Optimal influence under observational learning

IF 0.5 4区 经济学 Q4 ECONOMICS Mathematical Social Sciences Pub Date : 2024-02-01 DOI:10.1016/j.mathsocsci.2024.01.011
Nikolas Tsakas
{"title":"Optimal influence under observational learning","authors":"Nikolas Tsakas","doi":"10.1016/j.mathsocsci.2024.01.011","DOIUrl":null,"url":null,"abstract":"<div><p>We study the optimal targeting problem of a firm that is seeking to maximize the diffusion of a product in a society where agents learn from their neighbors. The firm can seed the product to a subset of the population and our goal is to find which the optimal subset to target is. We provide a condition that characterizes the optimal targeting strategy for any network structure. The key parameter in this condition is the agents’ decay centrality, which takes into account how close an agent is to others, in a way that distant agents are weighted less than closer ones.</p></div>","PeriodicalId":51118,"journal":{"name":"Mathematical Social Sciences","volume":"128 ","pages":"Pages 41-51"},"PeriodicalIF":0.5000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Social Sciences","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165489624000209","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

We study the optimal targeting problem of a firm that is seeking to maximize the diffusion of a product in a society where agents learn from their neighbors. The firm can seed the product to a subset of the population and our goal is to find which the optimal subset to target is. We provide a condition that characterizes the optimal targeting strategy for any network structure. The key parameter in this condition is the agents’ decay centrality, which takes into account how close an agent is to others, in a way that distant agents are weighted less than closer ones.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
观察学习下的最佳影响力
我们研究的是一个企业的最佳目标问题,该企业希望在一个代理人向邻居学习的社会中最大限度地传播产品。企业可以向人口的一个子集播种产品,而我们的目标是找到最佳目标子集。我们提供了一个条件,描述了任何网络结构下的最佳目标策略。该条件的关键参数是代理的衰减中心度,它考虑了代理与其他代理的距离,即距离远的代理的权重低于距离近的代理的权重。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Mathematical Social Sciences
Mathematical Social Sciences 数学-数学跨学科应用
CiteScore
1.30
自引率
0.00%
发文量
55
审稿时长
59 days
期刊介绍: The international, interdisciplinary journal Mathematical Social Sciences publishes original research articles, survey papers, short notes and book reviews. The journal emphasizes the unity of mathematical modelling in economics, psychology, political sciences, sociology and other social sciences. Topics of particular interest include the fundamental aspects of choice, information, and preferences (decision science) and of interaction (game theory and economic theory), the measurement of utility, welfare and inequality, the formal theories of justice and implementation, voting rules, cooperative games, fair division, cost allocation, bargaining, matching, social networks, and evolutionary and other dynamics models. Papers published by the journal are mathematically rigorous but no bounds, from above or from below, limits their technical level. All mathematical techniques may be used. The articles should be self-contained and readable by social scientists trained in mathematics.
期刊最新文献
Project selection with partially verifiable information On the decomposability of fractional allocations Node centrality based on its edges importance: The Position centrality Evidence disclosure with heterogeneous priors Very weakly dominant strategies
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1