Thoughts on the Urn Model
In the course, I found many topics particularly interesting. The one I like the best is the urn model. I am curious about its broader applications in real life, as it seems to be a useful tool for modeling information cascades and applying Bayesian principles. Additionally, it bears a resemblance to statistical procedures involving drawing data with replacement, such as bagging in machine learning.
I find the urn model intriguing, especially in scenarios where individuals make decisions based on information from others. When a student has information from multiple sources, they tend to follow what others are saying. In contrast, when they have only one previous person’s guess and their own draw, they are more likely to choose based on their own observation. This phenomenon resembles an information cascade. Interestingly, this concept also resonates with business development strategies. For instance, businesses can leverage the urn model in queueing systems to simulate customer arrivals and service requests. When customers observe a long waiting queue, they interpret it as a positive signal about the business’s popularity. There have been instances where new stores hire individuals to stand in line, creating a commercial effect and attracting customers.
Another relevant application I discovered is in machine learning, specifically in Bagging (Bootstrap Aggregating). This technique involves training multiple models on different subsets of the training data. The process is analogous to drawing samples with replacement from an urn. However, it’s important to note that if the subsets are too similar or contain repetitive data (similar to the case where many students hear the same information), it can lead to an information cascade effect— the model can result in a low training accuracy. This parallel between the urn model and machine learning practices underscores the versatility and applicability of these probabilistic concepts across various domains.
To conclude, these are only some thoughts that occurred to me throughout the class and they can be problematic. However, my favorite part of this class is that we get to integrate scientific models into social and economic settings. It encourages me to find the usefulness and application of what I learn!