Spigit, Inc.

Other

Lisa Purvis

Manas Hardas

James Joseph Gardner

Kate Bennet

A software voting or prediction system iteratively solicits participant preferences between members of a set, with a binary tree built used to minimize the number of iterations required. As each member of the set is considered, it is pairwise-compared with select members represented by nodes already in the binary tree, with iterations beginning at a root node of the tree and continuing to a leaf node. The newly considered member is placed as a new leaf node, and the tree is height-rebalanced as appropriate. Red-black tree coloring and tree rotation rules are optionally used for this purpose. Yes/no preference tallies are kept for each member of the set throughout the tree-building process and are ultimately used for scoring. Height-rebalancing of the tree helps minimize the number of iterations needed to precisely score each member of the set relative to its alternatives.

Scoring members of a set dependent on eliciting preference data amongst subsets selected according to a height-balanced tree

The present invention proposes that the value of a crowd network comes from network entropy and a very well engaged crowd has more value than a poorly engaged crowd. The engagement (and the resultant value generated from the engagement) of a crowd network can be measured in terms of the entropy of the network. The entropy is calculated as a function of the probability distribution of incoming and outgoing messages, which represents the entropy or uncertainty in the activity over the network. In this way, the activity occurring over a network in terms of message exchange is translated as a measure of the value of the network. An evolutionary algorithm is presented to optimize the entropy of a network by successively changing the network topology. Results indicate that the value of a crowd network very closely relate to its small world-ness, sparsity, and connectedness.

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Pleasanton, CA, United States of America

Lisa Purvis