This seems to be talking about situations where a vector of inputs has an optimal setting at extremes (convex), in contrast to situations where the optimal setting is a compromise (concave).
I’m inclined to say it’s a very different discussion than this one, as an agent’s resource utility function is generally strictly increasing, so wont take either of these forms. The optimal will always be at the far end of the function.
But no, I see the correspondence: Tradeoffs in resource distribution between agents. A tradeoff function dividing resources between two concave agents (t, where h is the hoard being divided between them, ta,b,h(r)=Ua(r)+Ub(h−r)) will produce that sort of concave bulge, with its optimum being a compromise in the middle, while a tradeoff function between two convex agents will have its optima at one or both of the ends.
This seems to be talking about situations where a vector of inputs has an optimal setting at extremes (convex), in contrast to situations where the optimal setting is a compromise (concave).
I’m inclined to say it’s a very different discussion than this one, as an agent’s resource utility function is generally strictly increasing, so wont take either of these forms. The optimal will always be at the far end of the function.
But no, I see the correspondence: Tradeoffs in resource distribution between agents. A tradeoff function dividing resources between two concave agents (t, where h is the hoard being divided between them, ta,b,h(r)=Ua(r)+Ub(h−r)) will produce that sort of concave bulge, with its optimum being a compromise in the middle, while a tradeoff function between two convex agents will have its optima at one or both of the ends.