I always wondered about people who could solve tough and confusing problems. Not because of the fact that they are smart. But, more due to the fact that their mind is involved in solving confusions all along. If you plot it in a time line of things, it is intuitive to believe that, given a problem, it will take a finite time to solve it. So, a given person who solves a problem will also be confused for a finite time before he finds the solution to the problem. If one's job were to solve a series of problems and assuming that when one problem is solved, there is always another problem to be dealt with, the state of confusion should persist throughout. Does that mean that people who solve problems are always in a state of confusion? Does that also imply that people who dont solve any problems dont get confused about anything? Is that why "Ignorance is Bliss"? I dont know...:)
Thursday, June 01, 2006
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12 comments:
I am confused! Doesn't confusion stem from not knowing which avenue to take when multiple avenues are open to you? In that sense isn't confusion a variable is the decision-making process?
Confusion can also result when you do not know which avenues are open to you. I was trying to characterize the state of facing difficult problems with uncertain outcomes to be the state of confusion...
Indha madhiri Kelvi ketta blog ezhudha matten..:)
Mindframes,
Ha ha..thats an interesting theory that a person is in a perpetual state of confusion if he keeps on solving problems. * chuckle*
Now ......for some CP. Having taken the decision analysis class,I would classify it as uncertainties and not confusions. We need to look at the uncertainties for the multiple avenues available and come to a decision. But, as you pointed out, we will be in a state of confusion regarding the uncertain outcomes till you take the decision analysis class on how to solve it....HO HO HO !!!
Saumya,
You are right in your thinking that uncertainty is a variable in the decision making process...ofcourse solving problems doesnot necessarily mean we will have good outcomes with our decisions.
When a given problem is solved, there is no state of confusion. so its probably like a spike graph that goes up and comes down as new problems are opened and solved
you are predicting a spike, assuming that the problem is solved quickly...:)... Also, what if the assumption is that there is an infinite supply of problems..:)..Just messing with u dude....
I am confused trying to understand this blog and in continous state of confusion even now :)
hmm we need a ton of assumptions here...if each problem is due to confusion and the solution of each problem leads to resolution of confusion atleast related to that problem...and then we have the start of a new problem with a new confusion...hmm okie taking that into infinite time u will realize that we can cancel out the positive (clearing confusion for one problem) with the negative (confusion created by the new problem)...therefore in the finite horizon model, if the stopping time is the last problem u solve, we can effectively say that we are never confused because everything cancels out...lol
I agree with you on the fact that confusion is not cumulative, in this context... (assuming all problems are solved before one ventures into another problem)... However, the state of confusion prevails. If you sample the person at any given time, wouldnt the probability of him being confused about "a" problem persist?..:)..Time never stops in this model...:)
@ mindframes: interesting but then there is a contradiction...because you mentioned in the article that the assumption was finite time...but if time never stops then thats a infinite horizon model...neways that does not derail the analysis...what we can do is simply set a finite time and add an epsilon (extremely small) value that can potentially make it infinite horizon..therefore technically we can reduce the infinite horizon problem into a finite horizon problem without loss of generality...
hmm my argument is that timing here is critical...if we assume discreet timelines, then the point in time when the probability is measured derails the analysis...but if we assume the analysis in continous time we can make the argument that at every instance when the probability is measured, one problem is solved while a new problem crops up...therefore the question is whethe we measure the sum of the two random variables or treat them as independent...my argument is we measure them as the sum..if that is the case then there wont exist a probability of being in the confused state...however it seems to me that your argument would be that the two events are independent...again i guess clearing that point will help..
BTW i like the summation of the two argument from a utility perspective...when u solve a problem u become happy...but when a new problem arises you are unhappy...so the effects balance each other...but on the other hand if we are to look at just one side of the coin which is the new problem that is created then we tend to be perenially unhappy, which might not be the case because there is always this balancing problem...therefore i think looking at the sum of the two rv's helps..what say you????
BTW sorry abt the first paragaph..i guess i misread the article a bit..looks like ur point is that it takes finite time to "solve" the problem but then problems keep coming which implies the timing on that could be infinite..but anywayz i think the argument holds even if you look at it from an infinite time perspective..
Hmm I love the idea behind this website, very unique.
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This site is one of the best I have ever seen, wish I had one like this.
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