John T. Murphy

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 3-D bone tissue growth using repast HPC: initial simulation design and performance results

Bone is one of the most implanted tissues worldwide. Bone tissue engineering deals with the replacement and regeneration of bone tissue; outcomes are determined by complex biological interactions, making it difficult to design an optimal tissue growth environment. Agent-Based Modeling (ABM) is a powerful tool to simulate such a system. We present a simulation of engineered bone tissue growth using the Repast HPC toolkit, an ABM tool for high-performance computing environments. We use this example to provide preliminary performance tests on new features (not yet publicly released) of Repast HPC that accommodate operations common to biological modeling: 3-Dimensional parallelized spatial simulation and diffusion in 3 dimensions. Repast HPC is a general ABM toolkit, and the performance documented here should be representative of performance on other simulations. Using the baseline Repast HPC tools provides flexibility for continued model development and improvement.

Integrated modeling of conflict and energy

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.

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.

MIRAGE: A Framework for Data-Driven Collaborative High-Resolution Simulation

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.