- Take your most acute/insightful intuition
- Find a simple-enough formal model/example
- Analyse/program the model
- Show some surprising/interesting results
- Write/publish your paper.
AI at the time presented a superfluity of interesting problems - truly it was bliss to be a researcher. Only later did I realise that the most profound questions ('What evolutionary pressures selected for natural, human intelligence?') led into a conceptual swamp, where the right questions could not even be identified, let alone solved.
In physics, subject to centuries of theoretical and mathematical development, the situation facing the new postgraduate researcher is overwhelming and intimidating. As Bob Henderson observes,
"What we’d created is called a “toy model”: an exact solution to an approximate version of an actual problem. This, I learned, is what becomes of a colossal conundrum like quantum gravity after 70-plus years of failed attempts to solve it. All the frontal attacks and obvious ideas have been tried. Every imaginable path has hit a dead end. Therefore researchers retreat, set up camp, and start building tools to help them attempt more indirect routes. Toy models are such tools. Rajeev’s river almost certainly didn’t run to The Grail. The hope was that some side stream of one of its many tributaries (Virasoro, Yamabe …) would.Bob Henderson recounts an insightful, often tragic tale of hope and disillusion. Beautifully written to boot. Give it a try.
"Actually that was my hope, not Rajeev’s. Rajeev, I believe, just liked doing the math. The thing was a puzzle he could solve, so he solved it. For him that was enough.
"I, of course, had my sights set on bigger game."
Via Peter Woit.