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Reason and Intuition01:37

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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 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.
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Rational Expressions01:28

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Rational expressions are algebraic fractions in which both the numerator and the denominator are polynomials. These expressions follow the arithmetic rules of numerical fractions but require extra care due to the presence of variables. A fundamental part of working with rational expressions is identifying values that make the expression undefined, typically those that result in division by zero or undefined radicals.Determining the DomainThe domain of a rational expression includes all real...
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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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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.
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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Rational Emotive Behavior Therapy01:24

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Cognitive-behavioral therapies (CBTs) are grounded in the belief that our thoughts profoundly influence our emotions and actions. Advocates of CBT emphasize three core assumptions: first, that cognitions are identifiable and measurable; second, that they are central to psychological functioning; and third, that irrational or maladaptive beliefs can be replaced with rational and adaptive ones. This transformative approach to therapy has paved the way for specific models such as Albert...
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Related Experiment Video

Updated: Nov 27, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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Bounded Rational Decision-Making from Elementary Computations That Reduce Uncertainty.

Sebastian Gottwald1, Daniel A Braun1

  • 1Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces elementary computation for decision-making, defining resource costs for uncertainty reduction. It establishes a framework for understanding computational processes with limited resources.

Keywords:
Bayesian inferencebounded rationalitydecision-makingdivergenceentropylimited resourcesmajorizationuncertainty

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Area of Science:

  • Computational Theory
  • Information Theory
  • Decision Science

Background:

  • Decision-making involves reducing uncertainty by eliminating alternatives.
  • Computational processes for decision-making are resource-intensive, limiting uncertainty reduction.
  • Existing theories lack a comprehensive framework for resource-constrained decision-making.

Purpose of the Study:

  • To introduce a novel concept of elementary computation for uncertainty reduction.
  • To develop resource cost functions that are monotonic with uncertainty reduction.
  • To provide a comprehensive framework for decision-making processes under limited resources.

Main Methods:

  • Defined elementary computation based on probability transfer principles.
  • Utilized concepts from majorization theory, T-transforms, and generalized entropies.
  • Developed order-preserving resource cost functions.

Main Results:

  • Established elementary computations as the inverse of Pigou-Dalton transfers for probability distributions.
  • Demonstrated that resource cost functions are monotonic with uncertainty reduction.
  • Proved new results in majorization theory, entropy, and divergence measures.

Conclusions:

  • The proposed framework offers a comprehensive approach to decision-making with limited computational resources.
  • Elementary computation provides a fundamental principle for understanding uncertainty reduction costs.
  • The findings advance the theoretical understanding of probability distributions and information measures.