Prof. WSR Oliver Baumann collaborates with two American colleagues to study resource allocation in hierarchical organizations, and how resources should be distributed between more well-known approaches and more innovative/risky ideas, depending on the structure of the organization.
The following figure illustrates two stylized organizational structures. In the model, a “senior manager” needs to decide how to allocate (limited) resources among his/her “middle managers”, who in turn need to decide how to allocate the resources they received among a set of uncertain alternatives (e.g., projects exploiting different technologies whose value is not fully understood). This set-up captures a typical learning process in organizations. Middle managers are specialists in the sense that they are dealing with concrete projects. By running a project, the middle managers can learn about the value of the underlying technology. Senior managers, on the other hand, are more generalists that learn about their middle managers, i.e., about the aggregate performance of the projects for which a particular middle manager is responsible.
Baumann and his research group develop simulation models that involve Monte Carlo methods. This approach relies on repeated random sampling to obtain numerical results and predict the probability of different outcomes. Therefore, it also creates a need for running many replications of the same model to obtain good approximations. This is where HPC resources come into the game.
“We can do things much faster, and we can do more than we ever could with our own machines. Computation is also cheaper and more easily available, and this means we get to experiment more.”Prof. WSR Oliver Baumann
The HPC resources that Baumann and his group use are distributed between Ohio State University, where his two colleagues work, and UCloud at the DeiC Interactive HPC facility at SDU.