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Bitcoin's Worldview and Why It Matters

There are a few questions I’ve received in response to my post on The Rise and (Inevitable) Fall of Bitcoin that are worth elaborating on.

Do you have a stake in Bitcoin?

I have no “skin in the game,” no stake, no monetary interest in Bitcoin or a Bitcoin-based enterprise. How soon will this happen? I think this process will take a long time to play out. There has yet to be an economy developed using Bitcoin and it has been purely used as a speculative vehicle. One of the first catalysts to inspire me to read about deeper about Bitcoin, beyond the sensational headlines, was when several prominent venture capitalists expressed interest and invested in Bitcoin-based enterprises. When Silk Road was shut down, shortly thereafter, I fully bought into the argument this would help nurture the legitimacy of Bitcoin. In my hypothesis, we are in the early stages of the rise period.

About this hypothesis, what do you mean by “operating hypothesis?”

Bitcoin is something entirely new, neither seen nor tried before and as such, it is impossible to perfectly predict what its path will look like. That doesn’t mean it’s not worth developing an understanding of, and having an opinion on where it could go. It also does not mean we can’t make an educated guess as to how it should transpire, while also leaving ourselves room to be intellectually flexible enough to evolve our opinions as events play out. As Bitcoin exists today and given the possible future paths within the rules already in place, this is fully what I expect to happen.

There will always be the opportunity for outside interventions to change the path for Bitcoin. For example, someone can build a parallel network, called something like “Bitbillz” and peg its value at 1:1 with Bitcoin, while offering anyone the capacity to freely exchange their Bitcoins for Bitbillz at no cost to the one making the transaction. This new Bitbillz money can then recreate its own rules-based system for the currency and its own economic paradigm with better rules.

Why do you care?

I am really intrigued by the idea of a digital currency. It has amazing potential on many levels. Such an economic development would dramatically alter the economic landscape for many global citizens. Some of the globe's worst currencies (think Zimbabwe) are in effect a punishment on the country’s citizens. Currency crises and volatility have actual costs on the lives of many people. The success of a digital currency would have interesting implications for citizens of developed nations as well, though I think the disruptive impact is overstated by the Bitcoin demagoguery and detractors alike. A digital currency, in my opinion, would never be more than a medium through which to buy some things easier or diversify one’s currency exposure. Most importantly though, the engineered mechanics of Bitcoin are brilliant and worth expanding upon to streamline, simplify and secure e-commerce transactions for everyone. This is indeed a majorly innovative breakthrough.

The problem I have is that Bitcoin was built with such a severe, fatal flaw that it’s success would inevitably hold the potential to do more harm than good. It’s early enough in the evolution of Bitcoin to create a digital currency with fewer problems, with more staying power, and most importantly, with a better worldview.

What did you mean by “ideological nature of some of these questions?”

Bitcoin was built with a worldview, and it’s a rather unattractive one. Bitcoin is the creation of Anarcho-Capitalist aka Market Anarchist, a fringe group at the intersection of outright anarchy and the Austrian School of Economics. This is something Bitcoin advocates purposely gloss over. Bitcoin’s early history, essentially from it’s first day up until October 1 of this year, 2013, was entirely created by, used by, financed by and for anarchists. Here are the words of one the man who built the first large Bitcoin community about why he was into Bitcoins (take note of who he is appealing to):

Hackers, anarchists, and criminals have been dreaming about these days since forever. Where you can turn on your computer, browse the web anonymously, make an untraceable cash-like transaction, and have a product in your hands, regardless of what any government or authority decides... This is about real freedom. Freedom from violence, from arbitrary morals and law, from corrupt centralized authorities, and from centralization altogether. While Silk Road and Bitcoin may fade or be crushed by their enemies, we've seen what free, leaderless systems can do. You can only chop off so many heads.

Bitcoin was built for a specific purpose, and by people with a specific fringe ideology. This particular school of anarchists believe strongly in hard money, with religious fervor. Instead of trying to learn about why gold standards don’t work in reality, they purposely took the single worst characteristic of the gold standard (its inelasticity of supply) to the logical extreme. This is what happens when people are blinded by extreme adherence to an extreme ideology. I don’t know what the “perfect” currency should look like, but I think many economists from all schools would agree that Bitcoin could do a whole lot better. Given that reality, why not try to improve the entire system before building an economy on top of it?

Well even if you don’t know what a “perfect” currency, isn’t Bitcoin worth trying because it’s the best we have? And shouldn’t you have to offer at least something that would be better?

Ok I’ll throw out one possible solution for thought, which is derivative of John Maynard Keynes’ idea proposed at the first Bretton Woods conference (he called it Bancor): maybe make the previously mentioned “Bitbillz” some kind of global reserve currency, where each Bitbill is backed by a basket of all of the world’s currencies (say 15-25 of the most liquid global currencies) in proportion to their relative share of GDP? 

I plotted what a hypothetical global reserve currency would look like, priced in dollars and benchmarked to 2011 global GDP levels. It's something worth thinking about.

When some currencies are strong, others will be weak, making it a less volatile, but still liquid kind of money. Further, arbitrageurs can then impose market discipline in the same way new shares of open-ended ETFs are created: when demand for Bitbillz outstrips supply arbitrageurs can buying up the exactly proportion and value of the constituent currencies and deposit them to mine/print/create new Bitbillz. The supply would have a finite constraint (the entire global monetary base), that should grow in-line with global GDP, and with a built-in natural check on volatility. Just an idea...I would love to hear if others have even better ones. It’s a question worth asking.


The Rise and (Inevitable) Fall of Bitcoin

Bitcoin is receiving much attention these days for its parabolic ascent. The attention seems to stem from people’s concerns with monetary policy and the growing disdain with government intervention and oversight, generally speaking. It is no coincidence that Bitcoin’s surge this year corresponds with the growing public backlash over these large issues. To that end, Bitcoin is a great story, but is it a great idea?

A Bitcoin Economy?

The textbook definition of money holds that it must be a medium of exchange, a unit of account and a store of value. Colloquially when many say "currency" they in fact mean "money," especially with regard to Bitcoin. We will ignore the argument as to whether Bitcoin is an effective store of value given its volatility and focus purely on the philosophical question of whether Bitcoin makes sense as money. For Bitcoin to truly emerge as “money” there must be an economy with the actual transfer of goods built on top of it. In such an economy, there will be some people who “save” money. This means that some will put off consumption today for the capacity to consume at a future date.

It’s hard to project exactly how much commerce will be done on Bitcoin, or how big the “economy” will be, but we do know that venture capitalists like Fred Wilson are investing in the Bitcoin ecosystem, and Marc Andreessen has expressed interest in following suit. Further, the US government’s arrest of the Silk Road founder and crackdown on the black market drug trade via Bitcoin could help lend great legitimacy to the broader Bitcoin economy. Given the evolution of credibility and interest amongst venture capitalists to grow an economy on Bitcoin, we can presume that Bitcoin’s price today reflects a belief that an economy will in fact be able to develop.

The Monetary Mechanics of Bitcoin

Before we can answer whether Bitcoin can work as “money,” let’s talk about the mechanics behind it (and by mechanics, we will ignore the cryptography and security element and focus purely on the monetary mechanics). Here are the mechanics for how Bitcoins are created:

The reward for solving a block is automatically adjusted so that roughly every four years of operation of the Bitcoin network, half the amount of bitcoins created in the prior 4 years are created. 10,500,000 bitcoins were created in the first 4 (approx.) years from January 2009 to November 2012. Every four years thereafter this amount halves, so it will be 5,250,000 over years 4-8, 2,625,000 over years 8-12, and so on. Thus the total number of bitcoins in existence will never exceed 21,000,000.

With this informatino, we can plot exactly what the money supply of Bitcoins will look like over time:

As such, we know that somewhere around 2040, the entire supply of Bitcoins will have been “created” and that no new incremental supply of will emerge from that point, onward.

The next important feature of Bitcoins that’s important to understand is the “granularity” of the currency. As they are constructed, each Bitcoin can be broken down into denominations of up to 8 decimal places (with 0.00000001 BTC being the smallest denomination). The currency was created this way, so that as Bitcoins increase in value, people are able to make purchases with fractions of the increasingly valuable coinage, rather than needing to use whole things at a time.

Lastly, and related to the idea of granularity is the deflationary bias embedded in Bitcoins, as explained by the Bitcoin community itself:

Because of the law of supply and demand, when fewer bitcoins are available the ones that are left will be in higher demand, and therefore will have a higher value. So, as Bitcoins are lost, the remaining bitcoins will eventually increase in value to compensate. As the value of a bitcoin increases, the number of bitcoins required to purchase an item decreases. This is a deflationary economic model.
So we know that people are trying to build a real economy on Bitcoin and we know mechanically how Bitcoin is designed to work, but how does it work in practice? For the purposes of this analysis, the two features of money that are most important are its role as a medium of exchange and its role as a store of value. As an economy grows, more Bitcoins will be used in exchange for goods and services, while at the same time, some who use Bitcoin will use their currency as a store of value in order to “save” money. In theory, Bitcoin is admittedly designed to increase in value over time (ie the deflationary model of currency) and therefore, “savers” will be rewarded simply by not spending their Bitcoins. This is a problem we’ll discuss shortly, but until then, I digress.
Over the past decade, the US has a fairly low savings rate compared with the rest of the world. Americans save between 11-12% of GDP per year. If we were to apply this savings rate to Bitcoin, already knowing the future path of the Bitcoin money supply, a problem starts to emerge--were 11.5% of Bitcoins saved per year, by 2021, 95.3% of the entire supply of the currency will have been stashed away as savings rendering commerce effectively impossible. A system of credit can be built on top of the Bitcoin economy (and most likely will be built), but while this can push back the date at which savings account for too large a share of the entire economy, it can only delay the inevitable. At some point, Bitcoins saved will start to approach total Bitcoins in circulation, making commerce effectively impossible.
Bitcoin is Good as Gold
This is in fact how the gold standard worked as money for years. While there is a finite amount of gold on planet earth, humans still have yet to mine all gold, and the supply of gold accordingly increases at some kind of modest annualized rate. When the price of gold spikes, miners are incentivized to increase their exploration and production efforts, and as such, spikes in the price of gold tend to come with spikes in production. Hard-money types tend to hate the growth of the money supply, though they like gold because the supply growth is not controlled by a centralized pseudo-government actor. Because no government controls supply growth, hard-money types are able to ignore the fact that gold’s supply growth tends to be lumpy (ie how the California Gold Rush led to a rapidly increasing money supply and its subsequent end led to stagnation) in focusing on how it is largely immune to inflations.
Bitcoin and gold are similar right now in how “mining” (alongside savings) is incentivized by a rising price. The increase of supply and rising price encourage those who have “saved” to spend by offering a price that is too good to pass up. This theoretically helps maintain a balance between commerce and savings over the long-run; however, we know theory and practice often differ and we’ll soon get to why, but first, let’s talk about how Bitcoin and gold differ. With gold, we have never had to contemplate a date at which the supply in circulation stops increasing, while with Bitcoin the end of supply growth has a known date. 

Here’s how the Bitcoin community explains this problem:

Worries about Bitcoin being destroyed by deflation are not entirely unfounded. Unlike most currencies, which experience inflation as their founding institutions create more and more units, Bitcoin will likely experience gradual deflation with the passage of time. Bitcoin is unique in that only a small amount of units will ever be produced (twenty-one million to be exact), this number has been known since the project's inception, and the units are created at a predictable rate.
In fact, infinite divisibility should allow Bitcoins to function in cases of extreme wallet loss. Even if, in the far future, so many people have lost their wallets that only a single Bitcoin, or a fraction of one, remains, Bitcoin should continue to function just fine. No one can claim to be sure what is going to happen, but deflation may prove to present a smaller threat than many expect.
Also, Bitcoin users are faced with a danger that doesn't threaten users of any other currency: if a Bitcoin user loses his wallet, his money is gone forever, unless he finds it again. And not just to him; it's gone completely out of circulation, rendered utterly inaccessible to anyone. As people will lose their wallets, the total number of Bitcoins will slowly decrease.
Therefore, Bitcoin seems to be faced with a unique problem. Whereas most currencies inflate over time, Bitcoin will mostly likely do just the opposite. Time will see the irretrievable loss of an ever-increasing number of Bitcoins. An already small number will be permanently whittled down further and further. And as there become fewer and fewer Bitcoins, the laws of supply and demand suggest that their value will probably continually rise.
Thus Bitcoin is bound to once again stray into mysterious territory, because no one exactly knows what happens to a currency that grows continually more valuable. Many economists claim that a low level of inflation is a good thing for a currency, but nobody is quite sure about what might happens to one that continually deflates. Although deflation could hardly be called a rare phenomenon, steady, constant deflation is unheard of. There may be a lot of speculation, no one has any hard data to back up their claims.
That being said, there is a mechanism in place to combat the obvious consequences. Extreme deflation would render most currencies highly impractical: if a single Canadian dollar could suddenly buy the holder a car, how would one go about buying bread or candy? Even pennies would fetch more than a person could carry. Bitcoin, however, offers a simple and stylish solution: infinite divisibility. Bitcoins can be divided up and trade into as small of pieces as one wants, so no matter how valuable Bitcoins become, one can trade them in practical quantities.
In fact, infinite divisibility should allow Bitcoins to function in cases of extreme wallet loss. Even if, in the far future, so many people have lost their wallets that only a single Bitcoin, or a fraction of one, remains, Bitcoin should continue to function just fine. No one can claim to be sure what is going to happen, but deflation may prove to present a smaller threat than many expect.

Why does all this matter and what am I getting at? 

Historically there have been points in time where the propensity to save (and thus not spend) has outstripped the supply of currency available to be saved, thus choking off the flow of commerce. This is a big part of what happened in the Great Depression and is something that both Keynesians and Monetarists alike agree on. When countries “left” the gold standard, they effectively devalued their currency all at once (and it's no coincidence that the recovery from the Great Depression started with such a devaluation). This similar mechanical move has happened in the floating-currency regime whereby countries who peg their currency to a foreign currency (like Argentina’s peso pegged to the dollar), must abandon the peg with a devaluation in order to protect their currency.

What happens when too much of Bitcoin’s supply gets stashed as savings? Granularity provides a built-in answer. 1 bitcoin will be divided by 10 in its purchasing power (note: 0.1 Bitcoins is presently known as a centibitcoin). This is how spending and commerce will then return to the Bitcoin economy. The Bitcoin community argues that this will be a slow, “gradual deflation,” which is “unheard of” in world history. There is a reason for this: gradual deflation is a paradox that simply does not and cannot exist.

Remember above I told you I would eventually get to why theory and practice differ once savings exceed a certain level, and prices rise? I didn’t forget. Theory and practice diverge because theory presupposes a certain kind of human rationality that simply does not exist. When the price of a currency itself starts to rise, and supply starts to get scarce, instead of prior savers now spending money and solving the problem of too little circulating money, reality has time-and-again demonstrated that people who previously were not savers, and were engaged in commerce, see that more money was made by saving money than investing it. In response, prior commercial actors decide they too can make more money by hoarding money than they can by investing it, and upon that realization, they too turn into incrementally new savers.  John Maynard Keynes dubbed this "The Paradox of Thrift" and the incorporation of paradox in the title is self-explanatory here. This positive feedback loop of saving begetting more saving continues until some kind of breakpoint is reached. This breakpoint is either a devaluation or a complete collapse in an economy, but either way, it is not and never has been pretty. 

Considering Bitcoins are the ultimate fiat currency, backed by neither a hard asset, nor the capacity to tax, faith in the currency is immensely important, but also far easier to destroy than build up. This harkens back to Warren Buffett’s wise observation that “it takes 20 years to build a reputation and five minutes to ruin it.” Once trust is lost in Bitcoin, it will be impossible to make it back.

Again, why does it matter?

Since I am speaking about this decline as inevitable, and obvious, let me explain why I think this even warrants conversation in the first place. I think Bitcoin is a fascinating experiment that will eventually have considerable value to help improve our knowledge of monetary systems, and to help dispel some of the myths that have built up in some recently popular economic circles. With the crisis, “hard-money” like the gold standard has become a popular “solution” despite the fact that we know both empirically and theoretically exactly how and why they don’t, won’t and never could work. Paul Krugman has tried to explain this problem with his explanation of the Capitol Hill Baby-Sitting co-op and Pascal-Emmanuel Gobry has highlighted this connection with Bitcoin, but considering the ideological nature of some of these questions, such proof will never be taken as positive. Bitcoin will eventually show us this reality in real-time.

The second source of my interest in Bitcoin is the role of feedback loops and reflexive processes in markets. George Soros’ Alchemy of Finance is one of my favorite market philosophy books and one I am a believer in. The essence of Soros’ “General Theory of Reflexivity” holds that markets are driven by feedback loops whereby prices influence the course of events, which influence prices, which in turn influence the course of events. I’ll let Soros further explain:


Feedback loops can be either negative or positive. Negative feedback brings the participants’ views and the actual situation closer together; positive feedback drives them further apart. In other words, a negative feedback process is self-correcting. It can go on forever and if there are no significant changes in external reality, it may eventually lead to an equilibrium where the participants’ views come to correspond to the actual state of affairs. That is what is supposed to happen in financial markets…
...By contrast, a positive feedback process is self-reinforcing. It cannot go on forever because eventually the participants’ views would become so far removed from objective reality that the participants would have to recognize them as unrealistic. Nor can the iterative process occur without any change in the actual state of affairs, because it is in the nature of positive feedback that it reinforces whatever tendency prevails in the real world. Instead of equilibrium, we are faced with a dynamic disequilibrium or what may be described as far-from-equilibrium conditions. Usually in far-from-equilibrium situations the divergence between perceptions and reality leads to a climax which sets in motion a positive feedback process in the opposite direction. Such initially self-reinforcing but eventually self-defeating boom-bust processes or bubbles are characteristic of financial markets, but they can also be found in other spheres.

Understanding and spotting feedback loops in financial markets is one of the most important things an investor can do. Feedback loops are one of the sources of my interest in the Santa Fe Institute and its work on markets as a complex adaptive system (see my recent interview with Michael Mauboussin which spends some time on feedback loops). In fact, feedback loops are one of the telltale features of complexity.  It is my operating hypothesis that Bitcoin is one such “positive feedback process” which will first lead to a spectacular risein prices, that will ultimately reverse and crumble into an even more remarkable decline.

As it stands today, per my thesis, Bitcoin’s “rise” should be in the early stages. This rise has been fueled by a combination of speculation and the promise for the development of actual commerce on Bitcoin. The adoption of broader uses for the currency will be the catalyst for the next stage of ascent. I’m not sure how far Bitcoin can go on the way up, nor am I sure exactly when the inflection point from rise to fall will occur, but one thing I am fairly certain of is that when the fall comes, it will be swift and violent. In the end, it's success will be its own demise. As they say, “what goes up on an escalator goes down on an elevator” and I would not want to be the one left holding the proverbial bag on the way down.


A Tale of 3 Tech IPOs and the Punditry Narrative

When LinkedIn IPO'd in May 2011, underwriters priced the offering at $45 per share. The stock closed the day up 109%, at $94/share. Henry Blodgett at Business Insider quickly published an article entitled "Congratulations LinkedIn, You Just Got Screwed Out of $130 Million," bashing the underwriters for misreading demand and reaping immense profits for themselves at the company's expense. Blodgett wasn't alone in critiquing LinkedIn's underwriters, as the media and financial commentators worked into a frenzy about just how poorly the IPO was priced. Jim Cramer chimed in with his own fury over how the underwriters "juiced" the IPO.

As Facebook geared up for its IPO, to much enthusiasm, people were asking whether Wall Street would avoid doing what it did to LinkedIn (aka "robbing" them) by pricing the offering fairly. A "fair price" would enable the company itself to maximize its own proceeds. In response, Facebook was priced as aggressively as possible. It was priced so aggressively that Blodgett called the IPO "Muppet Bait" for how dangerous a proposition buying into Facebook was for retail investors. We all know what happened next. There were no buyers of the shares hitting public markets, and the stock instantly entered a tailspin dubbed the "Facebook Faceplant." Here the underwriters were accused of "botching" the IPO at the expense of retail investors.

Leading up to Twitter's IPO, the biggest question was "how could we avoid another Facebook?" (too many such links to pick one worth sharing). Twitter purposely wanted to temper enthusiasm and price its offering low enough to encourage long-term investors to buy shares. Sure enough, Twitter opened over 70% above the $26 offering price and the company similarly left boatloads of money on the table a la LinkedIn. In each subsequent tech IPO, the prior "victim" ended up reaping the rewards.

Across these three big IPOs, we have seen punditry complain about the underwriters, company insiders, and the exchanges, amongst others, without ever looking into the proverbial mirror.  The media whirlwind surrounding these events has created a narrative which permeates society. This narrative then influences the actors in the next scene to attempt to avoid the pitfalls of the prior narrative, only to fall victim to new problems seen one long cycle ago. Punditry continuously drives a vicious cycle of reactionary moves with commensurate media complaints and the same problems sadly repeat themselves over and again. All we have learned in all this is that the underwriters simply can't win a PR windfall with these IPOs (though they do make plenty of money), and punditry will inevitably find a villain and a victim to create a story at its leisure. 


Disclosure: No position in any of the stocks mentioned.


Michael Mauboussin on the Santa Fe Institute and Complex Adaptive Systems

Michael Mauboussin of Credit Suisse is one of the best strategists on Wall Street and a thought leader who consistently introduces some of the most compelling topics to the financial community. It is therefore no surprise Mauboussin is now Chairman of the Board of Trustees at the Santa Fe Institute, an organization which specializes in the multi-disciplinary study of complex adaptive systems. I recently had the privilege of interviewing Mauboussin about his involvement with the Santa Fe Institute and his thoughts on complexity. Enjoy (and be sure to follow the links to some fascinating further readings):


Elliot: Now that you’re Chairman of the Board of Trustees at the Santa Fe Institute, what are your goals and visions for how to more broadly inject SFI’s lessons on complexity into the financial community’s understanding of markets?

Michael: In my role at SFI, the primary goal is to make sure that the Institute does, and can, do great science. The unifying theme is the study of complex adaptive systems. But the goal is to have a place where there’s support for important, transdisciplinary research. 

That said, I would love to continue to see this type of thinking work its way into our understanding of financial markets. That is happening to some degree. One example is Andrew Lo’s work on the Adaptive Market Hypothesis. Another example is Blake LeBaron’s work on markets using agent-based models. I think it’s a more complete way of viewing markets than a standard rational agent model or the assumption of the absence of arbitrage. The problem is that modeling complex adaptive systems is a lot messier than those other approaches.

Elliot: When we last met at an event introducing The Success Equation to SFI members in New York, I asked you what the right “success equation” is for a young investor. Your response was to “keep coming to these events.” How did you first learn about the Santa Fe Institute? And how did you come to embrace the SFI?

Michael: I first learned about SFI in 1995 at a Baltimore Orioles baseball game, where Bill Miller was my host and the proselytizer. He explained how this new research group dedicated to the study of complex systems was coming up with cool and useful insights about business and markets. Specifically, he was taken with Brian Arthur’s work on “increasing returns.” This work showed that under some conditions returns actually move sharply away from the mean. This is counter to classic microeconomic thinking that assumes returns are mean-reverting. 

In many ways I was primed for the message. I had been doing a lot of reading, especially in the area of science, and so this way of thinking made sense to me from the beginning.

Elliot: Did you have a bias towards one market philosophy before you adopted the complex adaptive system mental model?  

Michael: Although I had a solid liberal arts background before starting on Wall Street, I had very little background in business or finance. As a result, I had few preconceived notions of how things worked. It’s a challenge to come up with clear conclusions based on an observation of what happens in markets. On the one hand, you see clear evidence that some people do better than the indexes and that there are patterns of booms and crashes over the centuries. These suggest that markets are inefficient. On the other hand, there’s also clear evidence that it’s really hard to beat the market over time, and that the market is more prescient than the average investor. So for me, at least, there was an intellectual tug of war going on in my head. 

I have to admit to being struck by the beauty of the efficient markets hypothesis as described by the economists at the University of Chicago. At the forefront of this, of course, was Eugene Fama, who recently won the Nobel Prize in part for his work in this area. What’s alluring about this approach is that it comes with a lot of mental models. You can equate risk with volatility. You can build portfolios that are optimal relative to your preference for risk. And so forth. Because you can assume that prices are an unbiased estimate of value, you can do a lot with it. The market’s amazing ability to impound information into prices impresses me to this day.

So it was with this mental tug of war as a backdrop that I learned about the idea of complex adaptive systems. Suddenly, it all clicked into place. A simple description of a complex adaptive system has three parts. First, there are heterogeneous agents. These can be ants in an ant colony, neurons in your brain, or investors in a market. Second, these agents interact leading to a process called “emergence.” The product of emergence is a global system that has properties and characteristics that can’t be divined solely by looking at the underlying agents. Reductionism doesn’t work

What instantly drew me to this way of thinking is that it describes markets very well and it is very common in nature. The central beauty of this approach is that it provides some sense of when markets are likely to be efficient—in the classic sense—and when inefficiencies will creep in. Specifically, markets tend to be efficient when the agents operate in a truly heterogeneous fashion and the aggregation mechanism is working smoothly. Diversity is essential, both in nature and in markets, and the system has to be able to take advantage of that diversity. There are some neat examples in experimental economics to show how this works. It’s really wondrous. 

On the flip side, when you lose diversity the system can become very inefficient. And that’s also what we see in markets—diversity loss leads to booms and crashes. Now the loss in diversity can be sociological, in which we all start to believe the same thing, or it can be technical, such as the winding up or winding down of a leverage cycle. But here we have a framework that accommodates the fact that markets are pretty darned good with the fact that they periodically go haywire. And SFI was at the center of this kind of thinking.

Elliot: It’s interesting that your answer on what theory of markets you subscribe to is not in the “black or white” vein whereby one must be in one camp and one camp only. It seems like much of the divisiveness in today’s discourse (in many arenas) stems from people’s unwillingness to see these kinds of shades of grey, though as you suggest, that mentality is not for everyone.  Do you meet resistance from people when explaining your stance? Is there a way to get others to embrace “complexity” when people have an innate desire for linear, orderly explanations that are essentially either/or answers? 

Michael: Most of us are uncomfortable with ambiguity—we’d rather just have a point of view and stick to it. But in markets, the real answer clearly lies between the folks who believe that markets are perfectly efficient and those who believe it’s largely inefficient. By the way, if you think the market is mostly inefficient there is no reason to participate because even if you have a sense that you are buying a dollar at a discount there is no assurance that the market will ever recognize that value. So some degree of market efficiency is essential even for those who believe that markets have inefficiencies. 

My goal is less to get people to change their view and more to establish a better understanding of how things work. Once you learn about markets as a complex adaptive system and appreciate its implications, I find it difficult to go back to a more traditional point of view.  

Elliot: In More Than You Know, you said, “The best way to describe how I feel following a SFI symposium is intellectually intoxicated.” Are there steps you take following these events to transform the ideas you’ve learned and the relationships you’ve built into expanding the scope of your own knowledgebase? And how are you able to harness this intoxication into productive output? 

Michael: I wish I could be more systematic in this regard, but I think it’s fair to say that the ideas from SFI have permeated every aspect of my work. Perhaps a couple of examples will help make the point.

I’ve already mentioned conceptualizing markets as a complex adaptive system. This alone is a large step, because rather than simply moaning about the limitations of standard finance theory, you have a framework for thinking about what’s going on.

I’ve also already mentioned Brian Arthur’s work on increasing returns. Many businesses are being defined less by their specific market segment and more by the ecosystem they create. And it is often the case that in a battle of ecosystems, one will come out on top. So this set of steps provides a mental model to understand the process of increasing returns and, as important, how to identify them in real time.

Ideas from SFI have inspired my work in many other ways, from understanding power law distributions in social systems to network theory to collective decision making to the processes underlying innovation. I could go on. But suffice it to say that there is hardly an area of markets, business, or decision making where your thinking wouldn’t be improved by learning, and internalizing, the kinds of ideas coming out of the SFI.

Elliot: In More Than You Know, you also introduce Charlie Munger and SFI as “Two sources in particular [that] have inspired my thinking on diversity. The first is the mental-models approach to investing, tirelessly advocated by Berkshire Hathaway's Charlie Munger. The second is the Santa Fe Institute (SFI), a New Mexico-based research community dedicated to multidisciplinary collaboration in pursuit of themes in the natural and social sciences.” It seems only natural that adopting Charlie Munger’s perspective to mental models would lead one to the SFI. Can you talk about the synergies between these two worldviews in making you a better analyst? What role did your adoption of Munger’s framework play in your attraction to the SFI?

Michael: Charlie Munger is a very successful businessman. Probably the first thing to note about him is that he reads constantly. He’s a learning machine. There’s bound to be a good outcome if you dedicate yourself to reading good stuff over a long period of time. That alone should be inspiring.

So as I think about the synergies between the worldviews, a few thoughts come to mind. First, it’s essential to provide your mind with good raw material. That means exposing yourself to a lot of disciplines and learning the key tenets. It also means spending time with people who think differently than you do. 

Second, you have to be willing and able to make connections. What are the similarities between disease and idea propagation? What can an ant colony teach me about innovation? What do physical phenomena, such as earthquakes, tell us about social phenomena, such as stock market crashes? You need good raw material to make connections, but you also have to be careful to avoid superficial links.

Finally is the idea of thinking backwards. Munger is a big advocate for this. You observe that something is where it is: How did it get there? Why did it get there? There are some fascinating challenges in this regard right now. We know, for example, that the sizes of cities and companies follow power laws. Why? By what mechanism does this happen? No one really knows, and the prospect of solving those kinds of challenges is exciting.   

But I have to finish with the point that this approach to the world is not for everyone. The interest or capability to work in this fashion is far from universal. So I wouldn’t recommend this to everybody. Rather, I would encourage it if you have a proclivity to think this way.  

Elliot: You talk of a benefit of the mental models approach as having a diverse array of models that you can fit a given situation, rather than fitting a given situation to a one-size-fits-all model.  Can you shed some insight on a) how you built up your quiver of models; b) how you organize these models (either mentally or tangibly); and c) how you choose which model to use in a given situation?

Michael: Yes, I think the metaphor is that of a toolbox. If you have one tool only, you’ll try to apply it to all of the problems you see. And we all know people who are just like that.

The mental models approach seeks to assemble a box with many tools. The idea is to learn the big ideas from many disciplines. What are the main ideas from psychology? Sociology? Linguistics? Anthropology? Biology? And on and on. In many cases you don’t have to be a deep expert to get leverage from a big idea. One of my favorite examples is evolution. Spend some time really understanding evolution. It is a mental model that applies broadly and provides insights that other approaches simply can’t.

I’m not sure I’m much of an example, but I have strived to read widely. This in part has been inspired by the people and ideas I have encountered at SFI. Most of my organization comes through writing or teaching. For me, that is a way to consolidate my understanding. If I can’t effectively write or teach something, I don’t understand it. Now I’m sure I write about things I don’t understand as well, but I try my best to represent the science as accurately as possible.

As for choosing the right model, the key there is to look for a fit. One concept that intrigues me is that nature has taken on and solved lots of hard problems, and there’s a lot we can learn from observing how nature works. So you might learn how to run a committee more effectively if you understand the basic workings of a honeybee colony. Or you might have insight about the resources your company should allocate to experimentation by examining ant foraging strategies. 

The risk is that you take the wrong tool out of the toolbox. But I think that risk is a lot smaller than the risk of using the same tool over and over. I’ll also mention that the work of Phil Tetlock, a wonderful psychologist at the University of Pennsylvania, suggests that so-called “foxes,” people who know a little about a lot of topics, tend to be more effective forecasters than so-called “hedgehogs,” those with a single worldview. So not only is this an intellectually appealing way to go, there’s solid evidence that it’s useful in the real world. 

Elliot: When you cite how Brian Arthur’s work “showed that under some conditions returns actually move sharply away from the mean. This is counter to classic microeconomic thinking that assumes returns are mean-reverting.” It makes me think about feedback loops and this passage from More Than You Know: “Negative feedback is a stabilizing factor, while positive feedback promotes change. Too much of either type of feedback can leave a system out of balance.” Positive feedback loops are seemingly the force that drives conditions away from the mean. How can we think about feedback loops in a more constructive way and are there steps that we can take to understand when/where/how they will appear?  As a follow-up, is there a good mental model for thinking about when and how breakpoints appear in feedback loops?

Michael: There’s been a great deal written about this idea, albeit not necessarily using this exact language. One classic work on this is Everett Rogers’s book, Diffusion of Innovations. He was one of the first to describe how innovations—whether a new seed of corn or an idea—spread. From this a lot of other ideas emanated, including the idea of a tipping point, where momentum for diffusion accelerates. 

The Polya urn model is also useful in this context. A basic version of the model starts with balls of two colors, say black and white, in an urn at some ratio. You then randomly select one ball, match it with a ball of the same color, and replace it. For example, say you started with 3 black balls and 3 white balls, so 50 percent of the balls are black. Now you draw a ball, observe that it’s black, and return it to the urn with an additional black ball. So the percentage of black balls is now 57 percent (4/7). 

This urn model is very simple but demonstrates the principles behind positive feedback nicely. Specifically, it’s nearly impossible in advance to predict what’s going to happen, but once one color gets ahead sufficiently, it dominates the outcomes. (You can play a little more sophisticated version here.) It’s interesting to hit the simulator over and over to simply observe how the outcomes vary.

Another area where this model pops up is in format, or standard, wars. The classic example is Betamax versus VHS, but there are plenty of examples throughout history. Here again, as one standard gets ahead, positive feedback often kicks in and it wins the war.   

Now I don’t think there’s any easy way to model positive feedback, but these are some of the mental models that may help one consider what’s going on.

Elliot: You talk about Munger’s advice to think backwards and invert. I think your first book was Expectations Investing which provided a framework for estimating the embedded assumptions in an equity’s price. Yet you also warn that this way of thinking isn’t for everyone. Was this something you realized after sharing the ideas with many or were you always aware of this? Do you have any ideas for why this has a relatively narrow audience? Is there a natural tie-in to the behavioral biases of humans and why this doesn’t work for everyone? (For example, the human proclivity towards the narrative bias to explain past events) And if so, how can we think backwards more rationally and overcome these biases?

Michael: Steven Crist, the well-known handicapper, has a line about horse race bettors in his essay, “Crist on Value,” that I love to repeat. He says, “The issue is not which horse in the race is the most likely winner, but which horse or horses are offering odds that exceed their actual chances of victory. This may sound elementary, and many players may think they are following this principle, but few actually do.” Take out the word “horse” and insert the word “stock” and you’ve captured the essence of the problem. 

Our natural tendency is to buy what is doing well and to sell what is doing poorly. But as Crist emphasizes, it doesn’t really matter how fast the horse will run, it matters how fast the horse will run relative to the odds on the tote board. Great investors separate the fundamentals from the expectations, and average investors don’t. Most of us are average investors.   

My advice, then, is to try to be very explicit about segregating the fundamentals and the expectations. Sometimes high expectations stocks are attractive because the company will do better still than what’s in the price. Great. That’s a buy. Sometimes there are stocks with low expectations that are dear because the company can’t even meet those beat down results. That’s called a value trap. So, constantly and diligently ask and answer the question, “what’s priced in?” Doing so is very helpful.  


Navigating the Global Economy - Buttonwood Gathering 2013

I had the privilege of attending The Economist’s Buttonwood Gathering 2013 replete with a stacked lineup of speakers and panelists. At a conference such as Buttonwood, one of the most interesting elements is the opportunity to exchange ideas with attendees who are generally pretty brilliant in their own right. I had numerous conversations with other Gatherers on topics ranging including Mexico’s pro-market reforms, Canada’s housing bubble, the European banking environment, and much more. Measuring consensus on such topics at Buttonwood provides a great glimpse into what the “Smart Money” is thinking. Sure enough, smart money seems abundantly optimistic in Mexico’s steps toward and capacity to successfully implement said reforms, Canada’s housing bubble is very real, and the European banking environment will have to pivot from an arena of nationalistic-driven excess to centralized decency.

These are simply some topics I conversed about with fellow gatherers. The panels themselves covered a wide range of topics, from the global economy, to the emerging market landscape and today’s optimistic venture capital environment for technology. While it’s impossible to completely cover each topic and the panelists’ thoughts in this post, I want to share some of the points that were more striking and relevant to me personally in the themes and topics that I focus on.

The two-day event kicked off with a conversation on the “global economic outlook” between José Manuel González-Páramo, Robert Rubin and Nemat Shafik, moderated by Zanny Minton Beddoes. All the panelists echoed the theme that Europe was improving and a decent coefficient of global growth was moving from emerging back to developed markets. Robert Rubin took a strikingly pessimistic tone towards the US growth outlook, given his belief that the conventional narrative of a fiscal drag was overstated and the real problem remains lack of demand and therefore anemic consumption. Shafik explained how there is increasing decoupling and dispersion amongst the various emerging markets and how each unique country thought of in its own unique way. Gonzalez-Paramo mused that Europe had the greatest potential to outperform estimates in the coming months should the relevant parties continue on the path towards a formalized banking union.

The discussion on Europe offered a natural segue into the second panel covering “Europe’s Burden” with José Manuel Campa, Bruce Richards and Nicolas Veron. Véron explained how the stress tests in Europe would be completely different this time around. Rather than pure stress tests, the exercise would be an intensive Asset Quality Review (AQR) done by the ECB instead of the European Banking Authority. Richards seconded this sentiment, and noted that the EBA tests were “laughed at.” Richards further explained how Europe’s banks have $42 trillion in assets compared to a GDP of $13 trillion, far larger than the US, which has $15 trillion in assets on a GDP just shy of $17 trillion. While Europe’s economy “has bottomed” it will take time for the banks to grow out of their size problem, with the US Savings and Loan Resolution Trust Corporation wind-down offering the best analog. Richards called Europe today “the largest asset disposition in the history of the world” and said the opportunity is in the very early stages, with assets like Spanish Non-Performing Loans available for 3 cents on the dollar.

Next, Roger Altman and Thomas Horton spoke about the changing corporate landscape in the US. Altman insisted that “uncertainty” in the business community stemmed predominantly from a shortfall in demand in the economy and not from Washington. The biggest trend Altman has been watching is the rise in activism amongst shareholders, and the willingness of institutional shareholders to embrace activist proposals. Meanwhile, Horton opined that US tax policy’s limitations on repatriation offered a significant hurdle to prudent balance sheet management in corporate America and that regulatory uncertainty has been a particularly large obstacle for him personally in helping American Airlines emerge from bankruptcy.

Day two started with an interesting discussion on monetary policy between Mohamed El-Erian and Vincent Reinhart. Both gentlemen generally agreed that central bank policy cannot create supply, but that it can move demand. In this context, the risk/reward balance of further quantitative easing has shifted decisively towards the direction of risk, with little reward. While the Fed has emphasized the importance of forward guidance, they completely underestimated the market’s interpretation as to when tapering would begin. El-Erian worries that in this environment, people are being “pushed, not pulled into trades.” Reinhart stressed that in the future Yellen Fed, there will place a greater focus on the dual mandate. Further, she will take it as her responsibility to provide guidance that is both broader in scope and deeper in explanation.

Next, Jim Millstein and Mary Schapiro talked about the financial regulatory environment. Millstein highlighted how in Too Big To Fail, there is no market discipline happening in either the equity or debt markets for banks. As such, there is no natural free market check on these institutions considering debt is subsidized with the TBTF guarantee and equity is too large for an activist to impose changes. Ultimately, Millstein sees finance heading towards a more utility-like role in the economy. Schapiro expressed some concern that while a stronger regulatory regime has been constructed, it has effectively been rendered toothless by a lack of funding, but that ultimately she was optimistic regulators will find a middle ground and bridge some of the gaps present between political goals and regulatory reality.

Japan was next up in the Gathering’s coverage of global economies. Koichi Hamada and Paul Sheard both shared their belief that Abenomics so far is working, particularly on the monetary policy side. Hamada noted that excess capacity to GDP declined from 3% to 1.5% and inflation actually started moving in the right direction for once. The problem, both agreed, is that little light has been shed and little progress made on supply side reforms that are ultimately necessary for Abenomics to truly work. Both believe that in time this will happen, but for now, Abe will have to combat an entrenched and powerful bureaucracy to get his way. Sheard made the point that no central bank in world history has tried to dislodge deflation expectations knowing it will inevitably have to re-anchor inflation to a 2-2.5% target. Japan has plenty of room to do more when compared to the Fed, as the US central bank increased its balance sheet by 250% during the course of the crisis, in contrast to Japan’s 54% increase. Both explained how while many worry about Japan’s “demographic” challenges,” Japan does have an opportunity in that women make up a smaller percentage of the workforce than in most developed countries and there is considerable room to improve.

Robert Shiller and Lewis Alexander then held an interesting discussion about bubbles. Shiller started with a definition of a bubble: they are a price-mediated feedback between prices and market participants, with excessive enthusiasm, media participants, and regret from those who are not involved. The “psycho-economic phenomenon” is a defining characteristic that becomes ingrained in a culture and is related to long-term expectations that cannot be pinned down quantitatively. Alexander offered a distinction between those bubbles that are a systemic risk verse those that are not. Bubbles carry systemic risk only when they have a credit component. Thus, in the absence of a credit component, the risks of a bubble are not all that severe for society at large. The housing bubble was one such systemic risk event, though both emphasized this was clearly not the fault of the Federal Reserve Bank (as many skeptics proclaim). Home prices began their rise in 1997 and continued to rise even during periods within which the Fed was raising interest rates. Shiller explained that there simply was no correlation at all between the path of rates and home prices, and that the efficient market hypothesis was the real culprit for inducing a sense of complacency in market observers that all prices are rational. Further, right now, people are calling for bubbles everywhere and they can’t all be the Feds fault, as is evidenced by what Shiller said is “most likely” a bubble in Brazilian real estate. Though Alexander cautioned that the problem with monetary policy is how it is a “blunt tool” and influences all or nothing with regard to price, so some distortions can happen. These distortions are mainly in interest rate risk, not credit risk right now and he does not see accompanying systemic risk as a result.

The two Bagehot Lectures were given by Agustín Carstens and Alan Greenspan. Carstens discussed the role of emerging market central banks in a crisis environment. Central banks should continue to focus on keeping inflation under control, and could use some macroprudential policies to offer a countercyclical buffer, though such policy should be used “like tequilia--only in moderation.” EM central banks also should play a supervisory role to regulate the flows of currencies and help mitigate volatility, but monetary policy can’t do all this on its own. Many EMs need serious structural reforms and it’s unfortunate that these needs are only recognized on the down side of the cycle, not the up. This is equally true in other areas. For example, Mexico opened a permanent line of credit with the IMF when times were good, while now countries who would benefit from such a line don’t want to do so for fear of appearing to “need” it and in the process, looking vulnerable. Alan Greenspan then took to the stage. He explained how there is a significant bifurcation in our economy whereby capital investment of a less than 20 year duration is doing quite well and of greater than 20 years is in a deep slump. Greenspan believes this is the result of uncertainty in long-term planning and blames tax policy as the culprit. Right now in the US we are seeing one of the greatest spreads ever in term structure between 5 and 30 year Treasuries and this is a reflection of the gap between the short and long-term economies.

In the ensuing panel on fiscal priorities with Roger Ferguson, Laura D’Andrea Tyson and Carmen Reinhart, D’Andrea Tyson quickly launched into her rebuttal of Greenspan’s argument. She explained how the fiscal stimulus relative to GDP was rather small, and the premature austerity undertaken by the government since emerging from crisis has made the recovery slower than it needs to be. There is considerable excess capacity in our economy, and this is a far bigger culprit in weak long-term investing than anything else and this uncertainty is over demand, not politics. Carmen Reinhart agreed with most of these points and added that private sector deleveraging continues to be a headwind to growth. She also noted that the US has done particularly well relative to others around the globe, but worries about how the US will unwind it’s large fiscal deficit when all is said and done. Ferguson elaborated on how big the private sector short-fall was during the crisis and how much more the government could have stimulated the economy instead of leaving monetary policy as the “last man standing” to help. He complained that “politicians are acting Ricardian in a Keynesian world” and hurting, rather than helping our cause. He and D’Andrea Tyson remarked on how the negative real interest rates on Treasuries offer a serious opportunity for the government to borrow and invest in much-needed infrastructure projects, but unfortunately everyone in a position to do something is focused on discretionary spending as a problem when it’s really entitlements. If only discourse were more rational.

While this is hardly an exhaustive summary of the Buttonwood Gathering, these were some of the more relevant discussions on topics that I am concerned with. I took fairly extensive notes during the two days, and if anyone would like some more insight on any of the specific panels discussed here (or those that I didn’t mention), please feel free to leave a comment below or email me and I will be sure to answer.

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