Abstract: Example methods and systems are directed to simulating conditions for decision making. To help with decision making, simulated conditions may be used to generate probabilities of different events. Historical time-series data may be used to generate a probability distribution of values for simulated conditions. A user may be enabled to modify the probability distribution that was generated from the historical time-series data. The relationship between the value being simulated and other values may be represented by a knowledge graph. The knowledge graph may include nodes that represent arithmetic operations, input variables, external functions, database queries, and predictions. By running thousands of simulations with different values for input variables, as determined by the probability distributions for the input variables, a range of possible outcomes and their probabilities is generated. The simulation results are presented to a user to facilitate decision making.

Abstract: Information about time consumed in a task is combined with employment data about users performing the task to determine an associated cost of time. Based on the cost of performing the task and a predicted cost of automating the task, a predicted savings resulting from automation of the task is determined. From among multiple processes that are available to be automated, a recommendation is made to automate the task that benefits from the greatest savings. A user interface may be presented that identifies multiple processes and the automation savings available for each one. In response to an interaction with the user interface, bots are generated to automate the steps of a process.