An interactive agent-based framework for materialization-informed architectural design

Concepts of swarm intelligence are becoming increasingly relevant in the field of architectural design. An example is the use of agent-based modeling and simulation methods, which can help manage the complexity of building designs that feature many similar, but geometrically unique elements. Apart from leading to effective solutions and expanding the architectural design space, agent-based design methods can also be employed in integrated planning processes, in which the contributions of various disciplines take place in an integrated loop instead of being executed consecutively. We propose a computational framework for architectural design, in which agents represent building elements and/or joints between building elements. Behavior parameters, behavior weighting, and the environment can be modified in real-time while the agent system is running. Additionally, the designer can interact with individual agents directly, while slowing down or pausing agent movement if so desired. In the resulting design approach, the designer can globally adjust behavior parameters, while retaining local control over details where needed. To facilitate an integrative design process, domain-specific data and the results of external analysis can be included, either directly as input for agent behaviors, or by modifying the environment. We illustrate the potential of this computational framework using the example of the design of plate structures and show how this method can lead to quantifiable results while also attaining aesthetic goals. Furthermore, we provide an outlook toward possible further extensions of agent-based design methods in architecture.     

Modelling Primate Behavioral Ecology

Models play an important role in any mature science because they force us to make explicit our assumptions about how a phenomenon works and allow us to explore the way in which different variables influence a complex biological system. I review the principal kinds of models that could be used to study primate behavior and ecology: linear programming models, systems models, optimality models, stochastic dynamic programming models and agent-based simulation models. Although less use has been made of modelling in primatology than in some other areas of behavioral ecology, there is considerable scope for exploiting the predictive and explanatory power of models in the field.     

Conceptualizing microbe–plasmid communities as complex adaptive systems

Plasmids shape microbial communities’ diversity, structure, and function. Nevertheless, we lack a mechanistic understanding of how community structure and dynamics emerge from local microbe–plasmid interactions and coevolution. Addressing this gap is challenging because multiple processes operate simultaneously at multiple levels of organization. For example, immunity operates between a plasmid and a cell, but incompatibility mechanisms regulate coexistence between plasmids. Conceptualizing microbe–plasmid communities as complex adaptive systems is a promising approach to overcoming these challenges. I illustrate how agent-based evolutionary modeling, extended by network analysis, can be used to quantify the relative importance of local processes governing community dynamics. These theoretical developments can advance our understanding of plasmid ecology and evolution, especially when combined with empirical data.     

Integration of stochastic simulation with multivariate analysis: Short‐term facility fit prediction

This article describes a decision‐support tool to help pinpoint the potential root causes of sub‐optimal short‐term facility fit issues in biopharmaceutical facilities. This was achieved by creating a tool that integrated stochastic simulation with advanced multivariate statistical analysis. Process fluctuations in product titers in cell culture, step yields, and chromatography eluate volumes were mimicked using Monte Carlo simulation data derived using a stochastic discrete‐event simulation model. The resulting stochastic datasets, with the computed consequences on key metrics such as product mass loss and cost of goods, were examined using advanced multivariate statistical techniques. Principal component analysis combined with clustering algorithms was used to analyze the complex datasets from complete industrial batch processes for biopharmaceuticals. The challenge of visualizing the multidimensional nature of the dataset was addressed using hierarchical and k‐means clustering as well as stacked parallel co‐ordinate plots to help identify process fingerprints and characteristics of clusters leading to sub‐optimal facility fit issues. Industrially‐relevant case studies are presented that focus on technology transfer challenges for therapeutic antibodies moving from early phase to late phase clinical trials. The case study details how sub‐optimal facility fit can be alleviated by allocating alternative product pool tanks. The impact of this operational change is then assessed by reviewing an updated process fingerprint. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29: 368–377, 2013     

Exploring the Synergy Between Industrial Ecology and System of Systems to Understand Complexity

Two objectives are pursued in this article. First, from a methodological perspective, we explore the relationships among the constructs of complex adaptive systems, systems of systems, and industrial ecology. Through examination of central traits of each, we find that industrial ecology and system of systems present complementary frameworks for posing systemic problems in the context of sociotechnical applications. Furthermore, we contend that complexity science (the basis for the study of complex adaptive systems) provides a natural and necessary foundation and set of tools to analyze mechanisms such as evolution, emergence, and regulation in these applications. The second objective of the article is to illustrate the use of two tools from complexity sciences to address a network transition problem in air transportation framed from the system-of-systems viewpoint and shaped by an industrial ecology perspective. A stochastic simulation consisting of network theory analysis combined with agent-based modeling to study the evolution of an air transport network is presented. Patterns in agent behavior that lead to preferred outcomes across two scenarios are observed, and the implications of these results for decision makers are described. Furthermore, we highlight the necessity for future efforts to combine the merits of both system of systems and industrial ecology in tackling the issues of complexity in such large-scale, sociotechnical problems.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

Complexity in Urban Development and Management

Systems dynamics, cellular automata, agent-based modeling, and network analyses have been used in population, land use, and transport planning models. An overview of complex systems science as applied to urban development is presented, and examples are given of where the problems of housing people and anticipating their movements have been addressed with complex approaches, sometimes in concert with deterministic, large-scale urban models. Planning for cities today has additional environmental and social priorities in common with many topics that concern industrial ecology. The research agenda suggested here is that this, too, can be enriched with complex systems thinking and models to complement the often static assessment of environmental performance and better inform decision processes.     

Modeling Metal Flow Systems

Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models—an agent‐based model and a system dynamics model—both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.     

Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity

In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the basis of prior experimental evidence that longer processive lifetimes are enabled by larger myosin ensembles, it is hypothesized that emergent scaling laws could coincide with myosin-actin contact probability or system energy consumption. Because processivity is difficult to predict analytically and measure experimentally, agent-based computational techniques are developed to simulate processive myosin ensembles and produce novel processive lifetime measurements. It is demonstrated that only systems energy relationships hold regardless of isoform configurations or ensemble size, and a unified expression for predicting processive lifetime is revealed. The finding of such laws provides insight for how patterns emerge in stochastic mechanochemical systems, while also informing understanding and engineering of complex biological systems.     

Rangelands,pastoralists and governments: interlinked systems of people and nature

We analyse commercially operated rangelands as coupled systems of people and nature. The biophysical components include: (i) the reduction and recovery of potential primary production, reflected as changes in grass production per unit of rainfall; (ii) changes in woody plants dependent on the grazing and fire regimes; and (iii) livestock and wool dynamics influenced by season, condition of the rangeland and numbers of wild and feral animals. The social components include the managers, who vary with regard to a range of cognitive abilities and lifestyle choices, and the regulators who vary in regard to policy goals. We compare agent-based and optimization models of a rangeland system. The agent-based model leads to recognition that policies select for certain management practices by creating a template that governs the trajectories of the behaviour of individuals, learning, and overall system dynamics. Conservative regulations reduce short-term loss in production but also restrict learning. A free-market environment leads to severe degradation but the surviving pastoralists perform well under subsequent variable conditions. The challenge for policy makers is to balance the needs for learning and for preventing excessive degradation. A genetic algorithm model optimizing for net discounted income and based on a population of management solutions (stocking rate, how much to suppress fire, etc.) indicates that robust solutions lead to a loss of about 40% compared with solutions where the sequence of rainfall was known in advance: this is a similar figure to that obtained from the agent-based model. We conclude that, on the basis of Levin's three criteria, rangelands with their livestock and human managers do constitute complex adaptive systems. If this is so, then command-and-control approaches to rangeland policy and management are bound to fail.     

Complex mathematical analysis of photobioreactor system

Modeling as a tool solves extremely difficult tasks in life sciences. Recently, schemes of culturing of microalgae have received special attention because of its unique features and possible uses in many industrial applications for renewable energy production and high value products isolation. The goal of this review is to present the use of system analysis theory applied to microalgae culturing modeling and process development. The review mainly focuses on the modeling of the key steps of autotrophic growth under the integral biorefinery concept of the microalgae biomass. The system approach follows systematically a procedure showing the difficulties by modeling of sub‐systems. The development of microalgae kinetics and computational fluid dynamics (CFD) studies were analyzed in details as sub‐systems in advanced design of photobioreactor (PBR). This review logically follows the trends of the modeling procedure and clarifies how this approach may save time and money during the research efforts. The result of this work is a successful development of a complex PBR mathematical analysis in the frame of the integral biorefinery concept.     

Machine learning methods without tears: a primer for ecologists

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.     

Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa

Seasonal climate outlooks provide one tool to help decision-makers allocate resources in anticipation of poor, fair or good seasons. The aim of the 'Climate Outlooks and Agent-Based Simulation of Adaptation in South Africa' project has been to investigate whether individuals, who adapt gradually to annual climate variability, are better equipped to respond to longer-term climate variability and change in a sustainable manner. Seasonal climate outlooks provide information on expected annual rainfall and thus can be used to adjust seasonal agricultural strategies to respond to expected climate conditions. A case study of smallholder farmers in a village in Vhembe district, Limpopo Province, South Africa has been used to examine how such climate outlooks might influence agricultural strategies and how this climate information can be improved to be more useful to farmers. Empirical field data has been collected using surveys, participatory approaches and computer-based knowledge elicitation tools to investigate the drivers of decision-making with a focus on the role of climate, market and livelihood needs. This data is used in an agent-based social simulation which incorporates household agents with varying adaptation options which result in differing impacts on crop yields and thus food security, as a result of using or ignoring the seasonal outlook. Key variables are the skill of the forecast, the social communication of the forecast and the range of available household and community-based risk coping strategies. This research provides a novel approach for exploring adaptation within the context of climate change.     

Why Are Computational Neuroscience and Systems Biology So Separate?

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future.     

Structural complexity analysis for industrial ecosystems: A case study on LuBei industrial ecosystem in China

Natural ecosystems and human societies have evolved in many diverse ways and they are both complex systems. Our learning from the structure complexity of natural ecosystems can help us to redesign the structure of industrial system. Thus the materials and energy efficiency of industrial systems can be improved well to achieve the sustainable goals. In this paper, Structural Analysis Method for Industrial Ecosystems (SAMIE) is introduced and applied in the analysis of the structure complexity and efficiency of the industrial ecosystems. The industrial ecosystem is analyzed based on the industrial species’ classification, which is analogous to the natural ecosystem. A set of indicators are developed to evaluate the industrial system, in order to explore the problems of structural complexity, identify the limiting factors of industrial ecosystem evolution, and strengthen the capacity of adaptation and self-organization. A case study on LuBei industrial ecosystem in China has been selected to apply the SAMIE approach.     

Persistent Topology and Metastable State in Conformational Dynamics

The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain.     

Co-evolution of learning complexity and social foraging strategies

Variation in learning abilities within populations suggests that complex learning may not necessarily be more adaptive than simple learning. Yet, the high cost of complex learning cannot fully explain this variation without some understanding of why complex learning is too costly for some individuals but not for others. Here we propose that different social foraging strategies can favor different learning strategies (that learn the environment with high or low resolution), thereby maintaining variable learning abilities within populations. Using a genetic algorithm in an agent-based evolutionary simulation of a social foraging game (the producer-scrounger game) we demonstrate how an association evolves between a strategy based on independent search for food (playing a producer) and a complex (high resolution) learning rule, while a strategy that combines independent search and following others (playing a scrounger) evolves an association with a simple (low resolution) learning rule. The reason for these associations is that for complex learning to have an advantage, a large number of learning steps, normally not achieved by scroungers, are necessary. These results offer a general explanation for persistent variation in cognitive abilities that is based on co-evolution of learning rules and social foraging strategies.     

The Cultural Dynamics of Copycat Suicide

The observation that suicides sometimes cluster in space and/or time has led to suggestions that these clusters are caused by the social learning of suicide-related behaviours, or “copycat suicides”. Point clusters are clusters of suicides localised in both time and space, and have been attributed to direct social learning from nearby individuals. Mass clusters are clusters of suicides localised in time but not space, and have been attributed to the dissemination of information concerning celebrity suicides via the mass media. Here, agent-based simulations, in combination with scan statistic methods for detecting clusters of rare events, were used to clarify the social learning processes underlying point and mass clusters. It was found that social learning between neighbouring agents did generate point clusters as predicted, although this effect was partially mimicked by homophily (individuals preferentially assorting with similar others). The one-to-many transmission dynamics characterised by the mass media were shown to generate mass clusters, but only where social learning was weak, perhaps due to prestige bias (only copying prestigious celebrities) and similarity bias (only copying similar models) acting to reduce the subset of available models. These findings can help to clarify and formalise existing hypotheses and to guide future empirical work relating to real-life copycat suicides.