An Equilibrium Model of Experimentation on Networks

网络实验的平衡模型

• 批准号: 2149291
• 负责人: Simon Board
• 金额: $ 19.16万
• 依托单位: University of California-Los Angeles
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149291&HistoricalAwards=false
• 关键词: Equilibrium; Model; Experimentation; Networks

This project will investigate how the balance between discovery and diffusion depends on the nature of connections between people. Do more connections lead to faster technological progress? Is society better off in a loosely connected network or with many tight clusters? This project has two important features that differentiate it from the wider literature. First, by studying both diffusion and discovery within a single model, the project analyzes the interconnection between the two forces and how this depends on the social network. Second, the project considers forward-looking agents, whose incentives for original discovery are crowded out by the possibility of learning from others. The resulting model can be used to understand how policy changes (e.g., subsidizing innovation) affect society’s technological progress. The project also speaks to a growing empirical literature that studies how people learn about innovations from their neighbors (e.g. new production techniques or new consumer products). In their classic paper, Bala and Goyal (1998) study agents who learn from their neighbors in a network. To make analytical progress, they restrict attention to myopic, non-Bayesian agents, shortcutting strategic considerations and allowing them to solve the model as a sequence of static decision problems. In contrast, this project studies agents who are forward-looking and Bayesian, so social learning (both past and future) crowds out private experimentation. The key simplifying assumption is that agents learn via a perfect good news process. While each agent faces a rich strategy space, her “social learning curve” is described by a simple function of time, and her best response reduces to choosing a single number: the total amount of individual experimentation, as captured by a cutoff time. The model thus recovers the tractability of the reduced-form models of experimentation in a model of Bayesian learning and uses this to provide a clean characterization of initial experimentation and subsequent contagion. The project investigates learning and welfare as a function of the density and structure of the network. It studies two measures of network density: (i) the size of the core in a core-periphery network and (ii) the degree of random regular networks generated by the configuration model. For either measure, preliminary results suggest that aggregate (asymptotic) information decreases in network density, whereas welfare is hump-shaped in network density, with intermediate networks striking a balance between motivating individual discoveries and their social diffusion. The project will also investigate network structure, including models where links are one-directional (e.g., Twitter), bi-directional (e.g., LinkedIn) and clustered (e.g., Facebook). It will study the effect of such network architecture on learning and welfare in the context of tree networks. Trees both approximate large random networks and are highly tractable, allowing the researchers to characterize the social diffusion of information by simple ordinary differential equations. Collectively, this project will paint a clear picture about learning dynamics, information aggregation, and welfare in networks of forward-looking agents.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.

这个项目将研究发现和传播之间的平衡如何取决于人与人之间联系的本质。更多的连接会导致更快的技术进步吗？社会是松散连接的网络更好，还是紧密连接的集群更好？这个项目有两个重要的特点，区别于更广泛的文献。首先，通过在单一模型中研究扩散和发现，该项目分析了这两种力量之间的相互联系，以及这如何依赖于社会网络。其次，该项目考虑了前瞻性的代理人，他们对原始发现的激励被向他人学习的可能性所排挤。由此产生的模型可以用来理解政策变化（例如，补贴创新）如何影响社会的技术进步。该项目还涉及越来越多的实证文献，这些文献研究人们如何从邻居那里学习创新（例如新的生产技术或新的消费产品）。在他们的经典论文中，Bala和Goyal（1998）研究了网络中向邻居学习的代理。为了取得分析进展，他们将注意力限制在短视的非贝叶斯代理上，缩短战略考虑，并允许他们将模型作为一系列静态决策问题来解决。相比之下，这个项目研究的是具有前瞻性和贝叶斯的主体，因此社会学习（包括过去和未来）排挤了私人实验。关键的简化假设是，代理通过一个完美的好消息过程来学习。虽然每个智能体都面临丰富的策略空间，但她的“社会学习曲线”是由一个简单的时间函数描述的，她的最佳反应减少到选择一个数字：个体实验的总量，由截止时间捕获。因此，该模型恢复了贝叶斯学习模型中简化形式的实验模型的可追溯性，并利用这一点为初始实验和随后的传染提供了清晰的表征。该项目将学习和福利作为网络密度和结构的函数进行研究。它研究了网络密度的两个度量：(i)核心-外围网络中核心的大小和（ii）由配置模型生成的随机规则网络的程度。对于这两种测量，初步结果表明，网络密度的聚合（渐近）信息减少，而福利在网络密度中呈驼峰形，中间网络在激励个人发现和社会扩散之间取得平衡。该项目还将调查网络结构，包括单向链接（如Twitter）、双向链接（如LinkedIn）和集群链接（如Facebook）的模型。它将研究这种网络架构对树状网络背景下的学习和福利的影响。树既近似于大型随机网络，又易于处理，使研究人员能够通过简单的常微分方程来表征信息的社会扩散。总的来说，这个项目将描绘出一幅关于前瞻性代理网络中学习动态、信息聚合和福利的清晰图景。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。