by Sam Savage
Models vs. Modules
The discipline of probability management is defined by representing uncertainties as data, called SIPs, that obey both the laws of arithmetic and the laws of probability. One of the biggest implications of using SIPs is that formerly monolithic simulation models may now be decomposed into modules that are networked together through SIP Libraries, with the outputs of some models used as inputs to others. This is easy to explain. The hard part is getting people to understand it. So, here is a metaphor. Large simulation models are often like sandcastles, which eventually collapse under their own weight, or erode due to the tides of change. Modules are like Lego blocks, which can be assembled into structures. If you don’t like some part of a construction, or it becomes obsolete, you can snap off the old blocks and snap on new ones.
Examples
Royal Dutch Shell
Probability management arose out of work at Royal Dutch Shell starting in 2005 by Daniel Zweidler, then a manager at Shell, Stefan Scholtes, a Professor of Management Science at Cambridge, and me. It was driven by the fact that although Shell’s exploration engineers could simulate the daylights out of the Net Present Value of any particular venture, they couldn’t simulate their whole portfolio because the model was too big and would collapse under its own weight. See the foundational article in OR/MS Today.
The Lego blocks for the Shell portfolio were SIPs for each of the ventures, that is, arrays of thousands of Monte Carlo trials describing the range of possible NPVs for that particular venture. Hours were spent generating the library of SIPs for all ventures. But once that was done, because SIPs obey the laws of arithmetic, the SIP of NPV of any portfolio could be found nearly instantly by adding together the SIPs of its constituent ventures. This was done in an interactive Excel dashboard, which allowed managers, only a couple of levels below the CEO, to click things in and out of prospective portfolios. With every keyclick they would instantly see the consequences of their portfolio decisions in terms of both risk and return in a number of dimensions. That took place back in the Bad Old Days before Excel’s Data Table became powerful enough to bring interactive SIPmath simulation to all, so the portfolio model was contorted into a single row with hundreds of formulas, which were then copied down 1,000 times to refer to individual rows of the SIP Library. In subsequent years, we relied on Frontline System’s interactive simulation and stochastic optimization to continue the project with a more practical implementation. A training model used to teach Shell executives at Cambridge University is available for download. IMPORTANT: You need to enable macros to access the clickable scatter plot. Depending on the version of Excel, before opening, you may need to right click on it, go to Properties, then click Unblock.
A Pharmaceutical R&D Portfolio
A few years later, but still before I was aware of the breakthrough with the Excel Data Table, I worked on a similar problem with a large pharmaceutical firm. Although their analysts could simulate the daylights out of the Net Present Value of any particular R&D drug, they couldn’t simulate their whole portfolio because the model was too big and would collapse under its own weight. Furthermore, they needed to simulate it under numerous discount rates and other external factors like success probabilities for the drugs, market assumptions, etc. Again, it took hours on two separate computers to create the SIPs of the individual projects. There were roughly 50 drugs of each of two classes, and roughly 60 experiments with combinations of the external variables. And did I mention that it took 5,000 Monte Carlo trials to get it to converge? I’ll save you the math: 2*50*60*5000 gives you 30 million numbers, or 6,000 SIPs of 5,000 trials. But because SIPs obey the laws of arithmetic, we added all the SIPs of each type of drug together to get the SIP of NPV for the portfolio of all Type 1 drugs and the portfolio of all Type 2 drugs. That left us with 60 pairs of SIPs, one for each drug type and each experiment. The final dashboard with disguised data shows spinner controls that allowed us to scroll through the assumptions, which instantly pointed to the portion of the SIP Library resulting from that experiment.
At one point the assistant CFO wondered what the risk/return profile would look like if the lab were twice as big and could diversify over twice as many drugs. At first we figured it would take weeks to run a model with two hundred drugs to find out. But it took only minutes. One of the outputs was a SIP of 5,000 trials of the NPV of the entire portfolio. We simply copied that SIP, permuted it to model the NPV of a second similar but independent R&D lab, then added the two SIPs together. It was like snapping together Lego block models of two labs to get a model of a lab that was twice as big. And remember, SIPs also obey the laws of probability, so the resulting SIP told us all we needed to know about the risk and return of the imaginary larger and more diversified company.
Fast Forward to the Age of Chancification
Both the Shell and Pharma SIP libraries were used within single organizations in single dashboards for making critical decisions. But today, such libraries could be posted in the cloud and used collaboratively by hundreds of users with ChanceCalc (which runs simulations in native Excel using the Data Table).
As an example, the CDC can model the daylights out of future COVID-19 hospitalizations. Now imagine adding the models of all the hospitals in the country to the CDC model to better manage the pandemic, creating an enormous model that would collapse under its own weight. In a future blog I will describe how we have created SIP Libraries of predicted hospitalizations for each state, which may be stored in the cloud. In theory, these models could be snapped like Lego blocks into hospital management models nationwide to model surges in the current pandemic, or even cases of the good old-fashioned flu.
If you would like to learn more about ChanceCalc and Chancification:
Sign up to beta test ChanceCalc and provide feedback on the performance and tutorial.
If you are familiar with Monte Carlo simulation, download the SIPmath Modeler Tools.
Sign up for our Chancification webinars.
© Copyright 2021 Sam Savage