Interdisciplinary modeling: a case study of evolutionary economics

Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called “evolutionary economics” and has been a source of much debate among economists. Although philosophers have developed literatures on the nature of models and modeling, the unique issues surrounding this kind of interdisciplinary model building have yet to be independently investigated. In this paper, we utilize evolutionary economics as a case study in the investigation of more general issues concerning interdisciplinary modeling. We begin by critiquing the distinctions currently used within the evolutionary economics literature and propose an alternative carving of the conceptual terrain. We then argue that the three types of evolutionary economics we distinguish capture distinctions that will be important whenever resources of model-based theorizing are borrowed across distinct scientific domains. Our analysis of these model-building strategies identifies several of the unique methodological and philosophical issues that confront interdisciplinary modeling.     

Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.     

An agent-based modeling approach to represent infestation dynamics of the emerald ash borer beetle

Agent-based modeling (ABM) is a bottom-up approach capable of operationalizing complex systems. The approach can be used to reproduce the spatio-temporal patterns in ecological processes such as insect infestation by representing individual dynamics and interactions between “agents” and their environment from which complex behavior emerges. The emerald ash borer (Agrilus planipennis; EAB) is an invasive species native to south-east Asia which has infested and killed millions of ash trees (Fraxinus sp.) across the eastern United States as well as Ontario and Quebec in Canada. Efforts to model the insect's behavior are ongoing, but current models are limited to approaches that do not address the complexity that emerges from the dynamics between individual beetles and their varying spatial environments. The objective of this study is to develop an ABM to represent the interactions of the EAB and the emerging spatio-temporal pattern of the insect spread. The model is implemented on real datasets from the Town of Oakville, Ontario, Canada from 2008 to 2010. Tree inventory and land use data acquired from the Town of Oakville were used to represent the spatial environment of the EAB agents. The EAB interactions are implemented in the model as subroutines, each representing a stage in the EAB life cycle using a temporal resolution of one day. Model verification was performed based on the literature documenting the life cycle processes of the EAB to represent EAB behavior. The model is calibrated using the rate of spread observed in the Town of Oakville from 2008 to 2009 and is validated using datasets delimiting the spatial extent and severity of EAB infestation in 2009. When comparing simulated and observed data, there is a 72% agreement for the locations of the infestation. This indicates that the developed ABM approach offers a model able to capture the complex behavior of EAB where both the spatial extent and severity of infestation are simulated realistically. The model generates insights about the underlying processes governing EAB behavior, highlights areas of uncertainty in modeling the complex spatio-temporal patterns of EAB infestation, and is a useful tool for forest and pest management.     

Contributions of Fire Use Study to Land Use/Cover Change Frameworks: Understanding Landscape Change in Agricultural Frontiers

The impact of fire use and hazard in frontier settlement is a critical environmental concern that has been historically overshadowed by deforestation issues- and thus underexamined at local and regional scales by social scientists. Consequently, conceptual frameworks of LUCC change consider fire use as an outcome of land use decisions and neglect the capacity of burning choices to influence these decisions and subsequent land cover change. This paper examines the relationship of settlement, land use, and fire use. It considers recent LUCC frameworks, and then uses household surveys on fire use practices to discuss how the study of fire use can contribute to understanding frontier landscape change. Planting decisions, settlement history, location desires, and burning logistics work in combination to influence burning choices and thus LUCC.     

Environmental Impact Assessment of the Heterogeneity in Consumers’ Usage Behavior: An Agent‐Based Modeling Approach

The aim of this study is to develop a framework for understanding the heterogeneity and uncertainties present in the usage phase of the product life cycle through utilizing the capabilities of an agent‐based modeling (ABM) technique. An ABM framework is presented to model consumers’ daily product usage decisions and to assess the corresponding electricity consumption patterns. The theory of planned behavior (TPB), with the addition of the habit construct, is used to model agents’ decision‐making criteria. A case study is presented on the power management behavior of personal computer users and the possible benefits of using smart metering and feedback systems. The results of the simulation demonstrate that the utilization of smart metering and feedback systems can promote the energy conservation behaviors and reduce the total PC electricity consumption of households by 20%.     

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。     

Agent-based modelling as scientific method: a case study analysing primate social behaviour

A scientific methodology in general should provide two things: first, a means of explanation and, second, a mechanism for improving that explanation. Agent-based modelling (ABM) is a method that facilitates exploring the collective effects of individual action selection. The explanatory force of the model is the extent to which an observed meta-level phenomenon can be accounted for by the behaviour of its micro-level actors. This article demonstrates that this methodology can be applied to the biological sciences; agent-based models, like any other scientific hypotheses, can be tested, critiqued, generalized or specified. We review the state of the art for ABM as a methodology for biology and then present a case study based on the most widely published agent-based model in the biological sciences: Hemelrijk's DomWorld, a model of primate social behaviour. Our analysis shows some significant discrepancies between this model and the behaviour of the macaques, the genus used for our analysis. We also demonstrate that the model is not fragile: its other results are still valid and can be extended to compensate for these problems. This robustness is a standard advantage of experiment-based artificial intelligence modelling techniques over analytic modelling.     

Engineering the Virtual Enterprise: An Architecture-Driven Modeling Approach

The managerial and organization practices required by an increasingly dynamic competitive manufacturing, business, and industrial environment include the formation of “virtual enterprises.” A major concern in the management of virtual enterprises is the integration and coordination of business processes contributed by partner enterprises. The traditional methods of process modeling currently used for the design of business processes do not fully support the needs of the virtual enterprise. The design of these virtual enterprises imposes requirements that make it more complex than conventional intraorganizational business process design. This paper first describes an architecture that assists in the design of the virtual enterprise. Then it discusses business process reengineering (BPR) as a methodology for modeling and designing virtual organizations. While BPR presents many useful tools, the approach itself and the modeling tools commonly used for redesign have fundamental shortcomings when dealing with the virtual enterprise. However, several innovative modeling approaches provide promise for this problem. The paper discusses some of these innovative modeling approaches, such as object-oriented modeling of business processes, agent modeling of organizational players, and the use of ontological modeling to capture and manipulate knowledge about the players and processes. The paper concludes with a conceptual modeling methodology that combines these approaches under the enterprise architecture for the design of virtual enterprises.     

土地利用/覆盖变化对陆地生态系统碳循环的影响

土地利用/覆盖变化是学术界最为关注的环境变化问题之一,它能够影响陆地生态系统的生物多样性、水、碳和养分循环、能量平衡,引起温室气体释放增加等其它环境问题。不同类型的土地利用/覆盖变化对生态系统碳循环的作用不同,由高生物量的森林转化为低生物量的草地、农田或城市后,大量的CO₂将释放到大气中。全球土地利用/覆盖变化具有很强的空间变异性,对生态系统碳循环的影响同样具有明显的空间差异:热带地区的土地利用/覆盖变化造成大量的碳释放,而中高纬度地区土地利用/覆盖变化则表现为碳汇。目前,土地利用/覆盖变化引起的生态系统碳循环变化主要是通过模型模拟来估算的。尽管土地利用/覆盖变化及其相关过程与生态系统碳循环的关系已经比较清楚,但是,由于土地利用/覆盖变化过程复杂且影响广泛,对于如何量化两者之间的关系还存在很多不确定性。目前的量化过程主要是利用经验数据来实现的,机理性不强,使得对土地利用/覆盖变化造成的陆地生态系统CO₂释放量的估测差异很大。除了进一步加强长期定位研究以获得土地利用/覆盖变化与生态系统碳循环过程的定量关系外,土地利用/覆盖变化模型与植被动态模型、生态系统过程模型的耦合也是今后模型发展的主要方向之一。采用合理的管理措施能够大量增加土地利用/覆盖变化过程中的碳储存量,降低碳释放量,因此在模型中耦合管理措施来研究土地利用/覆盖变化过程对生态系统碳循环的影响是未来几年的工作重点。     

Diversity in auxology: between theory and practice. Proceedings of the 18th Aschauer Soiree, 13th November 2010

Auxology has developed from mere describing child and adolescent growth into a vivid and interdisciplinary research area encompassing human biologists, physicians, social scientists, economists and biostatisticians. The meeting illustrated the diversity in auxology, with the various social, medical, biological and biostatistical aspects in studies on child growth and development.     

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.     

Carpe diem: adaptation and devaluing the future

Organisms typically "discount the future" in their decision making, but the extent to which they do so varies across species, sexes, age classes, and circumstances. This variability has been studied by biologists, economists, psychologists, and criminologists. We argue that the conceptual framework required for an interdisciplinary synthesis of knowledge in this area is the evolutionary adaptationist analysis of reproductive effort scheduling pioneered by George Williams.     

Novel Multiscale Modeling Tool Applied to Pseudomonas aeruginosa Biofilm Formation

Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet) as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM) and constraint-based metabolic modeling. The hybrid model correctly recapitulates oxygen-limited biofilm metabolic activity and predicts increased growth rate via anaerobic respiration with the addition of nitrate to the growth media. In addition, a genome-wide survey of metabolic mutants and biofilm formation exemplifies the powerful analyses that are enabled by this computational modeling tool.     

Bridging the gap between micro - and macro-scale perspectives on the role of microbial communities in global change ecology

In order to understand the role microbial communities play in mediating ecosystem response to disturbances it is essential to address the methodological and conceptual gap that exists between micro- and macro-scale perspectives in ecology. While there is little doubt microorganisms play a central role in ecosystem functioning and therefore in ecosystem response to global change-induced disturbance, our ability to investigate the exact nature of that role is limited by disciplinary and methodological differences among microbial and ecosystem ecologists. In this paper we present results from an interdisciplinary graduate-level seminar class focused on this topic. Through the medium of case studies in global change ecology (soil respiration, nitrogen cycling, plant species invasion and land use/cover change) we highlight differences in our respective approach to ecology and give examples where disciplinary perspective influences our interpretation of the system under study. Finally, we suggest a model for integrating perspectives that may lead to greater interdisciplinary collaboration and enhanced conceptual and mechanistic modeling of ecosystem response to disturbance.     

Conformon-driven biopolymer shape changes in cell modeling

Conceptual models of the atom preceded the mathematical model of the hydrogen atom in physics in the second decade of the 20th century. The computer modeling of the living cell in the 21st century may follow a similar course of development. A conceptual model of the cell called the Bhopalator was formulated in the mid-1980s, along with its twin theories known as the conformon theory of molecular machines and the cell language theory of biopolymer interactions [Ann. N.Y. Acad. Sci. 227 (1974) 211; BioSystems 44 (1997) 17; Ann. N.Y. Acad. Sci. 870 (1999a) 411; BioSystems 54 (2000) 107; Semiotica 138 (1-4) (2002a) 15; Fundamenta Informaticae 49 (2002b) 147]. The conformon theory accounts for the reversible actions of individual biopolymers coupled to irreversible chemical reactions, while the cell language theory provides a theoretical framework for understanding the complex networks of dynamic interactions among biopolymers in the cell. These two theories are reviewed and further elaborated for the benefit of both computational biologists and computer scientists who are interested in modeling the living cell and its functions. One of the critical components of the mechanisms of cell communication and cell computing has been postulated to be space- and time-organized teleonomic (i.e. goal-directed) shape changes of biopolymers that are driven by exergonic (free energy-releasing) chemical reactions. The generalized Franck-Condon principle is suggested to be essential in resolving the apparent paradox arising when one attempts to couple endergonic (free energy-requiring) biopolymer shape changes to the exergonic chemical reactions that are catalyzed by biopolymer shape changes themselves. Conformons, defined as sequence-specific mechanical strains of biopolymers first invoked three decades ago to account for energy coupling in mitochondria, have been identified as shape changers, the agents that cause shape changes in biopolymers. Given a set of space- and time-organized teleonomic shape changes of biopolymers driven by conformons, all of the functions of the cell can be accounted for in molecular terms-at least in principle. To convert a conceptual model of the cell into a computer model, it is necessary to represent the conceptual model in an algebraic language. To this end, we have begun to apply the process algebra of Milner [Communicating and Mobile Systems: The pi-calculus, Cambridge University Press, Cambridge, 1999] to develop what is here called the "shape algebra," capable of describing complex and mobile patterns of interactions among biomolecules leading to cell functions.     

A chronology of plankton dynamics in silico: how computer models have been used to study marine ecosystems

Research on plankton ecology in the oceans has traditionally been conducted via two scientific approaches: in situ (in the field) and in vitro (in the laboratory). There is, however, a third approach: exploring plankton dynamics in silico, or using computer models as tools to study marine ecosystems. Models have been used for this purpose for over 60 years, and the innovations and implementations of historical studies provide a context for how future model applications can continue to advance our understanding. To that end, this paper presents a chronology of the in silico approach to plankton dynamics, beginning with modeling pioneers who worked in the days before computers. During the first 30 years of automated computation, plankton modeling focused on formulations for biological processes and investigations of community structure. The changing technological context and conceptual paradigms of the late-1970s and 1980s resulted in simulations becoming more widespread research tools for biological oceanographers. This period saw rising use of models as hypothesis-testing tools, and means of exploring the effects of circulation on spatial distributions of organisms. Continued computer advances and increased availability of data in the 1990s allowed old approaches to be applied to old and new problems, and led to developments of new approaches. Much of the modeling in the new millennium so far has incorporated these sophistications, and many cutting-edge applications have come from a new generation of plankton scientists who were trained by modeling gurus of previous eras. The future directions for modeling plankton dynamics are rooted in the historical studies.     

An agent-based model for predicting the prevalence of Trypanosoma cruzi I and II in their host and vector populations

Trypanosoma cruzi is the causative agent of Chagas disease, an endemic human parasitosis in Latin America. This protozoan is transmitted to human and other mammals by blood-feeding bugs belonging to the Triatominae subfamily. There are two strains (T. cruzi I and T. cruzi II) presenting different biological and ecological characteristics. An original agent-based model (ABM) was designed for predicting the prevalence (i.e., proportion of infected individuals in the total population at a given time) of T. cruzi I and II during single and mixed infections. The ABM was calibrated from experimental data retrieved from literature. It was shown that inclusion of reservoir hosts as supplementary type of agent in the model was necessary for obtaining realistic simulation results of the prevalence of the two strains. This is totally in agreement with experimental and field observations on the importance of reservoirs in the parasite transmission cycle. Proposals were made for refining the model. More generally, the advantages and limitations of the ABM in parasitology modeling have been discussed.     

Agent农业土地变化模型研究进展

农业土地变化是全球变化与可持续研究的热点,当前研究虽取得了长足进展,但仍存在诸多不足,集中表现在对农业土地系统复杂性与动态性的认识不够.近年来,基于Agent的农业土地变化研究(农业ABM/LUCC,Agent-based agricultural land change modeling)逐渐兴起,极大的丰富了传统研究的理论与方法,具体表现在:(1)农业ABM/LUCC将微观层面的人类个体行为整合进土地变化研究框架,有助于更加清楚的认识农业土地系统的“人类-自然”综合复杂性问题.(2)农业ABM/LUCC能够动态表达土地系统变化的内生反馈机制,有助于弥补传统的静态土地变化驱动机制分析的不足.(3)基于ABM/LUCC的农业土地利用格局动态研究是整合“人类-自然”综合研究的关键桥梁,农业ABM/LUCC能够与其他生物地球物理模型或经济模型动态嵌套,使多尺度、多维度综合模型研究成为可能.然而,农业ABM/LUCC研究也存在诸多挑战,如理论研究滞后于应用研究,大尺度应用难以开展,以及农户行为的模拟结果很难得到校验等.     

Rule-based modeling of chronic disease epidemiology: elderly depression as an illustration

Background

Rule-based Modeling (RBM) is a computer simulation modeling methodology already used to model infectious diseases. Extending this technique to the assessment of chronic diseases, mixing quantitative and qualitative data appear to be a promising alternative to classical methods. Elderly depression reveals an important source of comorbidities. Yet, the intertwined relationship between late-life events and the social support of the elderly person remains difficult to capture. We illustrate the usefulness of RBM in modeling chronic diseases using the example of elderly depression in Belgium.

Methods

We defined a conceptual framework of interactions between late-life events and social support impacting elderly depression. This conceptual framework was underpinned by experts'' opinions elicited through a questionnaire. Several scenarios were implemented successively to better mimic the real population, and to explore a treatment effect and a socio-economic distinction. The simulated patterns of depression by age were compared with empirical patterns retrieved from the Belgian Health Interview Survey.

Results

Simulations were run using different groupings of experts'' opinions on the parameters. The results indicate that the conceptual framework can reflect a realistic evolution of the prevalence of depression. Indeed, simulations combining the opinions of well-selected experts and a treatment effect showed no significant difference with the empirical pattern.

Conclusions

Our conceptual framework together with a quantification of parameters through elicited expert opinions improves the insights into possible dynamics driving elderly depression. While RBM does not require high-level skill in mathematics or computer programming, the whole implementation process provides a powerful tool to learn about complex chronic diseases, combining advantages of both quantitative and qualitative approaches.