The deceleration of global poverty reduction in the last decades suggests that traditional redistribution policies are losing their effectiveness. Alternative ways to work towards the #1 United Nations Sustainable Development Goal (poverty eradication) are required. NGOs have insistingly denounced the criminalization of poverty, and the social science literature suggests that discrimination against the poor (a phenomenon known as aporophobia) could constitute a brake to the fight against poverty. This paper describes a proposal for an agent-based model to examine the impact that aporophobia at the institutional level has on poverty levels. This aporophobia agent-based model (AABM) will first be applied to a case study in the city of Barcelona. The regulatory environment is central to the model, since aporophobia has been identified in the legal framework. The AABM presented in this paper constitutes a cornerstone to obtain empirical evidence, in a non-invasive way, on the causal relationship between aporophobia and poverty levels. The simulations that will be generated based on the AABM have the potential to inform a new generation of poverty reduction policies, which act not only on the redistribution of wealth but also on the discrimination of the poor.

翻译：过去几十年来，全球减贫速度的放缓表明，传统的再分配政策正在失去效力。我们需要寻求其他途径来实现联合国第一号可持续发展目标（消除贫困）。非政府组织不断谴责对贫困的定罪化，而社会科学文献表明，针对穷人的歧视（即仇贫现象）可能构成减贫斗争的阻碍。本文提出了一种基于智能体模型的方案，用以考察制度层面上的仇贫现象对贫困水平的影响。该仇贫现象智能体模型（AABM）将首先应用于巴塞罗那市的案例研究。由于仇贫现象已在法律框架中被识别，监管环境是该模型的核心。本文提出的AABM构成了以非侵入方式获取仇贫现象与贫困水平之间因果关系的经验证据的基石。基于该模型生成的模拟有望为新一代减贫政策提供信息，这些政策不仅作用于财富的再分配，也作用于对穷人的歧视问题。