Abstract: 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.

Abstract: The disclosure is directed to a system, method, and computer program for predicting the value of an idea based on crowd input. Users are prompted to vote as to whether variables for an idea (e.g., cost, time to implement, revenue/cost-savings) are greater than or less than proposed values. The proposed values are derived from probability distributions for the variables. Each time a vote is received for a variable, a new probability distribution for the variable is created, wherein parameters of the distribution (e.g., mean, shape parameter) are based on the vote data. The polling continues for each of the variables until a poll termination event occurs. The mean of the final probability distribution for each variable represents the system's estimate (or prediction) of the crowd's consensus value for the variable. Ideas are ranked based on the final consensus value estimates for the ideas.

Abstract: 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.

Abstract: 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.

Abstract: A rating system provides a mechanism whereby users can submit objects to be rated (ROs), and whereby users can submit ratings (ARs) regarding the ROs of other users. In a first novel aspect, each AR is multiplied by a weighting factor to determine a corresponding effective rating (ER). The weighting factor that is used to determine an ER from an AR is a function of the reputation RPT of the user who submitted the AR. In a second novel aspect, the weighting factor is also a function of a crowd voting probability value PT. In a third novel aspect, the weighting factor is also a function of the freshness RF of the AR. In a fourth novel aspect, a decay value D is employed in determining a user's reputation. ERs are used to determine a ranking of ROs. User reputation is used to determine a ranking of users.

Abstract: This disclosure provides a database, methods and associated software to implement and manage a prediction market. By collecting individual predictions as a set of predicted outcomes, such as a range of outcomes, the principles presented herein permit aggregation of overlapping predictions to generate a cumulative probability distribution, effectively using “crowd wisdom” to build a probability model of an event. Through the use of “roll-up” and “roll-down” techniques, this disclosure provides a way of applying new information not only to an event being predicted, but also to the outcomes of other, related events. Conversion to a common probability distribution format may be used to simplify and accelerate mathematical operations, easing the burden in quickly calculating and updating a cumulative probability model for events in each affected dimension in the predicted market.

Abstract: Various problems currently exist in the operation of using prediction markets for corporate forecasting. One of these problems we address is the problem related to the “consistency and consolidation of metrics”. We hierarchically structure and link sets of markets traded using an automated market maker and dynamically balance markets.

Abstract: An idea collaboration system includes an innovation market that accepts and displays user-submitted ideas; a discussion section that allows registered users to comment on and ranking ideas; and an idea merit section that displays an indication of merit for the ideas. The indication of merit for each idea is based on at least: (i) a number of different registered users who have submitted comments for the idea; (ii) a determination of user interest for the idea; and (iii) a reputation rank for registered users who provided a positive ranking for the idea.

Abstract: A method of ranking user reputation in a multi-user network includes using a display interface to display various concepts for discussion to multiple users of the network. The method includes receiving, from a first registered user via a data interface, a discussion post and a ranking for one of the displayed concepts. Other registered users submit responses to the first registered user's discussion post, such that each response includes an indication of whether the response is positive or negative. The method includes determining, via a processing device, a reputation rank for the first registered user. The reputation rank calculation is based on a number of discussion posts received from that registered user, as well as a number of positive responses received from the other registered users for the first registered user's discussion posts. The method includes displaying the reputation rank to the registered users via the display interface.