Here, we explore how humans tackle a complicated problem, such as reverse engineering a Bayesian network, as an iterative process involving a lot of try-and-fail, until convergence, closer to the true model.
Solving the Bayesian network involves exploration of new possible models, and exploitation of partial results already acquired during the multiple trials of the iterative process.
This repository is organized in the following way:
- A notebooks folder with multiple Jupyter notebooks
- Some dedicated Python libraries
- A manuscript folder
-Are travel restrictions and work restrictions not setting important constraints to mobility such that relaxing these constraints would change behavior dramatically?! ...I think that it may be important to mention these restrictions so as to not appear naive to them.
-Is there not a seasonality to the punctuated moves beyond the boundary of the city, such as summer vacation and Christmas with parents? In other words, are those punctuations not also quite predictable?
-Although we have not found documented evidence of memory for animals and nomad humans (such as e.g., hunter gatherers), they may similarly perform Lévy flights with memory, returning to previously visited spots for resources, which have replenished between two visits [→ is there a chance to find evidence for this, even if it’s qualitative? I am really surprised this has not been documented yet. I feel like this one might be too obvious to document? ...I think that there are too many other environmental and social constraints that this dynamic would play out in this form in which it might play out if most of these constraints were removed? Also, as one of the papers that I found points out, often the reason for moving isn't so much finding food but rather avoiding being food! ].