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相关概念视频

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Impression Management Techniques III: Aligning Actions01:29

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Aligning actions are communicative strategies individuals employ to maintain social harmony and preserve personal identity in the face of potential disruptions to social norms. These actions are particularly important in managing social impressions when one's behavior might be seen as inappropriate, incompetent, or morally questionable.Types of Aligning ActionsThe three principal types of aligning actions are disclaimers, accounts, and apologies.DisclaimersDisclaimers are preventive; they are...
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Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

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The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
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High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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走向人类-人工智能决策合作的科学:一个互补框架

Cleotilde Gonzalez1,2, Kate Donahue3, Daniel G Goldstein4

  • 1Social and Decision Sciences Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.

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概括

这项研究探讨了人 - 人工智能互补性,团队表现优于个人. 它提供了一个框架和设计原则,以有效,以人为中心的人工智能协作在关键决策.

关键词:
调整对齐的情况互补性 互补性 互补性人类AI团队合作

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科学领域:

  • 认知科学 认知科学
  • 人工智能 (AI) 是一种人工智能.
  • 人类因素 人类因素
  • 组织行为 组织行为
  • 伦理学 伦理学 伦理学

背景情况:

  • 人工智能 (AI) 越来越多地成为健康,安全,金融和治理领域关键决策过程的组成部分.
  • 主要的挑战已经从人类-人工智能协作转移到为最佳互补性构建这种互动.
  • 人类-人工智能互补意味着一个协同关系,人类-人工智能组合的团队超过了人类或人工智能独立运作的表现.

研究的目的:

  • 推进人类-人工智能决策合作的科学.
  • 根据集体智能和核心认知过程,提出理解和设计有效的人类-人工智能团队的框架.
  • 确定社会技术因素和设计原则对于实现人类-人工智能互补至关重要.

主要方法:

  • 从认知科学,人工智能,人类因素,组织行为和伦理学中获得的综合见解.
  • 提出了一个基于集体智能的框架,专注于推理,记忆和注意力.
  • 检查了社会技术因素 (团队组成,信任,心理模型,培训,任务结构) 并概述了设计原则以实现互补性.

主要成果:

  • 确定了影响人类-人工智能团队有效性的关键社会技术因素.
  • 概述了可操作的设计原则,以实现互补性,包括角色划分和持续评估.
  • 强调了透明度,信任和以人为中心的设计在人工智能协作中的重要性.

结论:

  • 人与人工智能的互补性可以通过小心地组织团队和任务来实现.
  • 有效的人类-人工智能团队需要注意认知过程,社会技术因素和道德考虑.
  • 拟议的框架和原则为开发高性能,适应性,透明性和可信度的人类-人工智能系统提供了路线图,这些系统与人类价值观保持一致.