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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
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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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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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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...
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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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Published on: September 10, 2018

A mechanism for value-sensitive decision-making.

Darren Pais1, Patrick M Hogan, Thomas Schlegel

  • 1Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America.

Plos One
|September 12, 2013
PubMed
Summary
This summary is machine-generated.

Honeybee swarm decision-making reveals that

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Area of Science:

  • Collective behavior
  • Decision-making mechanisms
  • Computational neuroscience

Background:

  • Collective decision-making in social insects like honeybees is crucial for survival.
  • Previous models often focus on accuracy, neglecting the value of chosen options.
  • Understanding the role of inhibitory signals is key to improving decision models.

Purpose of the Study:

  • To analyze a dynamical system model of honeybee swarm decision-making.
  • To investigate the impact of cross-inhibitory 'stop-signalling' on decision quality.
  • To explore how decision parameters influence value-based choices.

Main Methods:

  • Dynamical systems analysis of a novel decision-making model.
  • Mathematical modeling of collective choice mechanisms.
  • Simulation of swarm behavior and decision outcomes.

Main Results:

  • Cross-inhibition strength is a critical parameter influencing decisions based on value difference and mean value.
  • Cross-inhibition regulates deadlock resolution and enables adaptive time-dependent strategies.
  • The model demonstrates tuning of discrimination thresholds (similar to Weber's Law) and speed-accuracy trade-offs.

Conclusions:

  • Cross-inhibition significantly enhances collective decision-making by incorporating value considerations.
  • The model's principles may apply to biological systems (neural circuits) and artificial intelligence.
  • This research offers insights for designing decentralized decision-making algorithms.