Introduction to Decision Science
1. Introduction to Decision Science
Hello! In this video we'll discuss the characteristics of decision science and the main capabilities to support the decision-making process.2. Decision Science
So, what is Decision Science? It's a field of study that involves various disciplines to understand and improve decision-making processes by: Helping us to understand how decisions are made, unraveling all the internal and external factors involved in this process.3. Decision Science
Enabling us to maximize the desired outcomes of our decisions with the use of analysis tools.4. Decision Science
Providing guidance and methodology for the development and use of Decision Models to represent decision problems accurately.5. Decision Science
Assisting us with frameworks to mitigate the effects of uncertainties and risks involved in a decision.6. Decision Science
And, providing decision support systems allowing us to make informed decisions based on real-time data analysis.7. Fields in Decision Science
Decision Science embraces concepts and techniques from multiple subjects, such as mathematics, statistics, economics, psychology and computer science. The main fields of study in the scope of Decision Science are: Decision analysis: formalizing the decision problem, identifying objectives, defining alternatives, and quantifying trade-offs.8. Fields in Decision Science
Optimization Techniques: to find the best course of action or solution that maximizes objectives under given constraints.9. Fields in Decision Science
Risk analysis: to equip decision-makers with tools and techniques to assess and manage risks associated with decision alternatives.10. Fields in Decision Science
Behavioral analysis: To help us understand how individuals and groups make decisions, including the psychological aspects that may influence choices.11. Fields in Decision Science
and Decision Support Systems: to develop software platforms assisting decision-makers in structuring and analyzing decisions.12. Statistics in Decision Science
Statistics is crucial in Decision Science for analyzing data, modeling decision problems, making predictions, and optimizing outcomes. As examples, we have: Mean: The sum of all values in a dataset divided by the total number of values, representing the central tendency of the data.13. Statistics in Decision Science
Median: The middle value in a dataset when it is ordered from least to greatest. It divides the dataset into two equal parts, with half of the values below it, and the other half, above it.14. Statistics in Decision Science
The mode: Showcasing the value that appears most frequently in a dataset.15. Statistics in Decision Science
Standard deviation: measuring the dispersion of a specific value in the dataset around the mean.16. Statistics in Decision Science
and lastly, percentile: an expression of where a specific value in the dataset falls in the range of the overall data.17. Let's practice!
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