DIGITAL DEMOCRACY BENEFICIARY MANAGEMENT SYSTEM USING BAYESIAN STATISTICS, MACHINE LEARNING AND VOTING LOGIC

摘要

Digitized computer democracy systems allow for agents to make decisions on the behalf of clients and or beneficiaries by virtually polling their beliefs as conditions change over time. In the proposed innovation artificial intelligence, machine learning, and data analytics are used to dynamically predict the most important states to survey in a process. Beneficiaries are then surveyed about their preferences over various options in these surveyed states. When one of the states that is surveyed occurs, then voting logic and economic theory is used to make a decision on the appropriate course of action. When a state that has not been surveyed occurs, Bayesian methods are used to dynamically predict what a user's ballot would have looked like if that state had been surveyed. This Bayesian prediction is then tested, and if the result is found to be robust then a decision is reached using voting logic and economic theory. If the results are not robust, then the process is rerun to determine the most important states to survey. Using these analytic tools and techniques, the proposed invention provides a framework for practical and robust digital democracy applications for client and beneficiary management.