Self-organizing particle systems

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Advances in Complex Systems following peer review. The version of record, Malte Harder and Daniel Polani, ‘Self-organizing particle systems’, Advs. Complex Syst. 16, 1250089, published October 22, 2012, is available online via doi: https://doi.org/10.1142/S0219525912500890 Published by World Scientific Publishing.The self-organization of cells into a living organism is a very intricate process. Under the surface of orchestrating regulatory networks there are physical processes which make the information processing possible, that is required to organize such a multitude of individual entities. We use a quantitative information theoretic approach to assess self-organization of a collective system. In particular, we consider an interacting particle system, that roughly mimics biological cells by exhibiting differential adhesion behavior. Employing techniques related to shape analysis, we show that these systems in most cases exhibit self-organization. Moreover, we consider spatial constraints of interactions, and additionaly show that particle systems can self-organize without the emergence of pattern-like structures. However, we will see that regular pattern-like structures help to overcome limitations of self-organization that are imposed by the spatial structure of interactions.Peer reviewe

An Information Theoretic Investigation Of Complex Adaptive Supply Networks With Organizational Topologies

Supply networks exist throughout society in manufacturing and knowledge-intensive industries as well as many service industries. Organizations have been noted to behave as complex adaptive systems or information supply networks with both formal and informal structures. Thoroughly understanding supply network structure and behavior are critical to managing such organizations effectively, but their properties of complex adaptive systems make them more difficult to analyze and assess, forcing researchers to rely on unrealistic data or assumptions of behavior. This research proposes an information theoretic methodology to discover such complex network structures and dynamics while overcoming the difficulties historically associated with their study. Indeed, this was the first application of an information theoretic methodology as a tool to study complex adaptive supply networks. Moreover, managing these complex networks with formal and informal structures poses additional challenges because the effects of intervention can result in even more unpredictable effects. Noting that two primary functions of organizational networks are to transfer information between nodes and store information in the network, this research quantifies the effects of increased and decreased node performance on the ability of multiple organizational network topologies to accomplish these tasks. Multiple qualitative observations from previous researchers are quantitatively analyzed using information theoretic modeling and simulation. Results show an increased ability in local teams to store information within the network as well as a decreased ability by core-periphery networks to respond to increased information rates

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Measuring Information Storage and Transfer in Swarms

Spatial aggregation of animal groups give individuals many benefits that they would not be able to obtain otherwise. One of the key questions in the study of these animal groups, or “swarms”, concerns the way in which information is propagated through the group. In this paper, we examine this propagation using an information-theoretic framework of distributed computation. Swarm dynamics is interpreted as a type of distributed computation. Two localized informationtheoretic measures (active information storage and transfer entropy) are adapted to the task of tracing the information dynamics in a kinematic context. The observed types of swarm dynamics, as well as transitions among these types, are shown to coincide with well-marked local and global optima of the proposed measures. Specifically, active information storage tends to maximize as the swarm is becoming less fragmented and the kinematic history begins to strongly inform an observer about the next state. The peak of transfer entropy is observed to appear at the final stages of merging of swarm fragments, near the “edge of chaos ” where the system actively computes its next stable configuration. Both measures tend to minimize for either unstable or static swarm configurations. The results here show these measures can be applied to non-trivial models, most importantly, they can tell us about the dynamics within these model where we can not rely on visual intuitions