Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include agent-based models (ABMs) in which the agent definitions are decoupled from the simulation. In one or more aspects, some agents may have attributes and associated behaviors that permit them to be reused in different ABMs to simulate different systems.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: Computer-implemented methods and systems are provided for a procurement system. Consistent with disclosed embodiments, the procurement system may include at least one processor and at least one non-transitory memory containing instructions that, when executed by the at least one processor, cause the procurement system to perform operations. The operations may include creating, in a blockchain, a contract corresponding to a procurement request including a first procurement parameter, the contract including contract code enforcing procurement obligations. The operations may include receiving, from at least one user system, at least one additional procurement request including at least one second procurement parameter and indicating the contract. The operations may also include generating a contract execution result by executing the contract code using the first procurement parameter and the at least one second procurement parameter; and updating the blockchain based on the contract execution result.