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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.

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Presentation on theme: "Simulation of an Artificial Society with crime and punishment José Roberto Iglesias, Instituto de Física e Faculdade de Ciências Econômicas, UFRGS, Porto."— Presentation transcript:

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 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)

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 W i  [W min,W max ] triangular, [1,100] a time-dependent honesty index H i  [H min,H max ] 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 ≤ K v !

12 Arrested offenders and punishment  Probability of punishment: p 0 – … of small offences p 1 – … of large offences  Offender k goes to prison for months  Retribution: Offender k pays a fine f x S,

13 Honesty Dynamics If more than 50% of the crimes are punished, the honesty of all agents increases by the end of the month. Otherwise, the honesty decreases by the same proportion. This “correction” of the honesty is induced by the social perception of punishment or impunity. But the unpunished offender diminishes his honesty by  H and the punished offender increases his honesty by  H  H is a parameter of the model

14 Monthly results Simulation setting: N=1000, 240 months, N c =5% S=r*10*W v, f=0.25S Various p 0, p 1

15 Two kind of prison after-effects 1)“Contagion” with the honesty of inmates. So, additional diminution of honesty. 2)Leaving the prison ex-convicted should try to get a new job. The probability is given by 3) Where  is the time the inmate was in prison and is the average time in prison = 6 months 4) If he does not get a new job he earns the minimum wage.

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

17

18 Wealth and Gini coefficient

19 The Cost of Penalties

20 Histograms: Wealth

21 Correlations

22 What happens if p 1 < p 0 ? (Important crimes are punished with lower probability)

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

24 Wage distribution after crime

25 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

26 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

27 Muito obrigado por vossa atenção www.if.ufrgs.br/~iglesias «The degree of civilization in a society can be judged by opening the doors of its prisons» F. M. Dostoievski: House of the Death (F. M. Dostoievski: House of the Death)


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