scholarly journals Why statistical testing and confidence intervals should not be used in comparative life cycle assessments based on Monte Carlo simulations

2020 ◽  
Vol 25 (11) ◽  
pp. 2101-2105 ◽  
Author(s):  
Claudia von Brömssen ◽  
Elin Röös
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle ◽  
Monte Carlo Simulations ◽  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Statistical Testing ◽  
Life Cycle Assessments ◽  
Simulation Results

Abstract In the last years, it has been suggested to use statistical inferential methods, such as hypothesis testing or confidence intervals, to compare different products, services, or systems within comparative life cycle assessments based on Monte Carlo simulation results. However, the use of statistical inferential methods in such settings is fundamentally incorrect and should not be continued. In this article, we explain why and look closer at some related topics.

scholarly journals Simulasi Monte Carlo dalam Mengidentifikasi Peningkatan Penjualan Tanaman Mawar (Studi Kasus di Toko Bunga 5 Bersaudara Kota Solok)

2020 ◽  
Author(s):  
Dian Cyntia Dewi ◽  
S Sumijan ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Monte Carlo Simulations ◽  
Random Numbers ◽  
Cumulative Frequency ◽  
Simulation Process ◽  
The Monte Carlo Method ◽  
Simulation Results

Roses are one of the most popular types of plants in the community. The sale of roses at the flower shop of 5 siblings is increasingly in demand. Identifying the increase in sales is important in analyzing sales progress. At the present time the seller can only see a manual increase in sales that are most in demand. This study aims to determine predictions of the increase in sales of rose flowers with a monte carlo simulation accurately and accurately. The data that will be processed in this study in the last 2 years, namely 2018 and 2019, rose plants obtained at the 5 Brothers Flower Shop in Solok City. There are several types of roses in the predicted sales level. Then the data will be converted into the probability distribution into cumulative frequency and followed by generating random numbers so that they can determine random numbers. Next, we will group the boundary intervals of the random numbers that have been obtained and continue with the simulation process so that the simulation results and percentage accuracy are obtained using the Monte Carlo method. The results of this study on data processing from 2019 to 2020 have an accuracy of 90%. So this research is very appropriate in identifying the increase in sales for the following year. The design of this system determines the amount of increased sales of goods using the monte carlo method in a flower shop of 5 siblings. Monte Carlo simulations can be used to identify specific sales increases. The results obtained are quite accurate using the Monte Carlo method.

Coping with uncertainties in integrated urban water management

1997 ◽  
Vol 36 (8-9) ◽  
pp. 265-269
Author(s):  
Govert D. Geldof
Keyword(s):  
Water Management ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Interdisciplinary Approach ◽  
Urban Water ◽  
Urban Water Management ◽  
Integrated Water Management ◽  
Good Design ◽  
Management Techniques ◽  
Simulation Results

In the practice of integrated water management we meet complexity, subjectivity and uncertainties. Uncertainties come into play when new urban water management techniques are applied. The art of a good design is not to reduce uncertainties as much as possible, but to find the middle course between cowardice and recklessness. This golden mean represents bravery. An interdisciplinary approach is needed to reach consensus. Calculating uncertainties by using Monte Carlo simulation results may be helpful.

scholarly journals Cheating at Craps

2021 ◽  
Vol 48 (4) ◽  
pp. 53-61
Author(s):  
Andrea Marin ◽  
Carey Williamson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Modeling And Simulation ◽  
Analytical Model ◽  
Quantitative Evaluation ◽  
Mathematical Analysis ◽  
Simulation Modeling ◽  
Analytical Modeling ◽  
The World ◽  
Simulation Results

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.

scholarly journals A Comparison of Different Data-driven Procedures to Determine the Bunching Window

2021 ◽  
Vol 49 (2) ◽  
pp. 262-293
Author(s):  
Vincent Dekker ◽  
Karsten Schweikert
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Income Distribution ◽  
Prior Information ◽  
Structural Breaks ◽  
Data Driven ◽  
Taxable Income ◽  
Counterfactual Models ◽  
Simulation Results ◽  
Second Procedure

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

Should a hotter paramagnet transform quicker to a ferromagnet? Monte Carlo simulation results for Ising model

2021 ◽  
Author(s):  
Subir K Das ◽  
Nalina Vadakkayil
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
Simulation Results ◽  
Heating Up

For quicker formation of ice, before inserting inside a refrigerator, heating up of a body of water can be beneficial. We report first observation of a counterpart of this intriguing...

scholarly journals Monte Carlo Modeling and Design of Photon Energy Attenuation Layers for >10× Quantum Yield Enhancement in Si-Based Hard X-ray Detectors

Instruments ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 17
Author(s):  
Eldred Lee ◽  
Kaitlin M. Anagnost ◽  
Zhehui Wang ◽  
Michael R. James ◽  
Eric R. Fossum ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quantum Yield ◽  
Photon Energy ◽  
High Efficiency ◽  
High Energy ◽  
Yield Enhancement ◽  
X Ray ◽  
Simulation Results ◽  
Conversion Efficiencies

High-energy (>20 keV) X-ray photon detection at high quantum yield, high spatial resolution, and short response time has long been an important area of study in physics. Scintillation is a prevalent method but limited in various ways. Directly detecting high-energy X-ray photons has been a challenge to this day, mainly due to low photon-to-photoelectron conversion efficiencies. Commercially available state-of-the-art Si direct detection products such as the Si charge-coupled device (CCD) are inefficient for >10 keV photons. Here, we present Monte Carlo simulation results and analyses to introduce a highly effective yet simple high-energy X-ray detection concept with significantly enhanced photon-to-electron conversion efficiencies composed of two layers: a top high-Z photon energy attenuation layer (PAL) and a bottom Si detector. We use the principle of photon energy down conversion, where high-energy X-ray photon energies are attenuated down to ≤10 keV via inelastic scattering suitable for efficient photoelectric absorption by Si. Our Monte Carlo simulation results demonstrate that a 10–30× increase in quantum yield can be achieved using PbTe PAL on Si, potentially advancing high-resolution, high-efficiency X-ray detection using PAL-enhanced Si CMOS image sensors.

Polymer Loop Formation on a Functionalized Hard Surface: Quantitative Insight by Comparison of Experimental and Monte Carlo Simulation Results

Langmuir ◽  
2010 ◽  
Vol 26 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Zhenyu Haung ◽  
Haining Ji ◽  
Jimmy Mays ◽  
Mark Dadmun ◽  
Grant Smith ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Loop Formation ◽  
Hard Surface ◽  
Simulation Results

Gibbs-ensemble Monte Carlo simulation of H2–H2O mixtures

2021 ◽  
Author(s):  
Armin Bergermann ◽  
Martin French ◽  
Ronald Redmer
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Giant Planets ◽  
Gibbs Ensemble ◽  
Miscibility Gap ◽  
Ensemble Monte Carlo ◽  
Ensemble Monte Carlo Simulation ◽  
Gibbs Ensemble Monte Carlo

The miscibility gap in H2–H2O mixtures is investigated by conducting Gibbs-ensemble Monte Carlo simulations. Our results indicate that H2–H2O immiscibility regions may have a significant impact on the structure and evolution of ice giant planets.

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