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Wednesday, January 22, 2014

Bitcoin Threatens Giants Such As Visa To JP Morgan With Lower-Cost Payments / We Also Discuss: Algorithmic Government And Regulation

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Bitcoin Threatens Giants Such As Visa To JP Morgan With Lower-Cost Payments
We Also Discuss
Algorithmic Government And Regulation

Entrepreneurs from Silicon Valley to Wall Street say they don’t care much for Bitcoin as a currency to supplant the U.S. dollar. As a payment technology they could use to undercut Visa Inc. (V), Western Union Co. (WU) or Citigroup Inc. (C), they say they like that aspect a lot more.

As Bitcoin’s value gyrates, these investors have said that the headlines about its price and chatter about which retailers accept it obscure the real merit of what Satoshi Nakamoto, the name given to the anonymous person or people who created Bitcoin, delivered five years ago.

“At some point, I had an ‘aha!’ moment and realized that Bitcoin was best understood as a new software protocol through which you could rebuild the payments industry in ways that are better and cheaper,” Chris Dixon, a partner at Menlo Park, California-based venture capital firm Andreessen Horowitz, wrote in a blog post.

Bitcoin enthusiasts say they are building a system to move money across the Internet securely and at a lower cost than existing wire transfers, bank debits or remittances. If they can eliminate the friction created by middlemen and create easy-to-use consumer tools, Bitcoin businesses may claim a piece of the revenue and still deliver lower costs.

Already, some retailers are paying 1 percent to process transactions in Bitcoin, improving profit margins. Taking debit or credit cards, they may pay more than 3 percent to issuing banks. Bitcoin transactions log immediately, and are confirmed in as little as five minutes.

Volatile Currency

Skeptics counter that Bitcoin’s volatility -- the price swings resemble those of a thinly traded currency -- could wipe out cost savings in what is still a small business. They also say the startups, in addition to facing entrenched incumbents, will layer their own costs onto Bitcoin transactions, undermining the goal of a cheaper system.

A lower-cost virtual currency-driven payment system could take business from companies like Western Union that specialize in foreign remittances, as well as Visa and MasterCard Inc. (MA) Banks such as JPMorgan Chase and Co. (JPM), Citigroup and Bank of America Corp. reap billions in fees from the use of credit and debit cards, and are the main players in the direct-debit system.

“Perhaps the rise of Bitcoin will put pressure on big banks to get us a more modern payment system,” said Jennifer Tescher, president of the Center for Financial Services Innovation, a Chicago-based research group.

‘High Hurdle’

Bill Carcache, a Nomura Holdings Inc. analyst in New York, said any disruption is bound to move slowly. Visa and MasterCard have 35 million locations globally that accept their cards, and as many as 2 billion account holders.

“I’d never dismiss the risk posed by a new technology, but I think the network effect that Visa and MasterCard have achieved creates a high hurdle for new competitors seeking to disintermediate them,” said Carcache, who reiterated buy recommendations on Visa and MasterCard in a client note last month.

Bitcoin’s potential as a payments system is “an exotic topic within an exotic topic,” said Arjan Schutte, the founder of Los Angeles-based Core Innovation Capital, which invests in financial-services startups. It’s also the driver behind the most important venture capital investments in virtual currencies, according to Schutte, far more important than the price of Bitcoins.

“I have no interest in owning Bitcoin,” he said. “What I am entirely interested in is frictionless money transfer.”

Moving Cash

The global integration of financial markets obscures a central and costly problem: Moving the most basic asset, cash, can be expensive, slow and at times fraud-ridden.

When a consumer swipes a debit card at a store, the amount is immediately subtracted from the customer’s available cash. The merchant is paid only when the next collection of transactions clears, usually in a day, but sometimes at the cost of higher fraud levels, according to Shirley Inscoe of the Boston-based consultancy Aite Group LLC. When an immigrant gives cash to a Western Union agent, such as a convenience store, the money may be available quickly to a family member in another country only if the customer pays a premium.

Businesses paying suppliers through the automated clearing system to which all U.S. banks belong have no guarantee that payments will clear the same day, and the process uses a 40-year-old software protocol. International wire transfers can take four or five days, and are costly enough to make small payments uneconomic, Inscoe said.

Reaping Fees

Merchants pay about $48 billion in fees to banks for their customers’ payment-card use and banks pay much of that to Visa and MasterCard, according to Aite Group. Unauthorized charges, sometimes linked to data breaches like the one at Target Corp., cost about $5 billion per year. Remittance providers such as Western Union Co. reap about $37 billion in fees each year from people sending money home, according to the World Bank.

The Federal Reserve in September began a process for revamping payment systems. State banking supervisors also have formed their own group to examine how to reduce costs and increase speed, said Charles Vice, the commissioner of the Kentucky Department of Financial Institutions.

“We’ve not had a substantial look at clearing of payments in 40 years,” Vice said.

Asking Experts

Paul Cohen, a Visa spokesman didn’t respond to a request for a comment on Bitcoin’s role as a payments technology, and Seth Eisen at MasterCard declined to comment. Western Union, in an e-mailed statement, said it is tracking the Bitcoin market while for now handling only regular currency. Like many banks, Wells Fargo and Co. (WFC) is consulting “internal and external experts” to learn more about virtual currencies, spokeswoman Andrew Silver said.

The chief innovation of Bitcoin involves a solution to what is known as the double-spending problem. Any payment that is not cash handed over in person, such as entries in a bank account, could be duplicated, allowing people to spend the same money twice. A trusted third party, such as a payment system run by Visa or MasterCard that confirms identities and ownership, adds costs while preventing double-spending.

Nakamoto solved the problem by creating a public ledger that establishes ownership by anonymously recording who owns what Bitcoins. A volunteer network of computers validates every single transaction on the network by performing computations that prove they’ve done the work, and receive new Bitcoins in return, eliminating the need for a third party.

Security Risks

“That was the ‘breaking the sound-barrier moment’ for virtual currencies,” said Tony Gallippi, who co-founded BitPay Inc., an Atlanta-based Bitcoin payment processor.

Bitcoin transactions could mitigate the risks of breaches of the type reported by Target, Neiman Marcus Group Ltd. and other retailers, said Jerry Brito, director of the technology program at George Mason University’s Mercatus Center. With Bitcoin, a customer doesn’t leave behind the credentials necessary for additional, fraudulent transactions.

“You’re not giving the retailer anything but your Bitcoin address, the one from which you send the currency,” Brito said.

That address, revealed during the all-digital transaction, is useless without the private encryption key, which the customer does not reveal. By contrast, when using a payment card, a customer reveals an account number and pass code to a retailer.

“Think about that for a minute,” Gallippi told a congressional committee. “Why would you ever give someone full access to your $20,000 line of credit to pay them $20?”

‘Payment Rail’

In the jargon of the industry, Bitcoin is a “payment rail,” like Visa, Western Union or the bank-debiting system. Unlike those systems, Bitcoin is also the currency.

Bitcoin is “a bearer instrument,” said Dixon, the partner at Andreessen Horowitz, in which Bloomberg LP, the parent of Bloomberg News, is an investor. “You don’t need to know if I have the money -- it exists by virtue of the instrument,” he said.

The fact that it is a volatile currency is viewed by some as a downside in its use in transactions. A Bitcoin was worth about $875 on the CoinDesk Bitcoin Price Index yesterday, up from $609 a month earlier.

Some merchants use San Francisco-based Coinbase to process payments and immediately convert the virtual currency into dollars. Jeff Klee, chief executive officer of Bitcoin-accepting airline ticketing site Cheapair.com, said the emerging exchanges help mitigate the exchange-rate risks.

Volatility Protection

“If the price of Bitcoin dropped to zero tomorrow we would not feel the effects at all,” Klee said.

Mark Williams, a lecturer in finance at Boston University, said this model underscores how Bitcoin as a payment system cannot be divorced from Bitcoin as a currency.

“Companies that are claiming they accept Bitcoin as payment are actually accepting the coin only if someone else accepts the price risk,” Williams said.

The Institute of International Finance, an association of major global banks like Goldman Sachs Group Inc. (GS) and Citigroup, said Bitcoin’s volatility will limit its appeal. “It deters most large companies from accepting the digital currency as a form of payment,” the group said in a recent report.

The need for other companies like Coinbase to manage the Bitcoin-dollar nexus also undermines the goal of eliminating costly middlemen, said Aaron Greenspan, chief executive of Think Computer Corp., a software developer in Palo Alto, California.

‘In Circles’

“We’re going in circles here,” Greenspan said. “Bitcoin is supposedly decentralized but we’re creating these exchanges that centralize the trading, and at a cost.”

Peter Vessenes, the chairman of the Bitcoin Foundation, said the virtual currency, with software protocols open to any developer, won’t allow any exchange to become dominant, and therefore expensive.

“Because the Bitcoin network is open, it’s fundamentally different than, say, a Visa network,” Vessenes said. “You can’t build a propriety network and keep other competitors out. There is no centralization of power.”

Coinbase charges its merchant clients 1 percent to cash their Bitcoin into dollars, and the first $1 million in transactions are free.

Cutting Fees

Vinny Lingham, CEO of gift-card seller Gyft, said accepting Bitcoin still saves money even though it converts most of its Bitcoins back into dollars. He said that Gyft, which is based in San Francisco and sells cards for retailers including Amazon.com, Nike Inc. and Target, saves about 3 percent in swipe fees and fraud charges, and returns most of that to consumers in the form of redeemable points that can be redeemed for cards.

“If you really adopt Bitcoin as a company, you need to pass back the benefits to the consumers,” Lingham said. “You need to give people a reason to make payments” in Bitcoin.

Patrick Byrne, CEO of online retailer Overstock.com, estimates that 1 percent of its 2014 sales, or $13 million, will come from Bitcoin. Since its net margin is only 2 percent, the lower fees are a substantial savings.

Jacob Farber, a lawyer with Perkins Coie in Washington, said the uses of Bitcoin as a payments system could extend to bank treasuries moving cash within companies, wiring money overseas or sending money to other banks.

Back-Office Use

“Bitcoin could easily get picked up as back-office infrastructure for payments around the world,” Farber said.

That would jibe with how payments work today, since most consumers don’t know how payment cards work, only that they do, said Mary Dent, who runs the Palo Alto, California-based consultancy dcIQ.

The virtual currency’s success may be measured by innovators’ ability to create a new system while ditching the Bitcoin label. In the future, consumers may pay in dollars and merchants may receive the same, with Bitcoin having only an intermediary technological role.

“Who knows SMTP or HTTP?” Dent said, referring to the coding systems for e-mail and the World Wide Web. “Barely anybody. You just know that you can e-mail people and surf the Web. Someone is going to figure out how payments can be as easy and cheap as e-mail.”

That outcome would be very different than what a lot of libertarian-mined Bitcoin enthusiasts envisioned as its future: a decentralized currency free from the influence of governments or big banks, Williams said.

“If Bitcoin becomes commercialized, to be successful it will have to look and operate a lot like the financial firms the movement derided when this e-currency protocol was created,” Williams said. “Many Bitcoiners will view this as a sell-out, as the capital raise needed fund the commercialization of this venture would require large Wall Street backing.”

Why Bitcoin Matters

A mysterious new technology emerges, seemingly out of nowhere, but actually the result of two decades of intense research and development by nearly anonymous researchers.

Political idealists project visions of liberation and revolution onto it; establishment elites heap contempt and scorn on it.

On the other hand, technologists – nerds – are transfixed by it. They see within it enormous potential and spend their nights and weekends tinkering with it.

Eventually mainstream products, companies and industries emerge to commercialize it; its effects become profound; and later, many people wonder why its powerful promise wasn’t more obvious from the start.

What technology am I talking about? Personal computers in 1975, the Internet in 1993, and – I believe – Bitcoin in 2014.

One can hardly accuse Bitcoin of being an uncovered topic, yet the gulf between what the press and many regular people believe Bitcoin is, and what a growing critical mass of technologists believe Bitcoin is, remains enormous. In this post, I will explain why Bitcoin has so many Silicon Valley programmers and entrepreneurs all lathered up, and what I think Bitcoin’s future potential is.

First, Bitcoin at its most fundamental level is a breakthrough in computer science – one that builds on 20 years of research into cryptographic currency, and 40 years of research in cryptography, by thousands of researchers around the world.

Bitcoin is the first practical solution to a longstanding problem in computer science called the Byzantine Generals Problem. To quote from the original paper defining the B.G.P.: “[Imagine] a group of generals of the Byzantine army camped with their troops around an enemy city. Communicating only by messenger, the generals must agree upon a common battle plan. However, one or more of them may be traitors who will try to confuse the others. The problem is to find an algorithm to ensure that the loyal generals will reach agreement.”

More generally, the B.G.P. poses the question of how to establish trust between otherwise unrelated parties over an untrusted network like the Internet.

The practical consequence of solving this problem is that Bitcoin gives us, for the first time, a way for one Internet user to transfer a unique piece of digital property to another Internet user, such that the transfer is guaranteed to be safe and secure, everyone knows that the transfer has taken place, and nobody can challenge the legitimacy of the transfer. The consequences of this breakthrough are hard to overstate.

What kinds of digital property might be transferred in this way? Think about digital signatures, digital contracts, digital keys (to physical locks, or to online lockers), digital ownership of physical assets such as cars and houses, digital stocks and bonds … and digital money.

All these are exchanged through a distributed network of trust that does not require or rely upon a central intermediary like a bank or broker. And all in a way where only the owner of an asset can send it, only the intended recipient can receive it, the asset can only exist in one place at a time, and everyone can validate transactions and ownership of all assets anytime they want.

How Does This Work?

Bitcoin is an Internet-wide distributed ledger. You buy into the ledger by purchasing one of a fixed number of slots, either with cash or by selling a product and service for Bitcoin. You sell out of the ledger by trading your Bitcoin to someone else who wants to buy into the ledger. Anyone in the world can buy into or sell out of the ledger any time they want – with no approval needed, and with no or very low fees. The Bitcoin “coins” themselves are simply slots in the ledger, analogous in some ways to seats on a stock exchange, except much more broadly applicable to real world transactions.

The Bitcoin ledger is a new kind of payment system. Anyone in the world can pay anyone else in the world any amount of value of Bitcoin by simply transferring ownership of the corresponding slot in the ledger. Put value in, transfer it, the recipient gets value out, no authorization required, and in many cases, no fees.

That last part is enormously important. Bitcoin is the first Internetwide payment system where transactions either happen with no fees or very low fees (down to fractions of pennies). Existing payment systems charge fees of about 2 to 3 percent – and that’s in the developed world. In lots of other places, there either are no modern payment systems or the rates are significantly higher. We’ll come back to that.

Bitcoin is a digital bearer instrument. It is a way to exchange money or assets between parties with no pre-existing trust: A string of numbers is sent over email or text message in the simplest case. The sender doesn’t need to know or trust the receiver or vice versa. Related, there are no chargebacks – this is the part that is literally like cash – if you have the money or the asset, you can pay with it; if you don’t, you can’t. This is brand new. This has never existed in digital form before.

Bitcoin is a digital currency, whose value is based directly on two things: use of the payment system today – volume and velocity of payments running through the ledger – and speculation on future use of the payment system. This is one part that is confusing people. It’s not as much that the Bitcoin currency has some arbitrary value and then people are trading with it; it’s more that people can trade with Bitcoin (anywhere, everywhere, with no fraud and no or very low fees) and as a result it has value.

It is perhaps true right at this moment that the value of Bitcoin currency is based more on speculation than actual payment volume, but it is equally true that that speculation is establishing a sufficiently high price for the currency that payments have become practically possible. The Bitcoin currency had to be worth something before it could bear any amount of real-world payment volume. This is the classic “chicken and egg” problem with new technology: new technology is not worth much until it’s worth a lot. And so the fact that Bitcoin has risen in value in part because of speculation is making the reality of its usefulness arrive much faster than it would have otherwise.

Critics of Bitcoin point to limited usage by ordinary consumers and merchants, but that same criticism was leveled against PCs and the Internet at the same stage. Every day, more and more consumers and merchants are buying, using and selling Bitcoin, all around the world. The overall numbers are still small, but they are growing quickly. And ease of use for all participants is rapidly increasing as Bitcoin tools and technologies are improved. Remember, it used to be technically challenging to even get on the Internet. Now it’s not.

The criticism that merchants will not accept Bitcoin because of its volatility is also incorrect. Bitcoin can be used entirely as a payment system; merchants do not need to hold any Bitcoin currency or be exposed to Bitcoin volatility at any time. Any consumer or merchant can trade in and out of Bitcoin and other currencies any time they want.

Why would any merchant – online or in the real world – want to accept Bitcoin as payment, given the currently small number of consumers who want to pay with it? My partner Chris Dixon recently gave this example:

“Let’s say you sell electronics online. Profit margins in those businesses are usually under 5 percent, which means conventional 2.5 percent payment fees consume half the margin. That’s money that could be reinvested in the business, passed back to consumers or taxed by the government. Of all of those choices, handing 2.5 percent to banks to move bits around the Internet is the worst possible choice. Another challenge merchants have with payments is accepting international payments. If you are wondering why your favorite product or service isn’t available in your country, the answer is often payments.”

In addition, merchants are highly attracted to Bitcoin because it eliminates the risk of credit card fraud. This is the form of fraud that motivates so many criminals to put so much work into stealing personal customer information and credit card numbers.

Since Bitcoin is a digital bearer instrument, the receiver of a payment does not get any information from the sender that can be used to steal money from the sender in the future, either by that merchant or by a criminal who steals that information from the merchant.

Credit card fraud is such a big deal for merchants, credit card processors and banks that online fraud detection systems are hair-trigger wired to stop transactions that look even slightly suspicious, whether or not they are actually fraudulent. As a result, many online merchants are forced to turn away 5 to 10 percent of incoming orders that they could take without fear if the customers were paying with Bitcoin, where such fraud would not be possible. Since these are orders that were coming in already, they are inherently the highest margin orders a merchant can get, and so being able to take them will drastically increase many merchants’ profit margins.

Bitcoin’s antifraud properties even extend into the physical world of retail stores and shoppers.

For example, with Bitcoin, the huge hack that recently stole 70 million consumers’ credit card information from the Target department store chain would not have been possible. Here’s how that would work:

You fill your cart and go to the checkout station like you do now. But instead of handing over your credit card to pay, you pull out your smartphone and take a snapshot of a QR code displayed by the cash register. The QR code contains all the information required for you to send Bitcoin to Target, including the amount. You click “Confirm” on your phone and the transaction is done (including converting dollars from your account into Bitcoin, if you did not own any Bitcoin).

Target is happy because it has the money in the form of Bitcoin, which it can immediately turn into dollars if it wants, and it paid no or very low payment processing fees; you are happy because there is no way for hackers to steal any of your personal information; and organized crime is unhappy. (Well, maybe criminals are still happy: They can try to steal money directly from poorly-secured merchant computer systems. But even if they succeed, consumers bear no risk of loss, fraud or identity theft.)

Finally, I’d like to address the claim made by some critics that Bitcoin is a haven for bad behavior, for criminals and terrorists to transfer money anonymously with impunity. This is a myth, fostered mostly by sensationalistic press coverage and an incomplete understanding of the technology. Much like email, which is quite traceable, Bitcoin is pseudonymous, not anonymous. Further, every transaction in the Bitcoin network is tracked and logged forever in the Bitcoin blockchain, or permanent record, available for all to see. As a result, Bitcoin is considerably easier for law enforcement to trace than cash, gold or diamonds.

What’s The Future of Bitcoin?

Bitcoin is a classic network effect, a positive feedback loop. The more people who use Bitcoin, the more valuable Bitcoin is for everyone who uses it, and the higher the incentive for the next user to start using the technology. Bitcoin shares this network effect property with the telephone system, the web, and popular Internet services like eBay and Facebook.

In fact, Bitcoin is a four-sided network effect. There are four constituencies that participate in expanding the value of Bitcoin as a consequence of their own self-interested participation. Those constituencies are (1) consumers who pay with Bitcoin, (2) merchants who accept Bitcoin, (3) “miners” who run the computers that process and validate all the transactions and enable the distributed trust network to exist, and (4) developers and entrepreneurs who are building new products and services with and on top of Bitcoin.

All four sides of the network effect are playing a valuable part in expanding the value of the overall system, but the fourth is particularly important.

All over Silicon Valley and around the world, many thousands of programmers are using Bitcoin as a building block for a kaleidoscope of new product and service ideas that were not possible before. And at our venture capital firm, Andreessen Horowitz, we are seeing a rapidly increasing number of outstanding entrepreneurs – not a few with highly respected track records in the financial industry – building companies on top of Bitcoin.

For this reason alone, new challengers to Bitcoin face a hard uphill battle. If something is to displace Bitcoin now, it will have to have sizable improvements and it will have to happen quickly. Otherwise, this network effect will carry Bitcoin to dominance.

One immediately obvious and enormous area for Bitcoin-based innovation is international remittance. Every day, hundreds of millions of low-income people go to work in hard jobs in foreign countries to make money to send back to their families in their home countries – over $400 billion in total annually, according to the World Bank. Every day, banks and payment companies extract mind-boggling fees, up to 10 percent and sometimes even higher, to send this money.

Switching to Bitcoin, which charges no or very low fees, for these remittance payments will therefore raise the quality of life of migrant workers and their families significantly. In fact, it is hard to think of any one thing that would have a faster and more positive effect on so many people in the world’s poorest countries.

Moreover, Bitcoin generally can be a powerful force to bring a much larger number of people around the world into the modern economic system. Only about 20 countries around the world have what we would consider to be fully modern banking and payment systems; the other roughly 175 have a long way to go. As a result, many people in many countries are excluded from products and services that we in the West take for granted. Even Netflix, a completely virtual service, is only available in about 40 countries. Bitcoin, as a global payment system anyone can use from anywhere at any time, can be a powerful catalyst to extend the benefits of the modern economic system to virtually everyone on the planet.

And even here in the United States, a long-recognized problem is the extremely high fees that the “unbanked” — people without conventional bank accounts – pay for even basic financial services. Bitcoin can be used to go straight at that problem, by making it easy to offer extremely low-fee services to people outside of the traditional financial system.

A third fascinating use case for Bitcoin is micropayments, or ultrasmall payments. Micropayments have never been feasible, despite 20 years of attempts, because it is not cost effective to run small payments (think $1 and below, down to pennies or fractions of a penny) through the existing credit/debit and banking systems. The fee structure of those systems makes that nonviable.

All of a sudden, with Bitcoin, that’s trivially easy. Bitcoins have the nifty property of infinite divisibility: currently down to eight decimal places after the dot, but more in the future. So you can specify an arbitrarily small amount of money, like a thousandth of a penny, and send it to anyone in the world for free or near-free.

Think about content monetization, for example. One reason media businesses such as newspapers struggle to charge for content is because they need to charge either all (pay the entire subscription fee for all the content) or nothing (which then results in all those terrible banner ads everywhere on the web). All of a sudden, with Bitcoin, there is an economically viable way to charge arbitrarily small amounts of money per article, or per section, or per hour, or per video play, or per archive access, or per news alert.

Another potential use of Bitcoin micropayments is to fight spam. Future email systems and social networks could refuse to accept incoming messages unless they were accompanied with tiny amounts of Bitcoin – tiny enough to not matter to the sender, but large enough to deter spammers, who today can send uncounted billions of spam messages for free with impunity.

Finally, a fourth interesting use case is public payments. This idea first came to my attention in a news article a few months ago. A random spectator at a televised sports event held up a placard with a QR code and the text “Send me Bitcoin!” He received $25,000 in Bitcoin in the first 24 hours, all from people he had never met. This was the first time in history that you could see someone holding up a sign, in person or on TV or in a photo, and then send them money with two clicks on your smartphone: take the photo of the QR code on the sign, and click to send the money.

Think about the implications for protest movements. Today protesters want to get on TV so people learn about their cause. Tomorrow they’ll want to get on TV because that’s how they’ll raise money, by literally holding up signs that let people anywhere in the world who sympathize with them send them money on the spot. Bitcoin is a financial technology dream come true for even the most hardened anticapitalist political organizer.

The coming years will be a period of great drama and excitement revolving around this new technology.

For example, some prominent economists are deeply skeptical of Bitcoin, even though Ben S. Bernanke, formerly Federal Reserve chairman, recently wrote that digital currencies like Bitcoin “may hold long-term promise, particularly if they promote a faster, more secure and more efficient payment system.” And in 1999, the legendary economist Milton Friedman said: “One thing that’s missing but will soon be developed is a reliable e-cash, a method whereby on the Internet you can transfer funds from A to B without A knowing B or B knowing A – the way I can take a $20 bill and hand it over to you, and you may get that without knowing who I am.”

Economists who attack Bitcoin today might be correct, but I’m with Ben and Milton.

Further, there is no shortage of regulatory topics and issues that will have to be addressed, since almost no country’s regulatory framework for banking and payments anticipated a technology like Bitcoin.

But I hope that I have given you a sense of the enormous promise of Bitcoin. Far from a mere libertarian fairy tale or a simple Silicon Valley exercise in hype, Bitcoin offers a sweeping vista of opportunity to reimagine how the financial system can and should work in the Internet era, and a catalyst to reshape that system in ways that are more powerful for individuals and businesses alike.

Algorithmic Government And Regulation

Regulation is the bugaboo of today’s politics. We have too much of it in most areas, we have too little of it in others, but mostly, we just have the wrong kind, a mountain of paper rules, inefficient processes, and little ability to adjust the rules or the processes when we discover the inevitable unintended results.

One of the potential applications cryptocurrencies can bring in the future are non-monopolistic, open-source, and largely algorithmic governments. These governments will be transparent, inexpensive to operate, and efficiently serve the interests of its tax payers.

Consider, for a moment, regulation in a broader context. Your car’s electronics regulate the fuel-air mix in the engine to find an optimal balance of fuel efficiency and minimal emissions. An airplane’s autopilot regulates the countless factors required to keep that plane aloft and heading in the right direction. Credit card companies monitor and regulate charges to detect fraud and keep you under your credit limit. Doctors regulate the dosage of the medicine they give us, sometimes loosely, sometimes with exquisite care, as with the chemotherapy required to kill cancer cells while keeping normal cells alive, or with the anesthesia that keeps us unconscious during surgery while keeping vital processes going. ISPs and corporate mail systems regulate the mail that reaches us, filtering out spam and malware to the best of their ability. Search engines regulate the results and advertisements they serve up to us, doing their best to give us more of what we want to see.

What Do All These Forms Of Regulation Have In Common?

A Deep Understanding Of The Desired Outcome

Real-Time Measurement To Determine If That Outcome Is Being Achieved

Algorithms (i.e. A Set Of Rules) That Make Adjustments Based On New Data

Periodic, Deeper Analysis Of Whether The Algorithms Themselves Are Correct And Performing As Expected.

There are a few cases—all too few—in which governments and quasi-governmental agencies regulate using processes similar to those outlined above. Probably the best example is the way that central banks regulate the money supply in an attempt to manage interest rates, inflation, and the overall state of the economy. Surprisingly, while individual groups might prefer the US Federal Reserve to tighten or loosen the money supply at a different time or rate than they do, most accept the need for this kind of regulation.

Why Is This?

The desired outcomes are clear

There is regular measurement and reporting as to whether those outcomes are being achieved, based on data that is made public to everyone

Adjustments are made when the desired outcomes are not being achieved

Contrast this with the normal regulatory model, which focuses on the rules rather than the outcomes. How often have we faced rules that simply no longer make sense? How often do we see evidence that the rules are actually achieving the desired outcome?

Sometimes the “rules” aren’t really even rules. Gordon Bruce, the former CIO of the city of Honolulu, explained to me that when he entered government from the private sector and tried to make changes, he was told, “That’s against the law.” His reply was “OK. Show me the law.” “Well, it isn’t really a law. It’s a regulation.” “OK. Show me the regulation.” “Well, it isn’t really a regulation. It’s a policy that was put in place by Mr. Somebody twenty years ago.” “Great. We can change that!”

But often, there really is a law or a regulation that has outlived its day, an artifact of a system that takes too long to change. The Obama Administration has made some efforts to address this, with a process of both “regulatory lookback” to eliminate unnecessary regulations, and an increased effort to quantify the effect of regulations (White House, 2012).

But even this kind of regulatory reform doesn’t go far enough. The laws of the United States have grown mind-bogglingly complex. The recent healthcare reform bill was nearly two thousand pages. The US Constitution, including two hundred years worth of amendments, is about twenty-one pages. The National Highway Bill of 1956, which led to the creation of the US Interstate Highway system, the largest public works project in history, was twenty-nine pages.

Laws should specify goals, rights, outcomes, authorities, and limits. If specified broadly, those laws can stand the test of time.

Regulations, which specify how to execute those laws in much more detail, should be regarded in much the same way that programmers regard their code and algorithms, that is, as a constantly updated toolset to achieve the outcomes specified in the laws.

Increasingly, in today’s world, this kind of algorithmic regulation is more than a metaphor. Consider financial markets. New financial instruments are invented every day and implemented by algorithms that trade at electronic speed. How can these instruments be regulated except by programs and algorithms that track and manage them in their native element in much the same way that Google’s search quality algorithms, Google’s “regulations”, manage the constant attempts of spammers and black hat SEO experts to game the system?

Revelation after revelation of bad behavior by big banks demonstrates that periodic bouts of enforcement aren’t sufficient. Systemic malfeasance needs systemic regulation. It’s time for government to enter the age of big data. Algorithmic regulation is an idea whose time has come.

Open Data And Government As A Platform

There are those who say that government should just stay out of regulating many areas, and let “the market” sort things out. But there are many ways in which bad actors take advantage of a vacuum in the absence of proactive management. Just as companies like Google, Microsoft, Apple, and Amazon build regulatory mechanisms to manage their platforms, government exists as a platform to ensure the success of our society, and that platform needs to be well regulated!

Right now, it is clear that agencies like the SEC just can’t keep up. In the wake of Ponzi schemes like those of Bernie Madoff and Allen Stanford, the SEC has now instituted algorithmic models that flag for investigation hedge funds whose results meaningfully outperform those of peers using the same stated investment methods. But once flagged, enforcement still goes into a long loop of investigation and negotiation, with problems dealt with on a case-by-case basis. By contrast, when Google discovers via algorithmic means that a new kind of spam is damaging search results, they quickly change the rules to limit the effect of those bad actors. We need to find more ways to make the consequences of bad action systemic, rather than subject to haphazard enforcement.

This is only possible when laws and regulations focus on desired outcomes rather than the processes used to achieve them.

There’s another point that’s worth making about SEC regulations. Financial regulation depends on disclosure - data required by the regulators to be published by financial firms in a format that makes it easy to analyze. This data is not just used by the regulators themselves, but is used by the private sector in making its own assessments of the financial health of firms, their prospects, and other financial decisions. You can see how the role of regulators in requiring what is, in effect, open data, makes the market more transparent and self-policing.

You can also see here that the modernization of how data is reported to both the government and the market is an important way of improving regulatory outcomes. Data needs to be timely, machine readable, and complete. (See Open Government Working Group, 2007.) When reporting is on paper or in opaque digital forms like PDF, or released only quarterly, it is much less useful.

When data is provided in re-usable digital formats, the private sector can aid in ferreting out problems as well as building new services that provide consumer and citizen value. This is a goal of the US Treasury Department’s “Smart Disclosure” initiative (see http://www.data.gov/consumer/page/consumer-about). It is also central to the efforts of the new Consumer Financial Protection Bureau.

When government regulators focus on requiring disclosure, that lets private companies build services for consumers, and frees up more enforcement time to go after truly serious malefactors.

Regulation Meets Reputation

It is true that “that government governs best that governs least.” But the secret to “governing least” is to identify key outcomes that we care about as a society—safety, health, fairness, opportunity—encode those outcomes into our laws, and then create a constantly evolving set of regulatory mechanisms that keep us on course towards them.

We are at a unique time when new technologies make it possible to reduce the amount of regulation while actually increasing the amount of oversight and production of desirable outcomes.

Consider taxi regulation. Ostensibly, taxis are regulated to protect the quality and safety of the consumer experience, as well as to ensure that there are an optimal number of vehicles providing service at the time they are needed. In practice, most of us know that these regulations do a poor job of ensuring quality or availability. New services like Uber and Hailo work with existing licensed drivers, but increase their availability even in less-frequented locations, by using geolocation on smartphones to bring passengers and drivers together. But equally important in a regulatory context is the way these services ask every passenger to rate their driver (and drivers to rate their passenger). Drivers who provide poor service are eliminated. As users of these services can attest, reputation does a better job of ensuring a superb customer experience than any amount of government regulation.

Peer-to-peer car services like RelayRides, Lyft, and Sidecar go even further, bypassing regulated livery vehicles and allowing consumers to provide rides to each other. Here, reputation entirely replaces regulation, seemingly with no ill effect. Governments should be studying these models, not fighting them, and adopting them where there are no demonstrable ill effects.

Services like AirBnB provide similar reputation systems that protect consumers while creating availability of lodging in neighborhoods that are often poorly served by licensed establishments.

Reputation systems are a great example of how open data can help improve outcomes for citizens with less effort by overworked regulators and enforcement officials.

Sites like Yelp provide extensive consumer reviews of restaurants; those that provide poor food or service are flagged by unhappy customers, while those that excel are praised.

There are a number of interesting new projects that attempt to combine the reach and user-friendliness of consumer reputation systems with government data. One recent initiative, the LIVES standard, developed by San Francisco, Code for America, and Yelp, brings health department inspection data to Yelp and other consumer restaurant applications, using open data to provide even more information to consumers. The House Facts standard does the same with housing inspection data, integrating it with internet services like Trulia

Another interesting project that actually harnesses citizen help (rather than just citizen opinion) by connecting a consumer-facing app to government data is the PulsePoint project, originally started by the San Ramon, California fire department. After the fire chief had the dismaying experience of hearing an ambulance pull up to the restaurant next door to the one in which he was having lunch with staff including a number of EMR techs, he commissioned an app that would allow any citizen with EMR training to receive the same dispatch calls as officials.

The Role of Sensors in Algorithmic Regulation

Increasingly, our interactions with businesses, government, and the built environment are becoming digital, and thus amenable to creative forms of measurement, and ultimately algorithmic regulation.

For example, with the rise of GPS (not to mention automatic speed cameras), it is easy to foresee a future where speeding motorists are no longer pulled over by police officers who happen to spot them, but instead automatically ticketed whenever they exceed the speed limit.

Most people today would consider that intrusive and alarming. But we can also imagine a future in which that speed limit is automatically adjusted based on the amount of traffic, weather conditions, and other subjective conditions that make a higher or lower speed more appropriate than the static limit that is posted today. The endgame might be a future of autonomous vehicles that are able to travel faster because they are connected in an invisible web, a traffic regulatory system that keeps us safer than today’s speed limits. The goal, after all, is not to have cars go slower than they might otherwise, but to make our roads safe.

While such a future no doubt raises many issues and might be seen by many as an assault on privacy and other basic freedoms, early versions of that future are already in place in countries like Singapore and can be expected to spread more widely.

Congestion pricing on tolls, designed to reduce traffic to city centers, is another example. Systems such as those in London where your license plate is read and you are required to make a payment will be replaced by automatic billing. You can imagine the costs of tolls floating based not just on time of day but on actual traffic.

Smart parking meters have similar capabilities—parking can cost more at peak times, less off-peak. But perhaps more importantly, smart parking meters can report whether they are occupied or not, and eventually give guidance to drivers and car navigation systems, reducing the amount of time spent circling while aimlessly looking for a parking space.

As we move to a future with more electric vehicles, there are already proposals to replace gasoline taxes with miles driven—reported, of course, once again by GPS.

Moving further out into the future, you can imagine public transportation reinventing itself to look much like Uber. It’s a small leap from connecting one passenger and one driver to picking up four or five passengers all heading for the same destination, or along the same route. Smartphone GPS sensors and smart routing algorithms could lead to a hybrid of taxi and bus service, bringing affordable, flexible public transportation to a much larger audience.

The First Step is Measurement

Data driven regulatory systems need not be as complex as those used by Google or credit card companies, or as those imagined above. Sometimes, it’s as simple as doing the math on data that is already being collected and putting in place new business processes to act on it.

For example, after hearing of the cost of a small government job search engine for veterans ($5 million per year), I asked how many users the site had. I was told “A couple of hundred.” I was understandably shocked, and wondered why this project was up for contract renewal. But when I asked a senior official at the General Services Administration if there were any routine process for calculating the cost per user of government websites, I was told, “That would be a good idea!” It shouldn’t just be a good idea; it should be common practice!

Every commercial website not only measures its traffic, but constantly makes adjustments to remove features that are unused and to test new ones in their place. When a startup fails to gain traction with its intended customers, the venture capitalists who backed it either withdraw their funding, or “pivot” to a new approach, trying multiple options till they find one that works. The “lean startup” methodology now widely adopted in Silicon Valley considers a startup to be “a machine for learning,” using data to constantly revise and tune its approach to the market. Government, by contrast, seems to inevitably double down on bad approaches, as if admitting failure is the cardinal sin.

Simple web metrics considered as part of a contract renewal are one simple kind of algorithmic regulation that could lead to a massive simplification of government websites and reduction of government IT costs. Other metrics that are commonly used on the commercial web include time on site; abandon rate (people who leave without completing a transaction); and analysis of the paths people use to reach the desired information.

There is other data available as well. Many commercial sites use analysis of search queries to surface what people are looking for. The UK Government Digital Service used this technique in their effort to redesign the Gov.UK site around user needs rather than around the desires of the various cabinet offices and agencies to promote their activities. They looked what people were searching for, and redesigned the site to create new, shorter paths to the most frequently searched-for answers. (Code for America built a site for the city of Honolulu, Honolulu Answers, which took much the same approach, adding a citizen “write-a-thon” to write new, user friendly content to answer the most asked questions.)

This is a simpler, manual intervention that copies what Google does algorithmically when it takes search query data into account when evaluating which results to publish. For example, Google looks at what they call “long clicks” versus “short clicks.” When the user clicks on a search result and doesn’t come back, or comes back significantly later, indicating that they found the destination link useful, that is a long click. Contrast that to a short click, when users come back right away and try another link instead. Get enough short clicks, and your search result gets demoted.

There are many good examples of data collection, measurement, analysis, and decision-making taking hold in government. In New York City, data mining was used to identify correlations between illegal apartment conversions and increased risk of fires, leading to a unique cooperation between building and fire inspectors. In Louisville, KY, a department focused on performance analytics has transformed the culture of government to one of continuous process improvement.

It’s important to understand that these manual interventions are only an essential first step. Once you understand that you have actionable data being systematically collected, and that your interventions based on that data are effective, it’s time to begin automating those interventions.

There’s a long way to go. We’re just at the beginning of thinking about how measurement, outcomes, and regulation come together.

Risks of Algorithmic Regulation

The use of algorithmic regulation increases the power of regulators, and in some cases, could lead to abuses, or to conditions that seem anathema to us in a free society. “Mission creep” is a real risk. Once data is collected for one purpose, it’s easy to imagine new uses for it. We’ve already seen this in requests to the NSA for data on American citizens originally collected for purposes of fighting overseas terrorism being requested by other agencies to fight domestic crime, including copyright infringement! (See Lichtblau and Schmidt, 2013.)

The answer to this risk is not to avoid collecting the data, but to put stringent safeguards in place to limit its use beyond the original purpose. As we have seen, oversight and transparency are particularly difficult to enforce when national security is at stake and secrecy can be claimed to hide misuse. But the NSA is not the only one that needs to keep its methods hidden. Many details of Google’s search algorithms are kept as a trade secret lest knowledge of how they work be used to game the system; the same is true for credit card fraud detection.

One key difference is that a search engine such as Google is based on open data (the content of the web), allowing for competition. If Google fails to provide good search results, for example because they are favoring results that lead to more advertising dollars, they risk losing market share to Bing. Users are also able to evaluate Google’s search results for themselves.

Not only that, Google’s search quality team relies on users themselves—tens of thousands of individuals who are given searches to perform, and asked whether they found what they were looking for. Enough “no” answers, and Google adjusts the algorithms.

Whenever possible, governments putting in place algorithmic regulations must put in place similar quality measurements, emphasizing not just compliance with the rules that have been codified so far but with the original, clearly-specified goal of the regulatory system. The data used to make determinations should be auditable, and whenever possible, open for public inspection (See Bitcoin's BlockChain).

There are also huge privacy risks involved in the collection of the data needed to build true algorithmic regulatory systems. Tracking our speed while driving also means tracking our location. But that location data need not be stored as long as we are driving within the speed limit, or it can be anonymized for use in traffic control systems.

Given the amount of data being collected by the private sector, it is clear that our current notions of privacy are changing. What we need is a strenuous discussion of the tradeoffs between data collection and the benefits we receive from its use.

This Is No Different In A Government Context

In Conclusion:

We are just at the beginning of a big data algorithmic revolution that will touch all elements of our society. Government needs to participate in this revolution.

As outlined in the introduction, a successful algorithmic regulation system has the following characteristics: (Again)

A Deep Understanding Of The Desired Outcome

Real-Time Measurement To Determine If That Outcome Is Being Achieved

Algorithms (i.e. A Set Of Rules) That Make Adjustments Based On New Data

Periodic, Deeper Analysis Of Whether The Algorithms Themselves Are Correct And Performing As Expected.

Open data plays a key role in both steps 2 and 4. Open data, either provided by the government itself, or required by government of the private sector, is a key enabler of the measurement revolution. Open data also helps us to understand whether we are achieving our desired objectives, and potentially allows for competition in better ways to achieve those objectives.

Algorithmic Regulation Spreading Across Government?

I was very, very excited to learn that the City of Vancouver is exploring implementing a program started in San Francisco in which “smart” parking meters adjust their price to reflect supply and demand (story is here in the Vancouver Sun).

For those unfamiliar with the program, here is a breakdown. In San Francisco, the city has the goal of ensuring at least one free parking spot is available on every block in the downtown core. As I learned during the San Fran’s presentation at the Code for America summit, such a goal has several important consequences. Specifically, it reduces the likelihood of people double parking, reduces smog and greenhouse gas emissions as people don’t troll for parking as long and because trolling time is reduced, people searching for parking don’t slow down other traffic and buses as they drive around slowly looking for a spot. In short, it has a very helpful impact on traffic more broadly.

So how does it work? The city’s smart parking meters are networked together and constantly assess how many spots on a given block are free. If, at the end of the week, it turns out that all the spaces are frequently in use, the cost of parking on that block is increased by 25 cents. Conversely if many of the spots were free, the price is reduced by 25 cents. Generally, each block finds an equilibrium point where the cost meets the demand but is also able to adjust in reaction to changing trends.

Technologist Tim O’Reilly has referred to these types of automated systems in the government context as “algorithmic regulation” – a phrase I think could become more popular over the coming decade. As software is deployed into more and more systems, the algorithms will be creating market places and resource allocation systems – in effect regulating us. A little over a year ago I said that contrary to what many open data advocates believe, open data will make data political – e.g. that open data wasn’t going to depoliticize public policy and make it purely evidenced base, quite the opposite, it will make the choices around what data we collect more contested (Canadians, think long form census). The same is also – and already – true of the algorithms, the code, that will increasingly regulate our lives. Code is political.

Personally I think the smart parking meter plan is exciting and hope the city will consider it seriously, but be prepared, I’m confident that much like smart electrical meters, an army of naysayers will emerge who simply don’t want a public resource (roads and parking spaces) to be efficiently used.

We all interact daily with public infrastructure — roads, parks, mass transit, water supplies. Increasingly, this infrastructure contains sensors that collect and use data on our interactions with it. As the "Internet of things" evolves, the data held by public entities is a critical component of its architecture.

Early initiatives in this space have shown promise. In San Francisco, sensors detect, in real time, which parking places are occupied and which are available. Drivers can view parking availability through a mobile app. At the same time, the data is used to adjust parking prices based on demand. This not only improves a resident interaction with public infrastructure (who hasn’t driven around frustrated looking for parking?) but also helps the city set smarter pricing policies to regulate supply.

As the Internet of Things evolves, the data held by public entities is a critical component of its architecture.

In some cases, residents themselves become part of a network of sensors. Boston built the StreetBump app, which lets residents report road condition data using their smartphone’s accelerometer to detect bumps and potholes, sending information to the city. These examples are only the beginning, as residents, government and industries discover new uses for such data.

The idea is that using real-time data, regulation can change dynamically based on varying conditions in order to better achieve the desired outcomes. For example, speed limits can be algorithmically adjusted based on current road and traffic conditions to optimize for safe and efficient travel — as they already are on the London Orbital motorway.

This concept is compelling. As public officials, wouldn't we in fact be negligent if we did not use this knowledge to improve our environments and serve the public interest to the best of our ability? Isn't the responsibility of government to use resources like these inputs to make the smartest, most efficient decisions, as fast as possible?

As governments release more open data and contribute to this sensor network, imagine how far we can go. With better data, decisions can be based on what we know, instead of what we think. This can only lead to better policy and governance.

As we use technology and data to develop these systems and models of the future, we must be cautious to avoid the "creepy factor." A clear and proven way to do this is through open and standards-based releases of data. We are seeing more and more administrations passing laws or orders to mandate the publishing of certain data—from Philadelphia to Chicago. A path in which the expansion of these networks walks hand in hand with transparency is one on which we can advance, with accountability.

Unanswered Questions About Algorithmic Regulation

It suggests that the beginning and end of what we need to do is simply to increase the transparency of government and its engagement with citizens, and all will be well.  

While these are laudable goals, they are far from sufficient to bring government into the 21st century.  That's why I've always preferred to frame my government activism around the notion of "Algorithmic Regulation."

Algorithmic Regulation provides a new way to think about the great debate of today's politics: is there a way to tackle clear and present problems of society without simply throwing money at them, or having government take them on directly?

Anyone who uses the Internet, or a smartphone, can immediately grasp the power of a platform. Apple didn't write 800,000 iPhone apps, but they did create the opportunity for others to create them. Tim Berners-Lee didn't create Google, Facebook, or Twitter, but the web platform he created made them possible. 

So too with government. It creates and maintains roads, but not the destinations to which they go. It ensures that everyone has water, power, communications, education, and increasingly, healthcare, but not what each of us does with those things. It built a space program, launched weather and GPS satellites, but it doesn't do the nightly weather forecast, or give you maps and directions in your phone and in your car.  

Open data, as distinct from open government, is a powerful kind of platform. When government opens up data, like weather or GPS data, or the output of the scientific research it funds, society can build on that platform.  And it does.

There are also other aspects to the "Algorithmic Regulation" notion that are productive metaphors.  The best platforms rely on others for much, but not all, of their innovation. They provide clear rules of the road and protection from bad actors. They provide key system services, so that everyone doesn't have to reduplicate effort to recreate common infrastructure.

But there are many unanswered questions about Algorithmic Regulation:

Can interfaces to government be simple, beautiful, and easy to use?  2011 Code for America fellow Scott Silverman asked that question when he applied for the fellowship.  If government is to become a 21st century platform, it needs great design. In particular, it needs to focus on user-centered rather than government-centered design.  

How do we measure the result of government programs? Modern platforms are all about feedback loops - measuring what works, and doing more of it; understanding what doesn't work, and doing less of it. As Eric Ries, of Lean Startup fame, notes, a startup is a machine for learning.  Can government hold itself to the same standard?

How is the platform to be regulated?  Old-fashioned regulation provides an army of gatekeepers, bureaucrats who give permission and judge quality. Much as in an earlier era, publishers decided which books would see print, and which would remain on the slush pile.  But as Clay Shirky presciently observed, the Internet reversed this model, from "curate, then publish," to "publish, then curate." Anyone can publish to the web, but Google's algorithms work to surface the best content; anyone can publish a video on YouTube, but fans decide which ones will be watched.  Algorithms that mine collective opinion (pagerank and its equivalents) are the only way to keep up with the flood of content.

Algorithmic regulation isn't applicable to every situation.  I want my school's seismic inspection to be done in advance, not after it falls down in an earthquake. But even in areas like financial fraud, algorithmic regulation is already widespread in the private sector. How else do credit card companies, online advertisers, and ISPs weed out fraud and spam? It certainly should be applicable in less high-risk situations. Can I rely on user ratings to tell me where to stay on AirBnB or which Uber driver to trust?  I think so.  Bringing regulation into the 21st century is high on my wishlist.

What is the right balance between citizen participation and thoughtful representative governance? There are legitimate reasons for governance to be done in private. I believe it was James Madison who remarked that the U.S. Constitution could never have been developed out in the open, because of the tradeoffs the representatives of each of the colonies had to make.   Internet platforms again give some guidance.  They are not an anarchy, they have a carefully designed architecture of participation.  How can we redesign representative democracy for the 21st century, so that it is more participatory, without losing the benefits of thoughtful deliberation and forceful leadership?

While "Algorithmic Regulation" might seem to be less focused on the goals of the Knight News Challenge than "Open Government," I believe it provides fertile ground for investigation, thought, and the development of new systems that harness the power of the public to make of our government "a more perfect union" and a more powerful platform for the flourishing of our society.

Monty Henry, Owner

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