Modelling distributed decisionmaking in Command and Control using stochastic network synchronisation Abstract   We advance a mathematical representation of Command and Control as distributed decision makers using the Kuramoto model of networked phase oscillators. The phase represents a continuous PerceptionAction cycle of agents at each network node; the network the formal and informal communications of human agents and information artefacts; coupling the strength of relationship between agents; native frequencies the individual decision speeds of agents when isolated; and stochasticity temporal noisiness in intrinsic agent behaviour. Skewed heavytailed noise captures that agents may randomly xe2x80x98jumpxe2x80x99 forward (rather than backwards) in their decision state under time stress; there is considerable evidence from organisational science that experienced decisionmakers behave in this way in critical situations. We present a usecase for the model using data for military headquarters staff tasked to drive a twentyfour hour xe2x80x98battlerhythmxe2x80x99. This serves to illustrate how such a mathematical model may be used realistically. We draw on a previous casestudy where headquartersxe2x80x99 networks were mapped for routine business and crisis scenarios to provide advice to a military sponsor. We tune the model using the first data set to match observations that staff performed synchronously under such conditions. Testing the impact of the crisis scenario using the corresponding network and heavytailed stochasticity, we find increased probability of decision incoherence due to the high information demand of some agents in this case. This demonstrates the utility of the model to identify risks in headquarters design, and potential means of identifying points to change. We compare to qualitative organisational theories to initially validate the model.

Plasticity in Collective DecisionMaking for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lockins Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decisionmaking. Despite intensive research on models of collective decisionmaking, the implementation in multirobot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the decisionmaking process and by increasing the scenario difficulty. Most studies on largescale multirobot decisionmaking are limited to one instance of an iterated explorationdissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly. This is only possible by preventing lockins, a global consensus state of no return (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hopcount information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.