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Related Concept Videos

Decision Making01:20

Decision Making

249
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...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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.
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...
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Information Processing Approach01:30

Information Processing Approach

171
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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Heuristics01:21

Heuristics

153
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Revisionist Views of Adolescent and Adult Cognition01:24

Revisionist Views of Adolescent and Adult Cognition

112
A revisionist approach to Jean Piaget's theory of cognitive development has brought new insights that challenge and reinterpret his established ideas. Piaget proposed that the formal operational stage, emerging in adolescence, represents the culmination of cognitive maturity. During this stage, individuals are said to develop abstract thinking, engage in systematic problem-solving, and show a form of egocentrism, believing others are as preoccupied with their behavior as they are...
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Foundation models and intelligent decision-making: Progress, challenges, and perspectives.

Jincai Huang1,2, Yongjun Xu3,4,5, Qi Wang3,4,5

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Innovation (Cambridge (Mass.))
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PubMed
Summary
This summary is machine-generated.

Intelligent decision-making (IDM) leverages artificial intelligence (AI) and foundation models (FMs) for adaptive choices. This survey explores AI

Keywords:
agentartificial intelligencefoundation modelsintelligent decision-makinglarge language model

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Area of Science:

  • Artificial Intelligence
  • Decision Science

Background:

  • Intelligent decision-making (IDM) is crucial for automating and augmenting complex processes.
  • Modern IDM utilizes advanced frameworks like AI agents and reinforcement learning for adaptive choices.
  • Foundation models (FMs) are increasingly integrated, unifying diverse data modalities for enhanced decision-making.

Purpose of the Study:

  • To survey the evolution of Intelligent Decision-Making (IDM).
  • To examine advanced IDM paradigms incorporating Foundation Models (FMs).
  • To highlight the transformative impact of FMs on decision-making across scientific and industrial domains.

Main Methods:

  • Review of recent advances in multimodal foundation-based approaches.
  • Analysis of AI agents and high-level reinforcement learning frameworks.
  • Examination of the integration of diverse input modalities (vision, language, sensory data).

Main Results:

  • Foundation models (FMs) are pivotal, transforming decision-making and research capabilities.
  • Large-scale, multimodal data processing by FMs fosters adaptability and interdisciplinary breakthroughs.
  • IDM with FMs shows significant impact across healthcare, life sciences, and education.

Conclusions:

  • Foundation models represent a significant advancement in intelligent decision-making.
  • The integration of FMs enables more adaptive and effective decision processes.
  • Future work should focus on building efficient, adaptive, and ethical decision systems.