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Understanding the peer review process could help research and shed light on the mechanisms that underlie crowdsourcing. In this paper, we present an agent-based model of peer review built on three entities - the paper, the scientist and the conference. The system is implemented on a BDI platform (Jason) that allows to define a rich model of scoring, evaluating and selecting papers for conferences. Then, we propose a programme committee update mechanism based on disagreement control that is able to remove reviewers applying a strategy aimed to prevent papers better than their own to be accepted ("rational cheating"). We analyze a homogeneous scenario, where all conferences aim to the same level of quality, and a heterogeneous scenario, in which conferences request different qualities, showing how this affects the update mechanism proposed. We also present a first step toward an empirical validation of our model that compares the amount of disagreements found in real conferences with that obtained in our simulations. [ABSTRACT FROM AUTHOR]

We analyze a science collaboration network, i.e. a network whose nodes are scientists with edges connecting them for each paper published together. Furthermore, we develop a model for the simulation of discontiguous small-world networks that shows good coherence with empirical data. [ABSTRACT FROM AUTHOR]

This paper examines the evolution of agent strategies in a commons dilemma using a tag interaction model. Using a tag-mediated interaction model, individual's can determine their interactions based on their tag similarity. The experimental results show the significance of agent strategies that contribute to the overall value of the commons. The paper also examines the significance of viscosity on the emergence of cooperation in the commons dilemma. Viscosity represents a specific form of openness that will be examined in the paper. An initial series of experiments demonstrates the role of tags in the n-player dilemma while subsequent experiments examine the impact of openness on the evolution of agent strategies. The paper shows the emergence of cooperation through tag-mediated interactions. Simulation results show the emergence of strategies that contribute heavily to the value of the shared commons. The role of viscosity on the population strategy set is examined and this is similar to some forms of openness in multi-agent environments. [ABSTRACT FROM AUTHOR]

This special journal issue of Advances in Complex Systems presents a collection of papers describing current research delivered by recognized researchers actively working in various corners of the field of modeling of complex systems by cellular automata and related methods. All included papers are self-contained and present the latest developments in the areas where the authors work. Hence, all papers can be read independently, but it is strongly recommended to study the issue as a whole to get a general overview of the various methods and techniques from the field. The main aim of this special issue is to provide researchers from neighboring fields with sufficient information and vital examples of how to design models in complex systems. The whole issue is organized in such a way that common features occurring repeatedly in most models of complex systems are highlighted. [ABSTRACT FROM AUTHOR]

This paper establishes a trade credit network with a guarantee mechanism. Based on the established network model, the trade credit network and the guarantee network are visualized. The characteristics of the distributions of both networks and the firm size distribution are then researched. Concurrently, through computer simulations, the risk contagion among firms is analyzed on the basis of the interactions between the trade credit network and the guarantee network. The results are as follows: (1) the degree of the trade credit network takes on a power-law distribution, as does the upper tail of firm size. (2) For the degree distribution of the guarantee network, the test result of the power-law features changes with a varying value of ϖ based on the test method of Clauset, [Shalizi and Newman Power-law distributions in empirical data, SIAM Rev.51 (2009) 661–703]. The change in ϖ directly affects the establishment of the guarantee links, thus affecting the structure of the guarantee network and its power-law features. (3) Finally, the introduction of a guarantee mechanism aggravates the extent of the risk contagion among firms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects on performance of inducing organizational dynamics in collaborative search processes. Using an agent-based simulation, we analyze different temporal modes and levels of scope of change. The organizational properties under change are chosen randomly out of three dimensions of organizational design. Results suggest that inducing organizational dynamics has the potential to increase the effectiveness of search, but that the temporal change mode, the frequency and the scope of change in conjunction with the complexity of the search problem considerably affect the order of magnitude of this beneficial effect. The paper characterizes conditions under which different modalities of organizational change are beneficial. [ABSTRACT FROM AUTHOR]

The controlling of complex networks is one of the most challenging problems in modern network science. Accordingly, the required energy cost of control is a fundamental and significant problem. In this paper, we present the theoretical analysis and numerical simulations to study the controllability of complex networks from the energy perspective. First, by combining theoretical derivation and numerical simulations, we obtain lower bounds of the maximal and minimal energy costs for an arbitrary normal network, which are related to the eigenvalues of the state transition matrix. Second, we deduce that controlling unstable normal networks is easier than controlling stable normal networks with the same size. Third, we demonstrate a tradeoff between the control energy and the average degree (or the maximum degree) of an arbitrary undirected network. Fourth, numerical simulations show that the energy cost is negatively correlated with the degree of nodes. Moreover, the combinations of control nodes with the greater sum of degree need less energy to implement complete control. Finally, we propose a multi-objective optimization model to obtain the control strategy, which not only ensures the fewer control nodes but also guarantees the less energy cost of control. [ABSTRACT FROM AUTHOR]

In this paper, we present an agent-based model aiming at supporting decisions about the planning of shopping malls (SM), with a main focus about their environmental impact. The model forecasts the traffic flows induced by SM at the regional level on the whole road network of Canton Ticino (Switzerland), and has been used by the public administration as a support to decide master plan rules. The system has been modeled as an interaction space, a new type of modeling framework that includes both multi-agent systems and cellular automata as a particular case. We define a grammar to specify different agents' trips, and we associate a fuzzy logic based indicator to each one of these specifications. Similarly, we define attractiveness indicators for zones containing economical activities related to agents' trips. The use of these indicators permits to obtain agents' location choices of secondary activities with a great computational efficiency. Simulations include several environmental indicators, like concentrations of nitrogen dioxide, counting changes in a given small part of a road or an estimation of roads' level of service. [ABSTRACT FROM AUTHOR]

Computer simulations of complex food-webs are important tools for deepening our understanding of these systems. Yet most computer models assume, rather than generate, key system-level patterns, or use mathematical modeling approaches that make it difficult to fully account for nonlinear dynamics. In this paper, we present a computer simulation model that addresses these concerns by focusing on assumptions of agent attributes rather than agent outcomes. Our model utilizes the techniques of complex adaptive systems and agent-based modeling so that system level patterns of a marine ecosystem emerge from the interactions of thousands of individual computer agents. This methodology is validated by using this general simulation model to replicate fundamental properties of a marine ecosystem, including: (i) the predator-prey oscillations found in Lotka-Volterra; (ii) the stepped pattern of biomass accrual from resource enrichment; (iii) the Paradox of Enrichment; and (iv) Gause's Law. [ABSTRACT FROM AUTHOR]

In this paper, a new game theory based approach for the evacuees exit selection in emergency conditions is presented. It is founded on the theoretical concepts of multi-stage games with perfect information. In particular, an evacuee is a player and the available actions for a generic player are the accessible emergency exits. The developed mathematical model involves several psychological parameters, in order to make the emergency exit choice also affected by the individual character. Moreover, an evacuation simulation model incorporating this novel approach is shown. The model involves many other parameters and aspects attempting to obtain a satisfactory representation of the actual evacuation process and of the human behavior in emergency conditions. Finally, the effectiveness of the model is demonstrated with a simulative study. [ABSTRACT FROM AUTHOR]

Understanding the driving forces in the markets of their products is a basic necessity for any business. Quantitative models are either aggregated over large market segments or restricted to utility models of an individual's buying decision. While the aggregate models acknowledge that customer interactions are important they do not model them and hence have no way to adjust their model to changing business environments. This paper bridges the gap between individual decisions and the overall market behavior using agent based simulations to model the sales of computer chips in the high-end gamers market. The simulation environment is dynamic and models the succession of 19 products introduced over a 40 month time horizon which includes the recession of 2008-2010. Simulated sales are compared to actual sales data and are used to adjust the parametrization of the agents and their environment. We found that only two agent parameters are sufficient to obtain a very reasonable fit between simulations and data: The amount of money available for the gaming hobby and a parameter related to the gaming success of the high-end gamers. [ABSTRACT FROM AUTHOR]

In this paper, we report on the theoretical foundations, empirical context and technical implementation of an agent-based modeling (ABM) framework, that uses a high-performance computing (HPC) approach to investigate human population dynamics on a global scale, and on evolutionary time scales. The ABM-HPC framework provides an in silico testbed to explore how short-term/small-scale patterns of individual human behavior and long-term/large-scale patterns of environmental change act together to influence human dispersal, survival and extinction scenarios. These topics are currently at the center of the Neanderthal debate, i.e., the question why Neanderthals died out during the Late Pleistocene, while modern humans dispersed over the entire globe. To tackle this and similar questions, simulations typically adopt one of two opposing approaches, top-down (equation-based) and bottom-up (agent-based) models of population dynamics. We propose HPC technology as an essential computational tool to bridge the gap between these approaches. Using the numerical simulation of worldwide human dispersals as an example, we show that integrating different levels of model hierarchy into an ABM-HPC simulation framework provides new insights into emergent properties of the model, and into the potential and limitations of agent-based versus continuum models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-layer ant-based algorithm (MABA), which detects communities from networks by means of locally optimizing modularity using individual ants. The basic version of MABA, namely SABA, combines a self-avoiding label propagation technique with a simulated annealing strategy for ant diffusion in networks. Once the communities are found by SABA, this method can be reapplied to a higher level network where each obtained community is regarded as a new vertex. The aforementioned process is repeated iteratively, and this corresponds to MABA. Thanks to the intrinsic multi-level nature of our algorithm, it possesses the potential ability to unfold multi-scale hierarchical structures. Furthermore, MABA has the ability that mitigates the resolution limit of modularity. The proposed MABA has been evaluated on both computer-generated benchmarks and widely used real-world networks, and has been compared with a set of competitive algorithms. Experimental results demonstrate that MABA is both effective and efficient (in near linear time with respect to the size of network) for discovering communities. [ABSTRACT FROM AUTHOR]

Systems of technological networks (so-called Critical Infrastructures) might undergo strong perturbations leading to the reduction of their functionalities (crisis scenarios). Under perturbed conditions, infrastructures deliver their specific services in a range from a partial reduction up to a complete loss. Systems of complex technological networks (electrical, telecommunications, roads and railways, oil, gas and water pipelines etc.) are known to display large interdependences (a fault on one system spreads over the others); for this reason, it is relevant to devise simulators and decision support systems able to capture the complexity of such a "system of systems". The paper presents parts of the outcomes of a larger scientific project aiming at realizing a Decision Support System to predict the occurrence of undesired events and their consequences. In particular, we present a conceptual modeling approach consisting of a declarative language and a software implementation for the description and simulation of elements of Crisis Scenarios. The software application is conceived for risk managers, i.e. operators with a deep knowledge of critical infrastructure scenarios, but not necessarily with high-level IT or mathematical skill. [ABSTRACT FROM AUTHOR]

There is much empirical evidence that human decision-making under risk does not coincide with expected value maximization, and much effort has been invested into the development of descriptive theories of human decision-making involving risk (e.g. Prospect Theory). An open question is how behavior corresponding to these descriptive models could have been learned or arisen evolutionarily, as the described behavior differs from expected value maximization. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking, sequentiality of choice, and population dynamics in evolutionary environments. In this paper, we provide the results of several evolutionary game simulations designed to study the risk behavior of agents in evolutionary environments. These include several evolutionary lottery games where sequential decisions are made between risky and safe choices, and an evolutionary version of the well-known stag hunt game. Our results show how agents that are sometimes risk-prone and sometimes risk-averse can outperform agents that make decisions solely based on the maximization of the local expected values of the outcomes, and how this can facilitate the evolution of cooperation in situations where cooperation entails risk. [ABSTRACT FROM AUTHOR]

This paper reports the results of a multi-agent simulation designed to study the emergence and evolution of symbolic communication. The novelty of this model is that it considers some interactional and spatial constraints to this process that have been disregarded by previous research. The model is used to give an account of the implications of differences in the agents' behavior, which are embodied in a spatial environment. Two communicational dimensions are identified: the frequency with which agents refer to different topics over time and the spatial limitations on reaching recipients. We use the model to point out some interesting emergent communicational properties when the agents' behavior is altered by considering those two dimensions. We show the group of agents able to reach more recipients and less prone to changing the topic have the highest likelihood of driving the emergence and evolution of symbolic communication. [ABSTRACT FROM AUTHOR]

The question how social norms can emerge from microscopic interactions between individuals is a key problem in social sciences to explain collective behavior. In this paper, we propose an agent-based model to show that randomly distributed social behavior by way of local interaction converges to a state with a multimodal distribution of behavior. This can be interpreted as a coexistence of different social norms, a result that goes beyond previous investigations. The model is discrete in time and space, behavior is characterized in a continuous state space. The adaptation of social behavior by each agent is based on attractive and repulsive forces caused by friendly and adversary relations among agents. The model is analyzed both analytically and by means of spatio-temporal computer simulations. It provides conditions under which we find convergence towards a single norm, coexistence of two opposing norms, and coexistence of a multitude of norms. For the latter case, we also show the evolution of the spatio-temporal distribution of behavior. [ABSTRACT FROM AUTHOR]

This paper studies asynchronously-updated linear voter cellular automata models, with local and global interactions. In the locally-interacting model, sites copy the states of randomly-chosen sites in the local neighborhood; in the globally-interacting model, sites copy the states of sites chosen at random from the entire lattice. A multiscale model is also considered, mixing the two types of interactions. Such models can be used to model simple genetic drift, or the spread of opinions or ideas. The pair approximation moment-closure method is used to develop systems of differential equations describing the dynamics of the models with k states per site; stochastic spatially explicit simulations are also used. In simulations on large lattices, state frequencies among sites remain relatively constant at their initial values, while autocorrelations between adjacent sites move toward quasiequilibrium distributions and then remain constant with minor stochastic fluctuations. Full analytical solutions for the transient dynamics of the local autocorrelation (clustering) among adjacent sites are obtained with k = 2 states, and compared with simulations. Both show that increasing global interactions decreases spatial autocorrelation, and that even in the absence of long-distance interaction, there will still be mixing among the states. That is, the lattice will not converge to a quasiequilibrium configuration where regions in different states are maximally isolated from each other (completely segregated), but instead sites will have a strictly positive probability of being adjacent to sites in different states. [ABSTRACT FROM AUTHOR]

Shedding-type of card games are used as a fruit fly to study the evolution of institutional arrangements. Eleven types of rules are identified which leads to a spectrum of 2048 possible shedding games. Each game can be evaluated by the length and difficulty of the game and as such a fitness landscape of possible shedding games can be constructed. Building on cultural group selection simulations are performed with 100 groups which start with randomly throwing cards and evolving to games similar to UNO. Finally, experiments have been performed where characteristics of agents co-evolve with the rules of the game. [ABSTRACT FROM AUTHOR]

We report on replications of early experiments with FEARLUS, using larger numbers of agents, larger numbers of land parcels, and greater network connectivity than in the original work. We find that results from the larger-scale experiments differ from the smaller environments used previously. Whilst results from small communities of agents and environments should not be ignored just because the same effect is not observed in larger communities (and indeed vice versa), this work does raise the extent to which more general conclusions can be drawn from agent-based studies involving fixed population or environment sizes. [ABSTRACT FROM AUTHOR]

The volatility in a CPG market is modeled using a bottom-up simulation approach and validated against disaggregated supermarket transactions data. The simulation uses independent agents, each agent representing unique households in the data. A simple behavioral model incorporates household preferences for product attributes and prices. Our validation strategy tests the model predictions at both macro and micro levels and benchmarks the performance in each against a random choice model. The model significantly outperforms the benchmark at both levels. At the macro level, choices made by heterogenous agents accurately captures the volatility in market shares over time. This accuracy at the macro level is driven by the accuracy of predictions at the micro household level SKU and attribute choice. [ABSTRACT FROM AUTHOR]

While wealth distribution in the world is highly skewed and heavy-tailed, human talent — as the majority of individual features — is normally distributed. In a recent computational study by Pluchino et al. [Talent vs luck: The role of randomness in success and failure, Adv. Complex Syst. 21(03–04) (2018) 1850014], it has been shown that the combined effects of both random external factors (lucky and unlucky events) and multiplicative dynamics in capital accumulation are able to clarify this apparent contradiction. We introduce here a simplified version (STvL) of the original Talent versus Luck (TvL) model, where only lucky events are present, and verify that its dynamical rules lead to the same very large wealth inequality. We also derive some analytical approximations aimed to capture the mechanism responsible for the creation of such wealth inequality from a Gaussian-distributed talent. Under these approximations, our analysis is able to reproduce quite well the results of the numerical simulations of the simplified model in special cases. On the other hand, it also shows that the complexity of the model lies in the fact that lucky events are transformed into an increase of capital with heterogeneous rates, which yields a nontrivial generalization of the role of multiplicative processes in generating wealth inequality, whose fully generic case is still not amenable to analytical computations. [ABSTRACT FROM AUTHOR]

In order to unveil the value of network connectivity, discounted both in space and time, we formalize the construction of networks as an optimal control dynamic graph-theoretic problem. The network is based on a set of leaders and followers linked through edges. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs. The results show that the network equilibrium depends on the influence of leader nodes, while the network connectivity depends on the cohesiveness among followers. Through numerical simulations, we find that — past a threshold level of opportunity costs — the values of shadow prices become stationary. Likewise, the model outputs show that, at a fixed level of foregone gains, agents value the safeguard of connections less in time than in space. [ABSTRACT FROM AUTHOR]

We study the resilience of a multiplex socio-ecological system (SES) which we structure from the spheres composing the sustainability Venn diagram. The SES network is subject to dynamics of spread of a global reform through a knock-on effect, as a direct and indirect repercussion of a small-scale reform process that spreads out over time through network connectivity. The model outcomes reveal that high probability of reform completion on an SES layer through nodes previously reformed on other SES layers is necessary and sufficient to propagate the desired reform on that layer. The maximum asymptotic density of reformed nodes can only be achieved in the absence of risk of reform abrogation. When the risk is significant, it prevents the equilibrium density from reaching a steady state. The numerical simulation results show that the combination of likely probability of reform completion and of proportional influence of all layers yields the maximum magnitude of efficiency of the knock-on effect. We provide a formal argument that favors assigning equal weight to all aspects of sustainable development. [ABSTRACT FROM AUTHOR]

Statistical mechanics points out as fluctuations have a relevant role for systems near critical points. We study the effect of traffic fluctuations and the transition to congested states for a stochastic dynamical model of traffic on a road network. The model simulates a finite population that moves from one road to another according to random transition probabilities. In such a way, we mimic the traffic fluctuations due to the granular feature of traffic and the dynamics at the crossing points. Then the amplitude of traffic flow fluctuations is proportional to the average flow as suggested by empirical observations. Assuming a parabolic shaped flow-density relation, there exists an unstable critical point for the road dynamics and the system can perform a phase transition to a congested state, where some roads reach their maximal capacity. We apply a statistical physics approach to study the onset congestion and we characterize analytically the relation between the fluctuations amplitude and the appearance of congested nodes. We verify the results by means of numerical simulations on a Manhattan-like road network. Moreover we point out the existence of oscillating regimes, where traffic oscillations back propagate on the road network, whose onset depend sensitively from the traffic fluctuations and that have a strong influence on the hysteresis cycles of the systems when the traffic load is modulated. The comparison between the numerical simulations and the empirical traffic data recorded by an inductive-loop traffic detector system (MTS system) on the county roads of the Emilia Romagna region in Italy is shortly discussed. [ABSTRACT FROM AUTHOR]

The primary and secondary effects of various spatial and transportation policies can be evaluated with models of activity-travel behavior. Whereas existing activity-based models of travel demand simulate a typical day, dynamic models simulate behavioral response to endogenous or exogenous change, along various time horizons. The current study aims at developing a model of endogenous dynamics of activity-travel behavior. Endogenous dynamics are induced by stress, which is regarded as dissatisfaction with current habits. It is assumed that people try to alleviate stress by trying short-term changes, within the options known to them or by exploring new options. If these explorations prove to be unsuccessful, they will consider long-term changes, such as moving to a new residential location, buying a car, etc. Therefore, this self-improvement process can result in both short and long-term adaptations. In the proposed framework, choice-set formation is modeled, the key concepts of aspiration, activation, awareness and expected utility are integrated, while both rational and emotional mechanisms are taken into account. Numerical simulations are conducted in order to check the face validity of the model, as well as the impact of stress tolerance parameters on system performance. [ABSTRACT FROM AUTHOR]

For the simulation of pedestrians, a method is introduced to find routing alternatives from any origin position to a given destination area in a given geometry composed of walking areas and obstacles. The method includes a parameter which sets a threshold for the approximate minimum size of obstacles to generate routing alternatives. The resulting data structure for navigation is constructed such that it does not introduce artifacts to the movement of simulated pedestrians and locally pedestrians prefer to walk on the shortest path. The generated set of routes can be used with iterating static or dynamic assignment methods. [ABSTRACT FROM AUTHOR]

Opinion formation is an important element of social dynamics. It has been widely studied in the last years with tools from physics, mathematics and computer science. Here, a continuous model of opinion dynamics for multiple possible choices is analyzed. Its main features are the inclusion of disagreement and possibility of modulating external information/media effects, both from one and multiple sources. The interest is in identifying the effect of the initial cohesion of the population, the interplay between cohesion and media extremism, and the effect of using multiple external sources of information that can influence the system. Final consensus, especially with the external message, depends highly on these factors, as numerical simulations show. When no external input is present, consensus or segregation is determined by the initial cohesion of the population. Interestingly, when only one external source of information is present, consensus can be obtained, in general, only when this is extremely neutral, i.e., there is not a single opinion strongly promoted, or in the special case of a large initial cohesion and low exposure to the external message. On the contrary, when multiple external sources are allowed, consensus can emerge with one of them even when this is not extremely neutral, i.e., it carries a strong message, for a large range of initial conditions. [ABSTRACT FROM AUTHOR]

We analyze emotionally annotated massive data from Internet relay chat (IRC) as well as from BBC forum website and model the dialogues between chat participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution. This process is claimed to be responsible for a power-law distribution of the discussion lengths observed in the dialogues. We perform numerical simulations based on the noticed phenomenon obtaining a good agreement with the real data. Finally, we propose a method to artificially prolong the duration of the discussion that relies on the entropy of emotional probability distribution. [ABSTRACT FROM AUTHOR]

Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor-critic reinforcement learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control policy, i.e., it allows the controller to learn stabilizing the pole in the largest domain of initial conditions compared to the results obtained when using a single learning mechanism. This model can also find a successful control policy for goal-directed behavior, i.e., the robot can effectively learn to approach a given goal compared to its individual components. Thus, the study pursued here sharpens our understanding of how two different learning mechanisms can be combined and complement each other for solving complex tasks. [ABSTRACT FROM AUTHOR]

The recent emergence of GPGPU programming has resulted in a number of very efficient, but ultimately ad-hoc implementations of GPU accelerated simulations of complex systems. Because developing applications for the GPU is still a difficult and time consuming task, efficient GPU parallelizations of general purpose modeling frameworks are very useful. The dimer automaton is a stochastic modeling and simulation framework with a good balance of robustness, generality, and simplicity with capacity to model a wide range of phenomena. A major advantage of dimer automata is the ease in which they can be applied to any space that can be represented as a graph. Therefore, we have developed an efficient GPU implementation of dimer automata that runs up to 80 times faster than the serial implementation. [ABSTRACT FROM AUTHOR]

We present an analysis of the properties of a social network, formed by users participating in one of the most popular Polish political discussion forums, during a period of two years. We find that despite communication motivated mostly by controversy and negative emotions, the resulting social network is remarkably similar to networks found in discussion boards based on similarity of views. All collected messages have been analyzed to determine their content type, author's political sympathies and emotion level. This has allowed us to discover almost complete stability of the political sympathies of the users writing comments during the studied period. Using the data on reader evaluation of the comments we present estimates of the political sympathies of the "silent majority" of forum readers and found that they follow closely the distribution of sympathies of comment authors. The data from the observations are used as the input for an agent based computer simulation model, reproducing many observed statistical characteristics of the forum. [ABSTRACT FROM AUTHOR]

We discuss two different ways of evolution for chromosomes and genomes. Purifying selection dominates in large panmictic populations, where the Mendelian law of independent gene assortment is valid. If instead the populations are small, recombination processes are not effective enough to ensure an independent assortment of linked genes, and larger clusters of genes could be inherited as genetic units. There are whole clusters of genes which tend to complement in such conditions instead of single pairs of alleles like in the case of purifying selection. Computer simulations have shown that switching between complementation and purification strategies has the characteristic of a phase transition. This is also responsible for specific distribution of recombination events observed along eukaryotic chromosomes (a higher recombination rate is observed in subtelomeric regions than in central parts of chromosomes) and for sympatric speciation. [ABSTRACT FROM AUTHOR]

In this work we have used computer models of social-like networks to show by extensive numerical simulations that cooperation in evolutionary games can emerge and be stable on this class of networks. The amounts of cooperation reached are at least as much as in scale-free networks but here the population model is more realistic. Cooperation is robust with respect to different strategy update rules, population dynamics, and payoff computation. Only when straight average payoff is used or there is high strategy or network noise does cooperation decrease in all games and disappear in the Prisoner's Dilemma. [ABSTRACT FROM AUTHOR]

We introduce Network Automata, a framework which couples the topological evolution of a network to its structure. To demonstrate its implementation we describe a simple model which exhibits behavior similar to the "Game of Life" before recasting some simple, well-known network models as Network Automata. We then introduce Functional Network Automata which are useful for dealing with networks in which the topology evolves according to some specified microscopic rules and, simultaneously, there is a dynamic process taking place on the network that both depends on its structure but is also capable of modifying it. It is a generic framework for modeling systems in which network structure, dynamics, and function are interrelated. At the practical level, this framework allows for precise, unambiguous implementation of the microscopic rules involved in such systems. To demonstrate the approach, we develop a family of simple biologically inspired models of fungal growth. [ABSTRACT FROM AUTHOR]

Science is a fundamental human activity and we trust its results because it has several error-correcting mechanisms. It is subject to experimental tests that are replicated by independent parts. Given the huge amount of information available and the information asymetry between producers and users of knowledge, scientists have to rely on the reports of others. This makes it possible for social effects to influence the scientific community. Here, an Opinion Dynamics agent model is proposed to describe this situation. The influence of Nature through experiments is described as an external field that acts on the experimental agents. We will see that the retirement of old scientists can be fundamental in the acceptance of a new theory. We will also investigate the interplay between social influence and observations. This will allow us to gain insight in the problem of when social effects can have negligible effects in the conclusions of a scientific community and when we should worry about them. [ABSTRACT FROM AUTHOR]

An earlier study [24] concluded, based on computer simulations and some inferences from empirical data, that languages will change the more slowly the larger the population gets. We replicate this study using a more complete language model for simulations (the Schulze model combined with a Barabási–Albert network) and a richer empirical dataset [12]. Our simulations show either a negligible or a strong dependence of language change on population sizes, depending on the parameter settings; while empirical data, like some of the simulations, show a negligible dependence. [ABSTRACT FROM AUTHOR]

Firstly, the application of a cellular automata (CA) model to simulate the dynamics of lasers is reviewed. With this kind of model, the macroscopic properties of the laser system emerge as a cooperative phenomenon from elementary components locally interacting under simple rules. Secondly, a parallel implementation of this kind of model for distributed-memory parallel computers is presented. Performance and scalability of this parallel implementation running on a computer cluster are analyzed, giving very satisfactory results. This confirms the feasibility of running large 3D simulations — unaffordable on an individual machine — on computer clusters, in order to simulate specific real laser systems. [ABSTRACT FROM AUTHOR]

We investigate the performances of collective task-solving capabilities and the robustness of complex networks of automata using the density and synchronization problems as typical cases. We show by computer simulations that evolved Watts–Strogatz small-world networks have superior performance with respect to several kinds of scale-free graphs. In addition, we show that Watts–Strogatz networks are as robust in the face of random perturbations, both transient and permanent, as configuration scale-free networks, while being widely superior to Barabási–Albert networks. This result differs from information diffusion on scale-free networks, where random faults are highly tolerated by similar topologies. [ABSTRACT FROM AUTHOR]

Despite spectacular progress in biophysics, molecular biology and biochemistry our ability to predict the dynamic behavior of multicellular systems under different conditions is very limited. An important reason for this is that still not enough is known about how cells change their physical and biological properties by genetic or metabolic regulation, and which of these changes affect the cell behavior. For this reason, it is difficult to predict the system behavior of multicellular systems in case the cell behavior changes, for example, as a consequence of regulation or differentiation. The rules that underlie the regulation processes have been determined on the time scale of evolution, by selection on the phenotypic level of cells or cell populations. We illustrate by detailed computer simulations in a multi-scale approach how cell behavior controlled by regulatory networks may emerge as a consequence of an evolutionary process, if either the cells, or populations of cells are subject to selection on particular features. We consider two examples, migration strategies of single cells searching a signal source, or aggregation of two or more cells within minimal multiscale models of biological evolution. Both can be found for example in the life cycle of the slime mold Dictyostelium discoideum. However, phenotypic changes that can lead to completely different modes of migration have also been observed in cells of multi-cellular organisms, for example, as a consequence of a specialization in stem cells or the de-differentiation in tumor cells. The regulatory networks are represented by Boolean networks and encoded by binary strings. The latter may be considered as encoding the genetic information (the genotype) and are subject to mutations and crossovers. The cell behavior reflects the phenotype. We find that cells adopt naturally observed migration strategies, controlled by networks that show robustness and redundancy. The model simplicity allow us to unambiguously analyze the regulatory networks and the resulting phenotypes by different measures and by knockouts of regulatory elements. We illustrate that in order to maintain a cells' phenotype in case of a knockout, the cell may have to be able to deal with contradictory information. In summary, both the cell phenotype as well as the emerged regulatory network behave as their biological counterparts observed in nature. [ABSTRACT FROM AUTHOR]

We consider a simple model for catalyzed replication. Computer simulations show that a finite population moves in sequence space by diffusion analogous to the behavior of a quasispecies on a flat fitness landscape. The diffusion constant depends linearly on the per position mutation rate and the ratio of sequence length and population size. [ABSTRACT FROM AUTHOR]

We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reaction-diffusion systems, pattern formation phenomena, fluid flows, fracture processes and road traffic models. [ABSTRACT FROM AUTHOR]