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How To Avoid Regrets In Life

Discussion in 'Education & Personal Growth' started by Rose03, Jun 21, 2021.

  1. Rose03

    Rose03 Senior IL'ite

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    Hello everyone

    I don’t take decisions or rather find it very difficult to take decisions in life then I end up with regrets in life which I haunts me forever and spoils my happiness . what to do ?
     
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  2. anika987

    anika987 IL Hall of Fame

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    Grass is always greener on the other side.

    Human nature is as such we forget our blessings and do not look at our growth and hence room for regrets.

    there is always pros and cons to every decision.

    If it is the right decision,we are good.If it is wrong,we LEARN.

    There is nothing to regret in life.Life is a game Game.Play it.
     
    sonal1611 likes this.
  3. Hopikrishnan

    Hopikrishnan Platinum IL'ite

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    I noticed the forum in which you had started this thread. This point is very important, lest others come around and write long subjective essays on decisions, as well as point to their own personal experiences with such things.

    Avoiding regrets is a toughie. However, you can minimize regrets to a extremely negligible level. Quite a lot of research work happens in the AI field. For example, here is an abstract of a paper about Dynamic as well as Adaptive Regrets.

    Minimizing Dynamic Regret and Adaptive Regret Simultaneously
    arXiv - CS - Machine Learning Pub Date : 2020-02-06 , DOI: arxiv-2002.02085
    Lijun Zhang, Shiyin Lu, Tianbao Yang
    Regret minimization is treated as the golden rule in the traditional study of online learning. However, regret minimization algorithms tend to converge to the static optimum, thus being suboptimal for changing environments. To address this limitation, new performance measures, including dynamic regret and adaptive regret have been proposed to guide the design of online algorithms. The former one aims to minimize the global regret with respect to a sequence of changing comparators, and the latter one attempts to minimize every local regret with respect to a fixed comparator. Existing algorithms for dynamic regret and adaptive regret are developed independently, and only target one performance measure. In this paper, we bridge this gap by proposing novel online algorithms that are able to minimize the dynamic regret and adaptive regret simultaneously. In fact, our theoretical guarantee is even stronger in the sense that one algorithm is able to minimize the dynamic regret over any interval.​
     

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