Abstract
The purpose of a discrete-event simulation model (DES) is to assign individual characteristics to entities and use those characteristics to move and track entities or individuals through a series of discrete events defined in a given system. In this chapter, we present a discrete-event simulation model to examine how adherence to the specifics of the RNR plan might influence the churning of offenders in the prison system and its impact on recidivism outcomes. We use similar offender profiles, program categories, and assumptions as those presented in Chap. 7 to inform the model parameters. The model uses nationally based prison admission data from 1994-2006 to explore the impact of various levesl of RNR implementation. The model offered several scenarios, a baseline model to represent the current system and RNR informed options. The findings from the DES model illustrate the potential impact of the RNR framework on system outcomes. That is, the use of the RNR framework for treatment in prisons can reduce churning or return to prison. Adhering to the RNR principles would result in a 3.4 % reduction in the number of inmates returning to prison nationwide. By focusing on higher-risk offenders, the reduced reincarceration level would be increased to 5.5 %, and by matching offenders to quality programming, reincarceration levels would be reduced by 6.7 % over the baseline model (current practice). The impact of the RNR model has a cumulative effect to reduce reincarceration as people move through various stages in the justice system.
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Notes
- 1.
Some states did not report every year. For those states, the admissions from the previous year for which data were available were applied.
- 2.
Correctional programming can occur in different settings—prison, jail, probation/parole offices, community treatment provider, halfway house, and so on. It was not always possible to differentiate program setting for the meta-analysis (see Caudy et al., this volume).
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Pattavina, A., Taxman, F.S. (2013). Using Discrete-Event Simulation Modeling to Estimate the Impact of RNR Program Implementation on Recidivism Levels. In: Taxman, F., Pattavina, A. (eds) Simulation Strategies to Reduce Recidivism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6188-3_10
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