Operational Decision-Making for Reach Maximization of Incentive Programs that Influence Consumer Energy-Saving Behavior

实现影响消费者节能行为的激励计划最大化的运营决策

• 批准号: 1635611
• 负责人: Alexander Nikolaev
• 金额: $ 19.99万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1635611&HistoricalAwards=false
• 关键词: Operational; Decision; Making; Reach; Maximization

Environmental programs that exploit informational social influence have recently found success in fostering energy-conscious consumer behavior. By disseminating Home Energy Reports that compare individual households' energy spending patterns to those of their neighbors, these programs capitalize on the fact that people are more likely to adjust their behavior when simple information on its negative impacts on the environment is preceded by ''social proof'' that referent others have already started to behave pro-environmentally. While exploiting the power of social influence for the societal benefit is a breakthrough in itself, further research is required to boost the impact of such programs with direct incentives, so that they can be proactively controlled and their impact accelerated in an organized manner. This award supports fundamental research into the design and planning of randomized incentive programs -- social lotteries -- that inform consumers of their peers saving energy while being rewarded for doing so. The researched prescriptive methodologies enable calculated maximization of the ''reach'' of a social influence program or policy, and are expected to be applicable in economics, healthcare, education, etc., for increasing individual awareness of issues of societal concern. This project will further support the educational mission of the NSF by involving members of underrepresented student groups, senior design project advising, and conducting undergraduate student seminars.The research plan encompasses the modeling efforts to quantitatively capture the connection between the informational social influence and the impact of the randomized energy-saving incentive program; the development of mixed-integer programming, dynamic programming and decentralized network optimization algorithms for reach maximization under budgetary constraints; and the investigations into operational program planning under limited information about a targeted heterogeneous population. This project will produce new classes of problems and fundamental insights into operational planning of interventions exploiting social networks. Its products will allow for modeling of the mechanisms of social influence driven not directly by diffusion, as previously done, but by reach. The developed designs, recipes and tools will be evaluated with the data and advice provided by a collaborating energy provider. The improvements resulting from the introduction of the researched formalisms and algorithms will be characterized by gathering theoretical evidence, en route to preparing a pilot implementation.

利用信息社会影响的环保项目最近在培养节能消费者行为方面取得了成功。通过传播家庭能源报告，将单个家庭的能源消费模式与邻居的能源消费模式进行比较，这些项目利用了这样一个事实，即当人们对其对环境的负面影响有简单的信息时，他们更有可能调整自己的行为，因为“社会证明”表明其他人已经开始采取环保行动。利用社会影响力来获取社会效益本身就是一种突破，但如何通过直接激励来提升这类项目的影响力，使其受到主动控制，并有组织地加速其影响，还需要进一步的研究。该奖项支持对随机激励计划的设计和规划进行基础研究——社会彩票——告知消费者他们的同伴节约能源，同时也会因此获得奖励。经研究的规定性方法使社会影响方案或政策的“影响范围”得以计算最大化，并有望应用于经济、医疗保健、教育等领域，以提高个人对社会关注问题的认识。该项目将进一步支持美国国家科学基金会的教育使命，涉及代表性不足的学生群体成员，高级设计项目咨询，并举办本科生研讨会。研究计划包括建模工作，以定量捕捉信息社会影响与随机节能激励计划影响之间的联系；在预算约束下，混合整数规划、动态规划和分散网络优化算法的发展以及在有限信息下对目标异质人群的操作计划的调查。这个项目将产生新的问题类别，并对利用社会网络进行干预的操作计划产生基本见解。它的产品将允许对社会影响机制进行建模，而不是像以前那样直接由传播驱动，而是由覆盖范围驱动。开发的设计、配方和工具将根据合作能源供应商提供的数据和建议进行评估。引入所研究的形式和算法所带来的改进将以收集理论证据为特征，在准备试点实施的过程中。

项目成果

Seed activation scheduling for influence maximization in social networks

• DOI: 10.1016/j.omega.2017.06.002
• 发表时间: 2017-06
• 期刊: Omega
• 作者: Mohammadreza Samadi;R. Nagi;Alexander Semenov;Alexander G. Nikolaev

Robust communication network formation: a decentralized approach

• DOI: 10.1186/s40649-019-0072-3
• 发表时间: 2019-11
• 期刊: Computational Social Networks
• 作者: Christopher Diaz;Alexander G. Nikolaev;Abhinav Perla;Alexander Veremyev;E. Pasiliao

Integer programming models for detecting graph bipartitions with structural requirements

• DOI: 10.1002/net.21786
• 发表时间: 2018-06
• 期刊: Networks
• 影响因子: 2.1
• 作者: Chrysafis Vogiatzis;J. Walteros