Composable Cellular Automata

Mayer, Gary R.; Sarjoughian, Hessam S.

doi:10.1177/0037549709106341

Cited by 19 publications

(10 citation statements)

References 24 publications

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“…The computational laboratory is structured in a modular fashion as a hybrid modelling and simulation environment (Mayer et al 2006;Mayer 2009;Mayer & Sarjoughian 2009). There are a number of ways to model dynamic systems computationally, with varying strengths and weaknesses.…”

Section: A Computational Modelling Laboratory For Sessmentioning

confidence: 99%

Land use, water and Mediterranean landscapes: modelling long-term dynamics of complex socio-ecological systems

Barton

Ullah

Bergin

2010

Phil. Trans. R. Soc. A.

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The evolution of Mediterranean landscapes during the Holocene has been increasingly governed by the complex interactions of water and human land use. Different land-use practices change the amount of water flowing across the surface and infiltrating the soil, and change water's ability to move surface sediments. Conversely, water amplifies the impacts of human land use and extends the ecological footprint of human activities far beyond the borders of towns and fields. Advances in computational modelling offer new tools to study the complex feedbacks between land use, land cover, topography and surface water. The Mediterranean Landscape Dynamics project (MedLand) is building a modelling laboratory where experiments can be carried out on the long-term impacts of agropastoral land use, and whose results can be tested against the archaeological record. These computational experiments are providing new insights into the socio-ecological consequences of human decisions at varying temporal and spatial scales.

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Section: A Computational Modelling Laboratory For Sessmentioning

confidence: 99%

Land use, water and Mediterranean landscapes: modelling long-term dynamics of complex socio-ecological systems

Barton

Ullah

Bergin

2010

Phil. Trans. R. Soc. A.

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“…The MML integrates an agent-based model (ABM) of households practicing subsistence agriculture and/or pastoralism and a cellular automata landscape evolution model (LEM) that simulates the dynamics of surface erosion/deposition, vegetation change, and soil depth/fertility at the scale of local watersheds. Observational or modeled climatic data are used as input to the LEM (Barton et al, 2010(Barton et al, , 2012Mayer and Sarjoughian, 2009;Mitasova et al, 2013;Soto-15 Berelov, 2011). The components of the MML are modular and are dynamically coupled through an interaction model to manage overall model complexity (Davis and Anderson, 2004, p. 200;Gholami et al, 2014;Sarjoughian et al, 2013;Sarjoughian, 2006;Sarjoughian et al, 2015) (See S1 1.1).…”

Section: Definition and Descriptionmentioning

confidence: 99%

Modelling feedbacks between human and natural processes in the land system

Robinson¹,

Vittorio²,

Alexander³

et al. 2017

Preprint

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The unprecedented use of Earth's resources by humans, in combination with the increasing natural variability in natural processes over the past century, is affecting evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with 5 and quantification of human processes. Similarly, to mitigate risk and facilitate socio-economic development requires a better understanding of how the natural system (e.g., climate variability and change, extreme weather events, and processes affecting soil fertility) affects human processes. To capture and formalize our understanding of the interactions and feedback between human and natural systems a variety of modelling approaches are used. While integrated assessment models are widely 10 recognized as supporting this goal and integrating representations of the human and natural system for global applications, an increasing diversity of models and corresponding research have focused on coupling models specializing in specific human (e.g., decision-making) or natural (e.g., erosion) processes at multiple scales. Domain experts develop these specialized models with a greater degree of detail, accuracy, and transparency, with many adopting open-science norms that use new technology for 15 model sharing, coupling, and high performance computing. We highlight examples of four different approaches used to couple representations of the human and natural system, which vary in the processes represented and in the scale of their application. The examples illustrate how groups of researchers have attempted to overcome the lack of suitable frameworks for coupling human and natural systems to answer questions specific to feedbacks between human and natural systems. We draw from these 20 examples broader lessons about system and model coupling and discuss the challenges associated with maintaining consistency across models and representing feedback between human and natural systems in coupled models.Earth Syst. Dynam. Discuss., https://doi

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“…The agent model is a combination of discrete-event, rule-based agents and models that define an agent's relation to another (Mayer et al 2006;Mayer 2009;Mayer and Sarjoughian 2009). The discrete-event timing nature of the agents signifies that the agent conducts its actions in a discrete fashion but its current action may be interrupted by external factors.…”

Section: Agent Modelmentioning

confidence: 99%

Managing Hybrid Model Composition Complexity: Human–Environment Simulation Models

Sarjoughian

Mayer

Ullah

et al. 2015

Concepts and Methodologies for Modeling and Simulation

Self Cite

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It is becoming commonplace to use multiple types of models together for simulating multifaceted systems across many scientific disciplines. Indeed, in recent years, some approaches (referred to as multimodeling or multiformalism modeling) have been developed for representing a complex system as a set of subsystem models. Among these, there has been an interest in developing hybrid methods where structures and behaviors of models are explicitly accounted for. Furthermore, theories and approaches are proposed to define the interactions among heterogeneous model types. However, modeling a system this way brings about composition complexity that must also be managed. The complexities of hybrid modeling resulting from the interactions of the composed models can be reduced using interaction models, an approach referred to as polyformalism modeling (Sarjoughian 2006). Independently developing and utilizing such interaction models provides additional flexibility in system model design, modification, and execution for both the subsystem models and the resultant hybrid system model.

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Composable Cellular Automata

Cited by 19 publications

References 24 publications

Land use, water and Mediterranean landscapes: modelling long-term dynamics of complex socio-ecological systems

Land use, water and Mediterranean landscapes: modelling long-term dynamics of complex socio-ecological systems

Modelling feedbacks between human and natural processes in the land system

Managing Hybrid Model Composition Complexity: Human–Environment Simulation Models

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