The metaphor can be made mathematically precise if we first make the analogy between human decision-making and optimization methods like simulated annealing and genetic algorithms. These optimization methods look for a locally optimal solution, but add some sort of “noise” term to try to find a globally optimal solution. So if we suppose that someone who wants to stay in his own local minimum has a lower “noise” temperature than someone who is open-minded, then the metaphor starts to make sense on a much more profound level.
The metaphor can be made mathematically precise if we first make the analogy between human decision-making and optimization methods like simulated annealing and genetic algorithms. These optimization methods look for a locally optimal solution, but add some sort of “noise” term to try to find a globally optimal solution. So if we suppose that someone who wants to stay in his own local minimum has a lower “noise” temperature than someone who is open-minded, then the metaphor starts to make sense on a much more profound level.