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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/simulation-models-for-decision-making
课程评论：没有评论
课程名称：决策制定的模拟模型 课程概述：本课程主要面向大三和大四本科生或有兴趣学习模拟技术来解决商业问题的研究生。课程将介绍如何处理日常和复杂的商业问题，这些问题由于商业环境中的不确定性而没有唯一的正确答案。模拟建模使我们能够探讨各种结果，并保护个人或商业利益以免遭受不良后果。我们可以通过使用概率和逐步思维的概念来建模不确定性。逐步思维允许我们将问题分解为更小的组成部分，探讨相关事件之间的依赖关系，并关注由于未来不确定性而容易发生变化的问题方面。 课程将介绍高级Excel技术，以建模和执行模拟模型。课程中学习的许多Excel技术在模拟建模之外也非常有用。我们将学习蒙特卡罗模拟技术，其主要关注整体结果，以及离散事件模拟，其中可能更感兴趣的是相关事件之间的中间依赖关系。课程还将介绍一些通常在教科书中未涵盖的模拟建模实际问题。整个课程通过一些运行示例来展示概念并提供具体的建模实例。 完成课程后，学生将能够开发相对高级的模拟模型，以探索相对广泛的商业环境和结果。 课程大纲： - 第1周：概率概念 描述：不确定性给决策带来了挑战。数学上，我们通过定义多种可能的未来结果的概率来表示不确定性。本模块提供了对概率概念的概述，奠定了模拟建模的良好基础。我们还将首次接触基于Excel的模拟。 - 第2周：概率分布与蒙特卡罗模拟简介 描述：能够使用数学关系估计概率很重要，许多自然事件遵循或近似于一些定义良好的概率分布函数，例如均匀、指数和正态分布。有效构建模拟模型的重要性在于理解如何使用这些分布。此外，我们可能需要找出观察数据遵循的分布。本模块介绍了处理概率分布函数的细节，并引入了模拟模型的类型，以及一些处理可能不完整或无法准确匹配给定分布的真实数据的实用技巧。 - 第3周：蒙特卡罗模拟 描述：我们开始时表明，模拟是一种灵活的建模方法。本模块展示了这种灵活性。我们为咖啡店构建了四个蒙特卡罗模拟模型。这些模型在技术复杂性和复杂程度上逐步提高，以演示模型构建者在构建模型时必须考虑的各种问题，这些问题取决于需要回答的问题类型。讲解中解释了哪些模型可以回答某种类型的问题，以及某种类型的模型可能无法回答的问题。然后比较和讨论不同模型的结果，以理解选择特定模型的权衡。 - 第4周：反事实分析与离散事件模拟 描述：在本模块中，我们通过查看特例建模和进行反事实分析（检查可能不存在的情景或未实际实施的倡议）来结束蒙特卡罗模拟建模。然后，我们考察离散事件模拟的强大功能。离散事件模拟建模讨论的目标是让你了解事件之间的依赖关系以及如何在Excel中通过一些创新思维对这些依赖关系进行建模，尽管Excel本身并不支持离散事件模拟的任何功能。本部分的材料完全是原创的，旨在本课程中使用，书中找不到。
Name:Week 1: Probability Concepts
Description:Uncertainty leads to challenges in decision making. Mathematically, we represent uncertainty by defining probabilities when several of the outcomes are possible in the future. This modules provides an overview of probability concepts that are essential to lay a good foundation for simulation modeling. We will also get our first exposure to Excel based simulations.
Name:Week/Module 2: Probability Distributions and Introduction to Monte Carlo Simulations
Description:While being able to estimate probabilities using mathematical relationships is important, a lot of natural events follow or approximate some nicely defined probability distribution functions such as Uniform, Exponential and Normal Distributions. To effectively build simulation models, it is important to understand how to use these distributions. Further, we may need to find what distribution does our observed data follow. This module introduces the finer details of working with probability distribution functions and introduces the types of simulation models as well as some practice based tricks to work with real-world data that may not be complete or may not fit a given distribution exactly.
Name:Week 3: Monte Carlo Simulations
Description:We started by stating that simulation is one of the most flexible modeling approaches. This module demonstrates that flexibility. In this module, four Monte Carlo simulation models are built for a coffee shop. The models increase in technical complexity and sophistication to demonstrate various issues that modelers have to consider in building these models depending upon the type of questions that need to be answered. The lessons explain which models can answer certain type of questions and what questions may not be answered by a certain type of model. The results obtained from various models are then compared and discussed to understand the tradeoffs in choice of a particular model choice.
Name:Week 4: Counterfactual Analysis and Discrete Event Simulations
Description:In this module we wrap up the Monte Carlo Simulation modeling by looking at modeling special cases and doing counterfactual analysis (examining scenarios that may not have existed or initiatives that have not actually been implemented). We then examine the power of Discrete Event simulation. The goal of Discrete Event simulation modeling discussion is to introduce you to examine the dependencies in events and how these dependencies can be modeled in Excel with some innovative thinking, even though Excel does not natively support any functionality to support Discrete Event simulation. The material in this part is completely original and is designed for this course and will not be found in any books.
This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems. The course will introduce you to take everyday and complex business problems that have no one correct answer due to uncertainties that exist in business environments. Simulation modeling allows us to explore various outcomes and protect personal or business interests against unwanted outcomes. We can model uncertainties by using the concepts of probability and stepwise thinking. Stepwise thinking allows us to break down the problem in smaller components, explore dependencies between related events and allows us to focus on aspects of problem that are prone to changes due to future uncertainties. The course will introduce you to advanced Excel techniques to model and execute simulation models. Many of the Excel techniques learned in the course will be useful beyond simulation modeling. We will learn both Monte Carlo simulation techniques where overall outcome is of primary interest and discrete event simulation where intermediate dependencies between related events might be of interest. The course will introduce you to several practical issues in simulation modeling that are normally not covered in textbooks. The course uses a few running examples throughout the course to demonstrate concepts and provide concrete modeling examples. After taking the course a student will be able to develop fairly advanced simulation models to explore fairly broad range of business environments and outcomes.
