Abstract
In this study, a second-order adaptive social-cognitive agent model is introduced to examine prisoner recidivism. For comparison between different kinds of prisons and prison policies, recidivism rates from Norway and the USA are used. Two scenarios were used to model the effects of environmental, prison-related, and personal influences on recidivism rates. The presented adaptive social-cognitive agent model is based on a second-order reified network model. The model allows to computationally explore the effects on prisoner recidivism and the learning process for a prisoner’s social-cognitive state of mind as a main determinant of recidivism risk.
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Melman, D., Ploeger, J.B., Treur, J. (2020). A Second-Order Adaptive Social-Cognitive Agent Model for Prisoner Recidivism. In: De La Prieta, F., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection. PAAMS 2020. Communications in Computer and Information Science, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51999-5_13
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