In the last decades, there has been a deceleration in the rates of poverty reduction, suggesting that traditional redistributive approaches to poverty mitigation could be losing effectiveness, and alternative insights to advance the number one UN Sustainable Development Goal are required. The criminalization of poor people has been denounced by several NGOs, and an increasing number of voices suggest that discrimination against the poor (a phenomenon known as \emph{aporophobia}) could be an impediment to mitigating poverty. In this paper, we present the novel Aporophobia Agent-Based Model (AABM) to provide evidence of the correlation between aporophobia and poverty computationally. We present our use case built with real-world demographic data and poverty-mitigation public policies (either enforced or under parliamentary discussion) for the city of Barcelona. We classify policies as discriminatory or non-discriminatory against the poor, with the support of specialized NGOs, and we observe the results in the AABM in terms of the impact on wealth inequality. The simulation provides evidence of the relationship between aporophobia and the increase of wealth inequality levels, paving the way for a new generation of poverty reduction policies that act on discrimination and tackle poverty as a societal problem (not only a problem of the poor).

翻译：近几十年来，贫困减缓速度有所下降，表明传统的再分配式减贫方法可能正在失去效力，需要采取新的思路来推进联合国可持续发展目标中的首要任务。多家非政府组织指责对贫困人群的污名化，且越来越多声音指出，针对贫困人群的歧视（即“贫困恐惧症”现象）可能成为减贫障碍。本文提出新颖的贫困恐惧症智能体模型（AABM），通过计算手段证明贫困恐惧症与贫困之间的相关性。我们基于巴塞罗那市的真实人口数据及现行或议会讨论中的减贫公共政策构建了应用案例，并在专业非政府组织支持下，将政策分为歧视性政策与非歧视性政策两类，通过AABM观测其对财富不平等的影响。模拟结果证实了贫困恐惧症与财富不平等加剧之间的关联，为新一代从歧视入手、将贫困视为社会问题的减贫政策（而不仅是贫困人群自身问题）奠定了基础。