CoEvolution > OIMP > Fundamental concepts tree
Fundamental concepts tree
Initiator : Fabien
The goal is to be able to have a kind of cascading model of how my mind processes any information it encounters (end note 1). Anything has to conform to this model in order to accept it and add it in my then pre-existing network.
By expliciting this model I except to be able to refine it and correct it when necessary. The difficulty stands mostly on verifying its actual accuracy not to "reality" but to my actual implicitly used model.
- pattern recognition
- environmental pressure
- increasing return
- state of the art
- incorporated/embedded (as in Latour in SC22)
- chances of survival
- resources management
- competitive advantage
- game theory
- recursive representation
Previous versions vJuly102008at0922PM?, date2, date3 (use-the wiki-history), study of their evolution over time? and the meta-analysis of the process? (a form of cognitive capitalization).
Examples of uses
Here I will add few situations (new information accepted, rejected, creation of new information, ...) that will show how the model is used. Hopefully real time animation will replace textual depictions soon.
Example from a previous short discussion :
Let's imagine that you want to be smarter so that you can make nicer present to a girl you like and not mess it up each and every time.
You think :
- hey, what if I could make my mind more efficient ?
- But ... actually, how is my mind working ?"
- hmmm... well... I manipulate information, I compare them ...
Then instead of just thinking about it you write it down on a sheet of paper :
- ok so, I always do this then this then that blabla"
Then you smile and you think :
- ok, nice, but, Im a scientist! I need to check this, is it accurate or not?"
You then decide to use an fMRI or another technic to actually try to match your model to cold hard data. You design an experimental protocol and find that your model was kind of correct but not perfect. You refine it, change it and try it again. Still no perfect, so you do it again (perfect in the sense of realistic, accurate, not ideal). And you keep on repeating the process till you are satisfied.
Template of a concept tree
(positioned correctly in the tree)
name, date of added in the model, algorithm, version, sources
Here, by its very structure, example node requires example tree to be used.
Every node is a function that manipulates information (and thus can manipulate other nodes).
The position in the tree is determine by how often a node is used by others nodes. The more it's used, the "higher" it gets. The structure should be the result auto-organization and not explicit organizing (except if you want to make a "target" model to reach).
Template for a visual version to use as a desktop background
This should be evolved to a structured formalize description. Such a graph or tree could use RDF to facilitate its exploitation (existing tools and processes).
Brain imaging related hardware and software
See the listing of techniques, a short discussion I had with a friend regarding the possibility to use fMRI realistically and finally related Resources to explore.
Experiment for simplified automation
- Build your personal "clickable" FCT (Fundamental Concept Tree) Fx.
- Determine a goal G to reach.
- In order to reach G, use a serie (including eventually parallel use) of concepts Cy from Fx.
- F(x+1) builds itself with record of your path to reach G and compute statistics of Cy usage.
Repeat the experiment but this time F(x+1)
will propose potential Cy thanks to prediction (possibly using Markov chain).
- Could be simply Visual ant wiki based
- ... but if possible Ipv6 SSaS with API available on discoveries indexes
- (visually propose possible useful next concept based on (ANN with topology based on (FCT computational based on (explicit user input based on (repetitively inputted process of sequential concepts based on* (inputted computational concepts based (commonly agreed and understood language (based on simplification tools (based on... pmWiki ?))))))))
- implying increasing human ability to write code (programing mindset) and reversely increasing network ability to flexibly predict action based on statistical learning
- References to detailed concept should be developed too (internally but also externally with references).
- Personal concepts or never encountered concepts should be distinctive.
- Application of each concept should be expressed (why does it matter, how tools/solutions build from it or exploiting the concept are available now)
- test phase should precede each node
- the whole model should be coded
- differentiate higher-order (ex: evolution func or set of func) vs lower-order (ex: comparison func)
- find an experimental model to distinct what you think (or wish) you use and those you actually use on a daily basis (fMRI technics ? first manual pourcentages then Q&A ?) (end note 2)
- this fMRI way of confirmation could actually also a way to move toward one model to another, the loop process (try, check) could be used to shape from a model to a new "ideal" one (end note 2)
- dates of discovery and personal discovery should be added (for chronological analysis, especially updates and evolution with cohesion requirements).
- add examples of uses
- add the possibility to changes nodes (especially parent nodes) more easily
- improve exemple node (eventually based on "nodea") and allow/facilitate self-upgrade propagation
- provide easy to use templates and share them
- fundamental = not ALL concepts should be present, just the top of the hierarchy (that's why new concepts are not necessarily present)
- method for rigorous evaluation of changes (progresses ? regresses ? stabilization ?)
- including its own model. (back)