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Simulation of an Artificial Society with crime and punishment

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

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  1. 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

  2. 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)

  3. 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)

  4. “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

  5. Crime is as old as humankind

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

  7. Modeling crime… and punishment

  8. 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.

  9. Becker’s Utility We add the “honesty” factor as an additional constraint

  10. 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]

  11. 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 !

  12. 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,

  13. Monthly results • Simulation setting: • N=1000, 240 months, Nc=5% • S=r*10*Wv, f=0.25S • Various p0, p1

  14. Crime and punishment: results • averages over 240 months Left: With prison after-effects Right: Without

  15. Wealth and Gini coefficient

  16. The Cost of Penalties

  17. Histograms: Wealth

  18. Correlations

  19. Hysteresis What happens if the probability of punishment changes in time?

  20. 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

  21. 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

  22. 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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