Abstract: Methods and systems for determining a reallocation of resources are described. A device may determine initial allocation data that indicates a first amount of resources allocated to a plurality of areas. The device may determine a set of attacker expected rewards based on the initial allocation data. The device may determine a set of defender expected rewards based on the attacker expected rewards. The device may determine moving rewards indicating defensive scores in response to movement of the resources among the plurality of areas. The device may determine defender response rewards indicating defensive scores resulting from an optimal attack on the plurality of areas. The device may generate reallocation data indicating an allocation of a second amount of resources to the plurality of areas. The second amount of resources may maximize the moving rewards and the defender response rewards.

Abstract: An apparatus and method for optimizing a process, comprising: receiving live operational data associated with a plurality of sub-processes of a process; selecting a pre-trained regression model from a plurality of pre-trained regression models for each sub-process of the plurality of sub-processes; generating a system-wide optimization model comprising a multi-period mathematical program model, including: one or more decision variables; a plurality of constraints, wherein: a first constraint of the plurality of constraints comprises one of the pre-trained regression models, and a second constraint of the plurality of constraints comprises an operational constraint; and an objective function; generating, via the optimization model, an operating mode trajectory comprising a plurality of intermediate operating modes at a plurality of intermediate times during a planning interval; and displaying a set-point trajectory recommendation in a graphical user interface based on the operating mode trajectory.

Abstract: A system, computer program product, and method is described to provide a visualization tool which portrays the certain equivalent for one or more hypothetical (i.e. synthetic) or real probability distributions p(m), and optionally allows the user to manipulate that representation, resulting in associated changes to the underlying utility function u(m). In a first example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the probability distribution p(m), for a fixed utility function u(m). In a second example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the utility function u(m), assuming one or more fixed probability distributions p1(m), p2 (m), etc. In a third example, the decision maker can provide feedback through the visualization tool that would cause their utility function to be modified.

Abstract: A graphical event model method, system, and computer program product, include learning statistical and causal co-occurrence relationships among multiple event-types of data, requiring no complex input, and generating a representation that explains a mutual dynamic of the multiple event-types in a form of a graphical event model.

Abstract: A method of discovering and presenting associations between events includes discovering causal association scores for pairs of events in an event dataset, and generating a sequence of events based on the causal association scores.

Abstract: A system, method and program product for analyzing long term risk. A system is disclosed that includes a risk system for analyzing long-term risks, including: a risk knowledgebase that includes risk information associated with at least one domain; a risk model builder that builds a representation of a risk model based on inputs from a user interface and the risk knowledgebase, wherein the risk model includes risk factor nodes, risk event nodes and impact nodes; and a risk simulation engine that processes the representation and computes predicted outcomes.

Abstract: Methods and systems for determining a reallocation of resources are described. A device may determine initial allocation data that indicates a first amount of resources allocated to a plurality of areas. The device may determine a set of attacker expected rewards based on the initial allocation data. The device may determine a set of defender expected rewards based on the attacker expected rewards. The device may determine moving rewards indicating defensive scores in response to movement of the resources among the plurality of areas. The device may determine defender response rewards indicating defensive scores resulting from an optimal attack on the plurality of areas. The device may generate reallocation data indicating an allocation of a second amount of resources to the plurality of areas. The second amount of resources may maximize the moving rewards and the defender response rewards.

Abstract: Triggering a prioritized alert and provisioning an action may include receiving historical data associated with a set of projects, the historical data spanning multiple consecutive time periods. A hierarchical data structure is generated that includes occurrences of performance factors in the historical data. Based on the hierarchical data structure, Bayesian scores associated with the performance factors are derived, the Bayesian scores representing likelihood of the performance factors occurring in a given project. The performance factors are ranked based on the Bayesian scores. Based on ranking, an alert and an action may be automatically triggered.

Abstract: A risk modeling system, method and program product. A query orchestrator interfaces with users posing high-level queries and expanding high-level queries into lower level queries. A queryable risk extractor applies lower level queries to available risk-related knowledge to extract potential risks, e.g., to petrochemical resource production in a selected locale. A semantic enrichment unit applies semantic enrichment to extracted potential risks and selectively annotates the enriched results. A risk model builder generates a graphical risk model for display on a display. Risk analyst can use the graphical risk model to augment risk-related intelligence.

Abstract: A system, computer program product, and method is described to provide a visualization tool which portrays the certain equivalent for one or more hypothetical (i.e. synthetic) or real probability distributions p(m), and optionally allows the user to manipulate that representation , resulting in associated changes to the underlying utility function u(m). In a first example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the probability distribution p(m), for a fixed utility function u(m). In a second example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the utility function u(m), assuming one or more fixed probability distributions pi(m), p2 (m), etc. In a third example, the decision maker can provide feedback through the visualization tool that would cause their utility function to be modified.

Abstract: A task effort estimator may determine a probability distribution of an estimated effort needed to complete unfinished tasks in a project based on one or more of a set of completed tasks belonging to a project and attributes associated with the completed tasks belonging to the project, a set of completed tasks not belonging to the project and attributes associated with the completed tasks not belonging to the project, or the combination of both. A project completion predictor may determine a probability distribution of completion time for the project based on the probability distribution of an estimated effort needed to complete the unfinished tasks in the project, and one or more resource and scheduling constraints associated with the project.

Abstract: A method including, for a set of historical and/or ongoing business initiatives, determining key negative and positive performance factors by a computer from a structured taxonomy of negative and positive performance factors stored in a memory; modeling at least one of the performance factors for the ongoing business initiative or a new business initiative at at least one level of the hierarchical taxonomy. The key negative and positive performance factors are modeled based, at least partially, upon a likelihood of occurrence of the key negative performance factors during the business initiative, and based, at least partially, upon potential impact of the key performance factors on the business initiative. The method further includes providing the modeled performance factors in a report to a user, where the report identifies the modeled performance factors, and the potential impact of the at least one modeled performance factor.

Abstract: A method for determining an optimal multi-stage asset management policy includes providing a plurality of decision epochs and a number of admissible asset health levels for each decision epoch, providing a portfolio of assets over the admissible asset health levels in an initial decision epoch, providing a plurality of state transition probabilities between states of an underlying asset health dynamics process for the decision epochs, where each state corresponds to a percentage of assets that has a given health level in a given decision epoch, providing an action set to which admissible actions of the state transition probabilities belong, where an action changes a state transition probability, and determining cost functions of the admissible actions on a per-asset basis, where operational targets impose constraints on probabilities that the asset health of the portfolio of assets, in one or more decision epochs, is within a specified range.

Abstract: Based on a time series history of a random variable representing demand for at least one of a good and a service as a function of at least one controllable demand driver, obtain a quantile regression function that estimates a quantile of a demand distribution function; obtain a mixed- and/or super-quantile regression function that estimates conditional value at risk; and obtain a regression function that estimates mean of the demand distribution function. Joint optimization of: inventory of the at least one of a good and a service, and the at least one controllable demand driver, is undertaken based on the quantile regression function and the mixed- and/or super-quantile regression function, to obtain an optimal value for the at least one controllable demand driver and an implied optimal value for a stocking level. One or more exogenous demand drivers can optionally be taken into account.

Abstract: Adapting an existing portfolio optimizer to support one or more valuated dependencies without modifying the existing portfolio optimizer, may include translating one or more original elements and associated dependencies in a portfolio to be optimized based on said one or more valuated dependencies; invoking the existing portfolio optimizer with the translated one or more original elements and associated dependencies; and translating optimization results, if said optimization results contain translated one or more original elements, into a solution characterized in terms of said one or more original elements.

Abstract: Market demand is estimated for a firm under crisis, beginning with an operational demand forecast of a firm as input, correcting that forecast to account for the impact of a pandemic or other extraordinary market-disrupting circumstance, and providing a corrected operational demand forecast as output. The correction to account for the impact of an extraordinary market-disrupting circumstance is based on an estimated economic impact of the disruption and an estimated reduction of the firm's sales force attributable to the disruption.

Abstract: A task effort estimator may determine a probability distribution of an estimated effort needed to complete unfinished tasks in a project based on one or more of a set of completed tasks belonging to a project and attributes associated with the completed tasks belonging to the project, a set of completed tasks not belonging to the project and attributes associated with the completed tasks not belonging to the project, or the combination of both. A project completion predictor may determine a probability distribution of completion time for the project based on the probability distribution of an estimated effort needed to complete the unfinished tasks in the project, and one or more resource and scheduling constraints associated with the project.

Abstract: The system and method of the present disclosure allow the user to enumerate a set of risk factors for each financial metric time-profile projection, and respond to a set of questions that is linear in the number of risk factors. In one embodiment, the risk elicitation on each risk factor uses user input of likelihood of risk and impact and/or severity. On each of the risk factors for any given nominal, financial projection estimate, the inputs are systematically converted into a net impact distribution for that nominal, financial estimate.

Abstract: A method, system and computer program product for measuring a risk of an asset portfolio. The system estimates a ?-level CVaR (Conditional Value-at-Risk) of the asset portfolio by modeling interdependencies between assets in the asset portfolio. The modeling is based on Gaussian copula model.

Abstract: A method of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network. The method includes developing a multi-regime, regime switching stochastic model for determining day ahead/spot market energy prices using at least one historical profile and subjective opinion from at least one expert; and the multiple regimes correspond to a number of combinations of physical factors. A regime is identifiable by at least three factors. The method thus facilitates identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure.