Multiscale decision-making: Bridging temporal and organizational scales in hierarchical systems

Effective decision-making is a key prerequisite for a successful organization. Today’s organizations are large and continue to grow in size and scope. This development leads to higher complexity in managing and controlling organizations. Consequentially, selecting the right course of action has become more difficult for the individual decision-maker. Considering only immediate and local effects of actions reduces decision complexity but also decision quality. Effective decision-makers need to take into consideration the consequences of their actions on different time and organizational scales. In our research we develop a framework for multiscale decision-making, which gives decision-makers the ability to make opportune decisions in the face of multiscale system properties. Also, we provide organizations with a tool with which they can gauge the consequences of various organizational parameters on hierarchically interacting agents over multiple organizational and temporal scales. We begin our investigation with a model of two hierarchically interacting agents in a superior-subordinate relationship. The agents influence each other\u27s rewards and chances of success with their decisions. This bi-directional influence creates a game-theoretic situation. Using the concept of Nash equilibria, we determine the agents\u27 optimal strategies for different organizational parameters. Results are presented through phase diagrams, which graphically capture how variations in organizational parameters affect agent behavior. We extend this model of hierarchical interaction between two agents to a generalized tree-structured interaction network of many agents. This network resembles the typical organizational form of an enterprise. We visualize the hierarchical agent interaction with dependency graphs, which provide a compact representation of the organization and the associated parameters. In a final step, we extend the one-period model to allow for multiple time periods. We use Markov decision processes to model the multi-time-scale interactions. We take into consideration that decisions on lower organizational scales are made at a higher frequency than decisions at higher organizational scales. By considering the interdependence of decision frequency and hierarchal level, we fuse the temporal scale with the organizational scale which results in one comprehensive multiscale decision-making model