Concepts inLearning and incentives in user-generated content: multi-armed bandits with endogenous arms
User-generated content
User-generated content (UGC) covers a range of media content available in a range of modern communications technologies. It entered mainstream usage during 2005, having arisen in web publishing and new media content production circles. Its use for a wide range of applications, including problem processing, news, gossip and research, reflects the expansion of media production through new technologies that are accessible and affordable to the general public.
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Binomial distribution
In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Such a success/failure experiment is also called a Bernoulli experiment or Bernoulli trial; when n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance.
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Gamma distribution
In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. There are two different parameterizations in common use: With a shape parameter k and a scale parameter ¿. With a shape parameter ¿ = k and an inverse scale parameter ¿ = ¿¿, called a rate parameter. The parameterization with k and ¿ appears to be more common in econometrics and certain other applied fields, where e.g.
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Probability
Probability is ordinarily used to describe an attitude of mind towards some proposition of whose truth we are not certain. The proposition of interest is usually of the form "Will a specific event occur?" The attitude of mind is of the form "How certain are we that the event will occur?" The certainty we adopt can be described in terms of a numerical measure and this number, between 0 and 1, we call probability.
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Multi-armed bandit
In probability theory, the multi-armed bandit problem is the problem a gambler faces at a row of slot machines when deciding which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.
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Nash equilibrium
In game theory, Nash equilibrium (named after John Forbes Nash, who proposed it) is a solution concept of a game involving two or more players, in which each player is assumed to know the equilibrium strategies of the other players, and no player has anything to gain by changing only his own strategy unilaterally.
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Time complexity
In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the size of the input to the problem. The time complexity of an algorithm is commonly expressed using big O notation, which suppresses multiplicative constants and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e. , as the input size goes to infinity.
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