False Assumptions Underlying Modern Economics

The following is Part One of a two-part series on how our assumptions of reality powerfully influence the results we achieve. It explores false assumptions underlying modern economics and explains why these are leading the US and global economies to the edge of chaos. Part Two will explore a more hopeful new paradigm that is emerging in corporate and government economic thinking. 

False Assumptions Underlying Modern Economics
Why they are leading us to the edge of chaos

The following six assumptions are ubiquitous in policy decisions by governments, central banks and mainstream corporations.

  1. The economy is primary. However, in reality, it is a sub-system of earth’s biosphere and human society. The real system looks like this: Biosphere -> humanity -> economics.
  1. The goal is continuous consumption (GDP) growth. But such growth is impossible on a finite planet. The world is now deeply into ecological overshoot.
  1. Capital assets take precedence over living assets (humanity and Nature). This disregards the fact that living assets are the source of capital assets.
  1. Economies, markets and companies behave in rational, predictable ways. However, as complex, adaptive living systems, they can become unpredictable – especially when stressed – in ways that defy normal reversion-to-mean behavior.
  1. Debt is an effective tool for leveraging economic and corporate growth. However, as borrowers reach their debt limits, debt can also become a stressor that inhibits GDP and profit growth.
  1. Mathematical VaR (value at risk) models reduce risk. However, when used to promote risk taking, rather than to hedge, they can backfire badly as they did in the 2008 global market meltdown.1

Because of these false assumptions, the global economy has fallen into a system trap where the methods we use to achieve our economic goals end up subverting them. The risks of falling deeper into this trap have worsened from cycle to cycle since the 1970s as economies have pushed further and further beyond their ecological and debt limits.

Attempts to escape this trap via financial engineering have only made the problem worse by masking real issues and deluding decision makers into thinking things are better than they actually are.

Over the past few decades a series of sudden market crashes and disruptions costing trillions in financial loss have occurred. Their frequency and scale – which are far beyond the normal expectations of VaR models – suggest a system that is approaching a critical catastrophic state.

Exacerbating this problem, governments and central banks cover up negative feedbacks by changing accounting standards and regulations, and by trying to manage risk through altering key economic variables such as interest rates and exchange rates. By so blocking critical systemic feedback, they disguise important stress signals that might otherwise enable the system to adapt.

This brief will review some of the mentioned crashes and disruptions, which like cracks in a giant dam, suggest a need to radically rethink and change the way we manage ourselves and our economies.

Recent signs of instability

On January 15, 2015 an effort to disguise systemic risk in the global currency market backfired and sent financial markets into a panic. The trigger event was a decision by the Swiss National Bank to let the Franc seek its own level relative to the Euro, which caused the Franc to appreciate 30% against the Euro within the space of 16 minutes.

Described as a 20-standard-deviation event, this sudden revaluation was far beyond the scope of VaR models used by foreign exchange dealers, banks, brokers and hedge funds. Called “tail events” because they are so far out on the tail of a standard distribution, such events are now starting to show up with disturbing regularity.

“Goldman Sachs Chief Financial Officer Harvey Schwartz said on his morning’s earnings call that this (revaluation of the Franc) was something like a 20-standard-deviation move … A 7-standard-deviation move should happen about once every 390 billion days, or about once in a billion years. So this should be less frequent.”
Bloomberg (January 16, 2015)


The preconditions for this shock were set three years earlier when, in September 2011, European central bankers decided to set a fixed exchange rate between the Euro and the Swiss Franc. Introduced at the height of the Eurozone sovereign debt crisis, to discourage disruptive currency speculation and maintain a sense of regional stability, it in fact destabilized the region, while putting trillions of dollars of institutional assets at risk.

In short, central bankers’ attempt to correct an economic problem blew up because it treated a symptom of the problem (exchange rate speculation) rather than the problem itself, which was the European Central Bank (ECB) monetizing sovereign debt by printing more Euros. Known as “shifting the burden,” we see this archetypical official behavior over and over again as governments and bankers try to patch up a broken, ill-conceived system.

To economists, corporate leaders and investors, anomalies such as the sudden, sharp revaluation of the Swiss Franc, have now become all too common. No longer once-in-a-billion-years events, they now occur multiple times in a decade.

Consider the trillion-dollar stock market crash on May 6, 2010 – a reported 5+ standard deviation event. Starting at 2:32 PM New York City time, it lasted for approximately 36 minutes. Attributed to too much high frequency algorithmic trading against too little market liquidity, it quickly tripped the system into a critical state.

Why did this happen? It’s hard to say precisely, but beneath the covers we’ve had a glut of hot money – enabled by global central bank debt monetization – chasing profit in a slowing real economy. Rather than buying stocks as long-term investments, some hedge funds now trade based on algorithmic signals, which can change within a split second. A tidal wave of such trading can swamp a market until prices hit predetermined tripwires (known as “collars” or “circuit breakers”) at which point trading stops.

On October 15, 2014 a similar event happened in the world’s most liquid market: that for US Treasury bonds. This time prices did a radical round-trip: shooting up between 9:33 and 9:39 AM then cascading back down again between 9:39 and 9:45. Bloomberg attributed it to “high speed electronic trading” where algorithms essentially “fed on each other” in a rush of “self-trading.” The result, it concluded, was another “seven or eight standard deviation event.”2

Barely a year later on August 10 and 11, 2015 the Chinese Central Bank devalued the Yuan by 3.2 percent. Although seemingly small, this so-called “one-time adjustment” caused the Shanghai stock exchange to plunge 43%, wiping out $3 trillion in Chinese wealth plus additional trillions in other world markets.

The asymmetry between apparent cause and effect in the foregoing situations is staggering. As the system becomes stressed beyond its political-economic and ecological limits, seemingly small (or unseen) events now cause immense damage.

Early Signs of Systemic Stress:

Long before these 5 to 20 sigma flash crashes became recurrent events, we had early warning signals that tightly integrated global economic and ecological systems were approaching their limits. We see this particularly in the growth of global debt/GDP ratios and ecological overshoot from the late 1970s to the present.3 While each of these anomalies by themselves is disruptive, together they have created extreme stress as revealed in the previously noted disruptive market behavior.

The October 19, 1987 “Black Monday” crash is perhaps the starting point of this disruptive behavior. (After nearly a decade of rising debt/GDP ratios and ecological overshoot, the economy began to fracture.) Described by the New York Times as “the largest one-day market crash in history,” stock markets around the world suddenly plummeted, and the Dow Jones Industrials lost 22.6% of its value ($500 billion) before the day was over. Beyond its significance as an economic event, this crash launched a trend towards Federal Reserve market intervention to “support market liquidity.”4

At the time of the crash institutional investors had been aggressively bidding stocks, then hedging their positions by short selling stock index futures, which they regarded as a form of “insurance.”

According to the New York Times, this signified “the beginning of the destruction of markets … by computers programmed by fallible people and trusted by people who did not understand the computer programs’ limitations. As computers came in, human judgment went out.5

The Federal Reserve’s “Black Monday” intervention set a precedent that has been replicated over and over again by other world central banks in support of reckless market behavior. In the three decades since 1987 this behavior has become ubiquitous due to a reinforcing cycle where central banks provide market liquidity and computer-driven trading feed off this liquidity in increasingly reckless ways. This creates what economists call “moral hazard” because it enables investors to take risks (and harvest profits) that endanger the system while shifting the costs onto the general public.

In 1994, facing a bond market meltdown, Federal Reserve Chairman Greenspan did just this by virtually eliminating bank reserve requirements via the notorious “sweeps” program.6 This allowed banks to create ‘mirror accounts’ for each checking and savings account, enabling them to ‘sweep’ money out of those accounts each night just before midnight when a reserves ‘snapshot’ is taken. With nothing in those accounts at that moment (as they would appear to have zero balances), nothing had to be held in reserve against them. Then, a few minutes later, the money would be swept back in.

Beyond flooding banks with new liquidity, this sleight of hand gave aggressive fund managers new confidence that the Fed placed a higher priority on stabilizing capital markets than it did on allowing hyper-leveraged funds to suffer the consequences of their carelessness. And, indeed, as such Fed interventions on behalf of the banking industry became more common, a trend towards more aggressive speculation developed, resulting in deeper market crises and inevitably more costly Fed rescues.

The Asian financial crisis of July 1997 was another incident where hyper-leveraged investment activities imploded regional stock markets and currency values – with many losing 75% or more against the US dollar. It began when a Thai property developer failed to make a scheduled $3.1 million interest payment on an $80 billion Eurobond loan. Like the Thai economy itself, this venture was highly debt leveraged and effectively insolvent.

This single default called into question debt-leveraged investments throughout the region, causing fears of economic meltdown and contagion in global financial markets. To keep this from becoming worse, a consortium of central banks plus the International Monetary Fund (IMF) came to the rescue with $118 billion in credits. With the Fed coordinating US and global policy responses, commercial banks were asked to roll over short-term loans to regional borrowers and restructure them into medium-term loans.7

Shortly after this Asian meltdown came the LTCM (Long Term Credit Market) debacle. LTCM was a hedge fund that used derivatives to exploit temporary price differences between similar types of securities. With a “market-neutral” design, it expected to make profits regardless of whether prices went up or down. However, with $30 in debt for every dollar of capital, it was vulnerable to small errors, which eventually cost it dearly.

The flaws in LTCM’s strategy were exposed on August 17, 1998 when Russia suddenly devalued its currency and stopped payments on its debt, causing investors to seek safer and more liquid investments. The hedge fund, which had been betting that interest rate spreads in its portfolios would converge (revert to a statistical mean), was suddenly faced with sharp divergences, causing it to quickly lose 44% of its value.

Given the extensive list of Wall Street bank counterparties to LTCM’s trades, the Fed became concerned that creditors and depositors at these banks could crash the system if they tried to exit their positions at the same time. After considerable arm twisting it convinced 14 banks to put up $3.625 billion in capital in exchange for 90 percent of LTCM’s ownership – an unprecedented rescue operation because it was the first time a central bank had come to the assistance of a non-bank.

Thus in four years (1994 – 1998), the Fed undertook three rescue operations, with each one requiring radical interventions to keep the larger financial/economic system on an even keel. The financial backing it provided, incidentally, coincided with the dot-com bubble, which ended in the stock market collapse of 1999 – 2001.

Instead of letting markets clear previous errors of judgment (and discourage future hyper-leveraged risk-taking), the Fed’s response to the dot-com bust was to drop interest rates to one percent. This, in turn, became the fuel for the mid-decade housing bubble of reckless mortgage lending to sub-prime borrowers, which Wall Street leveraged with mortgage-backed securities (MBS). Adding to the frenzy of speculation, Fed researchers produced studies asserting there was no housing bubble.

Then on September 15, 2008 Lehman Brothers, the largest underwriter of such mortgages, declared bankruptcy. Within the space of a month, that caused a calamitous $10 trillion meltdown in global equity markets.

Like the speculative excesses of the prior two decades, Lehman’s demise was caused by false VaR assumptions about risk: first, risk to the mortgagees, whose loans were packaged into mortgage backed bonds; second risk to hyper-leveraged underwriters of those bonds; and third, risk to final MBS investors, who often bought them with borrowed money.

Like LTCM in 1998, Lehman had an aggressive leverage ratio of 31 to one (liabilities to equity). This meant a 3% to 4% decline in the value of its assets would entirely wipe out its equity. Adding to that risk, Lehman at the time had a portfolio of MBS valued at four times its shareholder equity. Consequently, when the US housing bubble began to burst in 2007 and sub-prime mortgagees began to default, Lehman was extremely vulnerable.

As the fourth largest investment bank in the US at the time of its demise, Lehman was a counterparty to hedge funds and banks all over the world. This caused a tsunami of fear among global financial institutions – prompting depositors, customers and creditors to withdraw from them. Concerned that such fear would become contagious and cause the global financial system to freeze up, the Fed again stepped into the breach.

This time it spent more than $16 trillion to bail out global corporations and banks.8 Although most bailout funds were later paid back, the sheer scale of the operation indicates the risk embedded in world markets at the time. Contemporaneously, the Fed also began several programs of quantitative easing (QE) – essentially, money printing – that led it to buy onto its balance sheet $3.7 trillion dollars of US Treasury bonds and MBS. Other world central banks did the same so that total QE is now more than $17 trillion.9

Looking back on the asymmetric consequences of Lehman’s failure, this bank with an assumed 2007 net worth of $22.5 billion, caused a $10 trillion global equity market meltdown, followed by a $16 trillion Fed rescue program and a further $17 trillion surge of world central bank money printing.

Looking further back, over the three decades since the 1987 Black Monday crash, we see here a repetitive cycle of increasing moral hazard: where greater and greater central bank interventions were followed by more egregious risk taking and ever more catastrophic system failure.

Today, this toxic situation has become so artificial and divorced from the real (living) economy, we now have a financial system propelled by mechanical algo-driven trading strategies, understood only by techies familiar with the deltas and gammas of derivatives trading, where risk is becoming increasingly concentrated in the hyper-leveraged global banking industry – a concentration that leaves scant room for error.

Which makes us wonder whether another bailout is even feasible.

Reflections from the Brink

According to the Institute for International Finance (IIF) world credit market debt at the end of third quarter 2016 was more than 325% of world GDP.10 This compares to ratios of 269% at year-end 2007 and 248% at year-end 2000 as revealed by a McKinsey Global Institute.11

But this is only part of the total risk we now face. Beyond explicit (visible) credit market debt, there is a multi-trillion-dollar bubble of implicit debt in under-funded public and private sector pension and healthcare plans. In a March 2016 white paper titled “The Coming Pension Crisis,” Citigroup says the unfunded liabilities of public pension plans in the world’s developed economies is $78 trillion – nearly double their published national debts.12 Troublesome as this is, it is likely a low-ball number.

According to Bridgewater Associates, the big picture in the US is more extreme. The following chart, presented on October 5, 2016 by CEO Ray Dalio at the Federal Reserve Bank of New York’s 40th annual Central Banking seminar, combines the explicit debt (lower two levels) and implicit debt (upper four) of the US as a percentage of GDP. Beyond the unfunded liabilities of the Social Security system, this includes those of Medicare and other “mandatory” government programs according to the US Congressional Budget Office (CBO).13

Consequently, when we tally explicit and implicit debt as a percentage of global GDP we get numbers that are orders of magnitude bigger than those of McKinsey and the IIF.

On top of that, the world’s largest banks continue to carry hundreds of trillions in leveraged derivatives positions that expose the global financial system to additional risk. Because these derivatives are actively traded between banks and other too-big-to-fail financial institutions, a counterparty failure in one segment of the market can send the whole system into crisis as happened with Lehman in 2008.

Deutsche Bank, whose counterparties (like Lehman’s) extend to the world’s largest financial institutions, exemplifies this excess with a derivatives position that at times has exceeded by 10 times Germany’s entire GDP. Adding fuel to such concerns, these derivatives or “Level 3 assets” account for 72 per cent of its “Tier 1 assets”, which are supposed to be the foundational assets that give the bank its strength.14

“In our opinion it is not so much funding issues but rather derivatives exposures that is more likely to trouble markets if Deutsche Bank concerns continue …This is especially true if these concerns propagate into a confidence crisis inducing a more rapid unwinding of derivative contracts.” – Nikolaos Panigirtoglou, JPMorgan. October 1, 2016 15


Adding to these risks is a trend towards “synthetic securitizations” where banks buy protection to backstop losses on their loan portfolios from pension funds, hedge funds and asset managers (including other banks).16

Worse, to enable these securitizations, these too-big-to-fail banks lend money to their insurers – in effect, issuing loans to backstop their own loans.

So the very banks that created the 1998 LTCM debacle and the 2008 MBS meltdown now insure themselves with debt-leveraged derivatives offered by their borrowers. Rather than diminishing risk, these synthetic securitizations further concentrate it in ways that will quickly spread contagion.

This concentration of risk within the banking industry brings to mind a memorandum signed by President Bush on May 5, 2006 that gives the Director of National Intelligence broad authority to exempt publicly traded companies from their accounting and disclosure requirements under section 13(b)(3)(A) of the Securities Exchange Act of 1934. Such exemption includes keeping accurate “books, records, and accounts” and maintaining “a system of internal accounting controls sufficient” to ensure the propriety of financial transactions and the preparation of financial statements in compliance with “generally accepted accounting principles.”17

Quietly inserted in the Federal Register on May 12, 2006 under the innocuous title “Assignment of Function Relating to Granting of Authority for Issuance of Certain Directives,”18 it represents one of many ways the US government continues to disguise, or paper over, the financial risks inherent in our economic system.

In short, rather than enabling us to exit the system trap we’ve been describing, these obfuscations take us deeper into the trap: towards constraints that give us less and less room maneuver.

Trying to disguise, or paper over, the economic and ecological deficits we are now running under our conceptual models of reality is no longer a viable option. No amount of cover or financial engineering will keep the system afloat. Unless we change our mental models of reality, we are headed for certain failure.

Considering our options

The overriding truth that emerges from the data presented here is that we cannot continue with business as usual. Which necessarily calls into question the assumptions underlying our economic system.

So the question is: Do we try to tweak (adapt) the present economic system, which evolved from the mechanistic assumptions of the industrial revolution, by changing it on the margins? Or do we undertake a radical overhaul and model our system on natural (living system) principles, which are more congruent with the living world in which we actually exist?

The first of these options is problematic because mechanistic systems and living ones are fundamentally different. One works by linear means and the other by non-linear. That begs the question: If the two are merged and end up in conflict, which one will predominate? So long as humanity holds to its “rationalist” belief that we, above all other species are guided by reason, and that we deserve to control Nature by virtue of our superior intellect, the linear approach will likely prevail. The calamitous experiences with computer-driven VaR models mentioned above affirm this likelihood.

The second option, although radical, is the likeliest because it enables us to live and work in harmony with the rest of life. While skeptics may argue that it has never been tried in a scalable way, that is not true. Some of the world’s most profitable companies have begun to mimic life in the ways they are organized and managed.

Most importantly, the deeper they go into life-mimicking strategies, the better their economic, ecological and social results have been in comparison with conventionally managed peers. And they achieve these results with significantly less debt-leverage than their conventionally managed peers.19

This is not to suggest that the life-mimicking approach is perfect. It isn’t. Companies that model best practices would be the first to say they still have a lot to learn. As Nike often says: “there is no finish line.” Nothing in Nature stands still. To survive and thrive, like all species, we must continuously learn and adapt.

Considering these questions from the perspective of risk management, it is worth noting: Risk concentrations, such as the one we see in our present system, do not exist in Nature. Given the redundancies that exist in diverse ecosystems, they don’t fall into critical states on their own. Rather, they adapt as they learn from experience.

So what can we learn from this emergent new model of the firm?

To begin, companies that mimic life share an understanding that they are sub-systems of the biosphere and society – not all-powerful managers of those larger systems. Consequently, they see their opportunities for financial returns in terms of strengthening these larger systems rather than dominating and exploiting them. Such companies also accept that their primary assets are living (people and Nature) because these are the source of their learning and all other assets. That mindset gives them an inner consistency and strength that conventionally managed firms lack.

The operating leverage in this approach is its appeal to the human heart. Most people want to make a difference in what they do. When they learn and work with their hearts as well as their minds, they are naturally more productive and innovative.

In harmony with these fundamental principles, life-mimicking companies have six shared attributes that may frame a way forward for other political-economic organizations. These include:

  1. Decentralized, self-organizing networked structures, whose component parts serve the health of the whole (such as the human nerve system and the cognitive architecture of our brains);
  2. Regenerative life strategies for survival, reproduction and strengthening cultural DNA through servant leadership and continuous self-evaluation;
  3. Frugal instincts that seek to optimize use of resources;
  4. Openness to feedback that enables adaptive learning;
  5. Symbiotic behaviors that link organizational well-being to the health of the larger systems in which they exist (biosphere, society); and
  6. Consciousness of organizational capabilities, interdependencies and limits.

Although broadly inclusive, this template is purposefully general. As companies and other organizations learn and evolve by experience, it will be improved.

Part Two Forthcoming: The Kalundborg Symbiosis – Where Hope Resides

There is an industrial city in Denmark that works on these very life-affirming principles. Since the early 1970s it has developed a model of collaboration between companies, governments, foundations and schools that regenerates local ecosystems while it adds value for host community and investors. This constitutes a multiple win scenario that, based on current evidence, can be replicated at national and global levels.

The three exchange-listed corporations at the center of Kalundborg, together with an energy company that is 50.1% state owned, all operate on living system principles. Together, they have created a vibrant learning community and a closed loop system of resource sharing, where effluents are converted into raw materials for further high value added enterprise.


1 According to the US Office of Comptroller of Currency (OCC), the combined VaR of the top 3 US bank holding companies (Bank of America, Citigroup and JPMorgan) was $493 million at mid-year 2008. Against this, these banks had $168 trillion in derivatives (notional value) on their books. The banks justified these huge positions by saying they were hedged with counterparties so that only .04% to .1% of their equity capital was actually at risk. Nevertheless, as a result of the September 2008 market crash, the 3 banks received $115 billion in US government bailouts, suggesting their VaR models missed the mark by over 99 percent. This doesn’t include back door bailouts where the Federal Reserve took additional trillions of sour loans off their books at face value.
3 According to the Global Footprint Network, “Since the 1970s, humanity has been in ecological overshoot, with annual demand on resources exceeding what Earth can regenerate each year. Today humanity uses the equivalent of 1.6 Earths to provide the resources we use and absorb our waste.”
For details on the Fed’s rapid response, see: “A Brief History of the 1987 Stock Market Crash with a Discussion of the Federal Reserve Response” (November 2006), pages 17 – 21.
New York Times
St Louis Fed
Federal Reserve History
Figure 1:
13 Full Text – Dalio’s Speech
14 For a diagram of Deutschebank’s global counterparties.
17 As reported in (August 2006): “A translation of the legalese in section 13(b)(3)(A) of the Act presents an unsettling prospect of adverse implications for the investing public. This memo effectively gives the Director of National Intelligence the ability to exempt companies from meeting their legal obligations as they pertain to keeping accurate books, records, and accounts and maintaining a system of internal accounting controls.
19 Joseph H. Bragdon, Companies That Mimic Life (Greenleaf Publishing, Ltd. 2016)


XHTML: You can use these tags: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>