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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.
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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Learning and Dynamic Decision Making.

Cleotilde Gonzalez1

  • 1Dynamic Decision Making Laboratory, Social and Decision Sciences Department, Carnegie Mellon University.

Topics in Cognitive Science
|November 12, 2021
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Summary
This summary is machine-generated.

This research introduces instance-based learning theory (IBLT) to explain dynamic decision-making in complex environments. It combines experiments and computational models to improve how humans make choices over time.

Keywords:
Dynamic decision makingInstance-based learning theoryLearning

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Area of Science:

  • Cognitive Science
  • Behavioral Economics
  • Computational Modeling

Background:

  • Decision-making research often overlooks dynamic, uncertain environments.
  • Existing theories lack practical guidelines for real-time choices.

Purpose of the Study:

  • Develop theoretical understanding of dynamic decision processes.
  • Improve human decision-making in complex, changing situations.
  • Introduce instance-based learning theory (IBLT) for dynamic environments.

Main Methods:

  • Laboratory experiments using dynamic games (individual and team-based).
  • Development of computational cognitive models specifying decision mechanisms.
  • Integration of experimental data with computational modeling.

Main Results:

  • Extrapolation of robust behavioral insights from dynamic games.
  • Creation of actionable cognitive models for decision processes.
  • Demonstration of IBLT's utility in diverse applications.

Conclusions:

  • Instance-based learning theory (IBLT) offers a robust framework for dynamic decision-making.
  • Integrating experimental and computational methods advances understanding of human choices.
  • This research bridges theoretical insights and practical applications in fields like cybersecurity and climate change.