Abstract: For several decades, the scientific means of grappling with complexity in human organizations has seen a strong disparity between broad qualitative models at one extreme, or, more recently, heavily data-driven workflow or agent-based models. In this talk, I explore the space for a model based on somewhat elementary coupled differential equations that also invokes more contemporary mathematics from network and complexity science. The model is a stochastic adaptation of the famous Kuramoto model of network coupled oscillators but represents both the social dimension of human organizational work, through graph theory representations, but also individual human cognitive aspects through representation of non-Gaussian stochasticity and the perception-action cycle.

In particular, the stochastic model allows for the modeling of so-called ​“recognition primed” decision-making where leaders may leap ahead in a decision process based on intuition and detecting patterns in the environment. The model also allows for representation of artificial/technological agents such as information objects, artefacts, or decision-support tools. In this respect, the model may genuinely represent distributed socio-technical systems whose function is informing and making decisions.

I show an example of application to an existent human organization of staff and systems in an operations center of a military headquarters. Using data available from a study whose focus was not necessarily aligned with the examination of synchronization of decision-making, I show how the model can be tuned and crudely validated. To this end, large-scale simulations were performed on an high-performance computing cluster, with support from collaborators at the Australian National University, to sample effectively the stochastic system. I illustrate the scope for this model to enable exploration of changes in organizational design, and the impact of future technologies such as artificial intelligence.

Bio: Alex Kalloniatis completed a Ph.D. in theoretical physics at the University of Adelaide.