Investigation of long memory in complex multi-state stochastic agent systems (09.3.3-LMT-K-712-19-0017)

Financed by European Social Fund.

We will propose an approximation of a multi-state agent model with a long memory by a single variable model. Simplifying the multi-state agent model by performing a variable elimination procedure would significantly reduce the resources of digital computations and allow us to compare the results of the analyses with analytical approximations derived by ourselves or other authors. Finding analytical approximations would help make the model more accessible and user-friendly in practice.

Duration: 2020-09-01 - 2022-08-31
Postdoctoral fellow: dr. Rytis Kazakevičius
Supervisor: habil. dr. Vygintas Gontis