Two-Level Geological Modeling: A Computational Approach to Teaching Decision Making Under Conditions of Geological Data Paucity

1995 ◽  
Vol I (3) ◽  
pp. 365-370 ◽  
Author(s):  
V. GOROKHOVSKI ◽  
D. NUTE
Keyword(s):  
Decision Making ◽  
Computational Approach ◽  
Geological Modeling ◽  
Geological Data ◽  
Approach To Teaching

scholarly journals Navier-Stokes Equation and Computational Scheme for Non-Newtonian Debris Flow

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Ignazio Licata ◽  
Elmo Benedetto
Keyword(s):  
Debris Flow ◽  
Flow Model ◽  
Computational Approach ◽  
Computational Scheme ◽  
Navier Stokes ◽  
Uniform Motion ◽  
Geological Data ◽  
Navier Stokes Equation ◽  
Grid Systems ◽  
Computing Effort

This paper proposes a computational approach to debris flow model. In recent years, the theoretical activity on the classical Herschel-Bulkley model (1926) has been very intense, but it was in the early 80s that the opportunity to explore the computational model has enabled considerable progress in identifying the subclasses of applicability of different sets of boundary conditions and their approximations. Here we investigate analytically the problem of the simulation of uniform motion for heterogeneous debris flow laterally confined taking into account mainly the geological data and methodological suggestions derived from simulation with cellular automata and grid systems, in order to propose a computational scheme able to operate a compromise between “global” predictive capacities and computing effort.

scholarly journals Computational approach to synthesis of the multiversion structure of distributed information decision-making support system

2021 ◽  
Vol 2094 (3) ◽  
pp. 032068
Author(s):  
I V Kovalev ◽  
N A Testoyedov ◽  
A A Voroshilova ◽  
D I Kovalev ◽  
D V Borovinskii
Keyword(s):  
Decision Making ◽  
Distributed System ◽  
Support System ◽  
Formal Model ◽  
Technical Condition ◽  
Computational Approach ◽  
Local Information ◽  
Distributed Information ◽  
Complex Control System ◽  
Decision Making Support

Abstract The computational approach to synthesis of the multiversion structure of distributed information decision-making support system is presented. A formal model of the local information system is given. This system is intended to ensure the functioning of a complex control system based on the multiversion approach and consisting of a set of multiversion objects. The problem of distribution of objects by local information subsystems has been solved. For a set of valid queries in a distributed system, the answers for the decision maker are formed sequentially without repeating queries. To take into account certain requirements regarding the structure of a distributed system, it is necessary to formulate these requirements in formalized constraints and introduce them into the mathematical description of the problem. Note that the effectiveness of the targeted use of the system depends both on the results of synthesis (structure and parameters of the system) and on the correct organization of the subsystem for monitoring its technical condition during operation.

scholarly journals Teaching Risk and Probability: Building the Monopoly® Board Game Into a Probability Simulator

2019 ◽  
Vol 5 (2) ◽  
pp. 133-143
Author(s):  
Joe Gazdula ◽  
Richard Farr
Keyword(s):  
Decision Making ◽  
Collaborative Learning ◽  
Competitive Environment ◽  
Board Game ◽  
Teaching Experiences ◽  
New Approach ◽  
Risk Environments ◽  
Analysis Technique ◽  
Approach To Teaching ◽  
Gaming Environment

This article describes the adaptation and use of the Monopoly® board game as a simple simulator to help introduce the principles of probability and risk. It focusses on teaching experiences in an undergraduate business program and offers a new approach to teaching probability and risk with dice to produce a collaborative simulated gaming environment. It allows students to be actively involved in developing the probability of throwing dice numbers and the analysis of risk and decision making in a competitive environment. Concluding with a probability analysis technique, it allows students to reflect on formal and informal approaches to decision making in risk environments and helps build communication for discussion, collaborative learning, and self-analysis among students.

Uncertainty Analysis in Geological Modeling for Coalbed Methane

2014 ◽  
Vol 1030-1032 ◽  
pp. 1407-1410
Author(s):  
Shuang Zhen Cao ◽  
Zhao Hui Xia ◽  
Ming Zhang ◽  
Yong Yang ◽  
Li Jiang Duan ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Quantitative Evaluation ◽  
Parameter Uncertainty ◽  
Coalbed Methane ◽  
Geological Modeling ◽  
Geological Data ◽  
Geological Conditions ◽  
Eastern Australia

The complexity of geological conditions results in ambiguity and uncertainty when interpreting geological data, bring difficulties to geological modeling, so quantitative evaluation of uncertainty of various geological parameters is important to establish accurate geological modeling. M coalbed methane field in eastern Australia was selected as an example to explain this process. The parameters and these uncertainty ranges were discussed firstly. And then, the impacts on calculated reserves of parameter uncertainty were analyzed. A better effect was attained in M coalbed methane field with this method.

scholarly journals Examining learning coherence in group decision-making: triads vs. tetrads

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tsutomu Harada
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Small Groups ◽  
Majority Rule ◽  
Group Decision ◽  
Computational Approach ◽  
Approach To Learning ◽  
Q Learning ◽  
Learning Framework ◽  
Better Than

AbstractThis study examined whether three heads are better than four in terms of performance and learning properties in group decision-making. It was predicted that learning incoherence took place in tetrads because the majority rule could not be applied when two subgroups emerged. As a result, tetrads underperformed triads. To examine this hypothesis, we adopted a reinforcement learning framework using simple Q-learning and estimated learning parameters. Overall, the results were consistent with the hypothesis. Further, this study is one of a few attempts to apply a computational approach to learning behavior in small groups. This approach enables the identification of underlying learning parameters in group decision-making.

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