减少信息级联对社会多样性的影响 社会学习

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摘要翻译：
众所周知，在线社交媒体和网络中的集体行为能够产生与群体智慧和羊群行为相关的非直觉动力。尽管这些主题在社会科学中已经得到了很好的研究，但互联网计算和电子商务的爆炸性增长使得理解它们在数字社会中的影响变得迫切。在这项工作中，我们探讨了社会多样性引入的随机性如何帮助参与推理过程的agents提高他们的集体绩效。我们的结果表明，社会多样性如何能够减少信息级联的不良影响，在信息级联中，理性的代理人选择忽略个人知识，以遵循一种主导的社会行为。社会多样性永远不可取的情况也被区分出来，这些发现对工程和社会场景的后果被讨论。
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英文标题：
《Social diversity for reducing the impact of information cascades on
  social learning》
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作者：
Fernando Rosas, Kwang-Cheng Chen and Deniz Gunduz
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Collective behavior in online social media and networks is known to be capable of generating non-intuitive dynamics associated with crowd wisdom and herd behaviour. Even though these topics have been well-studied in social science, the explosive growth of Internet computing and e-commerce makes urgent to understand their effects within the digital society. In this work we explore how the stochasticity introduced by social diversity can help agents involved in a inference process to improve their collective performance. Our results show how social diversity can reduce the undesirable effects of information cascades, in which rational agents choose to ignore personal knowledge in order to follow a predominant social behaviour. Situations where social diversity is never desirable are also distinguished, and consequences of these findings for engineering and social scenarios are discussed.
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PDF链接：
https://arxiv.org/pdf/1805.055

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关键词：多样性 社会学 Applications Optimization Consequences 减少 多样性 learning 社会 能够