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相关概念视频

Decision Making01:20

Decision Making

109
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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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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Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Updated: Jun 28, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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使用投机对手模型进行决策.

Jing Sun, Shuo Chen, Cong Zhang

    IEEE transactions on neural networks and learning systems
    |April 10, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的AI技术用于多代理系统,该技术仅使用本地数据来模拟对手. 这种方法在复杂环境中增强决策能力,优于现有的方法.

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    相关实验视频

    Last Updated: Jun 28, 2025

    Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
    13:04

    Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

    Published on: September 19, 2012

    12.1K
    The HoneyComb Paradigm for Research on Collective Human Behavior
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    科学领域:

    • 人工智能的人工智能
    • 多代理系统 多代理系统
    • 强化学习是一种强化学习.

    背景情况:

    • 对手建模在多代理系统中增强了代理决策.
    • 当前的方法需要访问对手的观察和行动,这往往是不可行的.
    • 需要使用仅使用局部信息的对手建模技术.

    研究的目的:

    • 介绍分布式对手辅助多代理演员-批评 (DOMAC),一种新的投机对手建模算法.
    • 仅使用局部信息 (代理人的观察,行动,奖励) 实现有效的对手建模.
    • 在复杂的多代理情景中改善代理决策和绩效.

    主要方法:

    • 在演员内部开发了投机的对手模型,以使用本地数据预测对手的行动.
    • 纳入分布式批评模型来估计政策质量评估的回报分配.
    • 正式推导出针对拟议的对手模型量身定制的政策梯度定理.

    主要成果:

    • 在多种多代理任务中,DOMAC成功地模拟了对手的行为.
    • 在基准环境 (MPE,Pommerman,SMAC) 中与最先进的方法相比,表现优越.
    • 与现有方法相比,在培训方面实现了更快的融合速度.

    结论:

    • DOMAC提供了一种有效的解决方案,用于仅使用局部信息进行对手建模.
    • 该算法在具有挑战性的多代理设置中增强了代理性能和决策.
    • DOMAC代表了人工智能投机对手建模的重大进步.