storing, in a storage, one or more agent complex probability distribution definitions, wherein each agent complex probability distribution definition comprises a plurality of agent attribute probability distribution definitions and agent behavior probability distribution definitions;

receiving, for a first simulation, a first simulation specification, wherein the first simulation specification comprises a first list of agent complex probability distribution definitions;

generating, using a random number generator, first agent attribute probability distributions for each of the first list of agent complex probability distribution definitions;

generating, based on the first agent attribute probably distributions, the first simulation;

generating, based on the first simulation and using the random number generator, first agent behaviors for first steps of the first simulation;

outputting, based on the first steps of the first simulation, a first synthetic dataset;

receiving, for a second simulation, a second simulation specification, wherein the second simulation specification comprises a second list of agent complex probability distribution definitions;

generating, using the random number generator, second agent attribute probability distributions for each of the second list of agent complex probability distribution definitions;

generating, based on the second agent attribute probably distributions, the second simulation;

generating, based on the second simulation and using the random number generator, second agent behaviors for second steps of the second simulation;

outputting, based on the second steps of the second simulation, a second synthetic dataset; and

training, based on the second synthetic dataset, a machine-learning model,

wherein the first list of agent complex probability distribution definitions and the second list of agent complex probability distribution definitions include at least one common agent complex probability distribution definition.