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相关概念视频

Modeling in Therapy01:26

Modeling in Therapy

44
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
44
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

95
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
95
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

92
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
92
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26
Modeling and Similitude01:12

Modeling and Similitude

245
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
245

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Watershed Planning within a Quantitative Scenario Analysis Framework
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对高度复杂,多系统问题的参与式建模:平衡定性理解和定量问题的挑战和建议.

Arielle R Deutsch1,2, Leah Frerichs3, Madeline Perry3

  • 1Avera Research Institute, Avera Health, Sioux Falls, SD, USA.

System dynamics review
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

开发复杂的参与型模型需要对利益相关者进行仔细的参与,界限的定义,以及定性/定量整合. 应对这些挑战是创造可用于系统变革的可操作见解的关键.

关键词:
多重系统模型多重系统模型质量质量整合 质量整合让利益相关者参与其中.

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科学领域:

  • 系统科学 系统科学
  • 参与式建模参与式建模
  • 计算社会科学 计算社会科学

背景情况:

  • 社区利益相关者参与增强了定性模型的开发.
  • 参与式建模面临着高度复杂,多系统交互的挑战.
  • 将定性模型转化为决策的定量模拟是困难的.

研究的目的:

  • 突出开发高复杂度,参与型模型的实际挑战.
  • 在系统动态项目中提出解决这些挑战的建议.
  • 为研究可翻译的定性多系统模型提供基础.

主要方法:

  • 一个正在进行的参与式建模项目的案例研究.
  • 专注于系统动态工具,用于高度复杂的多系统问题.
  • 确定利益相关者参与和模式整合的关键挑战.

主要成果:

  • 确定了三个主要挑战:利益相关者参与,界限定义和定性/定量整合.
  • 为应对这些具体挑战而提出的建议.
  • 强调需要在复杂的参与式建模中采用最佳实践.

结论:

  • 解决参与,边界和整合对于有效的参与建模至关重要.
  • 需要进一步研究开发可翻译的定性多系统模型的方法.
  • 改进的模型可以为系统变革的行动提供信息.