Los 10 principales competidores de aops.com

artofproblemsolving.com

Which math books are right for elementary to high school? Follow these guides and curriculum map with suggestions for Beast Academy, AoPS, and Contest Prep.

imacs.org

School Year ProgramSummer Program BOOK YOUR FREE CLASS IMACS MakesBetter Thinkersfor Life! Online Summer Programs Teaching Logic & Critical Thinking for over 30 Years School-Year Program Also Available! IMACS Makes Better Thinkers for Life! Online Summer Programs Teaching Logic & Critical Thinking for over 30 Years School-Year Program Also Available! What to Expect at IMACS ... Continue Reading

omegalearn.org

Omega Learn is a place to find all the educational resources you need for Elementary, Middle, or High School. We provide a variety of free classes, a comprehensive list of resources, and fun math competitions.

thethinkacademy.com

Top-rated after-school math programs, online and in-person, with tailored difficulty levels to help K-12 students excel in school classes and math competitions.

math.stackexchange.com

Q&A for people studying math at any level and professionals in related fields

mathworld.wolfram.com

A connected graph G is distance-regular if for any vertices x and y of G and any integers i,j=0, 1, ...d (where d is the graph diameter), the number of vertices at distance i from x and distance j from y depends only on i, j, and the graph distance between x and y, independently of the choice of x and y. In particular, a distance-regular graph is a graph for which there exist integers b_i,c_i,i=0,...,d such that for any two vertices x,y in G and distance i=d(x,y), there are exactly c_i...

ams.org

Advancing research. Creating connections.

arxiv.org

We perform a behavioral experiment of karma, a class of mechanisms for repeated resource allocation with attractive fairness and efficiency properties, in theory. Individuals in these mechanisms bid non-tradable credits that flow from resource consumers to yielders, like karma. Human subjects recruited on Amazon MTurk are repeatedly and randomly paired to bid karma according to time-varying and stochastic individual preferences or urgency to acquire resources. Treatments varied in the dynamic urgency process (frequent moderate urgency versus sporadic high urgency) and the richness of the bidding scheme (binary versus full range). Results are benchmarked against random allocation, and karma achieves a (almost) Pareto improvement over random, despite the MTurk subjects deviating significantly from the theoretically optimal Nash bidding policy. Maximum improvement is attained by subjects that deviate from Nash by up to one karma bid unit on average, and positive improvement is attained with average deviations of up to 3-4 bid units. These findings hold across all treatments, among which no significant differences are found, with the exception of the sporadic high urgency process with binary bidding treatment being (weakly) favorable over others. These results offer behaviorally robust lower bounds for the expected performance of karma in human populations. They also provide guidance for future testing and implementation of karma mechanisms in the real world.

mathoverflow.net

Q&A for professional mathematicians

semanticscholar.org

Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research.