Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

A COVID-19 Recovery Strategy Based on the Health System Capacity Modeling. Implications on Citizen Self-management

Versión preprint depositada sin articulo publicado dada la actualidad del tema. *Solicitud de los autoresConfinement ends, and recovery phase should be accurate planned. Health System (HS) capacity, specially ICUs and plants capacity and availability, will remain the key stone in this new Covid-19 pandemic life cycle phase. Until massive vaccination programs will be a real option (vaccine developed, world wield production capacity and effective and efficient administration process), date that will mark recovery phase end, important decisions should be taken. Not only by authorities. Citizen self-management and organizations self-management will be crucial. This means: citizen and organizations day a day decision in order to control their own risks (infecting others and being infected). This paper proposes a management tool that is based on a ICUs and plants capacity model. Principal outputs of this tool are, by sequential order and by last best data available: (i) ICUs and plants saturation estimation data (according to incoming rate of patients), (ii) with this results new local and temporal confinement measure can be planned and also a dynamic analysis can be done to estimate maximum Ro saturation scenarios, and finally (iii) provide citizen with clear and accurate data allow them adapting their behavior to authorities’ previous recommendations. One common objective: to accelerate as much as possible socioeconomic normalization with a strict control over HS relapses risk

Prediction of Flood Hydrograph in Small River Catchments Using System Modelling Approach

Floods remain to be one of the natural catastrophic disasters with serious adverse social and economic implications on individuals and communities all around the world. In Ireland, frequency of flood events have increased dramatically during the last forty years and is expected to continue to rise primarily due to changes in rainfall and temperature patterns as a result of the global climate change. Small river catchments are usually vulnerable to different types of flooding particularly those associated with “monster” rainfall events, which are characterised by short durations and high intensities. Therefore accurate prediction of flood hydrographs resulting from these rainfall events are vital for issuing timely and detailed warning to competent authorities in order to allow for efficient preparedness in the affected catchment and other downstream areas. The current study assess the performance of Unit Hydrograph model in predicting flood hydrograph due to extreme rainfall storms at three small river catchments with different physical and hydrological characteristics. Results suggest that the UH model is more powerful in simulating flood hydrographs at natural rural catchments than in urban catchments. The artificial drainage settings of the urban catchments could be the main reason for hindering the UH from simulating the characteristic behaviour of such type of catchments

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies

INVESTIGATION INTO MINE PILLAR DESIGN AND GLOBAL STABILITY USING THE GROUND REACTION CURVE CONCEPT

Pillars form an important support structure in any underground mine. A bulk of the overburden load is borne by the mine pillars. Thus, the strength of pillars has been a subject of detailed research over more than 6 decades. This work has led to the development of largely empirical pillar design formulations that have reduced the risk of pillar failures and mine collapse. Current research, however, has drawn attention to the fact that some of the assumptions used in the development of conventional pillar design methodologies are not always valid. Conventional pillar design methodology assumes that the pillars carry the dead weight of the overburden. This conventional method treats the pillars as passive structures. The limitation of this approach is that the self-supporting capacity of the overburden is not incorporated in pillar design. This suspension theory of pillar design treats the strata-pillar interaction problem as a classic case of static equilibrium, without detailing the interactions of the two structures. Globally, multiple pillar design methods have been developed, based on this suspension theory. Each of these methods approaches the calculation of pillar stability a little differently with respect to material properties, excavation geometries and stress conditions. Most of these design methods are derived empirically and lack a mechanics-based approach. Moreover, there is a lack of a unified pillar design methodology that can be used to design all types of mine pillars using a mechanics-based approach. The Ground Reaction Curve has been used as a means of correlating strata displacements to stress conditions. In addition, the Support Reaction Curve has been used in modeling the response of a support system under load, as a function of support properties and installation time with respect to opening development. In comparing the Ground Reaction Curves and Support Reaction Curves for different support systems, one can evaluate the effectiveness of installed support systems in maintaining the integrity of the excavated area(s). This approach has been widely used in designing secondary (artificial) support systems in both civil tunneling and the mining industry. Encouraged by the successful use of this single method in designing secondary support systems, this research revisits this concept for mine pillar design. This research investigates the utilization of the Ground Reaction Curve and Support Reaction Curve for the design of mine pillar support systems with respect to anticipated pillar loading and opening convergence. In addition, a conceptual three-tier solution to the pillar design problem, using a proper combination of numerical, analytical and data-driven methods is suggested, and a flowchart for the pillar design methodology is proposed. At the focus of this proposed method lies the Ground Reaction Curve (GRC) Concept. This research effort tries to verify the proposed pillar design flowchart using in-mine instrumentation and numerical modeling. For the purpose of this research, a deep longwall coalmine is instrumented to measure changes in pillar stress and associated roof convergence, due to mining activity. Subsequently, numerical models were developed in FLAC3D to model the geomechanical effects of underground longwall mining. The numerical modeling results are validated and calibrated using instrumentation data and a surface subsidence profile. The calibrated numerical models are further used to generate the Ground Reaction Curve for the overburden and Support Reaction Curve for the coal pillar. The comparison of both curves gives a detailed view of the overburden stability with respect to the mine pillar loading, in a more mechanics-based sense. The developed numerical approach can be used in future research and further development of this methodology for various mine types and different pillar support systems

THE ROLE OF SIMULATION IN SUPPORTING LONGER-TERM LEARNING AND MENTORING WITH TECHNOLOGY

Mentoring is an important part of professional development and longer-term learning. The nature of longer-term mentoring contexts means that designing, developing, and testing adaptive learning sys-tems for use in this kind of context would be very costly as it would require substantial amounts of fi-nancial, human, and time resources. Simulation is a cheaper and quicker approach for evaluating the impact of various design and development decisions. Within the Artificial Intelligence in Education (AIED) research community, however, surprisingly little attention has been paid to how to design, de-velop, and use simulations in longer-term learning contexts. The central challenge is that adaptive learning system designers and educational practitioners have limited guidance on what steps to consider when designing simulations for supporting longer-term mentoring system design and development deci-sions. My research work takes as a starting point VanLehn et al.’s [1] introduction to applications of simulated students and Erickson et al.’s [2] suggested approach to creating simulated learning envi-ronments. My dissertation presents four research directions using a real-world longer-term mentoring context, a doctoral program, for illustrative purposes. The first direction outlines a framework for guid-ing system designers as to what factors to consider when building pedagogical simulations, fundamen-tally to answer the question: how can a system designer capture a representation of a target learning context in a pedagogical simulation model? To illustrate the feasibility of this framework, this disserta-tion describes how to build, the SimDoc model, a pedagogical model of a longer-term mentoring learn-ing environment – a doctoral program. The second direction builds on the first, and considers the issue of model fidelity, essentially to answer the question: how can a system designer determine a simulation model’s fidelity to the desired granularity level? This dissertation shows how data from a target learning environment, the research literature, and common sense are combined to achieve SimDoc’s medium fidelity model. The third research direction explores calibration and validation issues to answer the question: how many simulation runs does it take for a practitioner to have confidence in the simulation model’s output? This dissertation describes the steps taken to calibrate and validate the SimDoc model, so its output statistically matches data from the target doctoral program, the one at the university of Saskatchewan. The fourth direction is to demonstrate the applicability of the resulting pedagogical model. This dissertation presents two experiments using SimDoc to illustrate how to explore pedagogi-cal questions concerning personalization strategies and to determine the effectiveness of different men-toring strategies in a target learning context. Overall, this dissertation shows that simulation is an important tool in the AIED system design-ers’ toolkit as AIED moves towards designing, building, and evaluating AIED systems meant to support learners in longer-term learning and mentoring contexts. Simulation allows a system designer to exper-iment with various design and implementation decisions in a cost-effective and timely manner before committing to these decisions in the real world

Experiment-Based Validation and Uncertainty Quantification of Partitioned Models: Improving Predictive Capability of Multi-Scale Plasticity Models

Partitioned analysis involves coupling of constituent models that resolve their own scales or physics by exchanging inputs and outputs in an iterative manner. Through partitioning, simulations of complex physical systems are becoming evermore present in scientific modeling, making Verification and Validation of partitioned models for the purpose of quantifying the predictive capability of their simulations increasingly important. Parameterization of the constituent models as well as the coupling interface requires a significant amount of information about the system, which is often imprecisely known. Consequently, uncertainties as well as biases in constituent models and their interface lead to concerns about the accumulation and compensation of these uncertainties and errors during the iterative procedures of partitioned analysis. Furthermore, partitioned analysis relies on the availability of reliable constituent models for each component of a system. When a constituent is unavailable, assumptions must be made to represent the coupling relationship, often through uncertain parameters that are then calibrated. This dissertation contributes to the field of computational modeling by presenting novel methods that take advantage of the transparency of partitioned analysis to compare constituent models with separate-effect experiments (measurements contained to the constituent domain) and coupled models with integral-effect experiments (measurements capturing behavior of the full system). The methods developed herein focus on these two types of experiments seeking to maximize the information that can be gained from each, thus progressing our capability to assess and improve the predictive capability of partitioned models through inverse analysis. The importance of this study stems from the need to make coupled models available for widespread use for predicting the behavior of complex systems with confidence to support decision-making in high-risk scenarios. Methods proposed herein address the challenges currently limiting the predictive capability of coupled models through a focused analysis with available experiments. Bias-corrected partitioned analysis takes advantage of separate-effect experiments to reduce parametric uncertainty and quantify systematic bias at the constituent level followed by an integration of bias-correction to the coupling framework, thus ‘correcting’ the constituent model during coupling iterations and preventing the accumulation of errors due to the final predictions. Model bias is the result of assumptions made in the modeling process, often due to lack of understanding of the underlying physics. Such is the case when a constituent model of a system component is entirely unavailable and cannot be developed due to lack of knowledge. However, if this constituent model were to be available and coupled to existing models of the other system components, bias in the coupled system would be reduced. This dissertation proposes a novel statistical inference method for developing empirical constituent models where integral-effect experiments are used to infer relationships missing from system models. Thus, the proposed inverse analysis may be implemented to infer underlying coupled relationships, not only improving the predictive capability of models by producing empirical constituents to allow for coupling, but also advancing our fundamental understanding of dependencies in the coupled system. Throughout this dissertation, the applicability and feasibility of the proposed methods are demonstrated with advanced multi-scale and multi-physics material models simulating complex material behaviors under extreme loading conditions, thus specifically contributing advancements to the material modeling community