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Related Concept Videos

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

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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.
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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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Decision Making: P-value Method01:09

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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.
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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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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Related Experiment Video

Updated: Sep 22, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts.

Hannah Elder1, Casey Canfield2, Daniel B Shank2

  • 1Technische Universität Berlin, Berlin, Germany, and University of Missouri-Columbia, Columbia, Missouri, USA.

Human Factors
|May 23, 2022
PubMed
Summary

This study shows that artificial intelligence (AI) recommendations improve performance in risky decisions, especially when indicating when not to act. However, users often under-value AI, particularly in high-stakes situations.

Keywords:
artificial intelligencecompliancedecision-makingreliancesignal detection theorytrust in automation

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Area of Science:

  • Human-computer interaction
  • Decision science
  • Artificial intelligence

Background:

  • AI decision support can enhance performance, but users often underutilize recommendations, especially with uncertain outcomes.
  • Increased AI reliability correlates with improved task performance through higher compliance and reliance.

Purpose of the Study:

  • To investigate the impact of AI recommendation presence and reliability on task performance and trust in risky decision-making.
  • To assess how AI influences collaborative decision-making models and user behavior.

Main Methods:

  • A between-subject design was employed with participants in high reliability AI, low reliability AI, or control conditions.
  • Participants made betting decisions in simulated basketball games, with compensation tied to performance.
  • Task performance (accuracy, signal detection) and trust behaviors (compliance, reliance) were evaluated.

Main Results:

  • AI recommendations boosted task performance and reliance on no-action recommendations, particularly in high reliability conditions.
  • Accuracy and sensitivity (d') improved with high reliability AI, but compliance and response bias (c) remained unaffected.
  • Participant behavior aligned with probability matching models only for compliance in the low reliability condition.

Conclusions:

  • AI recommendations are valuable in risky contexts, primarily for guiding 'no-action' decisions.
  • Designers must consider the influence of action versus no-action AI recommendations for effective interventions.