Woody perennials in temperate climates develop cold hardiness in the fall (acclimation) and lose cold hardiness in the spring (deacclimation) to survive freezing winter temperatures. Two main factors known to regulate deacclimation responses are dormancy status and temperature. However, the progression of deacclimation responses throughout the dormant period and across a range of temperatures is not well described. More detailed descriptions of dormancy status and temperature, as factors regulating deacclimation, are necessary to understand the timing and magnitude of freeze injury risks for woody perennials in temperate climates. In this study, we modeled deacclimation responses in cold-climate interspecific hybrid grapevine cultivars throughout the dormant period by integrating chill accumulation and temperature through the concept of deacclimation potential. We evaluated deacclimation and budbreak under multiple temperature treatments and chill unit accumulation levels using differential thermal analysis (DTA) and bud forcing assays. Deacclimation responses increased continuously following logistic trends for both increasing chill unit accumulation and increasing temperature. There are optimal temperatures where deacclimation rates increased but changes in deacclimation rates diminished below and above these temperatures. The cumulative chill unit range where deacclimation potential increased overlapped with the transition from endo- to ecodormancy. Therefore, deacclimation potential could provide a quantitative method for describing dormancy transitions that do not rely on the visual evaluation of budbreak. This information provides a more detailed understanding of when and how deacclimation contributes to increased risks by freezing injury. In addition, our descriptions could inform improvements to models predicting cold hardiness, dormancy transitions, and spring phenology.

Cold climate interspecific hybrid grapevines (CCIHG) selected for their superior midwinter cold hardiness have expanded grape production to cold climate regions. However, extreme weather events, such as polar vortexes and the high frequency of fall and spring freezes, often result in yield and vine losses. The main objective of this study was to evaluate changes in bud cold hardiness of five CCIHG cultivars grown in the upper Midwest to identify relative risk for freeze damage throughout the dormant period and to adapt a bud cold hardiness prediction model to CCIHG cultivars grown in cold climate regions. Bud cold hardiness was evaluated biweekly throughout the dormant period by measuring lethal temperatures for buds using differential thermal analysis (DTA). CCIHG cultivars in our study had an early acclimation response with increased levels of cold hardiness before the occurrence of freezing temperatures. Maximum levels of hardiness (-28 to -30°C) were observed both years in February; however, deeper levels of freezing stress resistance, probably attained by freeze dehydration, were not detected using DTA. CCIHG cultivars had a rapid deacclimation response that was accelerated with additional chilling accumulation during spring. The reparametrizing of a discrete-dynamic cold hardiness prediction model by expanding the range of ecodormant threshold temperatures for CCIHG resulted in predictions with an average root mean square error (RMSE) = 1.01. Although CCIHG cultivars have superior midwinter bud cold hardiness, fast deacclimation responses increase the risk of freeze damage during spring, thus this trait should be evaluated for future CCIHG cultivar release. The development of tools such as the discrete-dynamic cold hardiness prediction model for CCIHG cultivars will aid growers in decision-making to minimize damage and yield and vine losses.

Agent-based modeling has been widely applied to solve problems in contexts ranging from exploratory research studies to focused industrial practice. As the application of agent-based modeling has grown, developers have advocated a wide and diverse variety of development techniques. Some of these techniques inform elements of model creation, validation, or use. Others are comprehensive development methodologies. The popularity of agent-based modeling makes it impossible to include every development technique mentioned in the literature. Instead, this paper focuses on providing a representative sample that includes the techniques with the most practical utility along with those that are widely used or have received substantial attention. The sample reviewed in this paper includes techniques advocated specifically for agentbased modeling along with general software development approaches. Due to their importance, verification and validation techniques for agent-based modeling are specially highlighted. The paper concludes with a summary and recommendations on how best to build agent-based models.

Information about how human populations shift in response to various stimuli is limited because no single model is capable of addressing these stimuli simultaneously, and integration of the best existing models has been challenging because of the vast disparity among constituent model purposes, architectures, scales, and execution environments. To demonstrate a potential model coupling for approaching this problem, three major model components are integrated into a fully coupled system that executes a worldwide infection-infected routine where a human population requires a food source for sustenance and an infected population can spread an infection when it is in contact with the remaining healthy population. To enable high-resolution data-driven model federation and an ability to capture dynamics and behaviors of billions of humans, a high-performance computing agent-based framework has been created and is demonstrated in this chapter.

SchemaOnRead is a CRAN package that provides an extensible mechanism for importing a wide range of file types into R as well as support for the emerging schema-on-read paradigm in R. The schema-on-read tools within the package include a single function call that recursively reads folders with text, comma separated value, raster image, R data, HDF5, NetCDF, spreadsheet, Weka, Epi Info, Pajek network, R network, HTML, SPSS, Systat, and Stata files. It also recursively reads folders (e.g., schemaOnRead("folder")), returning a nested list of the contained elements. The provided tools can be used as-is or easily customized to implement tool chains in R. This paper's contribution is that it introduces and describes the SchemaOnRead package and compares it to related R packages.

Modeling complex natural and human systems to support policy or management decision making is becoming increasingly common. The resulting models are often designed and implemented by researchers or domain experts with limited software engineering expertise. To help this important audience, we present our experience and share lessons learned from the design and implementation of an agent-based model of agricultural production systems in the Argentine Pampas, emphasizing the software engineering perspective. We discuss the model's design including the model classes; the activity diagram, and data flow; the package and folder layout; the use of design patterns; performance optimization; initialization approaches; the analysis of results; and model measurement, validation, and verification.

In this paper we apply 3D printing and genetic algorithm-generated anticipatory system dynamics models to a homeland security challenge, namely understanding the interface between transnational organized criminal networks and local gangs. We apply 3D printing to visualize the complex criminal networks involved. This allows better communication of the network structures and clearer understanding of possible interventions. We are applying genetic programming to automatically generate anticipatory system dynamics models. This will allow both the structure and the parameters of system dynamics models to evolve. This paper reports the status of work in progress. This paper builds on previous work that introduced the use of genetic programs to automatically generate system dynamics models. This paper's contributions are that it introduces the use of 3D printing techniques to visualize complex networks and that it presents in more detail our emerging approach to automatically generating anticipatory system dynamics in weakly constrained, data-sparse domains.

Land exchange through rental transactions is a central process in agricultural systems. The land tenure regimes emerge from land transactions and structural and land use changes are tied to the dynamics of the land market. We introduce LARMA, a LAnd Rental MArket model embedded within the Pampas Model (PM), an agent-based model of Argentinean agricultural systems. LARMA produces endogenous formation of land rental prices. LARMA relies on traditional economic concepts for LRP formation but addresses some drawbacks of this approach by being integrated into an agent-based model that considers heterogeneous agents interacting with one another. PM-LARMA successfully reproduced the agricultural land tenure regimes and land rental prices observed in the Pampas. Including adaptive, heterogeneous and interacting agents was critical to this success. We conclude that agent-based and traditional economic models can be successfully combined to capture complex emergent land tenure and market price patterns while simplifying the overall model design.

Agent states and transitions between states are important abstractions in agent-based social simulation (ABSS). Although it is common to develop ad hoc implementations of state-based and transition-based agent behaviors, "best practice" software engineering processes provide transparent and formally grounded design notations that translate directly into working implementations. Statecharts are a software engineering design methodology and an explicit visual and logical representation of the states of system components and the transitions between those states. Used in ABSS, they can clarify a model's logic and allow for efficient software engineering of complex state-based models. In addition to agent state and behavioral logic representation, visual statecharts can also be useful for monitoring agent status during a simulation, quickly conveying the underlying dynamics of complex models as a simulation evolves over time. Visual approaches include drag-and-drop editing capabilities for constructing state-based models of agent behaviors and conditions for agent state transitions. Repast Simphony is a widely used, open source, and freely accessible agent-based modeling toolkit. While it is possible for Repast Simphony users to create their own implementations of state-based agent behaviors and even create dynamic agent state visualizations, the effort involved in doing so is usually prohibitive. The new statecharts framework in Repast Simphony, a subset of Harel's statecharts, introduces software engineering practices through the use of statecharts that directly translate visual representations of agent states and behaviors into software implementations. By integrating an agent statecharts framework into Repast Simphony, we have made it easier for users at all levels to take advantage of this important modeling paradigm. Through the visual programming that statecharts afford, users can effectively create the software underlying agents and agent-based models. This paper describes the development and use of the free and open source Repast Simphony statecharts capability for developing ABSS models.

The present paper summarizes the integration of two models, an energy security model and a national stability (conflict) model. The Energy Security Model uses system dynamics to represent national interactions in global markets for oil and natural gas. The Conflict Model employs multiscale agent-based modeling to represent international, national and subnational actors that must address complex scenarios in international relations. While this is a work in progress, the models are being integrated in order to support model interaction. So, instability in a major oil producing country can restrict global oil supplies and increase prices. Similarly, a fall in oil price might weaken a nation that is heavily dependent on oil revenue for stability. This overview provides an informative description of two alternate methods used to integrate two substantively distinct models.

BACKGROUND:Methicillin-resistant Staphylococcus aureus (MRSA) has been a deadly pathogen in healthcare settings since the 1960s, but MRSA epidemiology changed since 1990 with new genetically distinct strain types circulating among previously healthy people outside healthcare settings. Community-associated (CA) MRSA strains primarily cause skin and soft tissue infections, but may also cause life-threatening invasive infections. First seen in Australia and the U.S., it is a growing problem around the world. The U.S. has had the most widespread CA-MRSA epidemic, with strain type USA300 causing the great majority of infections. Individuals with either asymptomatic colonization or infection may transmit CA-MRSA to others, largely by skin-to-skin contact. Control measures have focused on hospital transmission. Limited public health education has focused on care for skin infections.METHODS:We developed a fine-grained agent-based model for Chicago to identify where to target interventions to reduce CA-MRSA transmission. An agent-based model allows us to represent heterogeneity in population behavior, locations and contact patterns that are highly relevant for CA-MRSA transmission and control. Drawing on nationally representative survey data, the model represents variation in sociodemographics, locations, behaviors, and physical contact patterns. Transmission probabilities are based on a comprehensive literature review.RESULTS:Over multiple 10-year runs with one-hour ticks, our model generates temporal and geographic trends in CA-MRSA incidence similar to Chicago from 2001 to 2010. On average, a majority of transmission events occurred in households, and colonized rather than infected agents were the source of the great majority (over 95%) of transmission events. The key findings are that infected people are not the primary source of spread. Rather, the far greater number of colonized individuals must be targeted to reduce transmission.CONCLUSIONS:Our findings suggest that current paradigms in MRSA control in the United States cannot be very effective in reducing the incidence of CA-MRSA infections. Furthermore, the control measures that have focused on hospitals are unlikely to have much population-wide impact on CA-MRSA rates. New strategies need to be developed, as the incidence of CA-MRSA is likely to continue to grow around the world.

Agent-based simulation (ABS) is an approach to modeling systems comprised of individual, autonomous, interacting “agents.” Agent-based modeling offers ways to more easily model individual behaviors and how behaviors affect others in ways that have not been available before. There is much interest in developing agent-based models for many application problem domains. Applications range from modeling agent behavior in supply chains and the stock market, to predicting the success of marketing campaigns and the spread of epidemics, to projecting the future needs of the healthcare system. Progress in the area suggests that ABS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories to aid in discovery. This brief tutorial introduces agent-based modeling and simulation by describing the basic ideas of ABS, discussing some applications, and addressing methods for developing agent-based models.

A novel agent-based model, the Electricity Market Complex Adaptive System (EMCAS) model, is designed to study market restructuring and the impact of new technologies on the power grid. The agent-based approach captures the complex interactions between the physical infrastructure and the economic behaviors of various agents operating in an electricity market. The electric power system model consists of power generating plants, transmission lines, and load centers. The electric power market is composed of generating company agents who bid capacity and prices into power pools administered by an Independent System Operator (ISO). The ISO agent balances supply and demand for day-ahead markets. EMCAS also simulates real-time market operation to account for the uncertainties in day-ahead forecasts and availability of generating units. This paper describes the model, its implementation, and its use to address questions of congestion management, price forecasting, market design, and market power.

Patterns have offered a powerful yet simple way to conceptualize and communicate ideas in many disciplines since Christopher Alexander introduced them in the late 1970s. Patterns in general, and design patterns in particular, became widely used for software development by the 1990s. They have subsequently been shown to be of substantial value in improving software quality and development efficiency. Several authors have suggested that there is great potential for patterns to improve the practice of agent-based modelling and simulation as well. We consider product and process patterns in this paper. Product patterns are a vocabulary for designing or implementing models. Process patterns are methods for designing, implementing, or using models. This paper's contribution is to identify a set of product and process patterns for agent-based modelling and simulation. The applicability of each proposed pattern is substantiated with published examples of their use within models or modeling libraries.

The computational study of complex systems increasingly requires model integration. The drivers include a growing interest in leveraging accepted legacy models, an intensifying pressure to reduce development costs by reusing models, and expanding user requirements that are best met by combining different modeling methods. There have been many published successes including supporting theory, conceptual frameworks, software tools, and case studies. Nonetheless, on an empirical basis, the published work suggests that correctly specifying model integration strategies remains challenging. This naturally raises a question that has not yet been answered in the literature, namely `what is the computational difficulty of model integration?' This paper's contribution is to address this question with a time and space complexity analysis that concludes that deep model integration with proven correctness is both NP-complete and PSPACE-complete and that reducing this complexity requires sacrificing correctness proofs in favor of guidance from both subject matter experts and modeling specialists.

Purpose Following Holland, complex adaptive systems (CASs) are collections of interacting, autonomous, learning decision makers embedded in an interactive environment. Modeling CASs is challenging for a variety of reasons including the presence of heterogeneity, spatial relationships, nonlinearity, and, of course, adaptation. The challenges of modeling CASs can largely be overcome by using the individual-level focus of agent-based modeling. Agent-based modeling has been used successfully to model CASs in many disciplines. Many of these models were implemented using agent-based modeling software such as Swarm, Repast 3, Repast Simphony, Repast for High-Performance Computing, MASON, NetLogo, or StarLogo. All of these options use modular imperative architectures with factored agents, spaces, a scheduler, logs, and an interface. Many custom agent-based models also use this kind of architecture. This paper’s contribution is to introduce and apply a theoretical formalism for analyzing modular imperative agent-based models of CASs. This paper includes an analysis of three example models to show how the formalism is useful for predicting the execution time and space requirements for representations of common CASs. Method The paper details the formalism and then uses it to prove several new findings about modular imperative agent-based models. Results It is proven that the asymptotic time and space performance of modular imperative agent-based modeling studies is computationally optimal for a common class of problems. Here ‘optimal’ means that no other technique can solve the same problem computationally using less asymptotic time or space. Modular imperative agent-based models are shown to be universal models, subject to the correctness of the Church-Turing thesis. Several other results are also proven about the time and space performance of modular imperative agent-based models. The formalism is then used to predict the performance of three models and the results are found to compare closely to the measured performance. Conclusions This paper’s contribution is to introduce, analyze, and apply a theoretical formalism for proving findings about agent-based models with modular agent scheduler architectures. Given that this kind of modeling is both computationally optimal and a natural structural match for many modeling problems, it follows that it is the best modeling method for such problems.

An agent-based model is used to simulate a developing region population, disease burden, health care infrastructure and estimate the impact of resource investment decisions on population health and health care costs. In this approach, the primary agents are individual health care facilities, capturing population characteristics, facility catchment population, and facility diagnostic capacity and strategies. Health facility investment decisions are represented by new hospital placement and capacity in selected jurisdictions. Impact on outcomes is simulated over a time horizon of up to 20 years. Data visualization is applied and used to compare multiple scenarios to help inform public health planning, investment and policy decision-making.

ReLogo is a new agent-based modeling (ABM) domain specific language (DSL) for developing agent-based models in the free and open source Repast Suite of ABM tools; the Java based Repast Simphony ABM toolkit and the C++ high performance computing Repast HPC toolkit both incorporate ReLogo. The language is geared towards a wide range of modeling and programming expertise, combining the sophisticated and powerful ABM infrastructure and capabilities in the Repast Suite with the ease of use of the Logo programming language and its associated programming idioms. This paper will present how ReLogo combines a number of concepts, including object-oriented programming, simple integration of existing code libraries, statically and dynamically typed languages, domain specific languages, and the use of integrated development environments, to create an ABM tool that is easy to learn yet is also capable of creating large scale ABMs of real world complex systems.