Abstract: Among other things, there is coordination of the timing and execution (a) of allocations of amounts of real-world resources to competing uses of the resources, relative to (b) dynamic assessments of an overall state of a changing real-world system, in order to alter the overall state of the real-world system. A request is received for an allocation of an amount of a real-world resource of the real-world system to a particular use of the resource as of a particular allocation time. From time to time, information is ingested representing states of facets of the real-world system, to maintain the information current relative to the particular allocation time. A current overall state of the real-world system is dynamically assessed, based on the current information representing states of the facets of the real-world system, as of a time that is current relative to the particular allocation time.

Abstract: Among other things, records are maintained for machine-executable intelligent assistance agents. The record for an assistance agent includes information about (1) a user to which the agent belongs, (2) a domain or facet of activity of the user, (3) a goal of the assistance agent within the domain or facet of activity, and (4) a principle of conduct of the assistance agent. Each assistance agent is executed to provide assistance only to the user to which the assistance agent belongs, only in the domain or facet, and only in accordance with the goal and the principle of conduct.

Abstract: Among other things, improvements are made in the operation of a computer that processes input data values and generates internal data values that depend on the input data values and on mappings defining the processing. Input data values are received from input data sources. The input data values conform to input data concepts. The input data values are processed to map them according to specified mappings to internal data values that conform to internal data concepts. At least one of the mappings is (a) from input data values that conform respectively to two or more different input data concepts or (b) to internal data values that conform respectively to two or more different internal data concepts, or (c) both. Updated information is applied about characteristics of at least one of the mappings that will affect the internal data values that are the result of the processing, the information being updated as late as the beginning of the processing of the input data values.

Abstract: Among other things, data is received representing a case for which an output premise is to be generated. The received data is processed to generate the output premise for the case. The processing includes both analytical processing and statistical processing. The analytical processing in the statistical processing are managed so that the output premise depends on results of the analytical processing that have been affected by the statistical processing, or on results of the statistical processing that have been affected by the analytical processing, or on both.

Abstract: Among other things, data is received representing a case for which an output premise is to be generated. The received data is processed to generate the output premise for the case. The processing includes both analytical processing and statistical processing. The analytical processing in the statistical processing are managed so that the output premise depends on results of the analytical processing that have been affected by the statistical processing, or on results of the statistical processing that have been affected by the analytical processing, or on both.

Abstract: Among other things, there is regularly received through a communication network from providers of financial products or from an aggregator or both, current information about transactions that occur in accounts of consumers of financial products that are maintained with providers of the financial products. The received current transaction information is stored in a database of information about the respective consumers. Machine learning is applied to the stored transaction information and other information about the consumers in the database to generate model profiles of transactions in accounts of corresponding categories of consumers for corresponding financial products. As current information about transactions is received, transactions that have occurred in the accounts of the consumers of the financial products are analyzed using the model profiles for the applicable categories of customers and financial products.