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  1. POEstimating difficulty of instances of NP problems
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    copied!<p>NP problems look like they are suitable for use as trapdoor functions or proofs of work, since they are difficult to solve, but easy to verify. Unfortunately, they seem a little hard to use in adversarial settings where an opponent can control problem selection because while worst-case problems are NP, particular instances can be solved very quickly.</p> <p>So: is there any algorithm which can take instances and estimate - more efficiently than trying to solve them - how hard or close to worst-case they are?</p> <p>(The context is musing about a Bitcoin protocol where the proofs-of-work were reusable and not useless hash checks. The obvious approach is to have a central authority issue, for each transaction block, a NP instance which corresponds to a real-world problem. But the central authority could be subverted, and start issuing easy problems which would render the network vulnerable to double-spends. One could accept problems from multiple authorities, or anyone, but the chosen-easy problem remains. If there were some way to <em>estimate</em> the difficulty of any problem presented to the network, then 'too easy' problems could simply be ignored, falling back to the hash race if necessary.)</p> <p>EDIT: jaxtr links me to <a href="http://www.cs.ubc.ca/~kevinlb/papers/2012-PredictingSatisfiability.pdf" rel="nofollow" title="Xu et al 2012">"Predicting Satisfiability at the Phase Transition"</a>, which gives algorithms which estimate hardness at 70% accuracy - but they don't seem to investigate whether the algorithm can be deliberately fooled. (As well, one can apparently generate SAT problems with specified probabilities of being satisfiable.)</p>
 

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