![]() ![]() Specific computer science problems and issues: Constraining someone’s choices can make a decision easier for them. ![]() In real life, we can try to be computationally kind to others.Relaxing a problem may trade off certainty for time/computational resources.When a queue or a to-do list gets too long, it’s better to close it off and drop things entirely. Queues bring up average throughput at the expense of delay or latency.Caching manages the trade-off between time and space.Sorting pre-empts the costs of a future search, so there is a trade-off between sorting and searching.Randomness can also help produce “good enough” solutions much quicker.Relaxing an intractable problem can make it tractable.Sometimes less is more, and simple models can outperform complex ones that overfit the data.Humans do surprisingly well at difficult computer science problems.Real-life problems are hard, and many are intractable (i.e.Having the best process does not guarantee the best outcome. Even the best algorithm can have a pretty high failure rate.We can apply algorithms to some of the problems we face in real life.Real-life problems are hard, and many are intractable.But even the best algorithm can have a pretty high failure rate. ![]()
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