The diffusion of development: Extending RCTs with agent-based modelling to understand spillovers of development interventions

随机对照试验（RCT）被广泛认为是评估发展干预措施影响的黄金标准。然而，随机对照试验只能帮助发展组织回答他们感兴趣的关键问题：他们的干预措施是否改善了人们的生计？毕竟，实验性的影响评估只能告诉我们，与对照个体相比，治疗个体的表现如何。与此同时，发展组织实施干预措施，希望利益超出其直接受益者。由于不可能向所有需要的人提供援助、培训或其他援助，这些组织往往希望其干预措施产生溢出效应，对整个社区产生持久和自我维持的影响，尽管只针对少数人。此外，有些干预措施似乎需要外溢效应，因为个人需要社区的支持才能做出改变生活的决定。我们建议通过将随机对照试验与基于代理的计算模型（ABM）相结合，改变研究人员和发展组织评估发展干预措施影响的方式。我们希望以一种新的方式应用基于代理的建模，通过使用它们来增强随机对照试验，并提供一种成本效益高的措施来衡量直接受益者之外的干预措施的广泛影响。这种方法大大改进了现有的实验设计，适用于研究溢出-不仅是因为它更灵活，更具成本效益，但因为它可以揭示更多的溢出效应。首先，现有的实验设计只允许我们观察严格定义的单位溢出。我们的方法将使我们能够观察整个人群的溢出效应。此外，由于我们可以观察到全人口范围的溢出效应，我们可以看到干预措施的变化如何影响宏观层面的结果。最后，虽然我们知道，当足够数量的个体采取了所需的行为时，干预措施通常会起作用或效果最好，但使用现有的实验设计很难观察这些阈值在哪里。我们的方法将使我们能够观察到临界点，在这个临界点上，治疗已经影响到足够多的人，随后的改变是容易接受的。我们将把我们提出的方法应用于发展干预-由国际开发部资助，由国际移民组织（移民组织）执行-旨在改善冈比亚和塞内加尔自营职业的前景。我们的最终目标是提供透明和全面的指导方针，以适当的实验设计，可以捕捉社区的影响;编程包，以实施在NetLogo的方法，一个免费和易于使用的ABM平台;和一个用户友好的应用程序进行初步测试和功率计算。

Randomised controlled trials (RCTs) are widely considered the gold-standard to assess the impact of development interventions. However, RCTs can only go so far in helping development organisations answer the key question that interests them: Do their interventions improve people's livelihoods? After all, experimental impact evaluations can only tell us how treated individuals fare in comparison to control individuals. Meanwhile, development organisations implement interventions in the hope that benefits extend beyond their direct beneficiaries. Because it is impossible to provide aid, training, or other assistance to everyone who needs it, such organisations often hope for spillover effects of their interventions that have long-lasting and self-sustainable effects on the whole community despite only targeting a few individuals. Moreover, it is plausible that some interventions require spillovers as individuals need support from their communities to make life-changing decisions. We propose to transform the ways that researchers and development organisations assess the impact of development interventions by combining RCTs with agent-based computational modelling (ABM). We hope to apply agent-based modelling in a novel way, by using them to enhance RCTs and providing a cost-effective measure of the widespread effects of interventions beyond the direct beneficiaries. This methodology greatly improves upon existing experimental designs that are adapted to study spillovers - not only because it is more flexible and more cost-effective, but because it can reveal much more about spillover effects. First, existing experimental designs only allow us to observe spillovers on strictly defined units. Our method will allow us to observe population-wide spillovers. Moreover, because we can observe population-wide spillovers, we can see how changes to interventions can shape outcomes at the macro-level. And finally, while we know that the interventions will often work or work best when a sufficient number of individuals have adopted the desired behaviour, it is very difficult to observe where these thresholds lie using existing experimental designs. Our method will allow us to observe tipping points at which the treatment has affected sufficient numbers of people that subsequent change is readily accepted.We will apply our proposed methodology to a development intervention - funded by DfID and implemented by the International Organisation for Migration (IOM) -- aimed at improving prospects for self-employment in the Gambia and Senegal. Our ultimate aim is to provide transparent and thorough guidelines on appropriate experimental designs that can capture community effects; programming packages to implement the methodology in NetLogo, a free and easy-to-use ABM platform; and a user-friendly app to conduct preliminary tests and power calculations.