Economic Potential of AI

 From McKinsey: The economic potential of generative AI: The next productivity frontier

The economic potential of generative AI: The next productivity frontier

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy—and the era is just beginning.

Sent from McKinsey Insights, available in the App Store and Play Store. ... '

Global Economics Intelligence Executive Summary

Things are tightening.  From McKinsey: 

Global Economics Intelligence executive summary, September 2022

October 11, 2022 | Article

Central banks sustain aggressive policy tightening; industrial activity picks up in emerging economies; financial-markets uncertainty works to strengthen the dollar.

Led by the US Federal Reserve, most central banks are now following a tightening course, increasing interest rates to fight inflation. With 75-basis-point hikes in September, the Fed and the European Central Bank (ECB) brought policy interest rates to ranges of 3–3.25% and 0.75–1.50%, respectively. Fed officials expect these rates to exceed 4% in 2023. For the ECB, the September hike was the largest in its history. ECB president Christine Lagarde and members of the ECB rate-setting council have signaled strongly that further hikes can be expected in the remaining two meetings of the year (Exhibit 1).  ... ' 

McKinsey: Global Economics Intelligence Executive Summary, September 2022

 Brief examination of economic changes ... 

Global Economics Intelligence executive summary, September 2022

October 11, 2022 | Article

Central banks sustain aggressive policy tightening; industrial activity picks up in emerging economies; financial-markets uncertainty works to strengthen the dollar.

ed by the US Federal Reserve, most central banks are now following a tightening course, increasing interest rates to fight inflation. With 75-basis-point hikes in September, the Fed and the European Central Bank (ECB) brought policy interest rates to ranges of 3–3.25% and 0.75–1.50%, respectively. Fed officials expect these rates to exceed 4% in 2023. For the ECB, the September hike was the largest in its history. ECB president Christine Lagarde and members of the ECB rate-setting council have signaled strongly that further hikes can be expected in the remaining two meetings of the year (Exhibit 1).

The policy makers have repeatedly stated that they are determined to bring down inflation, which was 8.3% in the United States in August and reached 10% in the eurozone in September (Exhibit 2). Business leaders share the concern, as suggested by the results of McKinsey’s latest global survey on economic conditions. Respondents from most regions cited inflation as the main risk to their home economies.

This battle against inflation, and the resulting change in policy direction, is fueling uncertainty in a crisis-weary global economy. Financial markets reacted quickly to interest-rate rises. Volatility indexes of traded assets uniformly increased, and most equity markets declined. Government bond yields climbed, and wary investors shifted wealth to dollar-denominated assets. The US dollar strengthened to historic levels against the pound and the euro. In Britain, where inflation is near 10%, the Bank of England (BoE) raised its key interest rate to 2.25%. The vulnerability of large economies to any additional shock was then starkly   .... ' 

Beyond GDP: A Framework for Measuring Economic Progress

 From: Irving Wladawsky-Berger:   A collection of observations, news and resources  ...

Beyond GDP: A Framework for Measuring Economic Progress 

“What is meant by economic progress, and how should it be measured?,” asked economists Diane Coyle and Leonard Nakamura in a recent paper  , Time Use and Household-Centric Measurement of Welfare in the Digital Economy. “The conventional answer is growth in real GDP over time or compared across countries, a monetary measure adjusted for the general rate of increase in prices. However, there is increasing interest in developing an alternative understanding of economic progress, particularly in the context of digitalization of the economy and the consequent significant changes Internet use is bringing about in production and household activity.”  .... ' 

The Coming Growth?

Good intro to a quite optimistic view of productivity, below is just his intro, pointing on to a more complete look.    I certainly hope so. 

The Coming Era of Productivity Growth  via  Irving Wladawsky-Berger  in his Blog

“The last 15 years have been tough times for many Americans, but there are now encouraging signs of a turnaround,” wrote economists Erik Brynjolfsson and Georgios Petropoulos in The Coming Productivity Boom,  a recent opinion article in the MIT Technology Review. “Productivity growth, a key driver for higher living standards, averaged only 1.3% since 2006, less than half the rate of the previous decade. But on June 3, the US Bureau of Labor Statistics reported that US labour productivity increased by 5.4% in the first quarter of 2021. What’s better, there’s reason to believe that this is not just a blip, but rather a harbinger of better times ahead: a productivity surge that will match or surpass the boom times of the 1990s.”  ...  '

Causal Machine Learning

Another area we experimented with, causal elements in the AI knowledge being used.  Would have liked to experiment with Alice. 

Microsoft Research Podcast

Econ2: Causal machine learning, data interpretability, and online platform markets featuring Hunt Allcott and Greg Lewis

Published June 2, 2021

Episode 122 | June 2, 2021 

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society. 

In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis. Together, they cover the connection between causal machine learning and economics research, the motivations of buyers and sellers on e-commerce platforms, and how ad targeting and data practices could evolve to foster a more symbiotic relationship between customers and businesses. They also discuss EconML, a Python package for estimating heterogeneous treatment effects that Lewis has worked on as part of the ALICE (Automated Learning and Intelligence for Causation and Economics) project at Microsoft Research. ... "

Emergence of Central Bank Digital Currencies

 As usual, an interesting piece from Irving's Blog (Below an intro, details and much more at the link) 

The Emergence of Central Bank Digital Currencies   By Irving Wladawsky   by IWB

Economist Digital Money V1Bitcoin was created in October of 2008 with the release of Bitcoin: A Peer-to-Peer Electronic Cash System, the original design paper which also introduced the blockchain architecture. A decade later, The Economist published a detailed evaluation of Bitcoin which succinctly concluded that “Bitcoin and other cryptocurrencies are useless.”

“Bitcoin, the first and still the most popular cryptocurrency, began life as a techno-anarchist project to create an online version of cash, a way for people to transact without the possibility of interference from malicious governments or banks,” it further argued. “A decade on, it is barely used for its intended purpose. Users must wrestle with complicated software and give up all the consumer protections they are used to. Few vendors accept it. Security is poor. Other cryptocurrencies are used even less.”

But last month, the May 8 issue of The Economist reached a very different conclusion in its assessment of central bank digital currencies (CBDCs), - i.e., e-dollars, e-yuans, or e-euros, - which it called “The digital currencies that matter.”

“Bitcoin has gone from being an obsession of anarchists to a $1trn asset class that many fund managers insist belongs in any balanced portfolio. … Yet, as our special report explains, the least noticed disruption on the frontier between technology and finance may end up as the most revolutionary: the creation of government digital currencies, which typically aim to let people deposit funds directly with a central bank, bypassing conventional lenders. These govcoins are a new incarnation of money. They promise to make finance work better but also to shift power from individuals to the state, alter geopolitics and change how capital is allocated. They are to be treated with optimism, and humility.”

Let me summarize a few of the special report’s key points.

Over 50 governments are exploring digital currencies. In October 2020, the Central Bank of The Bahamas issued the digital Sand Dollar, the first nationwide deployed CBDC. The Sand Dollar has the same value and consumer protections as the traditional Bahamian dollar, to which it can be instantly converted. The Bahamas also introduced the Sand Dollar prepaid card in collaboration with Mastercard, which can be used to pay for goods and services anywhere Mastercard is accepted.

China has a major e-yuan pilot underway. Over 500,000 individuals received 200 yuan ($30) from the government, which they can use to pay for goods and services using an e-yuan digital wallet offered by six commercial banks. Legally, e-yuans are as real as traditional hard cash. A few weeks ago, the US Digital Dollar Project announced that it will launch at least five programs over the next 12 months to explore the uses and designs of a US e-dollar. The European Central Bank has been developing the concept of the digital euro by conducting practical experiments and engaging with stakeholders and the broader public. And in April, the Bank of England announced the creation of a taskforce to coordinate the exploration of a potential UK CBDC. ... " 

Digital Yuan is Here

Implications remain to be broadly tested.   Details and underlyng security is of interest. 

China Creates Its Own Digital Currency, a First for Major EconomyA cyber yuan stands to give Beijing power to track spending in real time, plus money that isn’t linked to the dollar-dominated global financial system

By James T. Areddy in the WSJ

A thousand years ago, when money meant coins, China invented paper currency. Now the Chinese government is minting cash digitally, in a re-imagination of money that could shake a pillar of American power.

It might seem money is already virtual, as credit cards and payment apps such as Apple Pay in the U.S. and WeChat in China eliminate the need for bills or coins. But those are just ways to move money electronically. China is turning legal tender itself into computer code.

Cryptocurrencies such as bitcoin have foreshadowed a potential digital future for money, though they exist outside the traditional global financial system and aren’t legal tender like cash issued by governments.  .... " 

See also Coindesk's informative ongoing section on the Digital Yuan.

How AI Fits into Today's Economy

TNW looks into AI, and provides a non technical view, points to Prediction Machines Book. good starting place regarding economics involved.

A realistic picture of how AI fits into today’s economy

There’s a difference between a shiny new thing and a thing that works. You just need to look at the annual Consumer Electronics Show (CES) in Las Vegas to see how much of the technology we create just doesn’t cut it and gets tossed into the wastebin of innovation because it doesn’t find a working business model.

Where does artificial intelligence stand? Recent advances in machine learning have surely created a lot of excitement — and fear — around artificial intelligence. Game-playing bots that outmatch human champions. A text-generating AI that writes articles in mere seconds. Medical imaging algorithms that detect cancer years in advance.

How much of these technological advances are actually making it to the mainstream? How much of it is unwarranted hype? How will AI affect jobs? How is machine learning changing the business model of companies?

In their book Prediction Machines: The Simple Economics of Artificial Intelligence, professors Ajay Agrawal, Joshua Gans, and Avi Goldfarb, answer these and many other questions and paint a very realistic picture of the how machine learning fits into today’s economy.

Prediction Machines provides a very accessible and high-level overview of machine learning and the power and limits of the predictions provided by AI algorithms. The book is a must-read for business leaders and executives. But it is also a very valuable study for engineers and scientists who want to understand the implications of their innovations and how the technology they create integrates into the greater economy.

The book contains plenty of detailed and useful information and examples of how machine learning is changing how we do things. Here are some of my key takeaways.

The power of prediction machines
There are many misunderstandings about the meaning and difference of artificial intelligence, machine learning, and other related terms. There’s are also a lot of scientific discussions about AI’s advances toward human-level thinking and understanding and whether singularity is within reach or not.   .... " 

Simple Economics of the Blockchain

Considerable and interesting piece, well worth reading if you are considering blockchain use. And a proposition of the Blockchain's value.    Not very technical.

Some Simple Economics of the Blockchain
By Christian Catalini, Joshua S. Gans
Communications of the ACM, July 2020, Vol. 63 No. 7, Pages 80-90  10.1145/3359552

In October 2008, a few weeks after the Emergency Economic Stabilization Act rescued the U.S. financial system from collapse, Satoshi Nakamoto34 introduced a cryptography mailing list to Bitcoin, a peer-to-peer electronic cash system "based on crypto graphic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party." With Bitcoin, for the first time, value could be reliably transferred between two distant, untrusting parties without the need of an intermediary. Through a clever combination of cryptography and game theory, the Bitcoin 'blockchain'—a distributed, public transaction ledger—could be used by any participant in the network to cheaply verify and settle transactions in the cryptocurrency. Thanks to rules designed to incentivize the propagation of new legitimate transactions, to reconcile conflicting information, and to ultimately agree at regular intervals about the true state of a shared ledger (a blockchain)a in an environment where not all participating agents can be trusted, Bitcoin was also the first platform, at scale, to rely on decentralized, Internet-level 'consensus' for its operations. Without involving a central clearinghouse or market maker, the platform was able to settle the transfer of property rights in the underlying digital token (bitcoin) by simply combining a shared ledger with an incentive system designed to securely maintain it.

From an economics perspective, this new market design solution provides some of the advantages of a centralized digital platform (for example, the ability of participants to rely on a shared network and benefit from network effects) without some of the consequences the presence of an intermediary may introduce such as increased market power, ability to renege on commitments to ecosystem participants, control over participants' data, and presence of a single point of failure. As a result, relative to existing financial networks, a cryptocurrency such as Bitcoin may be able to offer lower barriers to entry for new service providers and application developers, and an alternative monetary policy for individuals that do not live in countries with trustworthy institutions. Key commitments encoded in the Bitcoin protocol are its fixed supply, predetermined release schedule, and the fact that rules can only be changed with support from a majority of participants. While the resulting ecosystem may not offer an improvement for individuals living in countries with reliable and independent central banks, it may represent an option in countries that are unable to maintain their monetary policy commitments. Of course, the open and "permissionless" nature of the Bitcoin network, and the inability to adjust its supply also introduce new challenges, as the network can be used for illegal activity, and the value of the cryptocurrency can fluctuate wildly with changes in expectations about its future success, limiting its use as an effective medium of exchange.

In the article, we rely on economic theory to explain how two key costs affected by blockchain technology—the cost of verification of state, and the cost of networking—change the types of transactions that can be supported in the economy. These costs have implications for the design and efficiency of digital platforms, and open opportunities for new approaches to data ownership, privacy, and licensing; monetization of digital content; auctions and reputation systems.   ..." 

Post Google Economics

Blog: Man on the Margin
Classical Economics in a Quantum World  By Michael Kendall

Brought to my Attention by the book:   'Life After Google',   by George Gilder

Be Hopeful about the Future

Good thoughts here, with the usual cautions coming from this source.

Pessimism dematerialized: Four reasons to be hopeful about the future

In More from Less, MIT Sloan School of Management’s Andrew McAfee argues that the “four horsemen of the optimist” are galloping to the planet’s rescue.

by Theodore Kinni in Strategy+Business    Further excerpts at the link:

If you’re a glass-half-full person, you’re going to love Andrew McAfee’s latest book, More from Less. Always optimistic, while still expressing minor notes of caution, McAfee, a research scientist at the MIT Sloan School of Management and cofounder and codirector of MIT’s Initiative on the Digital Economy (with frequent collaborator Erik Brynjolfsson), believes that life on this planet is getting better all the time. He also thinks that though humans face some big challenges, we have at our command all the resources needed to meet them.  ... " 

On an Economics of Eaches

Not a very new thing, but another way of looking at it, In Supply chain Brain.

Watch: The Economics of 'Eaches'
Thomas Goldsby: In SupplyChainBrain

Farewell to the age of economical bulk and pallet distribution. Now it's a question of managing "eaches." Thomas Goldsby, Haslam Chair of Logistics at the University of Tennessee, Knoxville, explains the new economics behind this trend.

SCB: Eaches are an essential part of today's supply chain, given the growth of e-commerce, and the need to pick small lots for customer orders. What do you mean by “the economics of eaches”?

Goldsby: We’re not talking about supply chains that are dependent upon full pallets moving in and out. Instead, at some point in the distribution of products, we're going to get down to less-than-pallet loads, and perhaps the individual item. When I refer to the economics of eaches, I'm talking about understanding the financial aspects associated with picking, packing, shipping and delivering those individual items.

SCB: Based on shifts in consumer buying behavior?

Goldsby: The way consumers shop today, it's no longer just a case of driving to a store, picking their own items from the shelf and filling up a shopping cart. Rather, it’s an industrial worker — an employee of a manufacturer, retailer or distributor — who’s performing that activity, as well as handling as the last-mile portion. My sense is that most companies don't fully appreciate the cost of breaking down the pallet and delivering to a desired destination.  .... " 

W. Brian Arthur Podcast

We connected with Arthur about modeling economies and enterprises at the SFI.

The Autonomous Economy  HBR Podcast

Listen and subscribe to this podcast via Apple Podcasts | Google Podcasts | RSS

One of the founders of complexity economics, W. Brian Arthur, joins Azeem Azhar to discuss how artificial intelligence is ushering us into the age of the autonomous economy with radical implications for our society.

In this podcast, Brian and Azeem also discuss:

Why the decision-making ability of AI will have a different class of impact on the economy than previous ‘landmark’ technologies like the printing press.

Whether it is possible to have publicly available decision-making enabled by artificial intelligence outside the proprietary enclaves of Silicon Valley monopolies.

Increasing Returns Theory and how to generate good governance in a digital economy with dominant market actors.  .... "

Lessons from Corporate Nudging

Mixing Behavioral Science, Economics and Psychology.    Its likely that this concept will influence AI methods as implemented in assistant technologies.  But there are lots of issues in such methods.  Utimately its a test of how machines manage people.   Important challenges hinted at here.

Lessons from the front line of corporate nudging  By Anna Güntner, Konstantin Lucks, and Julia Sperling-Magro in McKinsey

Executives setting up a behavioral-science unit should start by challenging themselves with six questions.

If you’re serious about setting up a behavioral-science team—or a nudge unit, as we’ll more colloquially refer to it in this article—you need to ask yourself some tough questions, such as what it should do, where it should sit, how you’ll know it’s succeeding, and whether you’re ready for the ethical tensions it may raise.

Lessons from the front line of corporate nudging

Subtle interventions to help people make better decisions are hardly new. Since the 1950s, behavioral scientists, using a mix of economics and psychology, have studied human irrationality and devised ways both to improve the choices made by consumers and influence how employees react in the workplace. Increasingly, over the past two decades, companies have used the insights of behavioral science to reduce bias in boardrooms, improve strategic decision making, provide benefits for customers, enhance the effectiveness of marketing campaigns, and avoid making bad bets on major acquisitions or investments.  ... " 

Game Theory and Society

A favorite topic is how game theory can be made practical   We tried that and got little out of it beyond descriptive rather than prescriptive models.   Is this new approach useful beyond that?  Note especially regarding networks and social dynamics.

What game theory tells us about politics and society
Economist Alexander Wolitzky uses game theory to model institutions, networks, and social dynamics.   By Peter Dizikes | MIT News Office

Digital and Your Business

A bit too much of an infographic, but makes its point.  Its about the business, the process, what it produces in context over time.   Its the economics.   In McKinsey"

"... Want to better understand what digital is doing to your business? Look to the underlying economics.  ... " 

Conversation with J Doyne Farmer

I recall following the work of J Doyne Farmer when we were interested in economic models that included arguments about complexity, at the Santa Fe Institute.  Here is a conversation with him that is a good update on what he is working on.  Note the mention of the Prediction Company, which I recall hearing about.  And the link to BiosGroup, which received $5 Million from P&G for Supply Chain modeling. 

Collective Awareness
A Conversation with J. Doyne Farmer [7.17.18] in the Edge

J. DOYNE FARMER is director of the Complexity Economics Programme at the Institute for New Economic Thinking at the Oxford Martin School, professor in the Mathematical Institute at the University of Oxford, and an external professor at the Santa Fe Institute. He was a co-founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. 

Always Out of Balance: Looking for Equilibria

Nicepiece, which includes a short, easy to understand video  that explains the basics of the Nash Equilibrium.  We sought to use these methods to understand competitive behavior.   Not very successfully I admit, but the approach did make us think of better models for the processes and integrated behaviors involved.  Even the supposed rationality of decisions were an issue.  It is what we called competitive modeled economics. What were the right counter behavior for competition?

Always Out of Balance  By Neil Savage  in  
Communications of the ACM, Vol. 61 No. 4, Pages 12-14

"Computational Theorists show that there is no easy way to to find Nash Equilibria, so game theory will have to look in new directions"....

When John Nash won the Nobel Prize in economics in 1994 for his contribution to game theory, it was for an elegant theorem. Nash had shown that in any situation where two or more people were competing, there would always be an equilibrium state in which no player could do better than he was already doing. That theorem has since been used to model all sorts of competitive systems, from markets to nuclear strategy to living creatures competing for finite resources.

"In some sense, it started not just game theory, but also modern economics," says Christos Papadimitriou, a professor of computer science at Columbia University. Nash's idea gave economists the ability to create hypotheses about market design, for instance. They could now ask what happened when a market reached equilibrium.

Nash's theorem is also an essential component of game theory, which had first been developed by computing pioneer John von Neumann. "Games are a mathematical thought experiment and we study them just because we want to understand how strategic rational players would behave in situations of conflict," Papadimitriou says. "And that's important because all of society is full of such situations."

Though Nash proved that at least one such Nash equilibrium existed for all games, what he did not do was predict how an equilibrium might be reached in a given situation. Was there, scientists wanted to know, an algorithm that would show players how to efficiently reach an equilibrium? After more than 65 years of researchers' studying that question, the answer turns out to be no, there is not. That means economists had better start rethinking some of their models. .... "

On AI Transforming the Economy

Also some about the AIIndex mentioned here recently.   Good points here,  what has been done is still only scratching the surface of what we would call 'intelligence'.  We are not there yet, but making progress that we have to pay attention to.

A.I. Will Transform the Economy. But How Much, and How Soon?
By The New York Times in the ACM News

There are basically three big questions about artificial intelligence and its impact on the economy: What can it do? Where is it headed? And how fast will it spread?

Three new reports combine to suggest these answers: It can probably do less right now than you think. But it will eventually do more than you probably think, in more places than you probably think, and will probably evolve faster than powerful technologies have in the past.

This bundle of research is itself a sign of the A.I. boom. Researchers across disciplines are scrambling to understand the likely trajectory, reach and influence of the technology — already finding its way into things like self-driving cars and image recognition online — in all its dimensions. Doing so raises a host of challenges of definition and measurement, because the field is moving quickly — and because companies are branding things A.I. for marketing purposes. .... "