- Apr 2022
It is difficult to see interdependencies This is especially true in the context of learning something complex, say economics. We can’t read about economics in a silo without understanding psychology, sociology and politics, at the very least. But we treat each subject as though they are independent of each other.
Where are the tools for graphing inter-dependencies of areas of study? When entering a new area it would be interesting to have visual mappings of ideas and thoughts.
If ideas in an area were chunked into atomic ideas, then perhaps either a Markov monkey or a similar actor could find the shortest learning path from a basic idea to more complex ideas.
Example: what is the shortest distance from an understanding of linear algebra to learn and master Lie algebras?
Link to Garden of Forking Paths
Link to tools like Research Rabbit, Open Knowledge Maps and Connected Papers, but for ideas instead of papers, authors, and subject headings.
It has long been useful for us to simplify our thought models for topics like economics to get rid of extraneous ideas to come to basic understandings within such a space. But over time, we need to branch out into related and even distant subjects like mathematics, psychology, engineering, sociology, anthropology, politics, physics, computer science, etc. to be able to delve deeper and come up with more complex and realistic models of thought.Our early ideas like the rational actor within economics are fine and lovely, but we now know from the overlap of psychology and sociology which have given birth to behavioral economics that those mythical rational actors are quaint and never truly existed. To some extent, to move forward as a culture and a society we need to rid ourselves of these quaint ideas to move on to more complex and sophisticated ones.
- Research Rabbit
- atomic notes
- visual thinking
- Connected Papers
- multidisciplinary studies
- Markov monkey
- Donut Economics
- Open Knowledge Maps
- behavioral economics
Oddly this article doesn't cover academia.edu but includes ResearchGate which has a content-sharing partnership with the publisher SpringerNature.
Matthews, D. (2021). Drowning in the literature? These smart software tools can help. Nature, 597(7874), 141–142. https://doi.org/10.1038/d41586-021-02346-4
Connected Papers uses the publicly available corpus compiled by Semantic Scholar — a tool set up in 2015 by the Allen Institute for Artificial Intelligence in Seattle, Washington — amounting to around 200 million articles, including preprints.
Semantic Scholar is a digital tool created by the Allen Institute for Artificial Intelligence in Seattle, Washington in 2015. It's corpus is publicly available for search and is used by other tools including Connected Papers.
In 2019, Smolyansky co-founded Connected Papers, one of a new generation of visual literature-mapping and recommendation tools.
- note taking
- information overload
- artificial intelligence
- tools for thought
- literature search
- Connected Papers
- Open Knowledge Maps
- Semantic Scholar
- Manfred Kuehn
- research methods
- Allen Institute for Artificial Intelligence
- Eddie Smolyansky
- literature review
- Mar 2022
There are some additional interesting questions here, like: how do you get to the edge quickly? How do you do that across multiple fields? What do you do if the field seems misdirected, like much of psychology?
- How do you get to the edge quickly?
I think this is where literature mapping tools come in handy. With such a tool, you can see how the literature is connected and which papers are closer to the edge of understanding. Some tools on this point include Connected Papers, Inciteful, Scite, Litmaps, and Open Knowledge Maps.
- How do you do that across multiple fields?
I think this requires taking an X-disciplinary approach that teeters on multiple disciplines.
- What do you do if the field seems misdirected, like much of psychology?
Good question. It is hard to re-orient a field unless you can find a good reason (e.g., a crisis) for a paradigm shift. I think Kuhn's writing on [The Structure of Scientific Revolutions(https://www.uky.edu/~eushe2/Pajares/Kuhn.html) may be relevant here.