Simulation Strategies to Reduce Recidivism

Part I. Advances in Simulation Modeling for Criminal Justice Planning and Management

1. Planning for the Future of the US Correctional System
April Pattavina, Faye S. Taxman

2. A Case Study in Gaps in Services for Drug-Involved Offenders
Faye S. Taxman, Matthew L. Perdoni

3. The Simulation Modelling Process
Andrew Greasley

4. The Empirical Basis for the RNR Model with an Updated RNR Conceptual Framework
Faye S. Taxman, April Pattavina, Michael S. Caudy, James Byrne, Joseph Durso

Part II. Simulation Inputs for an RNR Model

5. Creating Simulation Parameter Inputs with Existing Data Sources: Estimating Offender Risks, Needs, and Recidivism
Stephanie A. Ainsworth, Faye S. Taxman

6. The Responsivity Principle: Determining the Appropriate Program and Dosage to Match Risk and Needs
Erin L. Crites, Faye S. Taxman

7. Reducing Recidivism Through Correctional Programming: Using Meta-Analysis to Inform the RNR Simulation Tool
Michael S. Caudy, Liansheng Tang, Stephanie A. Ainsworth, Jennifer Lerch, Faye S. Taxman

Part III. Simulation Applications

8. RNR Simulation Tool: A Synthetic Datasets and Its Uses for Policy Simulations
Avinash Bhati, Erin L. Crites, Faye S. Taxman

9. A Simulation Modeling Approach for Planning and Costing Jail Diversion Programs for Persons with Mental Illness
David Hughes

10. Using Discrete-Event Simulation Modeling to Estimate the Impact of RNR Program Implementation on Recidivism Levels
April Pattavina, Faye S. Taxman

Part IV. Conclusion

11. Risk-Need-Responsivity (RNR): Leading Towards Another Generation of the Model
Faye S. Taxman, Michael S. Caudy, April Pattavina

Avainsanat: Social Sciences, Criminology & Criminal Justice, Social Policy