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Related Concept Videos

Decision Making01:20

Decision Making

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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
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...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...
The Availability Heuristic01:08

The Availability Heuristic

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):
Frustration and Conflict: Approach-Approach, Approach-Avoidance01:20

Frustration and Conflict: Approach-Approach, Approach-Avoidance

Frustration occurs when people are obstructed or prevented from achieving a desired goal or fulfilling a perceived need. For example, when someone's input is ignored in a discussion, it can lead to feelings of frustration. Conflict, however, arises from opposing interests, goals, or actions. Conflicts can take various forms based on the nature of these opposing desires or goals.
One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable options,...

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Decision making in context.

R M Haralick1

  • 1SENIOR MEMBER, IEEE, Departments of Electrical Engineering and Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

Bayesian decision theory explains why standard statistics ignore context due to prior probability and loss function assumptions. New methods incorporating context require combinatorial search for optimal decision-making.

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Area of Science:

  • Decision theory
  • Artificial intelligence
  • Statistical modeling

Background:

  • Traditional statistical methods often overlook contextual information.
  • Assumptions in prior probability functions and simple loss functions limit context integration.
  • Artificial intelligence constraints represent specific types of prior probability assumptions.

Purpose of the Study:

  • To explain from a Bayesian decision theoretic framework why context is ignored in standard statistical approaches.
  • To introduce context-aware loss functions and their implications for decision problems.
  • To provide a Bayesian perspective on probabilistic relaxation techniques.

Main Methods:

  • Analysis of joint prior probability functions and loss functions within a Bayesian decision theoretic framework.
  • Illustration of how artificial intelligence constraints relate to prior probability assumptions.
  • Development of context-aware loss functions and examination of the resulting decision problem structure.
  • Theoretical explanation of probabilistic relaxation using Bayesian principles.

Main Results:

  • Identified specific assumptions on joint prior probability and simplistic loss functions as reasons for ignoring context in standard statistics.
  • Demonstrated that artificial intelligence constraints are a form of assumption on joint prior probability.
  • Showcased context-aware loss functions that, with prior constraints, necessitate combinatorial state space search.
  • Presented a Bayesian theory explaining the mechanism of probabilistic relaxation.

Conclusions:

  • Contextual information can be integrated into statistical decision-making by carefully defining joint prior probabilities and employing context-aware loss functions.
  • The integration of context transforms decision problems into those requiring complex search strategies.
  • Bayesian decision theory provides a unified framework for understanding both standard statistical limitations and advanced techniques like probabilistic relaxation.