Persuading People on More and Less Divisive Issues
在或多或少存在分歧的问题上说服人们
基本信息
- 批准号:2214040
- 负责人:
- 金额:$ 35.82万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-15 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This project examines how people’s genuine beliefs about real world events inform their responses to public information, and how hard it is to persuade people to change their beliefs. This question is important as governments and experts often try to persuade citizens to take certain actions based on the information they collect. This happens in many settings ranging from an individual’s decision to adopt health precautions to a business owner’s decision to keep his/her business afloat in times of prolonged economic slowdown. Major inefficiencies and coordination failures could ensue if these recommendations made by the government are not followed. What is the best way to release information to the general public? What affordances prevent persuasion in practice from working as it does in theory? Studying how genuine beliefs respond to information provided by a knowledgeable source is a complex task since such beliefs are in general unknown and are likely to be different for different people, especially on divisive topics. This research develops a new mechanism of delivering information that allows testing for persuasion without needing to know participants’ initial believes, and hence allows us to separate different possible impediments to information policies. Using this mechanism, we test the efficacy of information policies across various domains including political and economic issues, sustainability concerns, and general knowledge questions. Specifically, our new mechanism circumvents unknown and heterogeneous prior beliefs by inducing a common posterior belief. We show that this new mechanism is optimal from the perspective of the information designer, i.e., attains the same expected payoff as the one in which the information designer knows each receiver’s prior. To test this mechanism, we conduct two sets of experiments: one in the laboratory with induced priors, and another in the field with genuine priors. In the second set of experiments, we first elicit homegrown priors people hold in a variety of domains and then examine their evolution in response to publicly provided information structures. In addition, we explore the propagation of information via cheap-talk communication between respondents, representing gossip in a population; and we study how this propagation is impacted by heterogeneity in respondents’ preferences and their beliefs about the distribution of preferences in the population, i.e., the degree of homophily.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该项目研究了人们对现实世界事件的真实信念如何影响他们对公共信息的反应,以及说服人们改变信念的难度有多大。这个问题很重要,因为政府和专家经常试图说服公民根据这些信念采取某些行动。这种情况发生在许多情况下,从个人采取健康预防措施的决定到企业主在经济长期放缓时期维持其业务的决定,如果提出这些建议,可能会导致严重的效率低下和协调失败。向公众发布信息的最佳方式是什么?在实践中,哪些因素阻碍说服发挥作用?研究真正的信念如何回应知识渊博的信息来源是一项复杂的任务。因为这些信念通常是未知的,并且对于不同的人来说可能会有所不同,特别是在有分歧的主题上,这项研究开发了一种新的信息传递机制,可以在不需要知道参与者最初信念的情况下进行说服力测试,从而使我们能够分开不同的可能使用这种机制,我们测试了各个领域的信息政策的有效性,包括政治和经济问题、可持续性问题和一般知识问题。具体来说,我们的新机制通过引入共同的后验信念来规避未知和异质的先验信念。我们证明,从信息设计者的角度来看,这种新机制是最优的,即获得与信息设计者知道每个接收者的先验相同的预期回报。为了测试这种机制,我们进行了两组实验:一组。在实验室在第二组实验中,我们首先引出人们在各个领域持有的本土先验,然后检查它们对公开提供的信息结构的演变。通过受访者之间的廉价谈话传播信息,代表群体中的八卦;我们研究受访者偏好的异质性以及他们对群体中偏好分布的信念(即同质性程度)如何影响这种传播; .此奖项通过使用基金会的智力价值和更广泛的影响审查标准进行评估,NSF 的法定使命被认为值得支持。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Marina Agranov其他文献
Static and Dynamic Underinvestment: An Experimental Investigation
静态和动态投资不足:实验研究
- DOI:
10.2139/ssrn.2540354 - 发表时间:
2014-12-18 - 期刊:
- 影响因子:0
- 作者:
Marina Agranov;Guillaume Fréchette;T. Palfrey;Emanuel Vespa - 通讯作者:
Emanuel Vespa
Persistence of power: Repeated multilateral bargaining
权力的持久性:反复的多边讨价还价
- DOI:
- 发表时间:
2016-12-01 - 期刊:
- 影响因子:0
- 作者:
Marina Agranov;Christopher S. Cotton;Chloe J. Tergiman - 通讯作者:
Chloe J. Tergiman
Stable Randomisation
稳定的随机化
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:3.2
- 作者:
Marina Agranov;P. Healy;Kirby Nielsen - 通讯作者:
Kirby Nielsen
Disentangling Suboptimal Updating: Task Difficulty, Structure, and Sequencing. ∗
解开次优更新:任务难度、结构和排序。
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Marina Agranov;P¨ellumb Reshidi;Alessandro Enke;Kristof Lizzeri;Kirby Madarasz;Pietro Nielsen;Leeat Ortoleva;Yariv - 通讯作者:
Yariv
Ranges of Randomization
随机化范围
- DOI:
10.1162/rest_a_01355 - 发表时间:
2023-07-24 - 期刊:
- 影响因子:8
- 作者:
Marina Agranov;Pietro Ortoleva - 通讯作者:
Pietro Ortoleva
Marina Agranov的其他文献
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