Summary of Slow Convergence Of Interacting Kalman Filters in Word-of-mouth Social Learning, by Vikram Krishnamurthy and Cristian Rojas
Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning
by Vikram Krishnamurthy, Cristian Rojas
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Theoretical Economics (econ.TH); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework utilizes Kalman filter agents to model word-of-mouth social learning. The approach involves sequential processing of noisy measurements by multiple Kalman filters. The covariance of the learned signal is shown to decrease exponentially with the number of agents, suggesting a slowing down of learning with increasing agent count. Additionally, artificially re-weighing the prior at each time step can achieve an optimal learning rate. This paper provides theoretical guarantees for the proposed method and highlights its potential applications in social learning settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how information spreads through a group of people using a type of algorithm called Kalman filter. Imagine you’re trying to learn something new, like a secret recipe. You start by asking someone who knows it, but their answer might not be perfect because they learned it from someone else too. This process keeps going until you get the correct information. The researchers found that as more people are involved in this learning process, it actually gets slower and less accurate. However, if we adjust how much importance we give to the previous knowledge when sharing new information, we can make the learning process faster and better. |
