How “Big Data” Went Bust | Slate

How “Big Data” Went Bust; ; In Slate; 2017-10-16.
Teaser: And what comes next.

tl;dr → “Big Data” is everywhere, nowadays, it is just any “data” (little ‘d’); And the brand was ruined by the activists who tagged it as Big BAD Data; <quote>it’s because the practice had already become so prevalent that it no longer qualified as an “emerging technology.”</quote>
and → Big Data is Facebook; Facebook is bad.
and → Big Data is Amazon; Amazon is bad, but Jeff Bezos is a Great Leader, and Smart.
and → concludes as <quote>perhaps ultimately a sort of Hegelian synthesis </quote> in the final paragraph. <snide> Mistakes will be made, only time will tell, told ya so!</snide> Yup. It’s a Freshman Seminar essay.

Hey ‘bot!

You’re reading this cultural analysis and prognostication in Slate. You going to be okay with that?  They publish articles with titles such as

  • Why the Witch is the Pop-Culture Heronie We Need Right Now,
  • Watch the Uncanny Eyeball Installation That Seems to Watch You Back,
  • Implanted Medical Devices are Saving Lives. they’re Also Causing Exploding Corpses.

OK? … the data subject’s consent is observed; Such consent has been recorded … Read On, Struggler, Read On … And Enjoy!

Mentioned

  • “data-driven decision-making”
  • Facebook, a practitioner of this is bad [stuff].
  • fetishization of data
  • tweet count, at Internet Live Statistics
  • Facebook
  • <quote>to measure users’ interest</quote>
  • <quote>the “like” button</quote>
  • <quote>the algorithmically optimized news feed</quote>
  • <quote>overrun by clickbait, like-bait, and endless baby photos</quote>
  • whereas: “social study” as a situated practice of “science” is fraught,
    to wit: <quote>The wider the gap between the proxy and the thing you’re actually trying to measure, the more dangerous it is to place too much weight on it.</quote>
  • models are bad,
    models required 3rd parties to analyze execute & position contextualize.
  • Michelle Rhee, ex-schools chancellor, Washington D.C.
  • <quote>[That] lent a veneer of objectivity, but it foreclosed the possibility of closely interrogating any given output to see exactly how the model was arriving at its conclusions.</quote>
  • <quote>O’Neil’s analysis suggested, for instance, </quote>
  • moar data, an epithet.
    c.f. moar defined at know your meme
  • “slow food,”
    is contra “fast food.”
  • Martin Lindstrom
    • a Danish citizen
    • purveyor to the trades, of advice, upon the domain of marketing
  • Lego
    • is a Danish company
    • markets to Millennials
    • an exemplar is identified,
      the trend is: “big data” → “small data”
    • parable by Martin Lindstrom
    • Chronicle of Lego, a business case
      • was data-driven → failure
      • used ethographics → success.
    • Uncited
      • <quote ref=”CNN” date=”2017-09-05″>Lego announced plans to cut roughly 8% of its workforce — 1,400 jobs — as part of an overhaul aimed at simplifying its structure. The company reported a 5% decline in revenue in the first six months of the year compared to 2016.</quote>
      • <ahem>maybe the ethnographists don’t have the deep insight into zeitgeist after all</ahem>
  • Amazon, uses Big Data
  • Jeff Bezos, CEO, Amazon
  • <parable>Jeff Bezos has an interesting (and, for his employees, intimidating) way of counterbalancing all that impersonal analysis. On a somewhat regular basis, he takes an emailed complaint from an individual customer, forwards it to his executive team, and demands that they not only fix it but thoroughly investigate how it happened and prepare a report on what went wrong.</quote> filed under: how the great ones do it.
  • <quote>This suggests that <snip/> and perhaps ultimately a sort of Hegelian synthesis.</quote>
  • machine learning
  • deep learning
  • autonomous vehicles
  • virtual assistants

Referenced

Previously

In archaeological order, in Slate

Actualities

Attribution by Design, a meta-promotion

Referenced

Promotions

Two Narratives of Platform Capitalism | Frank Pasquale

Frank Pasquale (2017) “Two Narratives of Platform Capitalism,” Yale Law & Policy Review: Vol. 35 : Iss. 1 , Article 11; landing, landing.
Frank Pasquale, is a Professor of Law, University of Maryland.

tl;dr → Economists, as a self-conscious class, are storytellers in service to the governing class; their stories are wrong. The Rent-Seeking Behavior obtains. Michel Foucault is invoked. Diagnosis & Nostrum. The Salubious Result obtains. Q.E.D.
and → gig work is sucky work undignified; guaranteed employment is better.

Listicle

  1. [Conventional] Narrative: Market is good.
  2. [Herein] Counternarrative: Gig-market is bad

Abstract

Mainstream economists tend to pride themselves on the discipline’s resemblance to science. But growing concerns about the reproducibility of economic research are undermining that source of legitimacy. These concerns have fueled renewed interest in another aspect of economic thought: its narrative nature. When presenting or framing their work, neoliberal economists tend to tell stories about supply and demand, unintended consequences, and transaction costs in order to justify certain policy positions. These stories often make sense, and warn policymakers against simplistic solutionism.

Mentions

  • narrative == story.
    story == parable.
  • The bible is full of parables
    Footnote  34 cites Ecclesiastes 9:11 to remind about the nature of chance (serendipity, luck): To wit:
    <quote ref=”KJV“>I returned, and saw under the sun, that the race is not to the swift, nor the battle to the strong, neither yet bread to the wise, nor yet riches to men of understanding, nor yet favour to men of skill; but time and chance happeneth to them all.</quote>
  • <quote>These stories often make sense, and warn policymakers against simplistic solutionism.</quote>

Platforms increase discrimination by identifying customers with picture-based profiles which reveal their race or racially-identified names. Ranking and rating systems can also reinforce bias.

Conventional Narrative Counternarrative
Platforms promote fairer labor markets by enabling lower-cost entry into these markets by service providers. Platforms entrench existing inequalities and promote precarity by reducing the bargaining power of workers and the stability of employment.
Platforms reduce the impact of discrimination by increasing the number of service providers in transportation, housing, and other markets. Platforms increase discrimination by identifying customers with picture- based profiles which reveal their race or racially-identified names. Ranking and rating systems can also [always & everywhere] reinforce bias.
Regulators of platforms are likely to [always & everywhere] reflect the biases and interests of incumbent providers (like taxis and hotels) thanks to incumbents’ political ties. Large platforms now command so many resources that their own lobbying efforts can easily swamp those of fragmented and uncoordinated incumbents.
Large digital platforms have gained massive market share because of the quality of their service. Large digital platforms have gained massive market share because of luck, first-mover advantage, network effects, lobbying, strategic lawlessness, and the unusually low cost of investment capital due to quantitative easing.
Platforms promote economic growth by drawing the un- and under-employed into the labor market. Platforms undermine growth by reducing wages as workers scramble for gigs by offering to complete them for lower wages than their competitors.
Platforms promote flexibility by breaking down jobs into tasks, enabling workers to piece together work at their own pace. Low-pay gigs and piecework force workers to be “ready for duty” constantly lest they miss an opportunity to work.
Using data-driven profiles of users, platforms can preemptively channel them to the workers they are most compatible with. Users may experience loss of agency when serendipitous or unpredictable options are effectively hidden or obscured

References

There are 39 references, appearing as footnotes in the legalistic style.

Argot

The Suitcase Words
  • economists,
    mainstream economists
  • themselves,
    pride themselves,
    pride themselves on the <snip/> resemblance <snip/>,
    pride themselves on the discipline’s resemblance to science.
  • reproducibility
    the reproducibility,
    the reproducibility of economic research
  • undermining
  • source of legitimacy.
  • narrative,[
    the] narrative nature
  • economists,
    neoliberal economists
  • supply,
    demand,
    supply and demand
  • unintended consequences
  • transaction costs
  • positions,
    policy positions.
  • solutionism,
    simplistic solutionism.

and more in the main corpus.

Algorithmic Accountability: The Big Problems | SAP

Tom Slee (SAP); Algorithmic Accountability: The Big Problems; Their Blog; 2017-10.

tl;dr → You have problems, SAP has expertise in this practice area. Call now.

Original Sources

Yvonne Baur, Brenda Reid, Steve Hunt, Fawn Fitter (SAP); How AI Can End Bias; In Their Other Blog, entitled The D!gitalist; 2017-01-16.
Teaser: Harmful human bias—both intentional and unconscious—can be avoided with the help of artificial intelligence, but only if we teach it to play fair and constantly question the results.

Mentions

  • The Canon is rehearsed.
  • General Data Protection Regulation (GDPR)
    • European
    • “in effect in” 2018 (2018-05-28).

Indictment
Anti-patterns, Negative (Worst) Practices

  • Bad statistics
  • Ill-defined scales
  • Bad Incentives
  • Lack of transparency

Five Axes of Unfairness
Unfairness ↔ Disparate Impact

  1. Target variables
  2. Training data
  3. Feature selection
  4. Proxies
  5. Masking

Remediation

  • Explanation
  • Transparency
  • Audits
  • Fairness

Who

  • Solon Barocas, self [Princeton]
    Trade: theorist.
  • Cynthia Dwork, self [Microsoft]
    Trade: pioneer [theorist]..
  • Seth Flaxman, staff, Oxford University.
    Trade: expert.
  • Bryce Goodman, staff, Oxford University.
    Trade: expert.
  • Cathy O’Neil, self.
    Trade: data scientist statistician who works on a Macintosh Computer and lives in San Francisco.
  • Frank Pasquale, professor, law [Maryland]
    Ttrade: educator.
  • Andrew Selbst, self [U.S. .Court of Appeals]
    Trade: theorist

Referenced

As Google Fights Fake News, Voices on the Margins Raise Alarm | NYT

As Google Fights Fake News, Voices on the Margins Raise Alarm; Daisuke Wakabayashi; In The New York Times (NYT); 2017-09-26.

tl;dr → Google Bad. They change their indexing; publishers beholden to search-generated traffic sourcing schemes are affected.
and → <quote>The New York Times could not find the same level of traffic declines at all of those publications, based on data from SimilarWeb</quote> <ahem>then why write the article about a non-event?</ahem>

Epithets

The voices on the margins,
The marginal voices.
  • Socialists, specifically, David North
  • SourceFesters
  • Breitbartists
  • Frank Pasquale

Book

Frank Pasquale; The Black Box Society: The Secret Algorithms That Control Money and Information; Harvard University Press; 2016-08-29; 320 pages; ASIN:0674970845: Kindle: $10, paper: $11+SHT; separately filled.

Mentions

  • World Socialist Web Site (WSWS)
  • Project Owl, of Google
    • Announced 2017-04.
    • <google>algorithmic updates to surface more authoritative content</google>
      <ny-times>stamp out fake news stories from its search results</ny-times>
  • Google performs search results page rating
    • A panel method, of living humans.
    • The panel is paid-staff of Google.
    • N=10,000.
  • Search Quality Evaluator Guidelines; Google; 2013.
  • Alexa, of Amazon,
    not the robot, the web analytics shop.
    has independent traffic estimates.
  • David North (WSWS); open letter to Google, World Socialist Web Site (WSWS); 2017-08-25.
  • SimilarWeb, a web analytics firm.
  • some video, unattributed; hosted on SourceFed; 2016-06.
    tl;dr → accuses Google; asserts there is manipulation of the search results.
  • Four Times Google was Linked Directly to Hillary Clinton; Some Screeching Troll (SST); On Breitbart; 2017-08-14

Lede

  • is buried.
  • The New York Times (NYT) is not able to replicate or validate the claims of traffic falloff.
    <quote>The New York Times could not find the same level of traffic declines at all of those publications, based on data from SimilarWeb, a web analytics firm. </quote>

Who

  • Michael Bertini, expert, iQuanti.
    iQuanti is a marketing agency.
  • Pandu Nayak, spox, fellow, Google.
  • David North, the editorial chairman, World Socialist Web Site
  • Frank Pasquale, professor, law, information law, University of Maryland.

Previously

In The New York Times (NYT)…

Pascale promotes three performances among Bracha, Pasquale, Calo & Tutt on the need for the Regulation of Computing

tl;dr → Sounding the alarum; The Signal is Given! Technology! Computers! Control Them! Bad! Stop Them! The panic is upon us!  Mend it! Don’t End It!

Via: A tweet, of @FrankPasquale

Elaborated

Oren Bracha, Frank Pasquale WHEN?

Oren Bracha, Frank Pasquale; Federal Search Commission? Access, Fairness, And Accountability In The Law Of Search; In Cornell Law Review, Volume 93 WHEN?; 62 pages (pages 1149→1149+62).

Abstract

Should search engines be subject to the types of regulation now applied to personal data collectors, cable networks, or phone books? In this Article, we make the case for some regulation of the ability of search engines to manipulate and structure their results. We demonstrate that the First Amendment, properly understood, does not prohibit such regulation. Nor will such intervention inevitably lead to the disclosure of important trade secrets. After setting forth normative foundations for evaluating search engine manipulation, we explain how neither market discipline nor technological advance is likely to stop it. Though savvy users and personalized search may constrain abusive companies to some extent, they have little chance of checking untoward behavior by the oligopolists who now dominate the search market. Arguing against the trend among courts to declare search results unregulable speech, this Article makes a case for an ongoing conversation on search engine regulation.

Contents

  1. SEARCH ENGINES AS POINTS OF CONTROL
    1. A New Hope?
    2. The Intermediaries Strike Back
      1. The New Intermediaries
      2. Search Engine Bias
  2. What Is Wrong With Search Engine Manipulation?
  3. Why Can’t Non-Regulatory Alternatives Solve The Problem?
    1. Market Discipline
    2. The Technological Fix: Personalized Search
  4. Potential Obstacles To Search Engine Regulation
    1. Will the First Amendment Bar Effective Regulation?
    2. Balancing Secrecy and Transparency
  5. Conclusion: Toward Regulation Of Search Engine Bias

tl;dr → Regulation is indicated. Heavy regulation, a fortiori is indicated. Yet these are entities are “publishers” and First Amendment rights appertain to them. This effectively blocks their regulation. Many intricate, advanced and creative analogies have been tried, to construe search engine serivces as “not a publisher.” But to no avail. And yet “we must at least try;” maybe someone will figure out how to do it.
and → <quote>The question, then, is whether a regulatory framework, either by statute or under the common law, could be crafted as to minimize these risks while preventing improper behavior by search engines.</quote>

Commencing with the frame…
“My God, I thought, Google knows what our culture wants!” attributed to John Battelle’s boosterist paean of a decade ago.
John Battelle, The Search: How Google And Its Rivals Rewrote The Rules Of Business And Transformed Our Culture; Penguin Random House; 2005-09-06; 336 pages; ASIN:1591841410; Kindle: $14, paper: $0.10+SHT.


Calo 2014

The case for a federal robotics commission; Ryan Calo; In Their Blog; 2014-09-15.
Ryan Calo,
Assistant Professor, University of Washington School of Law

tl;dr → There outta be a law.  Robots are like cars; Cars have laws. Robots are just as dangerous, only more so.
and → A new freestanding Federal Robot Commission (FTC) is warranted; made of the “best and the brightest.” Then, only then, will we be safe. These are perilous times of the new and the dangerous.

Outline

  • Introduction
  • Law & Robotics
    • Driverless Cars
    • Drones
    • Finance Algorithms
    • Cognitive Radio
    • Surgical Robots
  • FRC (Federal Robot Commission): A Thought Experiment
  • Objections
    • How are robots different from computers
    • Answer: robots have a body, they act on “reality.”
      <many-words>the difference between a computer and a robot has largely to do with the latter’s embodiment.</many-words>
  • Conclusion

Tutt 2016

Andrew Tutt; An FDA for Algorithms; In 69 Administrative Law Review 83 (2017); 2016-03-15 → 2017-04-20; 41 pages; ssrn:2747994

Abstract

[545 words; his point, and he does have one… An application of a precautionary principle is indiciated; these are dangerous machines run by dangerous people.]

The rise of increasingly complex algorithms calls for critical thought about how best to prevent, deter, and compensate for the harms that they cause. This paper argues that the criminal law and tort regulatory systems will prove no match for the difficult regulatory puzzles algorithms pose. Algorithmic regulation will require federal uniformity, expert judgment, political independence, and pre-market review to prevent – without stifling innovation – the introduction of unacceptably dangerous algorithms into the market. This paper proposes that a new specialist regulatory agency should be created to regulate algorithmic safety. An FDA for algorithms.

Such a federal consumer protection agency should have three powers. First, it should have the power to organize and classify algorithms into regulatory categories by their design, complexity, and potential for harm (in both ordinary use and through misuse). Second, it should have the power to prevent the introduction of algorithms into the market until their safety and efficacy has been proven through evidence-based pre-market trials. Third, the agency should have broad authority to impose disclosure requirements and usage restrictions to prevent algorithms’ harmful misuse.

To explain why a federal agency will be necessary, this paper proceeds in three parts. First, it explains the diversity of algorithms that already exist and that are soon to come. In the future many algorithms will be “trained,” not “designed.” That means that the operation of many algorithms will be opaque and difficult to predict in border cases, and responsibility for their harms will be diffuse and difficult to assign. Moreover, although “designed” algorithms already play important roles in many life-or-death situations (from emergency landings to automated braking systems), increasingly “trained” algorithms will be deployed in these mission-critical applications.

Second, this paper explains why other possible regulatory schemes – such as state tort and criminal law or regulation through subject-matter regulatory agencies – will not be as desirable as the creation of a centralized federal regulatory agency for the administration of algorithms as a category. For consumers, tort and criminal law are unlikely to efficiently counter the harms from algorithms. Harms traceable to algorithms may frequently be diffuse and difficult to detect. Human responsibility and liability for such harms will be difficult to establish. And narrowly tailored usage restrictions may be difficult to enforce through indirect regulation. For innovators, the availability of federal preemption from local and ex-post liability is likely to be desired.

Third, this paper explains that the concerns driving the regulation of food, drugs, and cosmetics closely resemble the concerns that should drive the regulation of algorithms. With respect to the operation of many drugs, the precise mechanisms by which they produce their benefits and harms are not well understood. The same will soon be true of many of the most important (and potentially dangerous) future algorithms. Drawing on lessons from the fitful growth and development of the FDA, the paper proposes that the FDA’s regulatory scheme is an appropriate model from which to design an agency charged with algorithmic regulation.

The paper closes by emphasizing the need to think proactively about the potential dangers algorithms pose. The United States created the FDA and expanded its regulatory reach only after several serious tragedies revealed its necessity. If we fail to anticipate the trajectory of modern algorithmic technology, history may repeat itself.

What Big Tech’s monopoly powers mean | Book Forum

What Big Tech’s monopoly powers mean; Staff; Book Forum; 2017-08-31.

tl;dr → It is all very bad. Others opined; they recite. Pointers are given (Book Forum actaully is a book review meta-site, after all)

Referenced

  • You Are The Product; John Lanchester; In London Review of Books; WHEN?
    (book) promotion

    • Tim Wu; The Attention Merchants: The Epic Scramble to Get Inside Our Heads From the Daily Newspaper to Social Media, How Our Time and Attention is Harvested and Sold; Vintage, reprint; 2017-08-19; 432 pages; ASIN:0804170045: Kindle: $14, paper: $12+SHT.
    • Antonio Garcia Martinez; Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley Inside the Silicon Valley Money Machine; Harper; 2016-06-28; 528 pages; ASIN:0062458191: Kindle: $15, paper: $7+SHT.
  • Who Owns The Internet: What Big Tech’s monopoly powers mean for our culture; Elizabeth Kolbert; In The New Yorker; 2017-08-28; separately filled.
    (book) promotion

    • Jonathan Taplin; Move Fast and Break Things: How Facebook, Google and Amazon have Cornered Culture and What It Means for All of Us; separately filled.
    • Franklin Foer; World Without Mind: The Existential Threat of Big Tech; separately filled.
  • Will Amazon take over the world?; Frank Pasquale; In Boston Review; WHEN?
    (book) promotion

    • Nick Srnicek, Platform Capitalism, (series) Theory Redux, Polity; 2016-12-27; 120 pages; ASIN:1509504877: Kindle: $8, paper: $11+SHT.
    • Trebor Scholz, Nathan Schneider; Ours to Hack and to Own: The Rise of Platform Cooperativism, A New Vision for the Future of Work and a Fairer Internet; OR Books; 2017-08-15; 252 pages; ASIN:1944869336: Kindle: $12, paper: $13+SHT.
  • On the kerfluffle at New America vs Google vs Open Markets;
    a.k.a. patronage is a wonderful thing when it is given; patronage is mean and nasty suckage when it is withdrawn
    <advice>Don’t bite the hand that feeds ya!<advice>

  • The hated ones: Amazon, Apple, Facebook, Google (AAFG)
    • Nationalise Google Facebook Amazon Data Monopoly Platform Public Interest; ; In The Guardian; 2017-08-13.
      Teaser: A crisis is looming. These monopoly platforms hoovering up our data have no competition: they’re too big to serve the public interest
      Riposte: let’s walk before we run; how about we nationalize The Guardian and see how that pans out before moving on to digest an organization that is run by adults?
      (book) promotion

      • Nick Smicek is a lecturer in digital economy, King’s College London.
      • Nick Srnicek, Platform Capitalism, Theory Redux, Polity; 2016-12-27; 120 pages; ASIN:1509504877: Kindle: $8, paper: $11+SHT.
    • Should America’s Tech Giant’s Be Broken Up?; Paula Dwyer; In Bloomberg; 2017-07-20.
      Teaser: Apple, Amazon, Google, and Facebook may be contributing to the U.S. economy’s most persistent ailments.
      tl;dr → Betteridge’s Law. Yes. Break ‘em up! Break ‘em up! Break ‘em up!
      (book&paper) promotions

      • Jonathan Taplin, age 70; Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy
      • David Autor (MIT) David Dorn (Zurich) Lawrence F. Katz (Harvard), Christina Patterson (MIT), John Van Reenen (MIT); The Fall of the Labor Share and the Rise of Superstar Firms; In Some Venue Surely, <sour>or maybe this is one of those half-decade duration “working papers” that the social scientists meditate upon before reporting out a “completed work” long after the effect has dematerialized <advice>give it a DOI number and be done with it, everyone else has already used or ignored the implications for policymakers concepts in the remediatory nostrums</advice></sour>; 2017-05-01; 74 pages; separately filled.
  • Trump damaged democracy, Silicon Valley will finish it off; Some Cub Reporter (SCR); In The Daily Beast; WHEN?
    Teaser: Donald Trump’s rise is, in a sense, just one symptom of the damage the are doing to America.

Toward a critical theory of corporate wellness | Gordon Hull & Frank Pasquale

Gordon Hull; More self-promotion: “Toward a critical theory of corporate wellness”; In His Blog; 2017-07-10.

tl;dr → employee wellness is but a surveillance-cum-control plane; a promotion of a forthcoming paper (you can read it for free if you’ve paid, which is a paywall)

Original Sources

Gordon Hull, Frank Pasquale; “Toward a critical theory of corporate wellness,”  In Biosocieties; soon.paywall.

Mentions

Abstract

In the U.S., “employee wellness” programs are increasingly attached to employer-provided health insurance. These programs attempt to nudge employees, sometimes quite forcefully, into healthy behaviors such as smoking cessation and exercise routines. Despite being widely promoted as saving on healthcare costs, numerous studies undermine this rationale. After documenting the programs’ failure to deliver a positive return on investment, we analyze them as instead providing an opportunity for employers to exercise increasing control over their employees. Based on human capital theory and neoliberal models of subjectivity that emphasize personal control and responsibility, these programs treat wellness as a lifestyle that employees must be cajoled into adopting, extending the workplace not just into the home but into the bodies of workers and entrenching the view that one belongs to one’s workplace. At the same time, their selective endorsement of health programs (many scientifically unsupported) produce a social truth of wellness framed as fitness for work. We conclude by arguing that the public health initiatives occluded by the private sector’s promotion of wellness programs would be a much better investment of resources.

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A Memo From MMT’s Legal Department | naked capitalism (New Economic Perspectives)

A Memo From MMT’s Legal Department; ; In naked capitalism, syndicated from New Economic Perspectives; 2017-07-18.
Devin Smith, staff, an economist, Corps of Engineers, United States Army.

Occasion

  • 1st International MMT Congress, 2017-09
    to be held at the University of Missouri-Kansas City.
  • A recital of
    • affiliated persons
    • promotional events

Mentions

  • Modern Monetary Theory (MMT)
  • Job Guarantee
  • Tax obligations create money
  • Tax obligations require wages.
  • Modern Money Network
    • founded 2012
  • Federal Reserve System
  • Rethinking Economics New York
    • a conference
    • Student-organized
    • Sponsors
      • Law School, Columbia University
      • Law School, New York University
      • Economics Department, The New School
  • Association for the Promotion of Political Economy and Law (APPEAL)

Bitcoin

Has a moneyness sense

Treasury currency
Commodity Futures Trading Commission commodity
Internal Revenue Service property

Quotes

<quote>“[A] renewed emphasis on a key legal insight (the government cannot default on debt it issues in its own currency) can lead to an adjustment to economic theory (MMT), which in turn informs a new legal proposal to get past the current, futile […] debate.
It’s a movement from legal insight to economic insight back to legal insight, or L – E – L.
… The legal foundations of MMT make it both scientifically, and normatively, a better theory of our economic system than the dominant paradigms of monetary policy.”</quote>, attributed to Frank Pasquale, circa 2014.

<quote>“Modern money theory explains that political and legal systems for creating, regulating, and distributing money are fundamental to economic prosperity and stability, necessarily shaping (not “distorting”) and facilitating private exchanges of goods and services.
… the economic costs and benefits of public money creation depend on the contingent, complex value-laden questions of how that money is spent and invested and how effectively taxes and other forms of regulation help steer economic and political activity toward the productivity, stability, and legitimacy that will help maintain currency value.”</quote>, attributed to Martha McCluskey, circa 2016.

Who

  • Rania Antonopoulos, Alternate Minister for Combatting Unemployment, Greece
  • Marshall Auerback
  • David Bholat, staff, Advanced Analytics Division, Bank of England
  • Richard Clarida, (former) Assistant Secretary, U.S. Treasury
  • Mathew Forstater
  • James K. Galbraith
  • Philip Harvey
    • Professor, Law, Rutgers University
    • (author) Rep. John Conyers H.R. 1000 towards full employment
      Versions: 114th Congress and many others
  • NAME Innes
  • Stephanie Kelton
  • NAME Knapp
  • NAME Keynes (surely you jeste)
  • Martha McCluskey, Professor, Law, University at Buffalo
  • Frank Pasquale, Professor, Law, University of Maryland
  • Zoltan Pozsar, (former) Senior Advisor, U.S. Treasury Department
  • Amar Reganti, ex-Deputy Director, Office of Debt Management at United States Treasury
  • Beardsley Ruml, Chairman of the Federal Reserve Bank of New York, once upon a time (at least in 1946-01)
  • Joseph Sommer, Legal Counsel, New York Federal Reserve Bank
  • Pavlina Tcherneva
  • Alexis Tsipras, (now-)Prime Minister, Greece,
  • Adair Turner, (former) Chairman, U.K. Financial Services Authority
  • Matias Vernengo, (former) Economic Research Director, Central Bank of Argentina.
  • Randy Wray

Referenced