1. Introduction to decision-making
I am Akshay Swaminathan. In this video, I will discuss how decision science is about extracting meaningful insights to make informed decisions.
2. Decision Science in action
This field plays a crucial role in various domains, from businesses determining their next product launch to doctors devising personalized treatment plans.
In this video, we will explore the fascinating world of data-driven decision-making, equipping ourselves with the tools and techniques necessary to navigate complex problems and arrive at optimal solutions.
We will discuss structured frameworks that facilitate a systematic approach to decision-making, enabling us to move beyond subjective intuition and embrace objective analysis.
3. Applying decision-making frameworks
Consider a scenario where you must choose between two job offers, each with advantages and disadvantages. A higher salary might necessitate relocation, while a lower salary could offer a better work-life balance. How do you arrive at the best decision? While gut feelings might play a role, a more robust approach involves systematically evaluating the pros and cons of each option. This is where decision-making frameworks prove invaluable.
4. Decision trees
One powerful framework is the decision tree. Envision a tree-like diagram where each branch represents a different decision or possible outcome. Starting at the root, we navigate down the branches, assessing the consequences of each choice until we reach a final decision.
Decision trees can be applied to a wide range of problems, from simple to complex. They allow us to incorporate probabilities and uncertainties into our analysis, providing a clear and concise visual representation of the decision-making process.
5. Expected value
In many real-world scenarios, decision outcomes are not guaranteed; there's an inherent element of chance. Expected value helps us quantify this uncertainty. It is a calculation considering the probabilities of different outcomes and their associated values.
For instance, if you're considering investing in a startup, there's a chance it could succeed, yielding substantial returns, or it could fail, resulting in financial loss. By calculating the expected value, we can weigh the potential gains against potential losses, gaining a clearer understanding of the investment's overall worth.
Expected value plays a critical role in decision tree analysis by enabling us to make decisions that are not only informed but also strategically optimized.
6. Other frameworks
Other frameworks exist in decision-making as well. Game theory analyzes strategic interactions where the outcome of a decision depends not only on your own choices but also on the choices of others. It's used in economics, political science, and even evolutionary biology to understand how individuals make decisions in competitive situations.
A common tool that data scientists use is A/B testing. This experimental approach randomly assigns groups and then compares outcomes. For example, large numbers of different customers might be randomly assigned to different versions of a website design, marketing email, or product feature. An outcome measure such as response rate or purchases is compared between these randomly assigned groups to see which performs better.
7. Let's practice!
This introduction has provided a glimpse into the world of data-driven decision-making, focusing on decision trees and expected value. These tools are indispensable for any aspiring data scientist, fostering a structured and analytical approach to problem-solving.
Remember, every great decision starts with a solid framework—embrace these tools, stay curious, and keep building your skills to unlock your full potential in this exciting field!