Scenario Analysis on Steroids

New 4.0 Enterprise SIPmath Modeler Tools

Herman Kahn

Scenario Analysis

Scenario analysis was invented by futurist Herman Kahn in the 1950s at the Rand Corporation. He was a friend of my dad’s and I remember him vividly from early childhood as energetic, humorous, and rotund. Perhaps not traits one would expect in the author of On Thermonuclear War, his controversial 1960 treatise of war in the nuclear age, which introduced the Doomsday Machine. In fact, Kahn is one of the characters upon whom Dr. Strangelove (of the movie) is based.

Scenario analysis involves forecasting major political and economic shifts through a few self-consistent narratives around potential futures. One does not specify probabilities of these scenarios up front but uses them to guide analysis into the unknown.

Scenario analysis has been widely used at Royal Dutch Shell and has been credited with preparing the company for the collapse of the Soviet Union. Here is a short docudrama of how it occurred.

“What would happen if the Soviet Union Collapsed?” … “Get out of town.” … “But what if it did?” … “That’s ridiculous!” … “But what if it did?” . . . . . . . . . . . . . “Maybe we’d better think this through.”

I am all for the out-of-the-box thinking that scenario analysis encourages. But if each of your handful of scenarios is rooted in the Flaw of Averages, where are you? Among other new features, the latest SIPmath Tools make it easy to combine interactive Monte Carlo with scenario analysis for the best of both worlds.

An Application – Climate Smart Agriculture

We have been assisting a team of environmental scientists at World Agroforestry (ICRAF) in exploring sets of climate smart agriculture projects in Africa, which will be the subject of a future webinar. Because all of these projects coexist in the same uncertain environment, there are strong portfolio effects, which can be modeled well with coherent SIP libraries. But beyond the sorts of uncertainties that are amenable to Monte Carlo simulation, there are potential world scenarios involving political upheaval, carbon pricing, etc., for which it is difficult to estimate probabilities. Therefore, we added the capability to the SIPmath Modeler Tools to run the same simulation through multiple experiments. You can then quickly scroll through either different portfolios of projects in one world scenario, or the same portfolio in multiple scenarios as shown in the graphics below.

Changing Portfolios

Changing Scenarios

Free Webinars

Brian Putt, our Chair of Energy Applications, and I will be offering a series of ongoing free webinars on the new SIPmath Tools. For a limited time, attendees will have the option to purchase the Enterprise Tools at a 30% discount, $150 off the regular price of $500. The first three webinars are listed below.

Introduction to the 4.0 Tools

This webinar will start with the basics of using either the Free or Enterprise versions of the SIPmath Modeler Tools for Excel. We will then briefly describe the exciting new features of the 4th generation tools below.

• Advanced HDR generator from Hubbard Decision Research

• Scatterplots of input and output cells

• Multi-scenario simulation and multi-scenario libraries

• Save and retrieve PMTable sheets for advanced analysis

Scenario Analysis on Steroids

This webinar will show how to create scenarios based on several variables such as discount rates, price levels, political upheaval, etc., which may easily be run through a single simulation model. The new Repeated Save command, coupled to Danny O’Neil’s “Enigma” formulas, automatically creates SIP Libraries containing multiple scenarios. These may be accessed by other models that can in turn filter the results by scenario. Topics include:

• Using the HDR Generator to coordinate models

• The “Enigma” formulas for performing experiments with arbitrary numbers of variables

• The Repeated Save command

• Filtering the results on Input and Output

More Power to the PMTable

The PMTable sheet is the heart of SIPmath in Excel, as it is the location of the Data Table that allows the simulation to run in native Excel. This webinar will show how to use the new “Save PMTable” command, which lets you save multiple versions of your analysis. For example, you may perform a multiple output simulation, save the resulting PMTable, then run a single output multiple experiment, save that PMTable, then return to the original. This also enables complex analysis techniques to include: 

• Storing a base or reference case for comparison

• Sensitivity analysis

• Tornado diagrams

• Critical path identification

© Copyright 2020 Sam L. Savage

COVID-19: The Solution is Obvious

Regardless of Your Political Position

by Sam L. Savage

The entire world is facing tradeoffs between public health and economics. But at a high level it is obvious what to do. We must choose between rational tradeoffs that balance these two objectives (the O’s in the graph above) or decisions that could be improved in both dimensions (X’s). There are many more X’s than O’s, and the O’s are hard to find. But we have the analytical technology to find them and should start now.

When we do find the O’s, some will favor healthcare outcomes, some economic outcomes, and some will be in between. So how should we choose among them? The way our country has traditionally made decisions that impact various stakeholders differentially, with democracy. Regardless of your politics, you want an O, not an X, and it’s nice that we can all agree on something.

So, what is the technology that can help us find the O’s? It is called stochastic optimization, and it has been used in the financial and insurance industries for decades. But how can you optimize when everything is so uncertain?  The word stochastic means explicitly modeling the uncertainty, as opposed to rolling it into a single average number as in the Flaw of Averages.

Modern Portfolio Theory

In the early 1950’s, future Nobel Prize winner Harry Markowitz was writing his doctoral dissertation on investing at the University of Chicago’s Department of Economics. The academic literature at the time prescribed maximizing average return. But Harry realized that this would have you investing all your money in the single hottest stock in the market. This flies in the face of not putting all your eggs in one basket. So, he explicitly added a new dimension to the investment problem: risk, measured as the uncertainty in return as shown below. Every investment is a point on this graph, and Harry calculated what he called the “Efficient Frontier,” an optimal risk/return tradeoff curve, arcing up from the origin.

This was the first stochastic optimization of which I am aware.

No rational investor would choose an investment to the right of the curve because going straight left to the curve would yield an investment with lower risk at the same return. Going straight up to the curve would yield an investment with more return at the same risk. So, an investment to the right of the curve is just plain nuts. Because the curve was found through optimization in the first place, nothing can exist to its left. And people first detected that Bernie Madoff was a fraud because he was promising the impossible on this graph.

A rational investor might pick any point on the curve depending on their risk attitude as shown. Harry’s 1952 paper on Portfolio Selection led to Modern Portfolio Theory (MPT), which revolutionized Wall Street, led to other stochastic optimization methods,  and ultimately garnered him a Nobel Prize in Economics in 1990.

SIP Libraries

In 2006, I helped Royal Dutch Shell apply MPT to finding efficient frontiers of risky exploration projects. A small prototype model (shown below) has risk on the horizontal axis and expected return on the vertical as in Harry’s original approach. It is available for download here. 

The Shell portfolio model was assembled from smaller models of the individual exploration projects using the concept of the SIP (Stochastic Information Packet). This is a data structure that represents uncertainties as auditable arrays of Monte Carlo trials and metadata. The project SIPs were interactively aggregated into portfolios in Excel, allowing managers just two steps below the CEO to add or remove projects in real time and see the resulting risk/return tradeoffs. The idea of SIP libraries, which had its foundations in the fields of financial engineering and insurance, has now been democratized by 501(c)(3) nonprofit ProbabilityManagement.org, of which Harry Markowitz and I were founding board members in 2013.

COVID-19

Meanwhile, back in the pandemic, there is so much uncertainty about the progression of the contagion, the effects of the disease itself, and human behavior in the face of it all, that the Flaw of Averages abounds.

Again, in theory, stochastic optimization can be applied to this problem, as we applied it at Shell. But due to the size and complexity, a single model would collapse under its own weight before producing useful results. In fact, I am not sure a single team of modelers could do it.

So, our nonprofit has begun experimenting with an approach that would allow teams in diverse disciplines to collaborate on this problem by decomposing it into manageable chunks. Models of contagion, government policy, and economics created separately in such common environments as Excel, R, and Python could be snapped together like Lego blocks using common SIP libraries. We have been working with colleagues at Kaiser Permanente and other healthcare organizations on this project and are actively seeking other potential partners.  

To leave you with something tangible, the blue curves in the graph display what we call the sample paths of a contagion model, with one for each of hundreds or thousands of simulated uncertain outcomes. Taken together, they can help us find the O’s. The red curve is the simulated “average” pandemic, which leads to an X.

© Copyright 2020 Sam L. Savage