Department of Biomathematics, University of California Los Angeles, Los Angeles, California, United States of America.
Department of Biomathematics, University of California Los Angeles, Los Angeles, California, United States of America ; Department of Mathematics, California State University at Northridge, Los Angeles, California, United States of America.
PLoS One. 2014 Jan 16;9(1):e85531. doi: 10.1371/journal.pone.0085531. eCollection 2014.
Motivated by recent efforts by the criminal justice system to treat and rehabilitate nonviolent offenders rather than focusing solely on their punishment, we introduce an evolutionary game theoretic model to study the effects of "carrot and stick" intervention programs on criminal recidivism. We use stochastic simulations to study the evolution of a population where individuals may commit crimes depending on their past history, surrounding environment and, in the case of recidivists, on any counseling, educational or training programs available to them after being punished for their previous crimes. These sociological factors are embodied by effective parameters that determine the decision making probabilities. Players may decide to permanently reform or continue engaging in criminal activity, eventually reaching a state where they are considered incorrigible. Depending on parameter choices, the outcome of the game is a society with a majority of virtuous, rehabilitated citizens or incorrigibles. Since total resources may be limited, we constrain the combined punishment and rehabilitation costs per crime to be fixed, so that increasing one effort will necessarily decrease the other. We find that the most successful strategy in reducing crime is to optimally allocate resources so that after being punished, criminals experience impactful intervention programs, especially during the first stages of their return to society. Excessively harsh or lenient punishments are less effective. We also develop a system of coupled ordinary differential equations with memory effects to give a qualitative description of our simulated societal dynamics. We discuss our findings and sociological implications.
受刑事司法系统最近采取的治疗和改造非暴力罪犯而不仅仅关注惩罚他们的努力的启发，我们引入了一个进化博弈论模型来研究“胡萝卜加大棒”干预计划对犯罪再犯的影响。我们使用随机模拟来研究一个群体的演化，其中个体可能会根据他们的过去历史、周围环境以及在累犯的情况下，根据他们因以前的罪行受到惩罚后可获得的任何咨询、教育或培训计划来犯罪。这些社会学因素体现在决定决策概率的有效参数中。玩家可以决定永久改过自新或继续从事犯罪活动，最终达到被认为不可救药的状态。根据参数选择，游戏的结果是一个大多数是有道德的、改过自新的公民或不可救药的社会。由于总资源可能有限，我们将每起犯罪的惩罚和康复成本的总和限制为固定，因此增加一项努力必然会减少另一项。我们发现，减少犯罪最成功的策略是优化资源分配，以便罪犯在受到惩罚后，特别是在他们重返社会的早期阶段，能够体验到有影响力的干预计划。过于严厉或宽松的惩罚效果较差。我们还开发了一个具有记忆效应的耦合常微分方程系统，以定性描述我们模拟的社会动态。我们讨论了我们的发现和社会学意义。
