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Contra Robert Shiller on Cryptocurrencies – Article by Adam Alonzi

Contra Robert Shiller on Cryptocurrencies – Article by Adam Alonzi

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Adam Alonzi


While warnings of caution can be condoned without much guilt, my concern is critiques like Dr. Shiller’s (which he has since considerably softened) will cause some value-oriented investors to completely exclude cryptocurrencies and related assets from their portfolios. I will not wax poetically about the myriad of forms money has assumed across the ages, because it is already well-covered by more than one rarely read treatise. It should be said, though it may not need to be, that a community’s preferred medium of exchange is not arbitrary. The immovable wheels of Micronesia met the needs of their makers just as digital stores of value like Bitcoin will serve the sprawling financial archipelagos of tomorrow. This role will be facilitated by the ability of blockchains not just to store transactions, but to enforce the governing charter agreed upon by their participants.

Tokens are abstractions, a convenient means of allotting ownership. Bradley Rivetz, a venture capitalist, puts it like this: “everything that can be tokenized will be tokenized the Empire State Building will someday be tokenized, I’ll buy 1% of the Empire State Building, I’ll get every day credited to my wallet 1% of the rents minus expenses, I can borrow against my Empire State Building holding and if I want to sell the Empire State Building I hit a button and I instantly have the money.” Bitcoin and its unmodified copycats do not derive their value from anything tangible. However, this is not the case for all crypto projects. Supporters tout its deflationary design (which isn’t much of an advantage when there is no value to deflate), its modest transaction fees, the fact it is not treated as a currency by most tax codes (this is changing and liable to continue changing), and the relative anonymity it offers.  

The fact that Bitcoin is still considered an asset in most jurisdictions is a strength. This means that since Bitcoin is de facto intermediary on most exchanges (most pairs are expressed in terms of BTC or a major fiat, many solely in BTC), one can buy and sell other tokens freely without worrying about capital gains taxes, which turn what should be wholly pleasurable into something akin to an ice cream sundae followed by a root canal. This applies to sales and corporate income taxes as well. A company like Walmart, despite its gross income, relies on a slender profit margin to appease its shareholders. While I’m not asking you to weep for the Waltons, I am asking you to think about the incentives for a company to begin experimenting with its own tax-free tokens as a means of improving customer spending power and building brand loyalty.

How many coins will be needed and, for that matter, how many niches they will be summoned to fill, remains unknown.  In his lecture on real estate Dr. Shiller mentions the Peruvian economist Hernando De Soto’s observation about the lack of accounting for most of the land in the world.  Needless to say, for these areas to advance economically, or any way for that matter, it is important to establish who owns what. Drafting deeds, transferring ownership of properties or other goods, and managing the laws of districts where local authorities are unreliable or otherwise impotent are services that are best provided by an inviolable ledger. In the absence of a central body, this responsibility will be assumed by blockchain. Projects like BitNation are bringing the idea of decentralized governance to the masses; efforts like Octaneum are beginning to integrate blockchain technology with multi-trillion dollar commodities markets.

As more than one author has contended, information is arguably the most precious resource of the twenty first century. It it is hardly scarce, but analysis is as vital to making sound decisions. Augur and Gnosis provide decentralized prediction markets. The latter, Kristin Houser describes it, is a platform used “to create a prediction market for any event, such as the Super Bowl or an art auction.” Philip Tetlock’s book on superforecasting covers the key advantages of crowdsourcing economic and geopolitical forecasting, namely accuracy and cost-effectiveness. Blockchains will not only generate data, but also assist in making sense of it.  While it is just a historical aside, it is good to remember that money, as Tymoigne and Wray (2006) note, was originally devised as a means of recording debt. Hazel sticks with notches preceded the first coins by hundreds of years. Money began as a unit of accounting, not a store of value.

MelonPort and Iconomi both allow anyone to start their own investment funds. Given that it is “just” software is the beauty of it: these programs can continue to be improved upon  indefinitely. If the old team loses its vim, the project can easily be forked. Where is crypto right now and why does it matter? There is a tendency for academics (and ordinary people) to think of things in the real world as static objects existing in some kind of Platonic heaven. This is a monumental mistake when dealing with an adaptive system, or in this case, a series of immature, interlocking, and rapidly evolving ecosystems. We have seen the first bloom – some pruning too – and as clever people find new uses for the underlying technology, particularly in the area of IoT and other emerging fields, we will see another bloom. The crypto bubble has come and gone, but the tsunami, replete with mature products with explicit functions, is just starting to take shape.

In the long run Warren Buffett, Shiller, and the rest will likely be right about Bitcoin itself, which has far fewer features than more recent arrivals. Its persisting relevance comes from brand recognition and the fact that most of the crypto infrastructure was built with it in mind. As the first comer it will remain the reserve currency of the crypto world.  It is nowhere near reaching any sort of hard cap. The total amount invested in crypto is still minuscule compared to older markets. Newcomers, unaware or wary of even well-established projects like Ethereum and Litecoin, will at first invest in what they recognize. Given that the barriers to entry (access to an Internet connection and a halfway-decent computer or phone) are set to continue diminishing, including in countries in which the fiat currency is unstable, demand should only be expected to climb.

Adam Alonzi is a writer, biotechnologist, documentary maker, futurist, inventor, programmer, and author of the novels A Plank in Reason and Praying for Death: A Zombie Apocalypse. He is an analyst for the Millennium Project, the Head Media Director for BioViva Sciences, and Editor-in-Chief of Radical Science News. Listen to his podcasts here. Read his blog here.

Review of Frank Pasquale’s “A Rule of Persons, Not Machines: The Limits of Legal Automation” – Article by Adam Alonzi

Review of Frank Pasquale’s “A Rule of Persons, Not Machines: The Limits of Legal Automation” – Article by Adam Alonzi

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Adam Alonzi


From the beginning Frank Pasquale, author of The Black Box Society: The Secret Algorithms That Control Money and Information, contends in his new paper “A Rule of Persons, Not Machines: The Limits of Legal Automation” that software, given its brittleness, is not designed to deal with the complexities of taking a case through court and establishing a verdict. As he understands it, an AI cannot deviate far from the rules laid down by its creator. This assumption, which is not even quite right at the present time, only slightly tinges an otherwise erudite, sincere, and balanced coverage of the topic. He does not show much faith in the use of past cases to create datasets for the next generation of paralegals, automated legal services, and, in the more distant future, lawyers and jurists.

Lawrence Zelanik has noted that when taxes were filed entirely on paper, provisions were limited to avoid unreasonably imposing irksome nuances on the average person. Tax-return software has eliminated this “complexity constraint.” He goes on to state that without this the laws, and the software that interprets it, are akin to a “black box” for those who must abide by them. William Gale has said taxes could be easily computed for “non-itemizers.” In other words, the government could use information it already has to present a “bill” to this class of taxpayers, saving time and money for all parties involved. However, simplification does not always align with everyone’s interests. TurboTax’s business, which is built entirely on helping ordinary people navigate the labyrinth is the American federal income tax, noticed a threat to its business model. This prompted it to put together a grassroots campaign to fight such measures. More than just another example of a business protecting its interests, it is an ominous foreshadowing of an escalation scenario that will transpire in many areas if and when legal AI becomes sufficiently advanced.  

Pasquale writes: “Technologists cannot assume that computational solutions to one problem will not affect the scope and nature of that problem. Instead, as technology enters fields, problems change, as various parties seek to either entrench or disrupt aspects of the present situation for their own advantage.”

What he is referring to here, in everything but name, is an arms race. The vastly superior computational powers of robot lawyers may make the already perverse incentive to make ever more Byzantine rules ever more attractive to bureaucracies and lawyers. The concern is that the clauses and dependencies hidden within contracts will quickly explode, making them far too detailed even for professionals to make sense of in a reasonable amount of time. Given that this sort of software may become a necessary accoutrement in most or all legal matters means that the demand for it, or for professionals with access to it, will expand greatly at the expense of those who are unwilling or unable to adopt it. This, though Pasquale only hints at it, may lead to greater imbalances in socioeconomic power. On the other hand, he does not consider the possibility of bottom-up open-source (or state-led) efforts to create synthetic public defenders. While this may seem idealistic, it is fairly clear that the open-source model can compete with and, in some areas, outperform proprietary competitors.

It is not unlikely that within subdomains of law that an array of arms races can and will arise between synthetic intelligences. If a lawyer knows its client is guilty, should it squeal? This will change the way jurisprudence works in many countries, but it would seem unwise to program any robot to knowingly lie about whether a crime, particularly a serious one, has been committed – including by omission. If it is fighting against a punishment it deems overly harsh for a given crime, for trespassing to get a closer look at a rabid raccoon or unintentional jaywalking, should it maintain its client’s innocence as a means to an end? A moral consequentialist, seeing no harm was done (or in some instances, could possibly have been done), may persist in pleading innocent. A synthetic lawyer may be more pragmatic than deontological, but it is not entirely correct, and certainly shortsighted, to (mis)characterize AI as only capable of blindly following a set of instructions, like a Fortran program made to compute the nth member of the Fibonacci series.

Human courts are rife with biases: judges give more lenient sentences after taking a lunch break (65% more likely to grant parole – nothing to spit at), attractive defendants are viewed favorably by unwashed juries and trained jurists alike, and the prejudices of all kinds exist against various “out” groups, which can tip the scales in favor of a guilty verdict or to harsher sentences. Why then would someone have an aversion to the introduction of AI into a system that is clearly ruled, in part, by the quirks of human psychology?  

DoNotPay is an an app that helps drivers fight parking tickets. It allows drivers with legitimate medical emergencies to gain exemptions. So, as Pasquale says, not only will traffic management be automated, but so will appeals. However, as he cautions, a flesh-and-blood lawyer takes responsibility for bad advice. The DoNotPay not only fails to take responsibility, but “holds its client responsible for when its proprietor is harmed by the interaction.” There is little reason to think machines would do a worse job of adhering to privacy guidelines than human beings unless, as mentioned in the example of a machine ratting on its client, there is some overriding principle that would compel them to divulge the information to protect several people from harm if their diagnosis in some way makes them as a danger in their personal or professional life. Is the client responsible for the mistakes of the robot it has hired? Should the blame not fall upon the firm who has provided the service?

Making a blockchain that could handle the demands of processing purchases and sales, one that takes into account all the relevant variables to make expert judgements on a matter, is no small task. As the infamous disagreement over the meaning of the word “chicken” in Frigaliment v. B.N.S International Sales Group illustrates, the definitions of what anything is can be a bit puzzling. The need to maintain a decent reputation to maintain sales is a strong incentive against knowingly cheating customers, but although cheating tends to be the exception for this reason, it is still necessary to protect against it. As one official on the  Commodity Futures Trading Commission put it, “where a smart contract’s conditions depend upon real-world data (e.g., the price of a commodity future at a given time), agreed-upon outside systems, called oracles, can be developed to monitor and verify prices, performance, or other real-world events.”  

Pasquale cites the SEC’s decision to force providers of asset-backed securities to file “downloadable source code in Python.” AmeriCredit responded by saying it  “should not be forced to predict and therefore program every possible slight iteration of all waterfall payments” because its business is “automobile loans, not software development.” AmeriTrade does not seem to be familiar with machine learning. There is a case for making all financial transactions and agreements explicit on an immutable platform like blockchain. There is also a case for making all such code open source, ready to be scrutinized by those with the talents to do so or, in the near future, by those with access to software that can quickly turn it into plain English, Spanish, Mandarin, Bantu, Etruscan, etc.

During the fallout of the 2008 crisis, some homeowners noticed the entities on their foreclosure paperwork did not match the paperwork they received when their mortgages were sold to a trust. According to Dayen (2010) many banks did not fill out the paperwork at all. This seems to be a rather forceful argument in favor of the incorporation of synthetic agents into law practices. Like many futurists Pasquale foresees an increase in “complementary automation.” The cooperation of chess engines with humans can still trounce the best AI out there. This is a commonly cited example of how two (very different) heads are better than one.  Yet going to a lawyer is not like visiting a tailor. People, including fairly delusional ones, know if their clothes fit. Yet they do not know whether they’ve received expert counsel or not – although, the outcome of the case might give them a hint.

Pasquale concludes his paper by asserting that “the rule of law entails a system of social relationships and legitimate governance, not simply the transfer and evaluation of information about behavior.” This is closely related to the doubts expressed at the beginning of the piece about the usefulness of data sets in training legal AI. He then states that those in the legal profession must handle “intractable conflicts of values that repeatedly require thoughtful discretion and negotiation.” This appears to be the legal equivalent of epistemological mysterianism. It stands on still shakier ground than its analogue because it is clear that laws are, or should be, rooted in some set of criteria agreed upon by the members of a given jurisdiction. Shouldn’t the rulings of law makers and the values that inform them be at least partially quantifiable? There are efforts, like EthicsNet, which are trying to prepare datasets and criteria to feed machines in the future (because they will certainly have to be fed by someone!).  There is no doubt that the human touch in law will not be supplanted soon, but the question is whether our intuition should be exalted as guarantee of fairness or a hindrance to moving beyond a legal system bogged down by the baggage of human foibles.

Adam Alonzi is a writer, biotechnologist, documentary maker, futurist, inventor, programmer, and author of the novels A Plank in Reason and Praying for Death: A Zombie Apocalypse. He is an analyst for the Millennium Project, the Head Media Director for BioViva Sciences, and Editor-in-Chief of Radical Science News. Listen to his podcasts here. Read his blog here.

Review of Philip Tetlock’s “Superforecasting” by Adam Alonzi

Review of Philip Tetlock’s “Superforecasting” by Adam Alonzi

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Adam Alonzi


Alexander Consulting the Oracle of Apollo, Louis Jean Francois Lagrenée. 1789, Oil on Canvas.

“All who drink of this treatment recover in a short time, except those whom it does not help, who all die. It is obvious, therefore, that it fails only in incurable cases.”

-Galen

Before the advent of evidence-based medicine, most physicians took an attitude like Galen’s toward their prescriptions. If their remedies did not work, surely the fault was with their patient. For centuries scores of revered doctors did not consider putting bloodletting or trepanation to the test. Randomized trials to evaluate the efficacy of a treatment were not common practice. Doctors like Archie Cochrane, who fought to make them part of standard protocol, were met with fierce resistance. Philip Tetlock, author of Superforecasting: The Art and Science of Prediction (2015), contends that the state of forecasting in the 21st century is strikingly similar to medicine in the 19th. Initiatives like the Good Judgement Project (GJP), a website that allows anyone to make predictions about world events, have shown that even a discipline that is largely at the mercy of chance can be put on a scientific footing.

More than once the author reminds us that the key to success in this endeavor is not what you think or what you know, but how you think. For Tetlock pundits like Thomas Friedman are the “exasperatingly evasive” Galens of the modern era. In the footnotes he lets the reader know he chose Friedman as target strictly because of his prominence. There are many like him. Tetlock’s academic work comparing random selections with those of professionals led media outlets to publish, and a portion of their readers to conclude, that expert opinion is no more accurate than a dart-throwing chimpanzee. What the undiscerning did not consider, however, is not all of the experts who participated failed to do better than chance.

Daniel Kahneman hypothesized that “attentive readers of the New York Times…may be only slightly worse” than these experts corporations and governments so handsomely recompense. This turned out to be a conservative guess. The participants in the Good Judgement Project outperformed all control groups, including one composed of professional intelligence analysts with access to classified information. This hodgepodge of retired bird watchers, unemployed programmers, and news junkies did 30% better than the “pros.” More importantly, at least to readers who want to gain a useful skillset as well as general knowledge, the managers of the GJP have identified qualities and ways of thinking that separate “superforecasters” from the rest of us. Fortunately they are qualities we can all cultivate.

While the merits of his macroeconomic theories can be debated, John Maynard Keynes was an extremely successful investor during one of the bleakest periods in international finance. This was no doubt due in part to his willingness to make allowance for new information and his grasp of probability. Participants in the GJP display open-mindedness, an ability and willingness to repeatedly update their forecasts, a talent to neither under- nor over-react to new information by putting it into a broader context,  and a predilection for mathematical thinking (though those interviewed admitted they rarely used an explicit equation to calculate their answer). The figures they give also tend to be more precise than their less successful peers. This “granularity” may seem ridiculous at first. I must confess that when I first saw estimates on the GJP of 34% or 59%, I would chuckle a bit. How, I asked myself, is a single percentage point meaningful? Aren’t we just dealing with rough approximations? Apparently not.

Tetlock reminds us that the GJP does not deal with nebulous questions like “Who will be president in 2027?” or “Will a level 9 earthquake hit California two years from now?” However, there are questions that are not, in the absence of unforeseeable Black Swan events, completely inscrutable. Who will win the Mongolian presidency? Will Uruguay sign a trade agreement with Laos in the next six months? These are parts of highly complex systems, but they can be broken down into tractable subproblems.

Using numbers instead of words like “possibly”, “probably”, “unlikely”, etc., seems unnatural. It gives us wiggle room and plausible deniability. They also cannot be put on any sort of record to keep score of how well we’re doing. Still, to some it may seem silly, pedantic, or presumptuous. If Joint Chiefs of Staff had given the exact figure they had in mind (3 to 1) instead of the “fair chance” given to Kennedy, the Bay of Pigs debacle may have never transpired. Because they represent ranges of values instead of single numbers, words can be retroactively stretched or shrunk to make blunders seem a little less avoidable. This is good for advisors looking to cover their hides by hedging their bets, but not so great for everyone else.

If American intelligence agencies had presented the formidable but vincible figure of 70% instead of a “slam dunk” to Congress, a disastrous invasion and costly occupation would have been prevented. At this point it is hard not to see the invasion as anything as a mistake, but even amidst these emotions we must be wary of hindsight. Still, a 70% chance of being right means there is a 30% chance of being wrong. It is hardly a “slam dunk.” No one would feel completely if an oncologist told them they are 70% sure the growth is not malignant. There are enormous consequences to sloppy communications. However, those with vested interests are more than content with this approach if it agrees with them, even if it ends up harming them.

When Nate Silver put the odds of the 2008 election in Obama’s favor, he was panned by Republicans as a pawn of the liberal media. He was quickly reviled by Democrats when he foresaw a Republican takeover of the Senate. It is hard to be a wizard when the king, his court, and all the merry peasants sweeping the stables would not know a confirmation bias from their right foot. To make matters worse, confidence is widely equated with capability. This seems to be doubly true of groups of people, particularly when they are choosing a leader. A mutual-fund manager who tells his clients they will see great returns on a company is viewed as stronger than a Poindexter prattling on about Bayesian inference and risk management.

The GJP’s approach has not spread far — yet. At this time most pundits, consultants, and self-proclaimed sages do not explicitly quantify their success rates, but this does not stop corporations, NGOs, and institutions at all levels of government from paying handsomely for the wisdom of untested soothsayers. Perhaps they have a few diplomas, but most cannot provide compelling evidence for expertise in haruspicy (sans the sheep’s liver). Given the criticality of accurate analyses to saving time and money, it would seem as though a demand for methods to improve and assess the quality of foresight would arise. Yet for the most part individuals and institutions continue to happily grope in the dark, unaware of the necessity for feedback when they misstep — afraid of having their predictions scrutinized or having to take the pains to scrutinize their predictions.

David Ferrucci is wary of the “guru model” to settling disputes. No doubt you’ve witnessed or participated in this kind of whimpering fracas: one person presents a Krugman op-ed to debunk a Niall Ferguson polemic, which is then countered with a Tommy Friedman book, which was recently excoriated  by the newest leader of the latest intellectual cult to come out of the Ivy League. In the end both sides leave frustrated. Krugman’s blunders regarding the economic prospects of the Internet, deflation, the “imminent” collapse of the euro (said repeatedly between 2010 and 2012) are legendary. Similarly, Ferguson, who strongly petitioned the Federal Reserve to reconsider quantitative easing, lest the United States suffer Weimar-like inflation, has not yet been vindicated. He and his colleagues responded in the same way as other embarrassed prophets: be patient, it has not happened, but it will! In his defense, more than one clever person has criticized the way governments calculate their inflation rates…

Paul Ehrlich, a darling of environmentalist movement, has screeched about the detonation of a “population bomb” for decades. Civilization was set to collapse between 15 and 30 years from 1970. During the interim 100 to 200 million would annually starve to death, by the year 2000 no crude oil would be left, the prices of raw materials would skyrocket, and the planet would be in the midst of a perpetual famine. Tetlock does not mention Ehrlich, but he is, particularly given his persisting influence on Greens, as or more deserving of a place in this hall of fame as anyone else. Larry Kudlow continued to assure the American people that the Bush tax breaks were producing massive economic growth. This continued well into 2008, when he repeatedly told journalists that America was not in a recession and the Bush boom was “alive and well.” For his stupendous commitment to his contention in the face of overwhelming evidence to the contrary, he was nearly awarded a seat in the Trump cabinet.

This is not to say a mistake should become the journalistic equivalent of a scarlet letter. Kudlow’s slavish adherence to his axioms is not unique. Ehrlich’s blindness to technological advances is not uncommon, even in an era dominated by technology. By failing to set a timeline or give detailed causal accounts, many believe they have predicted every crash since they learned how to say the word. This is likely because they begin each day with the same mantra: “the market will crash.”  Yet through an automatically executed routine of psychological somersaults, they do not see they were right only once and wrong dozens, hundreds, or thousands of times. This kind of person is much more deserving of scorn than a poker player who boasts about his victories, because he is (likely) also aware of how often he loses. At least he’s not fooling himself. The severity of Ehrlich’s misfires is a reminder of what happens when someone looks too far ahead while assuming all things will remain the same. Ceteris paribus exists only in laboratories and textbooks.

Axioms are fates accepted by different people as truth, but the belief in Fate (in the form of retroactive narrative construction) is a nearly ubiquitous stumbling block to clear thinking. We may be far removed from Sophocles, but the unconscious human drive to create sensible narratives is not peculiar to fifth-century B.C. Athens. A questionnaire given to students at Northwestern showed that most believed things had turned out for the best even if they had gotten into their first pick. From an outsider’s perspective this is probably not true. In our cocoons we like to think we are in the right place either through the hand of fate or through our own choices. Atheists are not immune to this Panglossian habit. Our brains are wired for stories, but the stories we tell ourselves about ourselves seldom come out without distortions. We can gain a better outside view, which allows us to see situations from perspectives other than our own, but only through regular practice with feedback. This is one of the reasons groups are valuable.

Francis Galton asked 787 villagers to guess the weight of an ox hanging in the market square. The average of their guesses (1,197 lbs) turned out to be remarkably close to its actual weight (1,198 lbs). Scott Page has said “diversity trumps ability.” This is a tad bold, since legions of very different imbeciles will never produce anything of value, but there is undoubtedly a benefit to having a group with more than one point of view. This was tested by the GJP. Teams performed better than lone wolves by a significant margin (23% to be exact). Partially as a result of encouraging one another and building a culture of excellence, and partially from the power of collective intelligence.

“No battle plan survives contact with the enemy.”

-Helmuth von Moltke

“Everyone has a plan ’till they get punched in the mouth.”

-Mike Tyson

When Archie Cochrane was told he had cancer by his surgeon, he prepared for death. Type 1 thinking grabbed hold of him and did not doubt the diagnosis. A pathologist later told him the surgeon was wrong. The best of us, under pressure, fall back on habitual modes of thinking. This is another reason why groups are useful (assuming all their members do not also panic). Organizations like the GJP and the Millennium Project are showing how well collective intelligence systems can perform. Helmuth von Moltke and Mike Tyson aside, a better motto, substantiated by a growing body of evidence, comes from Dwight  Eisenhower: “plans are useless, but planning is indispensable.”

Adam Alonzi is a writer, biotechnologist, documentary maker, futurist, inventor, programmer, and author of the novels A Plank in Reason and Praying for Death: A Zombie Apocalypse. He is an analyst for the Millennium Project, the Head Media Director for BioViva Sciences, and Editor-in-Chief of Radical Science News. Listen to his podcasts here. Read his blog here.