> ... I am resisting doing something out of my comfort/risk zone.
Interestingly, we resist because we perceive a loss of options or in other words - freedom, caused by the paralyzing anxiety. We usually feel free by being in our optimized space, where we have already invested a lot.
And the purpose of going out of out comfort zone is exactly the same - to maximize freedom or diversity of options. By embracing novelty, new options might be discovered and more freedom acquired.
It's the classic trafeoff between exploration and exploitation. Both exploration and exploitation are necessary to maximize results, and the balance between them is not a trivial issue. Always going out of your comfort zone can do as much harm as always avoiding novelty.
By the way, Reinforcement learning algorithms such as AlphaGo deal with the same issue. The simplest solution is to follow the optimized policy ("be in the comfort zone"), but from time to time to do random exploration steps in order to discover more of the state space and possibly find better strategies. In the beginning of the training sequence, it's more exploration, but later, more exploitation.
Interestingly, we resist because we perceive a loss of options or in other words - freedom, caused by the paralyzing anxiety. We usually feel free by being in our optimized space, where we have already invested a lot.
And the purpose of going out of out comfort zone is exactly the same - to maximize freedom or diversity of options. By embracing novelty, new options might be discovered and more freedom acquired.
It's the classic trafeoff between exploration and exploitation. Both exploration and exploitation are necessary to maximize results, and the balance between them is not a trivial issue. Always going out of your comfort zone can do as much harm as always avoiding novelty.
By the way, Reinforcement learning algorithms such as AlphaGo deal with the same issue. The simplest solution is to follow the optimized policy ("be in the comfort zone"), but from time to time to do random exploration steps in order to discover more of the state space and possibly find better strategies. In the beginning of the training sequence, it's more exploration, but later, more exploitation.