CIF: Small: Strategic Communication: Concepts, Methods, and Applications

CIF：小：战略沟通：概念、方法和应用

基本信息

批准号：
1910715
负责人：
Emrah Akyol
金额：
$ 23.5万
依托单位：
SUNY at Binghamton
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910715&HistoricalAwards=false
关键词：
CIF Small Strategic Communication Concepts

项目摘要

We are surrounded by networks of smart agents that exchange information with each other through communication channels, as in the case of Internet of Things and autonomous systems. This project considers problems related to communication between these smart and strategic agents referred to as 'strategic communication', when the agents have misaligned or conflicting objectives. Extensions of known strategic communication models to realistic communication settings that involve channel noise and data compression remains an open challenge. The goal of this project is to develop mathematical models of strategic communication for such realistic scenarios. An immediate application of this research pertains to trustworthy and transparent machine learning (ML). ML is increasingly being used in systems that make decisions which affect people, including setting prices for items, scoring credit and job applications, filtering news and social media updates, recommending routes and places, and controlling smart homes and autonomous cars. As ML algorithms make these decisions, it is natural to ask for their transparency, which, on the other hand, makes them vulnerable to manipulation and algorithmic bias. Designing efficient and transparent ML algorithms that are robust to manipulation and bias is a difficult challenge. This project will address this challenge by explicitly taking such possibilities into account in the ML algorithm development through robust strategic communication models. This research will address fundamental questions regarding strategic communication. This emerging research field requires revisiting key results in classical information theory and poses significant challenges, in terms of both analysis and optimization, requiring approaches and tools from multiple disciplines. The outcomes of the research are expected to constitute an essential step in understanding the interplay of game theory and economics with information theory, communications, and compression. Specific goals of this project are categorized into four groups. The first set of goals investigates optimal strategies in non-coded communication settings, with a particular focus on non-Gaussian sources and channels using tools from optimization in function spaces. The second set of goals concerns the transparency issues in ML. Building on the strategic communication models in non-coded scenarios, the project includes two research directions in this area: the quantification of the cost of transparency in various ML algorithms and the design of manipulation-aware ML algorithms. The third set of goals analyzes the role of compression in strategic communication scenarios, including the characterization and computation of fundamental limits, and the development of practical strategic data compression methods. The final set of goals explores networked strategic communication scenarios.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.

我们被智能代理网络包围，这些网络通过通信渠道（如物联网和自主系统）相互交流。该项目考虑了与这些智能和战略代理之间的沟通有关的问题，即当代理商未对准或相互矛盾的目标时，称为“战略交流”。已知的战略沟通模型扩展到涉及通道噪声和数据压缩的现实通信设置仍然是一个开放的挑战。该项目的目的是为这种现实情况开发战略沟通的数学模型。这项研究的立即应用与值得信赖和透明的机器学习（ML）有关。 ML越来越多地用于制定影响人们的决策的系统，包括设定项目的价格，信用和工作申请，过滤新闻和社交媒体更新，推荐路线和地点以及控制智能家居和自动驾驶汽车。随着ML算法做出这些决定，自然要要求其透明度，另一方面，这使它们容易受到操纵和算法偏见的影响。设计有力的操纵和偏见是一个艰难的挑战，设计有效且透明的ML算法。该项目将通过强大的战略沟通模型明确考虑ML算法开发中的这种可能性来应对这一挑战。这项研究将解决有关战略交流的基本问题。这个新兴的研究领域要求重新审视经典信息理论的关键结果，并在分析和优化方面提出了重大挑战，需要多个学科的方法和工具。预计该研究的结果将构成理解游戏理论与信息理论，通信和压缩的相互作用的重要步骤。该项目的具体目标分为四组。第一组目标调查了非编码通信设置中的最佳策略，特别关注使用功能空间中优化工具的非高斯来源和渠道。第二组目标涉及ML中的透明度问题。该项目以非编码方案的战略通信模型为基础，包括该领域的两个研究方向：各种ML算法中透明成本的量化以及操作感知的ML算法的设计。第三组目标分析了压缩在战略交流方案中的作用，包括基本限制的表征和计算以及实际战略数据压缩方法的发展。最后一组目标探讨了网络战略沟通方案。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。