Sunday, 16 August 2009
I've been browsing Jonah Lehrer's blog recently, and an article on grit caught my attention ( here ).
Grit is broadly defined as the resolve to continue in pursuing an aim, no matter the obstacles or distractions.
A quick afterthought.
How do you model "success" depending on grit?
A toy-model could comprise a space of "choices" at discrete time-points. There is some reward-function which is parametrized for "grit" and it determines the probability of changing a "choice" or sticking to it. Choices lead to "outcomes" at the end of the simulation.
To me it seems - the more grit you have, the less likely you'll be to optimize the reward function by exploring the space by jumping around a little.
How one defines the trajectory from choice to outcome should factor in that success does depend on spending some time on a project, so the reward function is increasing in time for fixed value of choice.
But still, if grit is very high, it seems that the initial (random?) choice will be the one trajectory followed till the end. Whereas for some "optimal" value, initially the space can be explored by jumping between trajectories.
Versions of this kind of reasoning permeate mundane things like deciding how much to work and how much to surf for fun during a typical work day, but also i guess long-term choices such as choice of career, home city, maybe in some version also the life partner, etc.