Meta-Stability of Interacting Adaptive Agents

The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Moment closure approximations in epidemiology

Moment closure approximation (MCA) is a method of obtaining dynamic deterministic approximations to models where spatiality is important. Such approximations track the time evolution of low-order correlations, for instance the correlation of disease status of nearest-neighbours in a square lattice. Thus they are able to capture aspects of population dynamics which traditional mean-field approximations are unable to. This thesis extends the techniques of moment closure approximation and develops novel applications for MCA in epidemiology. Most existing moment closures were intended as deterministic approximations to static regular lattices. However we develop deterministic approximations for dynamic network models and continuous space models. The purpose of applying MCA to a different set of models is not only to demonstrate their flexibility; we also explore the dynamical properties of such models with the moment closure tools we derive and with simulation data. Comparisons are then made between processes on regular lattices and processes in dynamic networks and in continuous space. Additionally, we answer questions relating to the epidemiology of sexually transmitted diseases and epidemics in populations embedded in two-dimensional continuous space. Some of the new techniques we develop can be applied to other models in ecology and epidemiology. We conclude that moment closure approximations continue to provide fertile ground for research, and that application of MCA to models other than static regular lattices can be worthwhile. Chapter 1 consists of background material and an introduction to moment closure approximations. In chapter 2 we look at the properties of moment closure approximations near critical points and during transient phases and consider their accuracy in such cases. Chapters 3 and 4 cover the application of pair approximations to sexually transmitted disease models, and chapter 5 is a preliminary study of a pair approximation for a continuous space model

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Managing Small-scale Fisheries: Alternative Directions and Methods

Human dependence on marine and coastal resources is increasing. Today, small-scale fisheries employ 50 of the world's 51 million fishers, practically all of whom are from developing countries. And together, they produce more than half of the world's annual marine fish catch of 98 million tonnes, supplying most of the fish consumed in the developing world. At the same time, increased fishery overexploitation and habitat degradation are threatening the Earth's coastal and marine resources. Most small-scale fisheries have not been well managed, if they have been managed at all. Existing approaches have failed to constrain fishing capacity or to manage conflict. They have not kept pace with technology or with the driving forces of economics, population growth, demand for food, and poverty. Worldwide, the management and governance of small-scale fisheries is in urgent need of reform. This publication looks beyond the scope of conventional fishery management to alternative concepts, tools, methods, and conservation strategies. There is, for example, broader emphasis on ecosystem management and participatory decision-making. Interested readers will include fishery managers, both governmental and nongovernmental; instructors and students in fishery management; development organizations and practitioners working on small-scale fisheries; and fishers and fishing communities that wish to take responsibility for managing their own resources

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc