Bottom-up Simulation Research Articles

Energy-mix Scenarios for Bolivia

Nowadays the Bolivian energy-mix is misbalanced due to the primary production of energy, which is focused in gaseous hydrocarbons, whereas the consumption is intensive in liquid hydrocarbons. At the same time the Bolivian electric system is mainly thermo, while the country present high hydro potential. In this framework this document makes reference to the trending evolution of the Bolivian energy-mix and proposes a mitigation scenarios based on the a) reduction of liquid hydrocarbons consumption; b) and introduction of renewable energies an energy efficiency measures in the electric system. Methodologically, the construction of such scenarios is developed by a bottom-up simulation for the time span 2007-2025. We based our estimations on previous results we obtained in the project Renewable Energies Generation in South America (REGSA), founded by the European Union.

The latency insertion method for simulations of phase-locked loops

In this paper, we present two methods for the simulations of phase-locked loops (PLL) based on the latency insertion method (LIM). First, we present a novel and simple behavioral model based simulation method that exploits the latency in the PLL formulation and utilizes a leapfrog time stepping discretization scheme to solve for the transient response of the PLL. Next, we apply LIM to the simulation of PLLs at the transistor level. Various PLL dynamic responses such as lock-in, pull-in and pull-out conditions are simulated and comparisons with analytical solutions are depicted when available. Results are also compared to traditional SPICE-based methods. Finally, a bottom-up behavioral simulation approach is illustrated by using LIM to generate individual models for the PLL components which are then used in an overall behavioral level simulation.

Mobilizing for Change

Theories on the establishment and propagation of political movements through mobilization have emerged and evolved over the last half century. Among the major theoretical frameworks that have been advanced are resource mobilization theory, political process theory, and culture theory. However, despite these developments, relatively few methodological approaches have applied bottom-up computational modeling and simulation in explaining movement development in conflicts. With developments made in computational methods, the integration of social theory with modeling and simulation is a natural progression in creating tools that allow analysts, policy makers, and researchers the means to assess the successes or failures of political movements during armed struggles. This article presents an agent-based model and simulation that applies several frequently used theoretical approaches to political mobilization and explores the extent to which group resources and identity shaped conflicts in Central Asia. Given their historical, cultural, political, economic, and geographical circumstances, the authors seek to determine why different movements experienced contrasting political mobilization outcomes. Results show that receiving outside resources could help a relatively weak group, with limited mobilization, overcome opposition that is initially better mobilized, while shared identity and sufficient risk taking are shown to be potentially strong factors in producing successful mobilization. More broadly, the approach advanced enables analysts and researchers to better anticipate future mobilization events and projected paths of conflict by developing and understanding cause and effect relationships within relevant theoretical frameworks.

The future of FMRI connectivity

"FMRI connectivity" encompasses many areas of research, including resting-state networks, biophysical modelling of task-FMRI data and bottom-up simulation of multiple individual neurons interacting with each other. In this brief paper I discuss several outstanding areas that I believe will see exciting developments in the next few years, in particular concentrating on how I think the currently separate approaches will increasingly need to take advantage of each others' respective complementarities.

Data-parallel techniques for simulating a mega-scale agent-based model of systemic inflammatory response syndrome on graphics processing units

Agent-based modeling is increasingly being used for computer simulation of complex biological systems. An agent-based model (ABM) is a bottom-up simulation where the bulk dynamics of the model result from the local interactions of its individual constituents or agents. However, due to emergent qualities of ABMs, bulk behaviors may be sensitive to the size of the model as determined by the population of individuals. Therefore, in certain circumstances it may be critical to closely match the simulation size with the actual system. This may be particularly true in biological systems, where multiple large-scale heterogeneous populations can range into millions or even billions of individual cells/agents. Most existing ABM simulation toolkits are designed for serial computing and canno*t effectively simulate such mega-scale systems from a run-time standpoint. In this paper, we investigate data-parallel ABM implementations on graphics processing units to address the scalability issue of ABMs. As an example, we have implemented an abstracted version of the Systemic Inflammatory Response Syndrome ABM. We also implemented a serial version to confirm statistical accuracy. Our results show that parallelization on graphics processing units offers a substantial gain in performance without a loss in accuracy.

Agent-based modeling within a cyberinfrastructure environment: a service-oriented computing approach

Agent-based models (ABM) allow for the bottom-up simulation of dynamics in complex adaptive spatial systems through the explicit representation of pattern–process interactions. This bottom-up simulation, however, has been identified as both data- and computing-intensive. While cyberinfrastrucutre provides such support for intensive computation, the appropriate management and use of cyberinfrastructure (CI)-enabled computing resources for ABM raise a challenging and intriguing issue. To gain insight into this issue, in this article we present a service-oriented simulation framework that supports spatially explicit agent-based modeling within a CI environment. This framework is designed at three levels: intermodel, intrasimulation, and individual. Functionalities at these levels are encapsulated into services, each of which is an assembly of new or existing services. Services at the intermodel and intrasimulation levels are suitable for generic ABM; individual-level services are designed specifically for modeling intelligent agents. The service-oriented simulation framework enables the integration of domain-specific functionalities for ABM and allows access to high-performance and distributed computing resources to perform simulation tasks that are often computationally intensive. We used a case study to investigate the utility of the framework in enabling agent-based modeling within a CI environment. We conducted experiments using supercomputing resources on the TeraGrid – a key element of the US CI. It is indicated that the service-oriented framework facilitates the leverage of CI-enabled resources for computationally intensive agent-based modeling.

Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change

A spectrum of methods exists for investigating and providing solutions for land use change. These methods can be broadly categorized as either ‘top-down’ or ‘bottom-up’ approaches according to how land use change is modeled and analyzed. Although there has been much research in recent years advancing the use of these techniques for both theoretical and practical applications, integrating top-down and bottom-up approaches for enhancing land use change modeling has received minimal attention. The objective of this study is to address this gap in the literature by bridging the bottom-up simulation of agent-based modeling and the top-down analytical capabilities of multi-objective decision-making by means of a heuristic modeling approach called reinforcement learning (RL). A model is developed in which computer agents representing households and commercial enterprises select locations to inhabit based on population densities and attractivity preferences. The land use change resulting from these dynamics is evaluated by a set of agents representing different stakeholders who are embedded with RL algorithms that allow them to influence the land use change process so that their objectives are addressed. The results demonstrate that bridging bottom-up and top-down models leads to negotiated land use patterns in which the desires and objectives of all individuals are constrained by behaviors of others. This study suggests that a movement toward a ‘modeling-in-the-middle’ approach is desirable to incorporate the real yet conflicting forces that shape land use change and that are rarely considered in unison.

Basic thermo-mechanical property estimation of a 3D-crosslinked epoxy/SiO 2 interface using molecular modelling

In this work we present a procedure for the construction of 3D networked epoxy moulding compounds and an estimation of basic thermodynamic properties by molecular dynamics simulations. Our investigations present part of general trend to extend failure analysis, reliability assessment and the development of packaging materials from the conventional discrete usage of simulation techniques to a more holistic approach of an interconnected multimethods-procedure, enabling bottom-up simulation of complex microsystems. Within that framework, the task at hand for detailed atomistic molecular modelling is to develop practical methods in order to take materials development as well as materials failure analysis to the nanoscale level. This paper reports a cross linking scheme for the construction of three dimensionally cross linked simulation packages and presents a first property analysis of an industry-oriented moulding compound material. First models and results are presented of model packages of ideal epoxy/silicon-dioxide interfaces.

Technology, expertise and social cognition in human evolution

Paleolithic stone tools provide concrete evidence of major developments in human behavioural and cognitive evolution. Of particular interest are evolving cognitive mechanisms implied by the cultural transmission of increasingly complex prehistoric technologies, hypothetically including motor resonance, causal reasoning and mentalizing. To test the relevance of these mechanisms to specific Paleolithic technologies, we conducted a functional magnetic resonance imaging study of Naïve, Trained and Expert subjects observing two toolmaking methods of differing complexity and antiquity: the simple 'Oldowan' method documented by the earliest tools 2.5 million years ago; and the more complex 'Acheulean' method used to produce refined tools 0.5 million years ago. Subjects observed 20-s video clips of an expert demonstrator, followed by behavioural tasks designed to maintain attention. Results show that observational understanding of Acheulean toolmaking involves increased demands for the recognition of abstract technological intentions. Across subject groups, Acheulean compared with Oldowan toolmaking was associated with activation of left anterior intraparietal and inferior frontal sulci, indicating the relevance of resonance mechanisms. Between groups, Naïve subjects relied on bottom-up kinematic simulation in the premotor cortex to reconstruct unfamiliar intentions, and Experts employed a combination of familiarity-based sensorimotor matching in the posterior parietal cortex and top-down mentalizing involving the medial prefrontal cortex. While no specific differences between toolmaking technologies were found for Trained subjects, both produced frontal activation relative to Control, suggesting focused engagement with toolmaking stimuli. These findings support motor resonance hypotheses for the evolutionary origins of human social cognition and cumulative culture, directly linking these hypotheses with archaeologically observable behaviours in prehistory.

VOLATILITY IN THE CONSUMER PACKAGED GOODS INDUSTRY — A SIMULATION BASED STUDY

The volatility in a CPG market is modeled using a bottom-up simulation approach and validated against disaggregated supermarket transactions data. The simulation uses independent agents, each agent representing unique households in the data. A simple behavioral model incorporates household preferences for product attributes and prices. Our validation strategy tests the model predictions at both macro and micro levels and benchmarks the performance in each against a random choice model. The model significantly outperforms the benchmark at both levels. At the macro level, choices made by heterogenous agents accurately captures the volatility in market shares over time. This accuracy at the macro level is driven by the accuracy of predictions at the micro household level SKU and attribute choice.

A realistic molecular model of cement hydrates

Despite decades of studies of calcium-silicate-hydrate (C-S-H), the structurally complex binder phase of concrete, the interplay between chemical composition and density remains essentially unexplored. Together these characteristics of C-S-H define and modulate the physical and mechanical properties of this "liquid stone" gel phase. With the recent determination of the calcium/silicon (C/S = 1.7) ratio and the density of the C-S-H particle (2.6 g/cm(3)) by neutron scattering measurements, there is new urgency to the challenge of explaining these essential properties. Here we propose a molecular model of C-S-H based on a bottom-up atomistic simulation approach that considers only the chemical specificity of the system as the overriding constraint. By allowing for short silica chains distributed as monomers, dimers, and pentamers, this C-S-H archetype of a molecular description of interacting CaO, SiO2, and H2O units provides not only realistic values of the C/S ratio and the density computed by grand canonical Monte Carlo simulation of water adsorption at 300 K. The model, with a chemical composition of (CaO)(1.65)(SiO2)(H2O)(1.75), also predicts other essential structural features and fundamental physical properties amenable to experimental validation, which suggest that the C-S-H gel structure includes both glass-like short-range order and crystalline features of the mineral tobermorite. Additionally, we probe the mechanical stiffness, strength, and hydrolytic shear response of our molecular model, as compared to experimentally measured properties of C-S-H. The latter results illustrate the prospect of treating cement on equal footing with metals and ceramics in the current application of mechanism-based models and multiscale simulations to study inelastic deformation and cracking.

Collateral Risk in Residential Mortgage Defaults

This paper presents a systematic framework for capturing the collateral-driven mortgage default risk. A forward-looking home price distribution model is developed that explicitly incorporates different sources of volatility in the market value of collateral houses. A consistent and computationally-efficient top-down approach of home price simulation is also introduced. We show that with the proper inclusion of all relevant sources of volatilities, the top-down approach provides close approximation to the results generated by a theoretically sound but computationally demanding bottom-up simulation approach. Using a numerical simulation, we demonstrate that a geographically-diversified mortgage pool entails a substantially lower level of systematic collateral driven mortgage default risk compared to a spatially-concentrated pool. However, the expected default risk is shown to remain unaffected, indicating that the benefit from geographic diversification is only realized through lower risk-based capital requirements, not in lower mortgage insurance premiums. Based on the US state level house price indices, the systematic risk of a state-concentrated mortgage pool is estimated to be about four times higher than that of a nationally-diversified mortgage pool. Our results also show that, among the different volatility components, omitting the cross-sectional dispersion of individual home prices would produce the largest bias in assessing home-price-based mortgage default risk.

Simulation of the long-term dynamic of a market-based transmission interconnection

Competitive markets set a framework in which unregulated market-based interconnections are allowed and encouraged. However, the presence of merchant transmission lines in the role of interconnector raises questions about the impact of these agents on the market conditions and system operation. The interconnector could be allowed to withhold capacity in order to keep the price difference (and profit of it) or be enforced by a must-offer provision to bid all its capacity. In this paper, the long-term dynamic of a market interconnection is studied and analyzed in a test system through a bottom-up simulation model. The outcomes are measured by the level of electricity prices along the simulated period and the annual amount of energy not supplied. The results show that the prices are strongly affected in the average value and in the standard deviation. The benefits of an interconnection as regards reliability are not hidden by the merchant nature of the interconnector. The results of the simulation show that letting the interconnector to control the bids of capacity offered to the markets is not detrimental to the markets. However, there is a lost of benefits compared with the case of a mandatory must-offer provision if compared at the same capacity of transmission line.

The potential role of Carbon Capture and Storage, under different policy options

This paper explores the potential role of CCS under different policy options in the Dutch electricity sector. A bottom-up simulation model is used to evaluate different policy scenarios. The results show that CCS plays only a moderate role for gradually rising CO2 prices. By using the scale advantages in CO2 transport already for the first CCS plants comparable to the level of large scale transport, the competitiveness of CCS is improved considerably. Furthermore, our results indicate that different policy options stimulate different types of CCS technologies. Finally, stimulating CCS and renewable energy sources simultaneously is more cost effective in the long run than stimulating solely CCS.

The design and use of an agent‐based model to simulate the 1918 influenza epidemic at Norway House, Manitoba

Agent-based modeling provides a new approach to the study of virgin soil epidemics like the 1918 flu. In this bottom-up simulation approach, a landscape can be created and populated with a heterogeneous group of agents who move and interact in ways that more closely resemble human behavior than is usually seen in other modeling techniques. In this project, an agent-based model was constructed to simulate the spread of the 1918 influenza pandemic through the Norway House community in Manitoba, Canada. Archival, ethnographic, epidemiological, and biological information were used to aid in designing the structure of the model and to estimate values for model parameters. During the epidemic, Norway House was a Hudson's Bay Company post and a Swampy Cree-Métis settlement with an economy based on hunting, fishing, and the fur trade. The community followed a traditional, seasonal travel pattern of summer aggregation and winter dispersal. The model was used to examine how seasonal community structures and associated population movement patterns may have influenced disease transmission and epidemic spread. Simulations of the model clearly demonstrate that human behavior can significantly influence epidemic outcomes.

Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

大学の組織構成要素のまとまりと要求面積充足度に基づいた既存校舎への室再配置計画

Many facilities already exist in university campus and it is important to make these facilities adjust to the change of university's organization. The theme of this paper is to acquire a method to place rooms in existing buildings. We proved this theme by using two organizations and two buildings. Both buildings have hierarchical structure and enormous patterns of layout plan. In order to search the optimal layout plan, we applied Neural network. By setting several parameters, the valuable plans are obtained with the bottom-up simulation program.

Climate change impacts on the thermal performance of Portuguese buildings. Results of the SIAM study

Results of the ” rst studies speci” cally dedicated to the impact of climate change on the thermal behaviour of buildings in Portugal are discussed within this paper. A top-down econometric/statistical analysis was attempted in order to correlate ‘ uctuations of temperature and energy consumption in the residential and services sectors, but the results were not conclusive. A bottom-up numerical thermal simulation of representative buildings for various regions of Portugal was more successful. Unlike most studies for northern countries, it is predicted that the energy demand for space conditioning in Portugal would greatly increase by the end of the twenty-” rst century, assuming ” xed characteristics of the building sectors. The heating season is shorter and the heating thermal load reduced (with HadCM3 model scenarios, less 250–410 kWh for residences, less 5–7 kWh/m2 for offices), but these economies are offset by an extended cooling season and large increases of the cooling thermal loads (additional 500–880 kWh for residences, 19–24 kWh/m2 for offices). Higher resolution studies using HadRM data resulted in yet more serious climate change impacts.

VISUAL DEBUGGING: A WAY OF ANALYZING, UNDERSTANDING AND COMMUNICATING BOTTOM‐UP SIMULATION MODELS IN ECOLOGY

ABSTRACT. Bottom‐up simulation models, i.e. individual‐based and spatially explicit models, are powerful new tools in ecology and natural resource management. However, they have to be implemented as more or less complex software and are therefore harder to analyze, understand, and communicate than mathematical models. Particularly critical is the problem of communication, which must be solved if bottom‐up models are to be scientifically credible. As a pragmatic solution to this problem, “visual debugging‘ is proposed, which means equipping simulation programs with a graphical user interface that integrates elements of conventional debugging and graphical representations of the model's state variables. The direct benefit of visual debugging is that the attitude of debugging, i.e. performing controlled experiments, is transferred to the overall development and analysis of the model. This facilitates acknowledging the hierarchical organization of ecological systems and using patterns observed in real systems for developing and validating the models. The benefit for communicating bottom‐up models is that peers can download the executable program from the internet and test and analyze the model on their own. Visual debugging is not meant as an exclusive alternative to other approaches of communicating bottom‐up simulation models, i.e. software libraries or general protocols of model description, but should be combined with these approaches. Overall, visual debugging integrates general principles of software engineering and specific requirements for modeling ecological systems and would therefore contribute to establishing an improved culture of analyzing, understanding, and communicating bottom‐up simulation models.

Visual debugging: a way of analyzing, understanding and communicating bottom-up simulation models in ecology

ABSTRACT. Bottom-up simulation models, i.e. individual-based and spatially explicit models, are powerful new tools in ecology and natural resource management. However, they have to be implemented as more or less complex software and are therefore harder to analyze, understand, and communicate than mathematical models. Particularly critical is the problem of communication, which must be solved if bottom-up models are to be scientifically credible. As a pragmatic solution to this problem, “visual debugging‘ is proposed, which means equipping simulation programs with a graphical user interface that integrates elements of conventional debugging and graphical representations of the model's state variables. The direct benefit of visual debugging is that the attitude of debugging, i.e. performing controlled experiments, is transferred to the overall development and analysis of the model. This facilitates acknowledging the hierarchical organization of ecological systems and using patterns observed in real systems for developing and validating the models. The benefit for communicating bottom-up models is that peers can download the executable program from the internet and test and analyze the model on their own. Visual debugging is not meant as an exclusive alternative to other approaches of communicating bottom-up simulation models, i.e. software libraries or general protocols of model description, but should be combined with these approaches. Overall, visual debugging integrates general principles of software engineering and specific requirements for modeling ecological systems and would therefore contribute to establishing an improved culture of analyzing, understanding, and communicating bottom-up simulation models.