Abstract: In the disclosure of the present innovation the system utilizes artificial intelligence, machine learning, and data analytics for practical and robust digital democracy applications to dynamically predict the most important states to survey in a process. In the present invention, beneficiaries are surveyed about their preferences over various options in these surveyed states. In the current invention, 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 and 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. In the system of the current invention, if the results are not robust, then the process is rerun to determine the most important states to survey.

Abstract: Data Shapley is an approach to understand the role of data in a decision-making process. The present invention involves a process to connect Data Shapley to a data analytics and machine learning based decision-making environment through the use of utility functions. In the present invention a problem is structurally analyzed using machine learning and data analytics to determine structural trends. Data is then analyzed using Data Shapley to determine what additional information is needed to make a decision. This allows for the relevant data to be collected to estimate utility functions for participants. Data Shapley is then used again to decompose the decision-making process and look for trends in the process, and machine learning is applied to see if there are commonalities across the criteria in the decision-making process. After this, the decision-making process selects a strategy as the decision.

Abstract: A business process is presented to analyze data using an ensemble of methods in a dynamic environment that adjusts and reconfigures the analytical methods and procedures based on the preferences of the user. Data is analyzed and cleaned to allow for analysis to allow for dynamic modeling and analysis using an ensemble, whereas an ensemble is a mix of multiple analytical procedures such as Long Short-Term Memory and regression in unison. This ensemble is dynamically optimized and adjusted using methods such as Markov Chain Monte Carlo to allow for efficient and scalable operations. These methods allow for dynamic systems that allow for modularity, such as the option to add stochastic memory to the system. Once the user is provided with an output from the system, the modularity of the system, combined with the efficient and scalable implementation, allows for the system to adjust itself based on inputs and the desire of the user.

Abstract: A business process is presented to construct a system for analyzing liquidity preferences, recommending a portfolio of assets, and recommending steps to rebalance a portfolio. Initial preferences over assets are elicited from a user, which are used to construct a universe of potential assets. An initial portfolio passes through an analytics process that uses decision theory, machine learning and time-series econometrics to characterize the relationship between assets in the universe of potential assets and assets in the initial portfolio. Liquidity structures among these assets are characterized across time, and a Markov Chain Monte Carlo based search process is run across these assets. Data analytics is used to further characterize the results of this search process, and the results are presented to the client via a user interface system.

Abstract: There is a need for dynamic and scalable optimization solutions to allow for generic portfolio construction as a way to enhance portfolio-suitability for clients. In the present innovation, a generalized asset mixer is optimized for client-specific portfolios by incorporating multiple decision-making perspectives in a cloud-based environment. First, the client specifies the constraints by which they wish to construct their portfolio, resulting in a metric which encompasses client preferences. Then, techniques that best encapsulate this metric are used to construct technique-based portfolios. Based on the importance of each of these constraints, a weight is assigned to each individual portfolio in order construct one master-portfolio which includes all common assets in each of the technique portfolios along with other unique assets that are determined through a modified Metropolis-Hastings procedure and, if the frame diverges, an additional Markov Chain weighting iterative procedure.

Abstract: 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.

Abstract: Artificial intelligence and optimization theory in an asset management process to develop a platform for the automated management of money managers in a portfolio construction process. The present innovation includes the process of connecting databases of securities information to asset manager information to create a linked database of information for the overview and management of portfolios. This linked database is then analyzed using a statistical optimization procedure, converted into a series of metrics and compressed and represented by a series of contrasts. These contrasts are analyzed using Data Shapley, Statistical Cointegration and a Democratized Digital Voter System to construct a utility function estimation. This estimated utility is typically high-dimensional in nature and is not always analytically solvable. In order to determine an optimum allocation convex hull optimization processes are run across the function to determine the appropriate weights to assign to each money manager.

Abstract: Data Shapley is an approach to understand the role of data in a decision-making process. The present invention involves a process to connect Data Shapley to a data analytics and machine learning based decision-making environment through the use of utility functions. In the present invention a problem is structurally analyzed using machine learning and data analytics to determine structural trends. Data is then analyzed using Data Shapley to determine what additional information is needed to make a decision. This allows for the relevant data to be collected to estimate utility functions for participants. Data Shapley is then used again to decompose the decision-making process and look for trends in the process, and machine learning is applied to see if there are commonalities across the criteria in the decision-making process. After this, the decision-making process selects a strategy as the decision.

Abstract: Artificial intelligence and machine learning in a clustering process to develop a utility model for asset allocation, engineering and other applications. The present invention defines a group of assets using specialized electronic circuits. The invention provides a utility function preference criterion to a user using a graphics interface implementing a first preference criterion, a second preference criterion and a third preference to be selected by a user. The invention operates in clustering the assets using a multi-level time series clustering approach using machine learning function to establish parameters for a utility function based upon the selected desired preference criterion. The invention operates to pass the assets through the utility function to assign a utility score to each of the assets, rank the assets based upon the assigned utility score of each asset, and create a portfolio of assets.

Abstract: Artificial intelligence and machine learning in a clustering process to develop a utility model for asset allocation, engineering and other applications. The present invention defines a group of assets using specialized electronic circuits. The invention provides a utility function preference criterion to a user using a graphics interface implementing a first preference criterion, a second preference criterion and a third preference to be selected by a user. The invention operates in clustering the assets using a multi-level time series clustering approach using machine learning function to establish parameters for a utility function based upon the selected desired preference criterion. The invention operates to pass the assets through the utility function to assign a utility score to each of the assets, rank the assets based upon the assigned utility score of each asset, and create a portfolio of assets.