The Teaching Model with Random Walkers

1. Agents move continuously in random directions across the space, simulating a spatially structured population.

2. When two agents come within the "Interaction Distance," they connect and begin an exchange of knowledge.

3. While connected, agents increase their knowledge based on their own missing knowledge, their peer's knowledge, and the learning rate. Both agents gain a fraction of their partner's knowledge.

4. Knowledge in this model is strictly cumulative. Interactions always result in gaining knowledge; knowledge is never lost during an interaction.

5. To prevent the entire population from becoming permanently omniscient, agents have a random chance to "die" at any moment. This resets their knowledge state immediately back to zero (ignorant), representing a new individual replacing the old one.

6. The teaching session ends naturally when the agents' random walks take them apart.

7. A particle can only engage in a teaching session with one other particle at a time, prioritizing the closest neighbor.