender kgoes to prison for months

  • Retribution: Offender k pays a fine f x S,


Monthly results

  • Simulation setting:

    • N=1000, 240 months, Nc=5%

    • S=r*10*Wv, f=0.25S

    • Various p0, p1


Crime and punishment: results

  • averages over 240 months

Left: With prison after-effects Right: Without


Wealth and Gini coefficient


The Cost of Penalties


Histograms: Wealth


Correlations


Hysteresis

What happens if the probability of punishment changes in time?


Conclusions

  • There is a first order phase transition in the criminality as a function of the probability of punishment

  • This transition is accompanied by changes in the assets and inequality of he full society.

  • Honesty coefficient (education) is an essential ingredient, along with the economic motivation of crime

  • Punishment is not just fines and prison but also economic aftereffects. The after-effects of prison may increase criminality. If prison do not recover the offenders, crime is the only issue


Ongoing and upcoming…

  • Rehabilitation: effects of incarceration on honesty indexes and wages

  • Treatment of recidivism

  • Underlying networks (social and criminal)

  • Comparison with the empirical data

    • Data from Rio Grande do Sul: correlations between size of the city, average education and criminaliry.

    • Shikida interviewed inmates in Paraná: Economic crime is the rule. But criminality seems not to be correlated with poverty

    • It is difficult to obtain the fraction of punished crimes to evaluate p0 and p1


Muito obrigado por vossa atenção

«The degree of civilization in a society can be judged by opening the doors of its prisons»

(F. M. Dostoievski: House of the Death)

www.if.ufrgs.br/~iglesias


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