scholarly journals Towards a Validation Methodology for Macroeconomic Agent-Based Models

2021 ◽  
Author(s):  
Sebastiaan Tieleman
Keyword(s):  
Emergent Properties ◽  
Agent Based Models ◽  
Important Distinction ◽  
Agent Based ◽  
Model Classification ◽  
Classification Framework ◽  
The Difference ◽  
To Come

AbstractAgent-based models provide a promising new tool in macroeconomic research. Questions have been raised, however, regarding the validity of such models. A methodology of macroeconomic agent-based model (MABM) validation, that provides a deeper understanding of validation practices, is required. This paper takes steps towards such a methodology by connecting three elements. First, is a foundation of model validation in general. Second is a classification of models dependent on how the model is validated. An important distinction in this classification is the difference between mechanism and target validation. Third, is a framework that revolves around the relationship between the structure of models of complex systems with emergent properties and validation in practice. Important in this framework is to consider MABMs as modelling multiple non-trivial levels. Connecting these three elements provides us with a methodology of the validation of MABMs and allows us to come to the following conclusions regarding MABM validation. First, in MABMs, mechanisms at a lower level are distinct from, but provide input to higher levels of mechanisms. Since mechanisms at different levels are validated in different ways we can come to a specific characterization of MABMs within the model classification framework. Second, because the mechanisms of MABMs are validated in a direct way at the level of the agent, MABMs can be seen as a move towards a more realist approach to modelling compared to DSGE.

scholarly journals Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Ramzi Idoughi ◽  
Thomas H. G. Vidal ◽  
Pierre-Yves Foucher ◽  
Marc-André Gagnon ◽  
Xavier Briottet
Keyword(s):  
Synthetic Data ◽  
Hyperspectral Data ◽  
Trace Gas ◽  
Gas Concentration ◽  
Components Analysis ◽  
The Difference ◽  
Long Wave Infrared ◽  
Radiance Estimation

Hyperspectral imaging in the long-wave infrared (LWIR) is a mean that is proving its worth in the characterization of gaseous effluent. Indeed the spectral and spatial resolution of acquisition instruments is steadily decreasing, making the gases characterization increasingly easy in the LWIR domain. The majority of literature algorithms exploit the plume contribution to the radiance corresponding to the difference of radiance between the plume-present and plume-absent pixels. Nevertheless, the off-plume radiance is unobservable using a single image. In this paper, we propose a new method to retrieve trace gas concentration from airborne infrared hyperspectral data. More particularly the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. It consists in performing a classification of the scene and then applying a principal components analysis based method to estimate the background radiance on each cluster stemming from the classification. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS) Imaging Hyper-Cam LW airborne acquisition above ethylene release. We finally show ethylene retrieved concentration map and estimate flow rate of the ethylene release.

scholarly journals Emulating Target Trials to Improve Causal Inference from Agent-Based Models

2021 ◽  
Author(s):  
Eleanor J Murray ◽  
Brandon D L Marshall ◽  
Ashley L Buchanan
Keyword(s):  
Causal Inference ◽  
Disease Transmission ◽  
Causal Effect ◽  
Potential Outcomes ◽  
Emergent Properties ◽  
Target Trial ◽  
Agent Based Models ◽  
Agent Based ◽  
Effect Estimation ◽  
Potential Outcomes Framework

Abstract Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key ‘no interference’ assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.

scholarly journals Image Classification of Beef and Pork Using Convolutional Neural Network in Keras Framework

2021 ◽  
Vol 5 (02) ◽  
pp. 245-248
Author(s):  
Salsa Bila ◽  
Anwar Fitrianto ◽  
Bagus Sartono
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Food Ingredient ◽  
Model Classification ◽  
Regularization Techniques ◽  
The Difference ◽  
Fully Connected ◽  
Selection Of

Beef is a food ingredient that has a high selling value. Such high prices make some people manipulate sales in markets or other shopping venues, such as mixing beef and pork. The difference between pork and beef is actually from the color and texture of the meat. However, many people do not understand these differences yet. In addition to socialization related to understanding the differences between the two types of meat, another solution is to create a technology that can recognize and differentiate pork and beef. That is what underlies this research to build a system that can classify the two types of meat. Convolutional Neural Network (CNN) is one of the Deep Learning methods and the development of Artificial Intelligence science that can be applied to classify images. Several regularization techniques include Dropout, L2, and Max-Norm were applied to the model and compared to obtain the best classification results and may predict new data accurately. It has known that the highest accuracy of 97.56% obtained from the CNN model by applying the Dropout technique using 0.7 supported by hyperparameters such as Adam's optimizer, 128 neurons in the fully connected layer, ReLu activation function, and 3 fully connected layers. The reason that also underlies the selection of the model is the low error rate of the model, which is only 0.111.Keywords: Beef and Pork, Model, Classification, CNN

A Thesaurus-Guided Text Analytics Technique for Capability-Based Classification of Manufacturing Suppliers

2017 ◽  
Author(s):  
Ramin Sabbagh ◽  
Farhad Ameri
Keyword(s):  
Vector Model ◽  
Text Analytics ◽  
Heavy Component ◽  
Classification Technique ◽  
Classification Framework ◽  
Capability Analysis ◽  
Realistic View ◽  
Capability Evaluation

Capability analysis is a necessary step in the early stages of supply chain formation. Most existing approaches to manufacturing capability evaluation and analysis use structured and formal capability models as input. However, manufacturing suppliers often publish their capability data in an unstructured format. The unstructured capability data usually portrays a more realistic view of the services a supplier can offer. If parsed and analyzed properly, unstructured capability data can be used effectively for initial screening and characterization of manufacturing suppliers specially when dealing with a large pool of prospective suppliers. This work proposes a novel framework for capability-based supplier classification that relies on the unstructured capability narratives available on the suppliers’ websites. Naïve Bayes is used as the text classification technique. One of the innovative aspects of this work is incorporating a thesaurus-guided method for feature selection and tokenization of capability data. The thesaurus contains the informal vocabulary used in the contract machining industry for advertising manufacturing capabilities. An Entity Extractor Tool (EET) is developed for the generation of the concept vector model associated with each capability narrative. The proposed supplier classification framework is validated experimentally through forming two capability classes, namely, heavy component machining and difficult and complex machining, based on real capability data.

Agent-based models of bounded rationality

2017 ◽  
Vol 23 (1/2) ◽  
pp. 2-12 ◽  
Author(s):  
Davide Secchi
Keyword(s):  
Bounded Rationality ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Special Issue ◽  
Agent Based Models ◽  
Content Type ◽  
Agent Based ◽  
Team Research ◽  
Complex Adaptive ◽  
To Come

Purpose The purpose of this editorial is to introduce the Special Issue “Agent-Based Models of Bounded Rationality” and to provide an overview of its rationale and main objectives. Design/methodology/approach After outlining the overall framework to justify the choice of agent-based modeling in relation to bounded rationality, an overview of the six papers published in the Special Issue is presented. Findings The paper argues that simulation of complex adaptive social systems is a way to set the ground for updating the concept of bounded rationality and prepare for it to still play a significant role in the years to come. Originality/value After its introduction, bounded rationality remained mostly used but seldom discussed in both its assumptions and its meaning. The originality of this introduction is to unveil some of the points that keep rationality still at the core of organization and team research.

scholarly journals Exploring Scientific Inquiry via Agent-Based Modelling

2021 ◽  
Vol 29 (4) ◽  
pp. 537-557
Author(s):  
Dunja Šešelja
Keyword(s):  
Scientific Inquiry ◽  
Agent Based Models ◽  
Scientific Rationality ◽  
Philosophical Literature ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Epistemic Function ◽  
Case Based

Abstract In this paper I examine the epistemic function of agent-based models (ABMs) of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni’s (2005) classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the last category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman’s (2010) ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM (Borg et al. 2019, 2017b, 2018), which provides insights into potential mechanisms underlying the efficiency of scientific inquiry.

Difference mode EXELFS with parallel-detection EELS

1992 ◽  
Vol 50 (2) ◽  
pp. 1262-1263
Author(s):  
Michael K. Kundmann ◽  
Ondrej L. Krivanek
Keyword(s):  
Edge Detection ◽  
Important Distinction ◽  
Edge Structure ◽  
Parallel Detection ◽  
Gain Variation ◽  
Ideal Solutions ◽  
Detector Cell ◽  
The Difference ◽  
Elemental Detection ◽  
Partial Correction

Parallel detection has greatly improved the elemental detection sensitivities attainable with EELS. An important element of this advance has been the development of differencing techniques which circumvent limitations imposed by the channel-to-channel gain variation of parallel detectors. The gain variation problem is particularly severe for detection of the subtle post-threshold structure comprising the EXELFS signal. Although correction techniques such as gain averaging or normalization can yield useful EXELFS signals, these are not ideal solutions. The former is a partial throwback to serial detection and the latter can only achieve partial correction because of detector cell inhomogeneities. We consider here the feasibility of using the difference method to efficiently and accurately measure the EXELFS signal.An important distinction between the edge-detection and EXELFS cases lies in the energy-space periodicities which comprise the two signals. Edge detection involves the near-edge structure and its well-defined, shortperiod (5-10 eV) oscillations. On the other hand, EXELFS has continuously changing long-period oscillations (∼10-100 eV).

Analisis Karakterisasi Teknologi Direct Sequence Spread Spectrum Dan Frequency Hopping Spread Spectrum

2019 ◽  
Vol 2 (2) ◽  
pp. 129-138
Author(s):  
Fawzan Galib Abdul Karim Bawahab ◽  
Elvan Yuniarti ◽  
Edi Kurniawan
Keyword(s):  
Spread Spectrum ◽  
Communication Systems ◽  
Multiple Access ◽  
Frequency Hopping ◽  
Direct Sequence Spread Spectrum ◽  
Direct Sequence ◽  
Frequency Hopping Spread Spectrum ◽  
Two Systems ◽  
The Difference

Abstrak. Pada penelitian ini, telah dilakukan analisa karakterisasi pada teknologi Direct Sequence Spread Spectrum dan Frequency Hopping Spread Spectrum, sebagai salah satu teknik multiple-access pada sistem komunikasi. Karakterisasi dilakukan untuk mencari bagaimana cara meningkatkan keoptimalan kedua sistem tersebut, dalam mengatasi masalah interferensi dengan sistem dan channel yang sama. Dan juga untuk menentukan veriabel apa yang mempengaruhi keoptimalan kedua sistem tersebut. Karakterisasi dilakukan dengan menentukan variabel-variabel yang mempengaruhi keoptimalan keduanya. Hasil dari karakterisasi, diketahui variabel-variabel yang mempengaruhi kemampuan sistem DSSS yaitu nilai frekuensi spreading (). Sedangkan untuk sistem FHSS yaitu nilai frekuensi spreading ( dan ) dan selisih antara frekuensi hopping data dengan frekuensi hopping interferensi . Kata Kunci: BER, DSSS, FHSS, Interference, Spread spectrum. Abstract. In this study, characterization of Direct Sequence Spread Spectrum and Frequency Hopping Spread Spectrum technologies have been done, as one of the multiple-access techniques in communication systems. Characterization is done to find out how to improve the ability of the two systems, in solving interference problems with the same system and channel. And also to determine what veriabel affects the ability of the two systems. Characterization is done by determining the variables that affect the ability of both. The results of the characterization, known variables that affect the ability of the DSSS system are the spreading frequency value (). As for the FHSS system, the spreading frequency value ( and ) and the difference between frequency hopping data with frequency hopping interference .

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