Multi-criteria classification of reward collaboration proposals
开发了一种自动分类奖励合作提案的机制，通过多准则方法提高透明度，帮助巴西公共安全系统打击有组织犯罪，兼顾被拘留者、警方和社区的利益。
This article develops a mechanism for automatically classifying rewarded collaboration proposals. The research’s purpose is to increase transparency in the rewarded collaboration process, thereby inviting more collaboration proposals, to aid in the fight against criminal organizations. The research focuses on critical facets of the public security system and of the organized crime in Brazil. Through rewarded collaboration, a new approach to plea bargaining is achieved that helps detect, disrupt, and ultimately dismantle illicit operations. This multi-criteria approach enables the consideration of the interests of detainees, the priorities of police institutions, and the perspective of the community. This approach results in the formation of a holistic understanding of the issue, taking into account the costs and benefits to society of punishing defendants whose guilt can be established. Composition of Probabilistic Preferences Trichotomic is the multi-criteria method employed to take imprecision into consideration while performing classification into predetermined classes. It enables the evaluation of each proposal independently. This boosts the system’s objectivity and consequently its attractiveness. Taking the interaction between the criteria into consideration, the analysis naturally applies to any number of evaluation criteria and individuals involved in the investigated crimes. Novel forms of interaction modeling are compared in practical instances.