Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.

 

Marshall JAR, Reina A, Bose T (2019) Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths. PLoS ONE 14(9): e0222906. https://doi.org/10.1371/journal.pone.0222906

Source: journals.plos.org

Self-organization is a general mechanism for the creation of new structural pattern of systems. A pattern, in essence, is a relationship, an architecture, a way of organizing, and a structure of order, which can only be explained by information activities. The characteristics of self-organization behavior, such as openness, nonlinearity, inner randomness, inner feedback, information network, and holographic construction, provide corresponding conditions and basis for the self-organizing evolution of the system from the aspects of environmental information function, maintenance and construction of the overall information framework of the system, and exploration of new information mode of the system. Based on the general process and mechanism of self-organization system evolution, its corresponding basic stages have the significance and value of information activities. Generally speaking, the process of system elements differentiating from the original system is the decoupling of information association between relevant elements and original systems. The convergence process of forming system elements is the initial exploration of forming a new information model; the nucleation process of some initial stabilization modes is the creation of information codons; the development of the system according to a particular pattern is ergodic construction of information feedback chain indicated by information codon; the diffusion of system self-replication is the expansion of the quantity of the information model; the variation in system self-replication is the innovation process of introducing new information pattern; environment-based selection and evolution correspond to the complex development of information pattern; and the alternation of old and new structures in system evolution corresponds to the formation process of the whole information network framework of the new system. In order to explain the self-organization’s characteristics, processes, and mechanisms of system evolution at a more comprehensive level, the complexity research program must pay enough attention to and give due status to the information factors and information science creed. Moreover, the information science research creed may also provide some basic theoretical paradigms with core theoretical significance for complex system research.

 

Information Characteristics, Processes, and Mechanisms of Self-Organization Evolution
Kun Wu and Qiong Nan

Complexity
Volume 2019, Article ID 5603685, 9 pages
https://doi.org/10.1155/2019/5603685

Source: www.hindawi.com

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

 

Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test

M.S. Talamali, T. Bose, M. Haire, X. Xu, J.A.R. Marshall, A. Reina. Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test. Swarm Intelligence 14(1):in press, 2020.
https://link.springer.com/article/10.1007/s11721-019-00176-9

Video: https://youtu.be/osQYuQ3cxmQ

Source: link.springer.com

Epidemics may contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex. There have been counter-intuitive observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. The article introduces a set of mathematical models to understand the role of conflicts in epidemics. The corresponding mathematical framework has the potential to explain the counter intuitive observations and the complex role of human conflicts in epidemics. This work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in conflict zones.

This approach exemplifies the role of non-linear thinking in complex systems like human societies. The work presented should be looked upon as a first step towards a quantitative model of disease spread in conflicts.

 

Towards a quantitative model of epidemics during conflicts

Soumya Banerjee

INDECS

Source: indecs.eu