Nicholson T. Collier

Complex adaptive systems modeling with Repast Simphony

PurposeThis paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent-based modeling software has collectively been under continuous development for more than 10 years.MethodIncludes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration.ResultsInclude a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system.ConclusionsWe discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts.

A Declarative Model Assembly Infrastructure for Verification and Validation

Model verification and validation (V&V) are critical to the long term use of agent-based models of social processes. This paper addresses one important aspect of social simulation V&V, specifically that of component-level V&V. In this paper the Repast Simphony (Repast S) declarative model assembly infrastructure for supporting component-level V&V is discussed.

Parallel agent-based simulation with Repast for High Performance Computing

In the last decade, agent-based modeling and simulation (ABMS) has been applied to a variety of domains, demonstrating the potential of this technique to advance science, engineering, and policy analysis. However, realizing the full potential of ABMS to find breakthrough research results requires far greater computing capability than is available through current ABMS tools. The Repast for High Performance Computing (Repast HPC) project addresses this need by developing a useful and useable next-generation ABMS system explicitly focusing on larger-scale distributed computing platforms. Repast HPC is intended to smooth the path from small-scale simulations to large-scale distributed simulations through the use of a Logo-like system. This article’s contribution is its detailed presentation of the implementation of Repast HPC as a useful and usable framework, a complete ABMS platform developed explicitly for larger-scale distributed computing systems that leverages modern C++ techniques and the ReLogo language.

Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation

BackgroundMethicillin-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.MethodsWe 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.ResultsOver 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.ConclusionsOur 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.

Large-Scale Agent-Based Modeling with Repast HPC: A Case Study in Parallelizing an Agent-Based Model

We present a case study for parallelizing a large-scale epidemiologic ABM developed with Repast HPC, the Chicago Social Interaction Model (chiSIM). The original serial model is a CA-MRSA model which tracks CA-MRSA transmission dynamics and infection in Chicago, and represents the spread of CA-MRSA in the population of Chicago. We utilize both within compute node parallelization using the OpenMP toolkit and distributed parallelization across multiple processes using MPI. The combined approach yields a 1350 % increase in run time performance utilizing 128 compute nodes.

Modeling the spread of community-associated MRSA

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) are strains of the bacterium S. aureus that are responsible for skin and soft tissue, blood, bone, and other infections that can be life threatening. CA-MRSA strains are resistant to standard antibiotics related to penicillins and have a high prevalence in the general community, as well as in healthcare facilities. CA-MRSA presents novel challenges for computational epidemiological modeling compared to other commonly modeled diseases. CA-MRSA challenges include modeling activities and contact processes of individuals in which direct skin contact can be an important infection pathway, estimating disease transmission parameters based on limited data, and representing behavioral responses of individuals to the disease and healthcare interventions. We are developing a fine-grained agent-based model of CA-MRSA for the Chicago metropolitan area. This paper describes how we are modeling CA-MRSA disease processes based on variants of standard epidemiological models and individual agent-based approaches.

Test-driven agent-based simulation development

Developing a useful agent-based model and simulation typically involves acquiring knowledge of the models domain, developing the model itself, and then translating the model into software. This process can be complex and is an iterative one where changes in domain knowledge and model requirements or specifications can cause changes in the software that in turn may require additional modeling and domain knowledge. Test-driven development is a software development technique that can help ameliorate this complexity by evolving a loosely coupled flexible design, driven by the creation of many small, automated unit tests. When the focus shifts to writing small tests that exercise the simulations behavior, the larger problem of translating a conceptual model into working code is decomposed into a series of much smaller, more manageable and highly focused translations. This paper explores the application of this technique to agent-based simulation development with examples from Repast Simphony, ReLogo and Repast HPC.

From desktop to large-scale model exploration with Swift/T

As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any “black box” application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.

The relogo agent-based modeling language

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.

Simulating water, individuals, and management using a coupled and distributed approach

Water is a key issue in sustainable urban development. SWIM (Simulating Water, Individuals and Management) is an agent-based model of water supply, management structure, and residential water consumer perception and behavior. Initial work applied data mining on newspaper articles to map networks of water management institutions and structures. SWIM extends this by linking an agent-based model of residential water consumption connected via networks of water managers to a global-scale hydrological model. In our case study, we focus on Tucson, Arizona, where management and social behaviors are well documented. Census data are used to create synthetic populations of consumers endowed with price sensitivity and behaviors impacting water use. Social networks, including those based on geographic proximity, allow water use behaviors to spread to others. We examine possible factors leading to recent attested declines in per-capita water use, leveraging ensemble runs on high-performance computing resources using the Swift parallel scripting language to strategically explore complex parameter spaces.