Modeling a Dynamic Trade War using Julia: Assumptions, Simulation, and Impacts on the US Economy

Modeling a Dynamic Trade War using Julia: Assumptions, Simulation, and Impacts on the US Economy

Building a simple dynamic simulation in Julia


This article explains a simplified simulation using game theory (the prisoner's dilemma) to analyze the impacts of imposing tariffs between the US and its major trading partners, highlighting potential short-term economic benefits such as increased revenues and domestic protection. The examples are coded in Julia and the files are available on github. https://github.com/etorkia/SharedDecisionModels/tree/main/PrisonnerGame
Author: Eric Torkia
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Is Oracle Crystal Ball still relevant?

Is Oracle Crystal Ball still relevant?

Are Excel Simulation Add-Ins like Oracle Crystal Ball the right tools for decision making? This short blog deliberates on the pros and cons of Oracle Crystal Ball.
Author: Eric Torkia
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Decision Science Developper Stack

Decision Science Developper Stack

What tools should modern analysts master 3 tier design after Excel?

When it comes to having a full fledged developper stack to take your analysis to the next level, its not about tools only, but which tools are the most impactful when automating and sharing analysis for decision making or analyzing risk on projects and business operations. 

Author: Eric Torkia
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The Need For Speed 2019

The Need For Speed 2019

Comparing Simulation Performance for Crystal Ball, R, Julia and @RISK

The Need for Speed 2019 study compares Excel Add-in based modeling using @RISK and Crystal Ball to programming environments such as R and Julia. All 3 aspects of speed are covered [time-to-solution, time-to-answer and processing speed] in addition to accuracy and precision.
Author: Eric Torkia
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Article rating: 3.8
Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data

Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data

How I Learned to Think of Business as a Scientific Experiment

Imagine playing a game in which someone asks you to infer the number of sides of a polyhedron die based on the face numbers that show up in repeated throws of the die. The only information you are given beforehand is that the actual die will be selected from a set of seven die having these number of faces: (4, 6, 8, 10, 12, 15, 18). Assuming you can trust the person who reports the outcome on each throw, after how many rolls of the die wil you be willing to specify which die was chosen?
Author: Robert Brown
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Article rating: 2.5
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Introduction to Tolerance Analysis (Part 1 / 13)

Aug 10 2010
36
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Tolerance Analysis is the set of activities, the up-front design planning and coordination between many parties (suppliers & customers), that ensure manufactured physical parts fit together the way they are meant to. Knowing that dimensional variation is the enemy, design engineers need to perform Tolerance Analysis before any drill bit is brought to raw metal, before any pellets are dropped in the hopper to mold the first part. Or, as the old carpenter's adage goes: Measure twice, cut once. 'Cause once all the parts are made, it would be unpleasant to find they don't go together. Not a good thing.

Remembering to pick right learning curve

Jun 17 2010
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This post presents 2 popular learning curve methods for estimating how a person or organization benefits from repeat learning.

This technique is key for the project risk analyst.

Algorithms and the New Millennium

Dr David Berlinski (2000) makes the historical observation that two great ideas have most influenced the technological progress of the Western world:

The first is the calculus, the second the algorithm. The calculus and the rich body of mathematical analysis to which it gave rise made modern science possible; but it has been the algorithm that has made possible the modern world. (Berlinski, p. xv)

Dr Berlinski concludes that:

The great era of mathematical physics is now over. The three-hundred-year effort to represent the material world in mathematical terms has exhausted itself. The understanding that it was to provide is infinitely closer than it was when Isaac Newton wrote in the late seventeenth century, but it is still infinitely far away…. The algorithm has come to occupy a central place in our imagination. It is the second great scientific idea of the West. There is no third. (Berlinski, pp. xv-xvi)

Source: Berlinski, D (2000). The Advent of the Algorithm: The 300-Year Journey from an Idea to the Computer. San Diego, CA: Harcourt.

Related Posts: Enter the Algorithm

Decision Warranties

According to Prof Ronald A Howard (1992):

Three of the warranties that I would like to have in any decision situation are that:
  1. The decision approach I am using has all the terms and concepts used so clearly defined that I know both what I am talking about and what I am saying about it;
  2. I can readily interpret the results of the approach to see clearly the implications of choosing any alternative, including of course, the best one; and
  3. The procedure used to arrive at the recommendations does not violate the rules of logic (common sense).

Plain and simple... Source: Howard, R A (1992), Heathens, Heretics, and Cults, Interfaces, 22(6), 15-27.

Collaborative modeling using predictive analytics

When building models we are often confronted with assumptions that evolve over time. In most cases it is important to capture these changes to keep our model relevant. Over the last decade, Business Intelligence solutions have created a culture of self-service IS information.  Given this democratization and decentralized access to data has created many opportunities and pitfalls for business analysts and decision-makers. We are going to outline some opportunities and pitfalls relating to shared modeling and a few strategies to get started.

This post presents the opportunities and challenges stemming from moving towards a distributed modeling paradigm in the organization. Also presented is a high-level integrated predictive/collaborative planning process.

Risk versus Uncertainty

Prof Frank H Knight (1921) proposed that "risk" is randomness with knowable probabilities, and "uncertainty" is randomness with unknowable probabilities. However, risk and uncertainty both share features with randomness. The illustration below explains the relationship of the concepts better than words...

Source: Knight, F H (2002/1921), Risk, Uncertainty and Profit, Washington, DC: BeardBooks.

 

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