Making cyber-human systems smarter

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-07-14 DOI:10.1016/j.is.2024.102428

Steven Alter

引用次数: 0

Abstract

The term smart is often used carelessly in relation to systems, devices, and other entities such as cities that capture or otherwise process or use information. This conceptual paper treats the idea of smartness in a way that suggests directions for making cyber-human systems smarter. Cyber-human systems can be viewed as work systems. This paper defines work system, cyber-human system, algorithmic agent, and smartness of systems and devices. It links those ideas to challenges that can be addressed by applying ideas that managers and IS designers discuss rarely, if at all, such as dimensions of smartness for devices and systems, facets of work, roles and responsibilities of algorithmic agents, different types of engagement and patterns of interaction between people and algorithmic agents, explicit use of various types of knowledge objects, and performance criteria that are often deemphasized. In combination, those ideas reveal many opportunities for IS analysis and design practice to make cyber-human systems smarter.

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让网络-人类系统更加智能

智能一词经常被不经意地用于系统、设备和其他实体（如城市），它们可以捕捉或以其他方式处理或使用信息。这篇概念性论文在论述智能这一概念时，提出了让网络人类系统变得更加智能的方向。网络人类系统可被视为工作系统。本文定义了工作系统、网络人类系统、算法代理以及系统和设备的智能性。本文将这些观点与可以通过应用管理者和信息系统设计师很少讨论（如果有的话）的观点来应对的挑战联系起来，这些观点包括：设备和系统的智能化维度、工作的各个方面、算法代理的角色和责任、人与算法代理之间不同类型的参与和互动模式、各种类型知识对象的明确使用，以及通常不被强调的性能标准。这些想法结合在一起，为信息系统分析和设计实践提供了许多机会，使网络-人系统变得更加智能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.