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Probability Laws01:49

Probability Laws

Overview
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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 $2,000...
Hindsight Biases01:12

Hindsight Biases

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?
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

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On the Robustness and Provenance of the Gambler's Fallacy.

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相关实验视频

Updated: Jul 7, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

贝叶斯学家犯下了徒的谬论

Kevin Dorst1

  • 1Department of Linguistics and Philosophy, Massachusetts Institute of Technology.

Cognitive science
|January 27, 2026
PubMed
概括

徒的谬论,即相信随机事件会自我纠正的信念,可能不是非理性的. 它可以是对不确定性和有限数据的合理反应,即使有大量数据集.

科学领域:

  • 认知科学 认知科学
  • 行为经济学是一种行为经济学.
  • 可能性理论概率理论.

背景情况:

  • 徒谬误通常被视为一种认知偏见,表明非理性.
  • 这种谬论涉及到期望随机过程自我纠正,例如预测连续之后的不同结果.

研究的目的:

  • 调查徒的谬论是否可以解释为一种理性的反应.
  • 为了建模具有因果不确定性和有限记忆的贝叶斯代理,遇到随机数据.

主要方法:

  • 模拟贝叶斯代理与不同程度的因果不确定性.
  • 观察代理对统计学上独立的随机过程的反应.
  • 在不同的结果序列之后分析代理预测.

主要成果:

  • 具有初始因果不确定性的贝叶斯元件表现出徒的谬论.
  • 代理人更快地排除了"条纹"的假设,而不是"切换"的假设,从而导致了不对称的信心.
  • 有限的内存加剧了错误,即使有大量数据,也会持续存在.

结论:

  • 徒的谬论可以在不确定性和有限的记忆条件下从理性贝叶斯推断中产生.
关键词:
贝叶斯更新是贝叶斯更新.因果推理原因推理徒的谬论 徒的谬论热手谬论 热手谬论 热手谬论记忆的限制 记忆的限制资源理性分析 资源理性分析

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  • 博者的谬误研究中观察到的经验趋势与这些理性,尽管有限,贝叶斯的反应一致.
  • 这表明明显的非理性可能源于适应不完整信息的策略.