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Nearly 50,000 drug offenders are annually offered treatment in lieu of traditional sentencing as a result of California's voter-approved Substance Abuse and Crime Prevention Act (SACPA). The challenges SACPA currently faces and their potential solutions may be similar to those faced by diversion programs in other regions. Stakeholders responsible for SACPA implementation were surveyed about the impact of recent budget cuts and were asked for suggestions on how to improve SACPA. Budget cuts undermined portions of the law that stakeholders agreed with and stakeholders themselves sought to circumvent portions of the law that they disagreed with. However, two areas of broad stakeholder agreement emerged: 1) funding should be increased, and 2) the option of brief jail sanctions for noncompliant participants should be made available. To address current challenges, key stakeholder groups should collectively draft and propose a revised version of the law using these areas of agreement as a foundation.

This article presents a neural network simulation of the out-grouphomogeneity effect (OHE). The model is a feedback network with delta-rule learning that has been previouslv used to simulate other aspects of stereotype learning, as well as causal learning and reasoning, and human memory. This simulation achieves 2 goals: First, we show that the model could successfully simulate the OHE. We argue that this is due to the error-correcting nature of delta-rule learning. Second. we show that each of aspects of the simulation influences the size of the OHE: (a) the ratio of in-group to out-group size, (b) the overall population size, (c) the learning rate, (d) the decay rate for weights, and (e) increased learning for extreme cases. The psychological relevance of these parameters and ways to study them are presented. Advantages of the model in terms of breadth of coverage for studying social cognitive phenomena are discussed.