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Related Concept Videos

Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Uncertainty and exploration in a restless bandit problem.

Maarten Speekenbrink1, Emmanouil Konstantinidis

  • 1Experimental Psychology, University College London.

Topics in Cognitive Science
|April 23, 2015
PubMed
Summary

Balancing exploration and exploitation is key for decision-making. This study found people often weigh the probability an option is the best, even when rewards change.

Keywords:
Dynamic decision makingExploration-exploitation trade-offRestless multi-armed bandit taskUncertaintyVolatility

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Decision Science

Background:

  • Effective decision-making in dynamic environments necessitates balancing exploiting known rewards with exploring for better options.
  • The 'restless bandit' task models scenarios where options' reward values change over time, posing a challenge for optimal strategy.

Purpose of the Study:

  • To investigate how individuals balance exploration and exploitation in a changing-reward environment.
  • To identify computational models that best explain human behavior in the restless bandit task.

Main Methods:

  • Participants engaged in a restless bandit task, making repeated choices between options with time-varying average rewards.
  • Computational models were compared to analyze participants' decision-making strategies.
  • Behavioral data was analyzed to determine the factors influencing choice.

Main Results:

  • A significant portion of participants demonstrated a strategy balancing exploration and exploitation.
  • Evidence suggests individuals considered the probability that a given option was the best available choice.
  • This probability-based evaluation appears to be a key mechanism for managing exploration-exploitation trade-offs.

Conclusions:

  • Human decision-making in changing environments often involves assessing the likelihood of an option being optimal.
  • This finding provides insight into the cognitive strategies employed to navigate uncertainty and optimize reward-seeking behavior.
  • The results support models that incorporate a 'probability of being the best' evaluation for balancing exploration and exploitation.