Skip to main content
SpringerLink
Log in

Using simulation modeling to evaluate sentencing reform in California: choosing the future

  • Published:
Journal of Experimental Criminology Aims and scope Submit manuscript

Abstract

Criminal sentencing reforms that have as their ostensible goal the protection of the public through the mechanism of selective incapacitation have proliferated in recent years. The most prominent of these types of reforms are the “Three Strikes” laws. Because these changes to sentencing policy work by extending the term of incarceration for affected offenders, rather than by changing the rate of incarceration, many years must pass before the effects of these kinds of changes can be measured and evaluated by conventional statistical methods. Data-validated dynamic systems simulation modeling offers the analyst an opportunity to evaluate prospectively the effects of such changes on prison populations. In addition to providing descriptive and evaluative information about the likely consequences of these reforms to the compositional dynamics of prison populations, dynamic systems simulation modeling also affords the analyst the opportunity to experiment upon the system to examine prospectively the likely effects of policy changes. In this paper, simulation models of the California criminal justice system are created and validated with historical data in order to provide a plausible baseline upon which to base future projections. Different policy scenarios are simulated to the year 2030 to assess experimentally the likely consequences to prison populations and to evaluate how well these policies target the “dangerous offenders” proponents that these policies promise to remove from society via incarceration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. The vast majority of these (over 80,000 offenders) were sentenced under the “Second Strike” provisions of the law. Third Strikers have thus far comprised fewer than 10% of affected offenders (LAO 2005).

  2. Zimring et al. (2001) assert that the broad impacts of the law were intended, not unintended (p. 23). I interpret the coming of the Three-Strikes law differently from these authors (see Auerhahn 2003: Chap. 3), though I do agree with the assertion of these authors that Three Strikes is “a penal practice without a theory” (Zimring et al. 2001: 7).

  3. It is important to note that most of the empirical research on selective incapacitation has evaluated the concept of selective incapacitation rather than specific policies that manifest this objective (e.g., Greenwood and Abrahamse 1982; Sherman et al. 1997: 9–17).

  4. Dynamic systems models have also been used to simulate various aspects of illicit drug use. Some of these models focus on large scale drug-distribution networks (Childress 1994a, b; Dombey-Moore et al. 1994), while others model populations of drug users (Jacobsen and Hanneman 1992; Homer 1993). This technique has also been applied to the evaluation of drug-control policy (Tragler et al. 2001; Rydell et al. 1996).

  5. The proceedings of this conference can be viewed at http://www.ojp.usdoj.gov/nij/ events/or/agenda.htm.

  6. This perspective derives, in part, from Thomas Schelling’s (1978) observation that any number of different processes may, in fact, lead to similar outcomes.

  7. Berkeley Madonna software (available for demonstration and purchase at www.berkeleymadonna.com) was used to create the simulation models.

  8. In modeling the system, a remarkable amount of consistency was observed in the timing of these changes. The ‘switching point’ for many of the rate modifiers—at all levels of the system, from arrest through incarceration—lay in the time period between 1985 and 1987. This should not come as a terrific surprise to anyone with even a cursory understanding of the history of American criminal justice policy reform as relates to sentencing.

  9. Explaining these differences, while a worthwhile endeavor, is beyond the scope of this analysis. Many researchers do attempt to explain them (e.g., Bridges and Steen 1998; Irwin 1985; Myers 1987; Swigert and Farrell 1976; Farrell and Holmes 1991), and the reader interested in the reasons such differences exist is referred to these authors For my purposes, these differences are merely noted as ‘social facts’ and incorporated into the modeling strategy in an effort to reproduce system dynamics as accurately as possible.

  10. The model is structured such that the ‘arrested’ population state clears on each iteration of the model. This means that 100% of the arrested population at t1 occupies a different state or leaves the model at t2. The general (non-criminal justice system involved) population is set up as a source/sink, as indicated here by the ellipse. In dynamic systems modeling, sources and sinks can be thought of as the boundaries of the model, in that they represent unlimited supplies of a particular input or resource (individuals who may become involved with the criminal justice system), and also serve as ‘absorbers’ of resources as they exit the model (individuals leaving the criminal justice system by varied mechanisms, such as charge dismissal, acquittal, or completion of probation or other sentences). Because of this, the equation generating the arrested system state takes a slightly different form from that of the other population states. A new arrested population is generated at each time-step by taking the initial value of the arrested population, in each of the 450 sub-models, and applying a multiplier to simulate growth or decline over time.

  11. Chaiken and Chaiken’s (1984) interpretations of the Rand data are occasionally contradictory and confusing. At times, they indicate a belief that there is a life-course progression of seriousness on the part of offenders (pp. 205–206); however, they note that the violent predators tend to be younger than other offenders (p. 209). Similarly, these authors emphasize, at various points, that several of the identified types of criminals do appear to be highly specialized in their modes of criminal behavior. However, the ‘violent predators,’ who commit the greatest proportion of crimes in the sample do not show such evidence of specialization. I find Wright and Rossi’s interpretation of similar findings in their research (1986: 76) a much more satisfying explanation of the patterns that emerge in the Rand data.

  12. The CDC was re-named the California Department of Corrections and Rehabilitation in July 2005.

  13. Some estimation was required for the prison and parole population subgroups, due to differences in the way in which offender criminal history information is tracked at the CDC and the requirements of the model. The CDC classifies offenders into one of four status categories: offenders who are not under the jurisdiction of the CDC at the time of offense commission (A); returned to custody for a new offense while on parole (B); returned to custody for technical violations (C); and returned to custody while in the process of mounting legal challenges to this return (D). For the purposes of the simulation model, all of these offenders are equivalent insofar as they occupy a model population state (e.g., prison, parole). The difficulty arises in that offenders designated as A status have no recorded criminal history information in the CDC system. This may be misleading, as while they are treated as a new entrant to the system, this is not necessarily the case. Offenders who have completed a term of parole or a prison sentence and are thus no longer under the supervision of the CDC would be classified as A status if arrested for a new crime. For this reason, offender criminal histories for Astatus offenders were estimated from the proportions of equivalent offenders (with respect to age, gender, race, and conviction offense) in the data provided by the CDC for offenders in other statuses (B, C, and D).

  14. In simulation modeling terms, this state functions as a sink. See note 10.

  15. This period spans the earliest and latest dates for which detailed complete electronic validation data were available from California state criminal justice agencies at the time of programming the simulation models.

  16. All system populations in the complete model specification met this standard; space limitations prevent me from reproducing them all here (see Auerhahn 2003: 105–107)

  17. Other researchers have reached similar conclusions. Caulkins (2001) found little substantive difference between different “strike-zones,” concluding that costs associated with incarceration far exceeded benefits for different variants of California’s Three-Strikes law. Blokland and Nieuwbeerta (2007) concluded much the same in their study of selective incapacitation scenarios in the Netherlands.

  18. Because the data are presented in 5-year intervals, it may appear at first glance to be a rapid jump from the previous data point. However, the proportional representation of women in the California prison population maintains a steady increase over the entire period under study, both retrospectively and prospectively.

References

  • Auerhahn, K. (1999). Selective incapacitation and the problem of prediction. Criminology, 37(4), 703–733.

    Article  Google Scholar 

  • Auerhahn, K. (2002). Selective incapacitation, three strikes, and the problem of aging prison populations. Criminology and Public Policy, 1, 3.

    Google Scholar 

  • Auerhahn, K. (2003). Selective incapacitation and public policy: Evaluating California’s imprisonment crisis. Albany, NY: SUNY Press.

    Google Scholar 

  • Auerhahn, K. (2004). California’s incarcerated drug offender population yesterday, today, and tomorrow: Evaluating the war on drugs and proposition 36. Journal of Drug Issues, 34(1), 95–120.

    Google Scholar 

  • Austin, J., Clark, J., Hardyman, P., & Henry, D. A. (1999). The impact of ‘three strikes and you’re out. Punishment and Society, 1(2), 131–162.

    Article  Google Scholar 

  • Avi-Itzhak, B., & Shinnar, R. (1973). Quantitative models in crime control. Journal of Criminal Justice, 1, 185–217.

    Article  Google Scholar 

  • Bales, W. D., & Dees, L. G. (1992). Mandatory minimum sentencing in Florida: Past trends and future implications. Crime and Delinquency, 38(3), 309–329.

    Article  Google Scholar 

  • Belkin, J., Blumstein, A., Glass, W., & Lettre, M. (1972). JUSSIM, an interactive computer program and its uses in criminal justice planning. pp. 467–477 in Proceeding, Project SEARCH. Sacramento, CA: Committee on Security and Privacy.

  • Berk, R. A., Fovell, R. D., Schoenberg, F., & Weiss, R. E. (2001). The use of statistical tools for evaluating computer simulations. Climactic Change, 51, 119–130.

    Article  Google Scholar 

  • Blokland, A. A. J., & Nieuwbeerta, P. (2007). Selectively incapacitating frequent offenders: Costs and benefits of various penal scenarios. Journal of Quantitative Criminology, 23, 327–353.

    Article  Google Scholar 

  • Bloom, B., Chesney-Lind, M., & Owen, B. (1994). Women in California prisons: Hidden victims of the war on drugs. San Francisco: Center on Juvenile and Criminal Justice.

    Google Scholar 

  • Blumstein, A., Cohen, J., & Nagin, D. (eds.), (1978). Deterrence and Incapacitation: Estimating the Effects of Criminal Sanctions on Crime Rates. Washington DC: National Academy of Sciences.

  • Blumstein, A., & Graddy, E. (1982). Prevalence and recidivism in index arrests: A feedback model. Law and Society Review, 16(2), 265–290.

    Article  Google Scholar 

  • Blumstein, A., & Koch, G. (1977). A prolegomenon for a macro model for criminal justice planning: JUSSIM III. Criminal Justice Planning.

  • Blumstein, A., & Larson, R. (1969). Models of a total criminal justice system. Operations Research, 17, 199–232.

    Article  Google Scholar 

  • Bridges, G. S., & Crutchfield, R. D. (1988). Law, social standing, and racial disparities in imprisonment. Social Forces, 66(3), 699–724.

    Article  Google Scholar 

  • Bridges, G. S., & Steen, S. (1998). Racial disparities in official assessments of juvenile offenders: Attributional stereotypes as mediatiing mechanisms. American Sociological Review, 63(4), 554–570.

    Article  Google Scholar 

  • Byrne, D. (1997). Simulation – A way forward? Sociological Research Online, 2, 2 http://www.socresonline.org.uk/2/2/4.html Access date: January 30, 2008.

    Article  Google Scholar 

  • California Board of Corrections, Jail Planning and Construction Division (1987). State of the Jails in California: Report #4. Board of Correction: Sacramento, California.

    Google Scholar 

  • California Department of Corrections and Rehabilitation (2005). California Prisoners and Parolees, 2005. Available online at: http://www.cdcr.ca.gov/ReportsResearch /OffenderInfoServices/Annual/CalPris/CALPRISd2005.pdf /OffenderInfoServices/Annual/CalPris/CALPRISd2005.pdf. Accessed June 20, 2007.

  • California Department of Corrections and Rehabilitation (2006). Prison Census Data as of December 31, 2006. Available online at: http://www.cdcr.ca.gov/ReportsResearch/ OffenderInfoServices /Annual/Census/CENSUSd0612.pdf. Access date June 20, 2007.

  • California Department of Justice (1997). California Criminal Justice Profile 1996. Sacramento, CA: Criminal Justice Statistics Center.

    Google Scholar 

  • California Penal Code (2006). Available online at http://www.leginfo.ca.gov/cgi-bin/calawquery?codesection=pen&codebody=&hits=20. Access date: July 25, 2007.

  • Canela-Cacho, J. A., Blumstein, A., & Cohen, J. (1997). Relationship between the offending frequency (l) of imprisoned and free offenders. Criminology, 35(1), 13–175.

    Article  Google Scholar 

  • Cao, J., Bennett, G., & Zhang, K. (2000). Direct execution simulation of load balancing algorithms with real workload distribution. The Journal of Systems and Software, 54, 227–237.

    Article  Google Scholar 

  • Cassidy, R. G. (1985). Modelling a criminal justice system. In D. P. Farrington, & R. Tarling (Eds.), Prediction in Criminology (pp. 193–207). Albany: SUNY Press.

    Google Scholar 

  • Cassidy, R. G., Peters, M. H., & Turner, R. E. (1981). Interrelation of criminal justice system resources and workload. Infor, 19(2), 91–112.

    Google Scholar 

  • Cassidy, R. G., & Turner, R. E. (1978). Criminal justice system behavior. Behavioral Science, 23, 99–108.

    Article  Google Scholar 

  • Caulkins, J. P. (2001). How large should the strike zone be in ‘three strikes and you’re out’ sentencing laws? Journal of Quantitative Criminology, 17, 227–246.

    Article  Google Scholar 

  • Chaiken, J. M., & Chaiken, M. R. (1982). Varieties of Criminal Behavior. Santa Monica, CA: Rand.

    Google Scholar 

  • Chaiken, M. R., & Chaiken, J. (1984). Offender types and public policy. Crime and Delinquency, 30, 195–226.

    Article  Google Scholar 

  • Childress, M. (1994a). A system description of the Marijuana Trade. Santa Monica: RAND.

    Google Scholar 

  • Childress, M. (1994b). A system description of the Heroin Trade. Santa Monica: RAND.

    Google Scholar 

  • Cohen, J. (1978). The incapacitative effects of imprisonment: A critical review of the literature. In A. Blumstein, J. Cohen, & D. Nagin (Eds.), Deterrence and Incapacitation: Estimating the Effects of Criminal Sanctions on Crime Rates (pp. 187–243). Washington DC: National Academy of Sciences.

    Google Scholar 

  • Cohen, J. (1983). Incapacitation as a strategy for crime control: Possibilities and pitfalls. In M. Tonry, & N. Morris (Eds.), Crime and Justice: An Annual Review of Research (Volume 5). Chicago: University of Chicago Press.

    Google Scholar 

  • Cohen, J., Fields, K., Lettre, M., Stafford, R., & Walker, C. (1973). Implementation of the JUSSIM Model in a Criminal Justice Planning Agency. Journal of Research in Crime and Delinquency, vol: 117–131.

  • Costello, A., Garnett, R. and Schiraldi, V. (1991) Parole violators in California: A waste of money, a waste of time. San Francisco: Center on Juvenile and Criminal Justice.

    Google Scholar 

  • Crawford, C., Chiricos, T., & Kleck, G. (1998). Race, racial threat, and sentencing of habitual offenders. Criminology, 36(3), 481–511.

    Article  Google Scholar 

  • Davis, C., Estes, R., & Schiraldi, V. (1996). Three Strikes: The New Apartheid. San Francisco: Center on Juvenile and Criminal Justice.

    Google Scholar 

  • Dombey-Moore, B., Resetar, S., & Childress, M. (1994). A system description of the Cocaine Trade. Santa Monica: RAND.

    Google Scholar 

  • Dorny, C. N. (1993). Understanding dynamic systems: Approaches to modeling, analysis, and design. Englewood Cliffs, New Jersey: Prentice-Hall.

    Google Scholar 

  • Ehler, S., Schiraldi, V., & Lotke, E. (2004). Racial divide: An examination of the impact of California’s three strikes law on African-Americans and Latinos. Washington, DC: Justice Policy Institute.

    Google Scholar 

  • Farrell, R. A., & Holmes, M. D. (1991). The social and cognitive structure of judicial decision-making. Sociological Quarterly, 32, 529–542.

    Article  Google Scholar 

  • Foote, C. (1993). The Prison Population Explosion: California’s Rogue Elephant. San Francisco: Center on Juvenile and Criminal Justice.

    Google Scholar 

  • Forrester, J. W. (1968). Principles of Systems. Cambridge: MIT Press.

    Google Scholar 

  • Forrester, J. W. (1969a). Urban dynamics. Cambridge: MIT Press.

    Google Scholar 

  • Forrester, J. W. (1969b). A deeper knowledge of social systems. Technology Review, 71(6), 21–30.

    Google Scholar 

  • Friedrich, B. (1999). California Ballot Propositions 1944-Present. Compiled and edited by Barbara Friedrich, Head of Reference, Heafey Law Library, Santa Clara University School of Law.

  • Gilchrist, A. (1988). Numerical weather prediction and climate simulation. Reports on Progress in Physics, 51, 1203–1226.

    Article  Google Scholar 

  • Gorton, J., & Boies, J. L. (1999). Sentencing guidelines and racial disparity across time: Pennsylvania Prison Sentences in 1977, 1983, 1992, and 1993. Social Science Quarterly, 80(1), 37.

    Google Scholar 

  • Greenberg, D. (1975). The incapacitative effect of imprisonment: Some estimates. Law and Society Review, 9, 541–580.

    Article  Google Scholar 

  • Greenwood, P. W., & Abrahamse, A. (1982). Selective Incapacitation. Santa Monica: RAND.

    Google Scholar 

  • Hanneman, R. A. (1988). Computer-assisted theory building: Modelling dynamic social systems. Beverly Hills, CA: Sage.

    Google Scholar 

  • Hanneman, R. A., Collins, R., & Mordt, G. (1995). Discovering theory dynamics by computer simulation: Experiments on state legitimacy and imperialist capitalism. In P. Marsden (Ed.), Sociological Methodology 1995 (pp. 1–46). Washington, DC: American Sociological Association.

    Google Scholar 

  • Hanneman, R. A., & Patrick, S. (1997). On the uses of computer-assisted simulation modeling in the social sciences. Sociological Research Online 2:2. Available online at http://www.socresonline.org.uk/socresonline/2/2/5.html.

  • Homer, J. B. (1993). A systems dynamics model for National Cocaine Prevalence. Systems Dynamics Journal, 9(1), 49–78.

    Article  Google Scholar 

  • Irwin, J. (1985). The jail: Managing the underclass in American Society. Berkeley: University of California Press.

    Google Scholar 

  • Jacobsen, C., & Bronson, R. (1985). Simulating violators. Arlington, Virginia: Operations Research Society of America.

    Google Scholar 

  • Jacobsen, C., & Hanneman, R. A. (1992). Illegal drugs: Past, present, and possible futures. Journal of Drug Issues, 22, 105–120.

    Google Scholar 

  • Jones, B. (1998). A History of the California Initiative Process. Sacramento: Secretary of State’s Office.

    Google Scholar 

  • Kizziah, M. (2004). Women in California Prisons. California Women’s Commission. Sacramento, CA. Available online at: http://women.ca.gov/doc.asp?id=143. Access date: July 23, 2006.

  • Kovandzic, T. V. (2001). The impact of Florida’s habitual offender law on crime. Criminology, 39, 179–203.

    Article  Google Scholar 

  • Kovandzic, T. V., Sloan, J. J., & Vieraitis, L. M. (2004). Striking out’ as crime reduction policy: The impact of three strikes laws on crime rates in U.S. Cities. Justice Quarterly, 21(2), 207–239.

    Article  Google Scholar 

  • Legislative Analyst’s Office (1995). The “Three Strikes and You’re Out” Law: A Preliminary Assessment. Available online at http://www.lao.ca.gov/analysis_1995 /3strikes.html. Access date July 16, 2007.

  • Legislative Analyst’s Office (2005). A Primer: Three Strikes – The Impact After More than A Decade. Available online at http://www.lao.ca.gov/2005 /3_Strikes /3_strikes_ 102005.htm. Access date: July 16, 2007.

  • Livingston, M., Stewart, A., & Palk, G. (2005). A Micro-Simulation Model of the Juvenile Justice System in Queensland. Paper presented at the International Congress on Modelling and Simulation, December 2005, Melbourne, Australia. Available online at http://www.mssanz.org.au/modsim05/papers/livingston.pdf. Access date: July 26, 2007.

  • Menzies, R. J., Webster, C. D., & Sepejak, D. S. (1985). The dimensions of dangerousness: Evaluating the accuracy of psychometric predictions of violence among forensic patients. Law and Human Behavior, 9, 49–70.

    Article  Google Scholar 

  • Mihata, K.. (1997). The Persistence of ‘Emergence’. In R. Eve, S. Horsfall, & M. E. Lee (Eds.), Chaos, complexity, and sociology: Myths, models, and theories (pp. 30–38). Thousand Oaks California: Sage.

    Google Scholar 

  • Miles, T. J., & Ludwig, J. (2007). The silence of the Lambdas: Deterring incapacitation research. Journal of Quantitative Criminology, 23, 287–301.

    Article  Google Scholar 

  • Monahan, J. (1981). Predicting violent behavior: An assessment of clinical techniques. Beverly Hills, CA: Sage.

    Google Scholar 

  • Moore, M. H., Estrich, S. R., McGillis, D., & Spelman, W. (1984). Dangerous Offenders: The Elusive Target of Justice. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Myers, M. A. (1987). Economic inequality and discrimination in sentencing. Social Forces, 65(3), 746–766.

    Article  Google Scholar 

  • Nagin, D. (1998). Deterrence and incapacitation. In M. Tonry (Ed.), The handbook of crime and punishment (pp. 345–368). New York: Oxford University Press.

  • Ohlin, L. E., & Remington, F. J. (1993). Discretion in Criminal Justice: The Tension Between Individualization and Uniformity. Albany: SUNY Press.

    Google Scholar 

  • Petee, T. (1994). Recommended for release on recognizance: Factors affecting pretrial release recommendations. Journal of Social Psychology, 134(3), 375–382.

    Google Scholar 

  • Plous, F. K. (2000). Taming the Traffic Tiger. Planning, 66, 22–25.

    Google Scholar 

  • Pursula, M. (1999). Simulation of traffic systems: An overview. Journal of Geographic Information and Decision Analysis, 3, 1–8.

    Google Scholar 

  • Rydell, C. P., Caulkins, J. P., & Everingham, S. S. (1996). Enforcement or treatment? Modeling the relative efficacy of alternatives for controlling cocaine. Operations Research, 44(5), 687–695.

    Google Scholar 

  • Schelling, T. C. (1978). Micromotives and Macrobehavior. New York: W.W. Norton.

    Google Scholar 

  • Schiraldi, V., & Godfrey, M. (1994). Racial Disparities in the Charging of Los Angeles County’s “Third Strike” Cases. San Francisco: Center on Juvenile and Criminal Justice.

    Google Scholar 

  • Schrag, P. (1998). Paradise Lost: California’s Experience, America’s Future. Berkeley: University of California Press.

    Google Scholar 

  • Shannon, R. E. (1975). System simulation: The art and science. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Sherman, L. W., Gottfredson, D., MacKenzie, D., Eck, J., Reuter, P., & Bushway, S. (1997). Preventing crime: What works, what doesn’t, what’s promising: A report to the United States Congress. College Park, MD: University of Maryland. Available online at http://www.ncjrs.gov/works/wholedoc.htm. Access date: July 25, 2007.

  • Sherman, M., & Hawkins, G. (1981). Imprisonment in America: Choosing the future. Chicago and London: University of Chicago Press.

    Google Scholar 

  • Shinnar, S., & Shinnar, R. (1975). The effects of the criminal justice system on the control of crime: A quantitative approach. Law and Society Review, 9, 581–611.

    Article  Google Scholar 

  • Spelman, W. (1994). Criminal incapacitation. New York: Plenum.

    Google Scholar 

  • Steadman, H. J., & Cocozza, J. J. (1974). Careers of the criminally insane. Lexington, MA: D.C. Heath and Company.

    Google Scholar 

  • Steury, E. H., & Frank, N. (1990). Gender bias and pretrial release: More pieces of the puzzle. Journal of Criminal Justice, 18, 417–432.

    Article  Google Scholar 

  • Swigert, V. L., & Farrell, R. A. (1976). Murder, Inequality and the Law: Differential treatment in the legal process. Lexington, MA: D.C. Heath and Company.

    Google Scholar 

  • Thornberry, T. P., & Jacoby, J. E. (1979). The criminally insane: A community follow-up of mentally ill offenders. Chicago: University of Chicago Press.

    Google Scholar 

  • Tragler, G., Caulkins, J. P., & Feichtinger, G. (2001). Optimal dynamic allocation of treatment and enforcement in illicit drug control. Operations Research, 49(3), 352–362.

    Article  Google Scholar 

  • Turner, S. (1999). Profiling inmates in the Los Angeles County jail: Risks, recidivism, and release options. Paper Presented at National Institute of Justice Annual Conference on Criminal Justice Research and Evaluation, July 18–21, 1999, Washington, DC.

  • U.S. Department of Justice, Bureau of Justice Statistics (1993). Survey of Inmates of State Correctional Facilities 1991 [Computer File]. Conducted by U.S. Department of Commerce, Bureau of the Census. ICPSR ed. Ann Arbor, MI: Inter-University Consortium for Political and Social Research [producer and distributor].

  • Wolfgang, M. E., Figlio, R. M., & Sellin, T. (1972). Delinquency in a Birth Cohort. Chicago: University of Chicago Press.

    Google Scholar 

  • Wright, J. D., & Rossi, P. H. (1986). Armed and considered dangerous: A survey of felons and their firearms. Aldine de Gruyter: New York.

    Google Scholar 

  • Zedlewski, E. W. (1987). Making confinement decisions. Washington, DC: National Institute of Justice.

    Google Scholar 

  • Zimring, F. E., & Hawkins, G. (1988). The New Mathematics of Imprisonment. Crime and Delinquency, 34, 425–436.

    Article  Google Scholar 

  • Zimring, F. E., & Hawkins, G. (1995). Incapacitation: Penal Confinement and the Restraint of Crime. New York: Oxford University Press.

    Google Scholar 

  • Zimring, F. E., Hawkins, G., & Kamin, S. (2001). Punishment and democracy: Three strikes and you’re out in California. New York: Oxford University Press.

    Google Scholar 

  • Zulch, G., Rinn, A., & Strate, O. (2001). Dynamic analysis of changes in decisional structures of production systems. International Journal of Production Economics, 69, 239–252.

    Article  Google Scholar 

Data Sources

Download references

Author information

Authors and Affiliations

  1. Department of Criminal Justice, Temple University, Philadelphia, PA, USA

    Kathleen Auerhahn

Authors
  1. Kathleen Auerhahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kathleen Auerhahn.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Auerhahn, K. Using simulation modeling to evaluate sentencing reform in California: choosing the future. J Exp Criminol 4, 241–266 (2008). https://doi.org/10.1007/s11292-008-9056-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11292-008-9056-2

Keywords

Search

Navigation