The Structure and Dynamics eJournal welcomes articles, book reviews, data, simulations, research material, and special issues that examine aspects of human evolution, social structure and behavior, culture, cognition, or related topics. Our goal is to advance the historic mission of anthropology in the broadest sense to describe and explain the range of variation in human biology, society, culture and civilization across time and space. Submissions of databases, software tutorials, programs, and teaching materials are welcomed, as are communications on research materials of interest to a wide variety of science and social science researchers, including networks, dynamical models, and complexity research and related genre.
Volume 6, Issue 2, 2013
This paper examines the effectiveness of partition in ceasing violence during ethnic conflict. Wigmore-Shepherd’s 2012 study argued that ethnic conflict is often due to the congruence between ethnic and political identity, allowing political conflicts to become ‘ethnicised’ and ethnic conflict to eclipse the original political dispute. Therefore this paper hypothesises that ethnic homogenisation via partition can allow the original political conflict to re-emerge in a potentially violent manner. The hypothesis is tested by an agent based model adapted from the model used in the 2012 study. The model finds that in the instances where there is not a perfect congruence between ethnic and political identity, politically motivated violence does persist in the ethnic enclaves. It was found that a lower level of congruence would result in a higher level of post-partition violence. Furthermore the act of migration itself can encourage spikes of ethnically motivated violence and agents of different ethnicity cross paths to reach their enclave.
Robust Intelligence (RI) under uncertainty: Mathematical foundations of autonomous hybrid (human-machine-robot) teams, organizations and systems
To develop a theory of Robust Intelligence (RI), we continue to advance our theory of interdependence on the efficient and effective control of systems of autonomous hybrid teams composed of robots, machines and humans working interchangeably. As is the case with humans, we believe that RI is less likely to be achieved by individual computational agents; instead, we propose that a better path to RI is with interdependent agents. However, unlike conventional computational models where agents act independently of neighbors, where, for example, a predator mathematically consumes its prey or not as a function of a random interaction process, dynamic interdependence means that agents dynamically respond to the bi-directional signals of actual or potential presence of other agents (e.g., in states poised to fight or flight), a significant increase over conventional modeling complexity. That this problem is unsolved, mathematically and conceptually, precludes hybrid teams from processing information like human teams operating under challenges and perceived threats. To simplify this problem, we use bistable models for interdependence with a focus on teams and firms as we increase complexity to the level of systems. As part of the problem, in this paper, and countering simplification, sentient multi-agent systems require an aggregation process like data fusion. But the conventional use of fusion for the control of mobile systems hinges on mathematical convergence into patterns, increasing uncertainty whenever divergent information has the potential to process information into knowledge. The goals of our research are: First, to analyze why valid models of interdependence are difficult to build. Second, to reduce uncertainty in decision-making by moderating convergence processes in data aggregation (e.g., fusion) with differential clustering between alternative (orthogonal) views that check convergence processes and promote information processing (e.g., second opinions from independent physicians; prosecutor-defense attorneys; Republicans-Democrats in Congress; opposed scientists, like Bohr-Einstein). Third, in line with our theoretical expectations, we plan to lay the groundwork for agent-based systems to model the stability from the cooperative contexts associated with teams, and the instability from the competitive contexts associated with multiple teams or firms that constitute systems. Our result will be a new theory of interdependence; a new model of data aggregation; and new agent-based models of interdependence.
Archaeological, historical, and ethnographic sources on the pastoralism of Inner Asia provide evidence for a resilient, but highly volatile steppe adaptation that developed several thousand years ago. This study explores some fundamental aspects of pastoralist settlement and social systems as they developed following the Bronze Age. The analysis uses the agent-based computational model, HouseholdsWorld, to simulate aspects of mobility, population density, kinship structures, and herd dynamics relating to emerging social territories and the implications for sustainable landscape use. Comparisons with archaeological data show the potential impacts of social controls on habitation distributions and mobility. When overarching social controls were in place distinctive territorial differences emerged. When social controls were less centralized individual households became wealthier. In regions with dense populations, expanding the scope of landscape knowledge allowed micro-mobility to effectively mitigate social restrictions. As a result population expanded, but became poorer. In less densely inhabited regions greater knowledge of the landscape expanded the mid-range of wealth distribution without expanding the number of poor.
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