Self Improvement Through Exploration
Better living through algorithms
The Explore vs. Exploit Algorithm
The explore-exploit algorithm is a method used in machine learning and artificial intelligence to balance the trade-off between exploration (trying new things) and exploitation (using what is known to be successful). The algorithm generally involves iteratively choosing between actions that will yield the highest expected reward (exploitation) and actions that will provide new information (exploration).
You can use this mental model to help you decide on when you should rely on old favorites or explore something new. By exploring we take risks to gain information, by exploiting we gain the highest expected reward by leveraging current strengths. The goal is to find a balance that allows you to learn and improve over time while still achieving a high level of performance and satisfaction.
Everyday Application of Explore vs. Exploit Algorithm
The explore-exploit algorithm can be applied in many areas of everyday life, such as your personal development, your career, or even your everyday leisure activities. Here are a few examples:
Personal development: When learning a new skill, an individual may choose to explore different techniques and methods, while at the same time exploiting their current knowledge and understanding of the subject. This allows for a balance between trying new things and making progress in a specific area.
Career choices: When considering job opportunities, an individual may choose to explore new fields or industries while at the same time exploiting their current skills and experience. This allows for a balance between taking risks and leveraging current strengths.
Leisure activities: When choosing what to do in one's free time, an individual may choose to explore new hobbies and activities while at the same time exploiting their current interests and passions. This allows for a balance between trying new things and enjoying familiar pastimes.
When to Explore or Exploit?
The optimal ratio of exploration to exploitation depends on the specific problem and the context in which it is being applied. In some cases, it may be more beneficial to heavily favor exploration in order to gather as much information as possible, while in other cases exploitation may be more important in order to achieve a high level of performance.
In practice, the optimal ratio of exploration to exploitation can be determined through trial and error and by monitoring the performance of the system over time. Which option you choose is also based on your personality. If you are risk averse, you will exploit more than you explore and if you are risk taker you will want to explore more than exploit. Regardless, you should always have a bit of exploration in your activities, so that you may discover new things and limit uncertainty.
Some rules of thumb:
In new problems or problems of high uncertainty, a higher initial exploration rate (or low exploitation rate) is more beneficial.
In problems of lower uncertainty, where the number of possible actions is small and the effects of each action are well understood, a lower initial exploration rate (or high exploitation rate) can be applied.
Start with high exploration every time you are faced with a new problem, and over time decrease exploration with exploitation as you become more familiar with your topic or environment.
It's also worth noting that in some problems, the best approach might not be to balance between exploration and exploitation but to focus on one of them at a time, alternating between exploration and exploitation phases.
Conclusion
So, the next time you're hungry, consider whether you'll explore a new restaurant or exploit a known favorite. Exploring a new restaurant could be exciting and you might discover a new favorite place to eat. However, it could also be a disappointment if the food is not to your liking or the service is poor. On the other hand, when exploiting a known favorite you'll enjoy the food and the service, but you might miss out on finding the best restaurant you’ve ever been to.
Further Reading