Refining self-propelled particle models for collective behaviour

Swarming, schooling, flocking and herding are all names given to the wide variety of collective behaviours exhibited by groups of animals, bacteria and even individual cells. More generally, the term swarming describes the behaviour of an aggregate of agents (not necessarily biological) of similar size and shape which exhibit some emergent property such as directed migration or group cohesion. In this paper we review various individual-based models of collective behaviour and discuss their merits and drawbacks. We further analyse some one-dimensional models in the context of locust swarming. In specific models, in both one and two dimensions, we demonstrate how varying the parameters relating to how much attention individuals pay to their neighbours can dramatically change the behaviour of the group. We also introduce leader individuals to these models with the ability to guide the swarm to a greater or lesser degree as we vary the parameters of the model. We consider evolutionary scenarios for models with leaders in which individuals are allowed to evolve the degree of influence neighbouring individuals have on their subsequent motion

Feature-Aware Verification

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl

Developing a conceptual model for exploring emergence

Emergence is a fundamental property of complex systems and can be thought of as a new property or behaviour which appears due to non-linear interactions within the system; emergence may be considered to be the 'product' or by-product of the system. For example, within social systems, social capital, the World Wide Web, law and indeed civilization in general may be considered emergent, although all within different time scales. As our world becomes increasingly more interconnected, understanding how emergence arises and how to design for and manage specific types of emergence is ever more important. To date, the concept of emergence has been mainly used as an explanatory framework (as used by Johnson 2001), to inform the logic of action research (Mitleton-Kelly 2004) or as a means of exploring the range of emergent potential of simulation of real complex systems (Axelrod 2003). If we are to improve our ability to manage and control emergence, we need first to directly study the phenomenon of emergence, its causes and consequences across real complex systems

Self-Configuring Socio-Technical Systems: Redesign at Runtime

Modern information systems are becoming more and more socio-technical systems, namely systems composed of human (social) agents and software (technical) systems operating together in a common environment. The structure of such systems has to evolve dynamically in response to the changes of the environment. When new requirements are introduced, when an actor leaves the system or when a new actor comes, the socio-technical structure needs to be redesigned and revised. In this paper, an approach to dynamic reconfiguration of a socio-technical system structure in response to internal or external changes is proposed. The approach is based on planning techniques for generating possible alternative configurations, and local strategies for their evaluation. The reconfiguration mechanism is presented, which makes the socio-technical system self-configuring, and the approach is discussed and analyzed on a simple case study

Solar Flux Emergence Simulations

We simulate the rise through the upper convection zone and emergence through the solar surface of initially uniform, untwisted, horizontal magnetic flux with the same entropy as the non-magnetic plasma that is advected into a domain 48 Mm wide from from 20 Mm deep. The magnetic field is advected upward by the diverging upflows and pulled down in the downdrafts, which produces a hierarchy of loop like structures of increasingly smaller scale as the surface is approached. There are significant differences between the behavior of fields of 10 kG and 20 or 40 kG strength at 20 Mm depth. The 10 kG fields have little effect on the convective flows and show little magnetic buoyancy effects, reaching the surface in the typical fluid rise time from 20 Mm depth of 32 hours. 20 and 40 kG fields significantly modify the convective flows, leading to long thin cells of ascending fluid aligned with the magnetic field and their magnetic buoyancy makes them rise to the surface faster than the fluid rise time. The 20 kG field produces a large scale magnetic loop that as it emerges through the surface leads to the formation of a bipolar pore-like structure.Comment: Solar Physics (in press), 12 pages, 13 figur

Measuring autonomy and emergence via Granger causality

Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness