Circling the Drain

“It’s Time to Retire ‘Crap Circles’,” argues Gardiner Morse in the HBR. I wholeheartedly agree. He’s assembled a lovely collection of examples. Some violate causality amusingly:

“Through some trick of causality, termination leads to deployment.”

Morse ridicules one diagram that actually shows an important process,

The friendly-looking sunburst that follows, captured from the website of a solar energy advocacy group, shows how to create an unlimited market for your product. Here, as the supply of solar energy increases, so does the demand — in an apparently endless cycle. If these folks are right, we’re all in the wrong business.

This is not a particularly well-executed diagram, but the positive feedback process (reinforcing loop) of increasing demand driving economies of scale, lowering costs and further increasing demand, is real. Obviously there are other negative loops that restrain this one from delivering infinite solar, but not every diagram needs to show every loop in a system.

Unfortunately, Morse’s prescription, “We could all benefit from a little more linear thinking,” is nearly as alarming as the illness. The vacuous linear processes are right there next to the cycles in PowerPoint’s Smart Art:

Linear thinking isn’t a get-out-of-chartjunk-free card. It’s an invitation to event-driven unidirectional causal thinking, laundry lists, and George Richardson’s Dead Buffalo Syndrome. What we really need is more understanding of causality and feedback, and more operational thinking, so that people draw meaningful graphics, employing cycles where they appropriately describe causality.

h/t John Sterman for pointing this out.

Thorium Dreams

The NY Times nails it in In Search of Energy Miracles:

Yet not even the speedy Chinese are likely to get a sizable reactor built before the 2020s, and that is true for the other nuclear projects as well. So even if these technologies prove to work, it would not be surprising to see the timeline for widespread deployment slip to the 2030s or the 2040s. The scientists studying climate change tell us it would be folly to wait that long to start tackling the emissions problem.

Two approaches to the issue — spending money on the technologies we have now, or investing in future breakthroughs — are sometimes portrayed as conflicting with one another. In reality, that is a false dichotomy. The smartest experts say we have to pursue both tracks at once, and much more aggressively than we have been doing.

An ambitious national climate policy, anchored by a stiff price on carbon dioxide emissions, would serve both goals at once. In the short run, it would hasten a trend of supplanting coal-burning power plants with natural gas plants, which emit less carbon dioxide. It would drive some investment into low-carbon technologies like wind and solar power that, while not efficient enough, are steadily improving.

And it would also raise the economic rewards for developing new technologies that could disrupt and displace the ones of today. These might be new-age nuclear reactors, vastly improved solar cells, or something entirely unforeseen.

In effect, our national policy now is to sit on our hands hoping for energy miracles, without doing much to call them forth.


h/t Travis Franck

Defense Against the Black Box

Baseline Scenario has a nice account of the role of Excel in the London Whale (aka Voldemort) blowup.

… To summarize: JPMorgan’s Chief Investment Office needed a new value-at-risk (VaR) model for the synthetic credit portfolio (the one that blew up) and assigned a quantitative whiz (“a London-based quantitative expert, mathematician and model developer” who previously worked at a company that built analytical models) to create it. The new model “operated through a series of Excel spreadsheets, which had to be completed manually, by a process of copying and pasting data from one spreadsheet to another.” The internal Model Review Group identified this problem as well as a few others, but approved the model, while saying that it should be automated and another significant flaw should be fixed. After the London Whale trade blew up, the Model Review Group discovered that the model had not been automated and found several other errors. Most spectacularly,

“After subtracting the old rate from the new rate, the spreadsheet divided by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a factor of two and of lowering the VaR . . .”

Microsoft Excel is one of the greatest, most powerful, most important software applications of all time. …

As a consequence, Excel is everywhere you look in the business world—especially in areas where people are adding up numbers a lot, like marketing, business development, sales, and, yes, finance. …

But while Excel the program is reasonably robust, the spreadsheets that people create with Excel are incredibly fragile. There is no way to trace where your data come from, there’s no audit trail (so you can overtype numbers and not know it), and there’s no easy way to test spreadsheets, for starters. The biggest problem is that anyone can create Excel spreadsheets—badly. Because it’s so easy to use, the creation of even important spreadsheets is not restricted to people who understand programming and do it in a methodical, well-documented way.

This is why the JPMorgan VaR model is the rule, not the exception: manual data entry, manual copy-and-paste, and formula errors. This is another important reason why you should pause whenever you hear that banks’ quantitative experts are smarter than Einstein, or that sophisticated risk management technology can protect banks from blowing up. …

System Dynamics has a strong tradition of model quality control, dating all the way back to its origins in Industrial Dynamics. Some of it is embodied in software, while other bits are merely habits and traditions. If the London Whale model had been an SD model, would the crucial VaR error have occurred? Since the model might not have employed much feedback, one might also ask, had it been built with SD software, like Vensim, would the error have occurred?

There are multiple lines of defense against model errors:

  • Seeing the numbers. This is Excel’s strong suit. It apparently didn’t help in this case though.
  • Separation of model and data. A model is a structure that one can populate with different sets of parameters and data. In Excel, the structure and the data are intermingled, so it’s tough to avoid accidental replacement of structure (an equation) by data (a number), and tough to compare versions of models or model runs to recover differences. Vensim is pretty good at that. But it’s not clear that such comparisons would have revealed the VaR structure error.
  • Checking units of measure. When I was a TA for the MIT SD course, I graded a LOT of student models. I think units checking would have caught about a third of conceptual errors. In this case though, the sum and average of a variable have the same units, so it wouldn’t have helped.
  • Fit to data. Generally, people rely far too much on R^2, and too little on other quality checks, but the VaR error is exactly the kind of problem that might be revealed by comparison to history. However, if the trade was novel, there might not be any relevant data to use. In any case, there’s no real obstacle to evaluating fit in Excel, though the general difficulties of building time series models are an issue where time is relevant.
  • Conservation laws. SD practitioners are generally encouraged to observe conservation of people, money, material, etc. Software supports this with the graphical stock-flow convention, though it ought to be possible to do more. Excel doesn’t provide any help in this department, though it’s not clear whether it would have mattered to the Whale trade model.
  • Extreme conditions tests. “Kicking the tires” of models has been a good idea since the beginning. This is an ingrained SD habit, and Vensim provides Reality Check™ to automate it. It’s not clear that this would have revealed the VaR sum vs. average error, because that’s a matter of numerical sensitivity that might not reveal itself as a noticeable change in behavior. But I bet it would reveal lots of other problems with the model boundary and limitations to validity of relationships.
  • Abstraction. System Dynamics focuses on variables as containers for time series, and distinguishes stocks (state variables) from flows and other auxiliary conversions. Most SD languages also include some kind of array facility, like subscripts in Vensim, for declarative processing of detail complexity. Excel basically lacks such conventions, except for named ranges that are infrequently used. Time and other dimensions exist spatially as row-column layout. This means that an Excel model is full of a lot of extraneous material for handling dynamics, is stuck in discrete time, can’t be checked for DT stability, and requires a lot of manual row-column fill operations to express temporal phenomena that are trivial in SD and many other languages. With less busywork needed, it might have been much easier for auditors to discover the VaR error.
  • Readable equations. It’s not uncommon to encounter =E1*EXP($D$3)*SUM(B32:K32)^2/(1+COUNT(A32:K32)) in Excel. While it’s possible to create such gobbledygook in Vensim, it’s rare to actually encounter it, because SD software and habits encourage meaningful variable names and “chunking” equations into comprehensible components. Again, this might have made it much easier for auditors to discover the VaR error.
  • Graphical representation of structure. JPMorgan should get some credit for having a model audit process at all, even though it failed to prevent the error. Auditors’ work is much easier when they can see what the heck is going on in the model. SD software provides useful graphical conventions for revealing model structure. Excel has no graphics. There’s an audit tool, but it’s hampered by the lack of a variable concept, and it’s slower to use than Vensim’s Causal Tracing™.

I think the score’s Forrester 8, Gates 1. Excel is great for light data processing and presentation, but it’s way down my list of tools to choose for serious modeling. The secret to its success, cell-level processing that’s easy to learn and adaptable to many problems, is also its Achilles heel. Add in some agency problems and confirmation bias, and it’s a deadly combination:

There’s another factor at work here. What if the error had gone the wrong way, and the model had incorrectly doubled its estimate of volatility? Then VaR would have been higher, the CIO wouldn’t have been allowed to place such large bets, and the quants would have inspected the model to see what was going on. That kind of error would have been caught. Errors that lower VaR, allowing traders to increase their bets, are the ones that slip through the cracks. That one-sided incentive structure means that we should expect VaR to be systematically underestimated—but since we don’t know the frequency or the size of the errors, we have no idea of how much.

Sadly, the loss on this single trade would probably just about pay for all the commercial SD that’s ever been done.


The Trouble with Spreadsheets