http://www.casmodeling.com The latest research articles published by Complex Adaptive Systems Modeling 2013-04-26T00:00:00Z PurposeThe performance of an optimistic parallel discrete event simulator (PDES) in terms of the total simulation execution time of an experiment depends on a large set of variables. Many of them have a complexand generally unknown relationship with the simulation execution time. In this paper, we describe anagent-based performance model of a PDES kernel that is typically used to simulate large-sized complex networks on multiple processors or machines. The agent-based paradigm greatly simplifies themodeling of system dynamics by representing a component logical process (LP) as an autonomousagent that interacts with other LPs through event queues and also interacts with its environment whichcomprises the processor it resides on.MethodWe model the agents representing the LPs using a "base" class of an LP agent that allows us to usea generic behavioral model of an agent that can be extended further to model more details of LPbehavior. The base class focuses only on the details that most likely influence the overall simulationexecution time of the experiment. Results: We apply this framework to study a local incentive based partitioning algorithm where each LP makesan informed local decision about its assignment to a processor, resulting in a system akin to a selforganizing network. The agent-based model allows us to study the overall effect of the local incentive-based cost function on the simulation execution time of the experiment which we consider to be theglobal performance metric. Conclusion: This work demonstrates the utility of agent-based approach in modeling a PDES kernel in order toevaluate the effects of a large number of variable factors such as the LP graph properties, load balancing criteria and others on the total simulation execution time of an experiment. http://www.casmodeling.com/content/1/1/12 Aditya Kurve Khashayar Kotobi George Kesidis Complex Adaptive Systems Modeling 2013, null:12 2013-04-26T00:00:00Z doi:10.1186/2194-3206-1-12 /content/figures/2194-3206-1-12-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 12 2013-04-26T00:00:00Z PDF PurposeLarge-scale optimization tasks have many applications in science and engineering. There are many algorithms to perform such optimization tasks. In this manuscript, we aim at using consensus in multi-agent systems as a tool for solving large-scale optimization tasks.MethodThe model is based on consensus of opinions among agents interacting over a complex networked structure. For each optimization task, a number of agents are considered, each with an opinion value. These agents interact over a networked structure and update their opinions based on their best-matching neighbor in the network. A neighbor with the best value of the objective function (of the optimization task) is referred to as the best-matching neighbor for an agent. We use structures such as pure random, small-world and scale-free networks as interaction graph. The optimization algorithm is applied on a number of benchmark problems and its performance is compared with a number of classic methods including genetic algorithms, differential evolution and particle swarm optimization. Results: We show that the agents could solve various large-scale optimization tasks through collaborating with each other and getting into consensus in their opinions. Furthermore, we find pure random topology better than small-world and scale-free topologies in that it leads to faster convergence to the optimal solution. Our experiments show that the proposed consensus-based optimization method outperforms the classic optimization algorithms. Conclusion: Consensus in multi-agents systems can be efficiently used for large-scale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks. http://www.casmodeling.com/content/1/1/11 Omid Askari-Sichani Mahdi Jalili Complex Adaptive Systems Modeling 2013, null:11 2013-04-24T00:00:00Z doi:10.1186/2194-3206-1-11 /content/figures/2194-3206-1-11-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 11 2013-04-24T00:00:00Z XML We propose an agent-based model for peer selection in the Internet at the Autonomous System (AS) level.The proposed model, called GENESIS-CBA, is based on realistic constraints and provider selection mechanism,with ASes acting in a myopic and decentralized manner to optimize a cost-related fitness function. We introducea new peering scheme, Cost-Benefit-Analysis, which, unlike existing peering strategies, gives the ASes the abilityto analyze the impact of each peering link on their economic fitness. Using this analysis ASes engage in only thosepeering relations which can have a positive impact on their fitness. Our proposed model captures key factors thataffect the network formation dynamics: highly skewed traffic matrix, policy-based routing, geographic co-locationconstraints, and the costs of transit/peering agreements. As opposed to analytical game-theoretic models, whichfocus on proving the existence of equilibria, GENESIS-CBA is a computational model that simulates the networkformation process and allows us to actually compute distinct equilibria (i.e., networks) and to also examine thebehavior of sample paths that do not converge. We find that such oscillatory sample paths occur in about 7% ofthe runs, and they always involve tier-1 ASes. GENESIS-CBA results in many distinct equilibria that are highlysensitive to initial conditions and the order in which ASes (agents) act. This implies that we cannot predict theproperties of an individual AS in the Internet. However, certain properties of the global network or of certainclasses of ASes are predictable. http://www.casmodeling.com/content/1/1/10 Aemen Lodhi Amogh Dhamdhere Constantine Dovrolis Complex Adaptive Systems Modeling 2013, null:10 2013-04-24T00:00:00Z doi:10.1186/2194-3206-1-10 /content/figures/2194-3206-1-10-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 10 2013-04-24T00:00:00Z PDF We examine the role of information-based measures in detecting and analysingphase transitions. We contend that phase transitions have a general character,visible in transitions in systems as diverse as classical flocking models,human expertise, and social networks. Information-based measures such as mutualinformation and transfer entropy are particularly suited to detecting thechange in scale and range of coupling in systems that herald a phasetransition in progress, but their use is not necessarily straightforward,possessing difficulties in accurate estimation due to limited sample sizes andthe complexities of analysing non-stationary time series. These difficulties aresurmountable with careful experimental choices. Their effectiveness inrevealing unexpected connections between diverse systems makes them a promisingtool for future research. http://www.casmodeling.com/content/1/1/9 Terry Bossomaier lionel Barnett Michael Harré Complex Adaptive Systems Modeling 2013, null:9 2013-04-08T00:00:00Z doi:10.1186/2194-3206-1-9 /content/figures/2194-3206-1-9-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 9 2013-04-08T00:00:00Z PDF Purpose Complex networks seem to be ubiquitous objects in contemporary research, both in the natural and social sciences. An important area of research regarding the applicability and modeling of graph- theoretical-oriented approaches to complex systems, is the probabilistic inference of such networks. There exist different methods and algorithms designed for this purpose, most of them are inspired in statistical mechanics and rely on information theoretical grounds. An important shortcoming for most of these methods, when it comes to disentangle the actual structure of complex networks, is that they fail to distinguish between direct and indirect interactions. Here, we suggest a method to discover and assess for such indirect interactions within the framework of information theory.Methods Information-theoretical measures (in particular, Mutual Information) are applied for the probabilistic inference of complex networks. Data Processing Inequality is used to find and assess for direct and indirect interactions impact in complex networks.Results We outline the mathematical basis of information-theoretical assessment of complex network structure and discuss some examples of application in the fields of biological systems and social networks.Conclusions Information theory provides to the field of complex networks analysis with effective means for structural assessment with a computational burden low enough to be useful in both, Biological and Social network analysis. http://www.casmodeling.com/content/1/1/8 Enrique Hernández-Lemus Jesús Siqueiros-García Complex Adaptive Systems Modeling 2013, null:8 2013-04-08T00:00:00Z doi:10.1186/2194-3206-1-8 /content/figures/2194-3206-1-8-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 8 2013-04-08T00:00:00Z PDF PurposeA crowd of pedestrians is a complex system in which individuals exhibit conflicting behavioural mechanisms leading to self-organisation phenomena. Computer models for the simulation of crowds represent a consolidated type of application, employed on a day-to-day basis to support designers and decision makers. Most state of the art models, however, generally do not consider the explicit representation of pedestrians aggregations (groups) and their implications on the overall system dynamics. This work is aimed at discussing a research effort systematically exploring the potential implication of the presence of groups of pedestrians in different situations (e.g. changing density, spatial configurations of the environment). Methods: The paper describes an agent-based model encompassing both traditional individual motivations (i.e. tendency to stay away from other pedestrians while moving towards the goal) and an adaptive mechanism representing the influence of group presence in the simulated population. The mechanism is designed to preserve the cohesion of specific types of groups (e.g. families and friends) even in high density and turbulent situations. The model is tested in simplified scenarios to evaluate the implications of modelling choices and the presence of groups. Results: The model produces results in tune with available evidences from the literature, both from the perspective of pedestrian flows and space utilisation, in scenarios not comprising groups; when groups are present, the model is able to preserve their cohesion even in challenging situations (i.e. high density, presence of a counterflow), and it produces interesting results in high density situations that call for further observations and experiments to gather empirical data. Conclusions: The introduced adaptive model for group cohesion is effective in qualitatively reproducing group related phenomena and it stimulates further research efforts aimed at gathering empirical evidences, on one hand, and modelling efforts aimed at reproducing additional related phenomena (e.g. leader-follower movement patterns). http://www.casmodeling.com/content/1/1/7 Giuseppe Vizzari Lorenza Manenti Luca Crociani Complex Adaptive Systems Modeling 2013, null:7 2013-03-27T00:00:00Z doi:10.1186/2194-3206-1-7 /content/figures/2194-3206-1-7-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 7 2013-03-27T00:00:00Z XML PurposeBioincising is a biotechnological process for improving the permeability of refractory wood such as Norway spruce heartwood using the wood-decay fungus Physisporinus vitreus. The degradation of the bordered pit membranes by P. vitreus in its first stage of wood colonization enhances the uptake of preservatives and wood modification substances, whereas the strength of the material is not significantly reduced. Methods: We propose to study bioincising by means of a mathematical model, because many factors affect the growth and effects of P. vitreus in Norway spruce in such a complex way that an evaluation of the optimal incubation conditions (i.e. water activity, temperature or pH) is very expensive or even not possible solely using laboratory experiments. Results: Using a hyphal growth model we demonstrate here for the first time how to optimize bioincising by linking the microscopic growth behavior of P. vitreus with macroscopic system properties of the wood. Moreover, we propose universal measures of wood-decay fungi, i.e., penetration velocity, penetration work and penetration capacity, which may figure as measures for the efficiency of wood colonization. For example, our simulation shows that an increase of the hyphal growth rate (i.e. changing the incubation conditions) from 1 to 2 μm·d-1 results in an increase of the mycelium’s growth velocity from 0.8 to 1.75 μm·d-1 and an increase of the penetration capacity from 0.5 to 0.6 10-3·mm2·d-1 using a pit degradation rate of 2 μm·d-1. Conclusions: Information about the penetration velocity, penetration work and penetration capacity is of significance for both its biotechnological use and the study of the colonization strategy of wood-decay fungi in general. http://www.casmodeling.com/content/1/1/6 Matthias Fuhr Mark Schubert Chris Stührk Francis Schwarze Hans Herrmann Complex Adaptive Systems Modeling 2013, null:6 2013-03-27T00:00:00Z doi:10.1186/2194-3206-1-6 /content/figures/2194-3206-1-6-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 6 2013-03-27T00:00:00Z XML This paper proposes a method based on complex networks analysis, devised to perform clustering on multidimensional datasets. In particular, the method maps the elements of the dataset in hand to a weighted network according to the similarity that holds among data. Network weights are computed by transforming the Euclidean distances measured between data according to a Gaussian model. Notably, this model depends on a parameter that controls the shape of the actual functions. Running the Gaussian transformation with different values of the parameter allows to perform multiresolution analysis, which gives important information about the number of clusters expected to be optimal or suboptimal.Solutions obtained running the proposed method on simple synthetic datasets allowed to identify a recurrent pattern, which has been found in more complex, synthetic and real, datasets. http://www.casmodeling.com/content Giuliano Armano Marco Alberto Javarone Complex Adaptive Systems Modeling 2013, null:5 2013-03-13T00:00:00Z doi:10.1186/2194-3206-1-5 /content/figures/2194-3206-1-5-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 5 2013-03-13T00:00:00Z XML Considerable advance has been made in recent years in the research field of pattern formation by segregation of tissue cells. Research has become more quantitative partly due to more in-depth analysis of experimental data and the emergence modeling approaches. In this review we present experimental observations, including some of our new results, on various aspects of two and three dimensional segregation events and then summarize the computational modeling approaches. http://www.casmodeling.com/content/1/1/4 Elod Mehes Tamas Vicsek Complex Adaptive Systems Modeling 2013, null:4 2013-03-13T00:00:00Z doi:10.1186/2194-3206-1-4 /content/figures/2194-3206-1-4-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 4 2013-03-13T00:00:00Z XML We introduce PyCX, an online repository of simple, crude, easy-to-understand sample codes for various complex systems simulation, including iterative maps, cellular automata, dynamical networks and agent-based models. All the sample codes were written in plain Python, a general-purpose programming language widely used in industry as well as in academia, so that students can gain practical skills for both complex systems simulation and computer programming simultaneously. The core philosophy of PyCX is on the simplicity, readability, generalizability and pedagogical values of simulation codes. PyCX has been used in instructions of complex systems modeling at several places with successful outcomes. http://www.casmodeling.com/content/1/1/2 Hiroki Sayama Complex Adaptive Systems Modeling 2013, null:2 2013-03-13T00:00:00Z doi:10.1186/2194-3206-1-2 /content/figures/2194-3206-1-2-toc.gif Complex Adaptive Systems Modeling 2194-3206 ${item.volume} 2 2013-03-13T00:00:00Z XML