Policy learning can alter the perceptions of both the seriousness and the causes of a policy problem, thus also altering the perceived need to do something about the problem. This then allows for the informed weighing of different policy options. Taking a social network perspective, we argue that the role of social influence as a driver of policy learning has been overlooked in the literature. Network research has shown that normatively laden belief change is likely to occur through complex contagion—a process in which an actor receives social reinforcement from more than one contact in its social network. We test the applicability of this idea to policy learning using node-level network regression models on a unique longitudinal policy network survey dataset concerning the Reducing Deforestation and Forest Degradation (REDD+) initiative in Brazil, Indonesia, and Vietnam. We find that network connections explain policy learning in Indonesia and Vietnam, where the policy subsystems are collaborative, but not in Brazil, where the level of conflict is higher and the subsystem is more established. The results suggest that policy learning is more likely to result from social influence and complex contagion in collaborative than in conflictual settings.

政策学习可以改变人们对政策问题的严重性和原因的认识，从而也改变人们认为需要对问题做点事情的感觉。然后，这允许对不同的策略选项进行明智的权衡。从社会网络的角度来看，我们认为社会影响力作为政策学习驱动力的作用在文献中被忽略了。网络研究表明，充满负荷的行为可能会因复杂的传染而发生规范上的改变，在这一过程中，参与者从其社交网络中的多个联系人中获得了社交支持。我们在有关巴西减少森林砍伐和森林退化（REDD +）计划的独特纵向政策网络调查数据集上，使用节点级网络回归模型测试了该想法对政策学习的适用性，印度尼西亚和越南。我们发现，网络连接解释了印度尼西亚和越南的政策学习情况，这两个国家之间的政策子系统是协作的，而在巴西和巴西则不是，那里的冲突程度更高并且子系统更加成熟。结果表明，与冲突环境相比，政策学习更有可能来自协作中的社会影响和复杂的传染。