reply to u/dotinvoke at https://www.reddit.com/r/Zettelkasten/comments/175a6tr/are_links_still_better_than_search_in_the_age_of/

The value in the process is making a ratchet of ideas which is highly customized to building your own lines of thought or "associative trails" if you prefer Vannevar Bush's framing.

If your idea worked, then one could "simply" rely on Google's database and a variety of associated tools to act as your zettelkasten—Bob's your uncle and you're done! In practice, you'll find that this doesn't work well. You can experiment, but I think you'll find that your own limited choices of links will work far better than the infinite number of adjacent possible links that a digital system may create on your behalf. If you're already fighting information overload, you don't want to add link overload to your list of problems.

Put in a different light, it can be interesting to randomly flip a coin and go left on heads and right on tails to see where you might end up, particularly if you're unsure. But if you actively make your own choices, you're more likely to be happier with what you see along the way and where you end up.

This isn't true. The Stochastic Parrots paper outlines other avenues for reining in the harms of language models like GPT's.

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. FAccT ’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3442188.3445922.

Would the argument here for stochastic parrots also potentially apply to or could it be abstracted to Markov monkeys?

Article on brown noise and it's impact on ADHD focus. Suggested by Adam A. Provides useful links to primary sources for experiments, topics, and people

Google Scholar is needed to access annotations in context "Listen to the noise: noise is beneficial for cognitive performance in ADHD"

Author's self-described "most intriguing result"

Home - COVID 19 scenario model hub. (n.d.). Retrieved July 5, 2021, from https://covid19scenariomodelinghub.org/

$$\left\{\mathcal{I}_{n}, n \geq 0\right\}$$

是一组滤链

• 滤链定义在$$(\Omega, \mathscr{F}, \mathscr{P})$$

• 滤链满足$$\mathcal{I}{0} \subseteq \mathcal{I}{1} \subseteq \mathcal{I}_{2} \subseteq \cdots$$

Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.

Kiss, I. Z., Miller, J., & Simon, P. L. (2017). Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer International Publishing. https://doi.org/10.1007/978-3-319-50806-1

Miller, J. C., & TIng, T. (2019). EoN (Epidemics on Networks): A fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks. Journal of Open Source Software, 4(44), 1731. https://doi.org/10.21105/joss.01731

Murphy, C., Laurence, E., & Allard, A. (2020). Deep learning of stochastic contagion dynamics on complex networks. ArXiv:2006.05410 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.05410

Della Rossa, F., & DeLellis, P. (2020). Stochastic master stability function for noisy complex networks. Physical Review E, 101(5), 052211. https://doi.org/10.1103/PhysRevE.101.052211

Mehrotra, S., Rahimian, H., Barah, M., Luo, F., & Schantz, K. (2020 May 02). A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19. Naval Research Logistics (NRL). https://doi.org/10.1002/nav.21905