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Related Concept Videos

Decision Making01:20

Decision Making

370
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: 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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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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Heuristics01:21

Heuristics

201
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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Trial and Error and Algorithm01:12

Trial and Error and Algorithm

220
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Econometric Views (EViews)01:29

Econometric Views (EViews)

310
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Interactive and Visualized Online Experimentation System for Engineering Education and Research
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Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making.

Md Naimul Hoque, Klaus Mueller

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    Summary
    This summary is machine-generated.

    This study introduces a new causality-based model for interpretable algorithmic decision-making. Unlike complex black box models, this approach is inherently understandable for both experts and non-experts, improving trust and comprehension.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Explainable AI (XAI)

    Background:

    • Algorithmic decision-making systems are increasingly prevalent.
    • Many systems are complex 'black box' models, necessitating auxiliary explanation models.
    • Existing explanations often lack accessibility for non-expert users, leading to misinterpretation.

    Purpose of the Study:

    • To demonstrate a predictive and interactive model based on causality that is inherently interpretable.
    • To provide a method that does not require auxiliary models for explanation.
    • To enable both expert and non-expert users to comprehensively understand algorithmic decision-making.

    Main Methods:

    • Development of 'Outcome Explorer,' a causality-guided interactive interface.
    • Evaluation through think-aloud sessions with three expert users.
    • User study with 18 non-expert users to assess ease of understanding.

    Main Results:

    • Expert users found the tool comprehensive for their explanation needs.
    • Non-expert users could easily understand the inner workings of the model.
    • The causality-based approach proved effective in enhancing model interpretability.

    Conclusions:

    • A causality-guided interactive model offers inherent interpretability without auxiliary components.
    • This approach significantly improves understanding for both technical and lay users.
    • Outcome Explorer demonstrates a viable solution for enhancing trust and transparency in algorithmic decision-making.