The complexity of modern military operations create a demand for efficient collaborative decision making and problem solving. Additionally, as military units operate in increasingly dynamic environments, the ability to respond to changing circumstances becomes paramount for mission success. An effective response rests on correct dissemination and transfer of information across the command and control structure, and thus is critically linked to the network of human interactions. In this paper, we take an agent-based modeling approach to collective problem solving. We investigate three key factors affecting the performance in collaborative environments: (1) the structure of network used to share information between agents, (2) the search strategies adopted by agents, and (3) the complexity of problems facing the group. In particular we study how the trade-off between exploitation of known solutions and exploration for novel ones is related to the efficiency of collective search. Additionally we consider the role of agent behavior: propensity for risk-taking and trustworthiness, as well as the dynamic nature of social connections. Finally, we outline the directions for future work regarding the efficiency of problem solving on military-like command and control structures.
KEYWORDS: Social networks, Analytical research, Data modeling, Algorithm development, Network security, Network architectures, Statistical analysis, Information science, Systems modeling, Web 2.0 technologies
Within networks one can identify motifs that are significant recurring patterns of interaction between nodes. Here motifs are sub-graphs that occur more frequently than would be explained by random connections. Graphs can be used to model internal network structures of human groups, or links between groups, with group dynamics being governed by these structures. Graphs can also model behavior in engineered systems, and internal network structures can significantly affect dynamic behavior. A graph may only be partially visible (such as in hostile or coalition environments), however detectable network motifs may in some cases be reflective of the entire graph. We outline a research plan and describe basic network motifs and their properties, along with current analytic techniques for static and dynamic settings. We offer suggestions as to how network motif techniques can be applied to intra- or inter- group behavior, for example to detect whether multiple groups behave as a co-operative alliance, or whether coalition networks inter-operate in positive ways. As an example, we examine a complex time-series graph dataset relevant to coalition focused aspects of the class of networks under study, specifically related to the social network resulting from the authorship of academic papers within a coalition. We provide details of the basic analysis of this network over time and outline how this can be used as one of the datasets for our planned network motif research activities, especially with regards to the temporal and evolutionary aspects.
KEYWORDS: Modeling, Visualization, Analytical research, Defense and security, Visual process modeling, Social psychology, Systems modeling, Computer simulations, Computer science
Psychological theories of inter-group behaviour offer justified representations for interaction, influence, and motivation for coalescence. Agent-based modelling of this behaviour, using evolutionary approaches, further provides a powerful tool to examine the implications of these theories in a dynamic context. In particular, this can enhance our understanding of the escalation of hostility and warfare, and its mitigation, contributing to policy and interventions. In this paper we propose a framework through which social psychology can be embedded in computation for the examination of inter-group behaviour. We examine how various social-psychological theories can be embedded in evolutionary models, and identify ways in which visualisation can support the objective assessment of emergent behaviour. We also discuss how real-world data can be used to parameterise scenarios on which modelling is conducted.
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