First and foremost, I wish to extend the heartiest of hellos and the warmest of welcomes to everyone here. I recently was introduced to the world of machine learning through some wondrous circumstance, and have been sitting here for a week straight in complete awe of some applications that it could be used for.
I am currently applying for fellowships for a PhD program and have decided to incorporate a machine learning algorithm at its core. I do not know how well machine learning can be applied to my thesis, as I do not have a coding / math background, but rather a political science/ philosophy background. Nonetheless, my symbolic logic classes have been seeing me through all this code and math.
For my thesis, I wish to redefine [negative] democracy in the philosophical rendition of positive democracy. To sum the definitions of the respective versions of democracy woefully short, negative democracy pertains to the absence of constraints to freedom, whereas positive democracy ensures the ability to access those freedoms (e.g you have the freedom to go to a top university, but because you do not have any resources you do not actually have the freedom).
The part where machine learning comes in is in the creation of various activations layers, which will deduce out based upon my input variables (eg. income inequality, gender inequality, race inequality, etc.) to deduce out a output ranging from 0 to 1 which gives definition as to how ‘democratic’ a country is based upon my definition (input variables).
Mind you, my machine learning competence is basic to give an overestimation; but for-whatever reason, I find this very intuitive and fun (in a philosophical sense). Thereby, I humbly ask for any help in the creation of this thesis, would it be viable? Doable in 3-4 years? How many layers should I be looking at? What would be the recommend number of training examples? ( I was thinking just the OECD countries to start)
Thank you so very much for your advice and kind attention,
-Nam