Embarking on a Journey of Inquiry

or years, I have read prevailing economic consensus with considerable bemusement. "Keep my own counsel", I would oft repeat. Pillorying prominent academics is unwise, I brooded. Yet, my reticence to speak must be cast aside. Economics is no longer a mere academic topic. It is a potent weapon for shaping political discourse. Everyone trained in the complexity sciences has a civic duty to critique academic theories and models grounded in faulty assumptions. The misrepresentation of dogma as science has risen to treasonous levels. It is time that we reclaim the public commons from entrenched interests, then exercise our newfound freedom by weighing the tradeoffs of various economic ideologies more earnestly.

With this missive, I commence an exploration into the confluence of several disciplines.  The central locus will be the hazy margin between economics, finance, and political science.  I will fix my gaze upon these topics through a lens cast in the crucible of the hard sciences: systems engineering, dynamical systems, computer science, information theory, chaos, and complex adaptive systems.  My central goal is to explore how the dominant economic theories (neoclassical and neo-Keynesian economics) have evolved into unfalsifiable, hubristic dogmas of profound consequential danger to prudent public policy formation.

My purpose is not to denigrate the achievements of the major economic schools.  Rather, my principal aim is to place them into a more appropriate context—to kick them off their undeserved pedestal...so that we may evaluate them without the pretense of law that currently shields them from question.  We must examine how, in their quest to emulate the hard sciences, economists fell into a reductionist trap.  They adopted the deep mathematical roots of physics without thoroughly understanding the mathematics they were borrowing, nor designing experiments to make their predictions falsifiable.  They are thus stuck defending the indefensible through inertia, and the weight of quadrillions of dollars of notional assets risk-managed through techniques whose mathematical foundations are internally inconsistent.

We must probe the ample evidence indicting econometric models as poor predictors of real world dynamics.  As Ole Peters and Murray Gell-Mann have pointed out, economists entirely (mis)apply probability and expected value theory to systems where they're inapplicable—a point I'll return to frequently in subsequent posts.  This belief in the general applicability of Gaussian (bell curve) statistics has led economists to champion models—whose pillars are immanently incompatible with both human nature and reason itself—as unassailable.  I can think of no better place to start this scrutiny than the rational investor hypothesis, which will be the topic of my next few musings.

As I endeavor to make my first steps on this journey, my desire is to foster a broader dialogue about cutting edge science.  Far too few are aware of the Santa Fe Institute's work into rethinking economics.  That a body of publications and symposia stretching back over thirty years is practically unacknowledged within both the cloistered world of neoclassical economics, and the broader public sphere, disquiets me.  Even fewer seem to be aware of Steve Keen's quixotic battle from within the academy to rethink econometric models in a manner that obeys fundamental double-book accounting principles (which ultimately makes one think a lot harder about the construction of real-world macro models).

To form a groundswell, more people need to be aware of the intellectual divide — and the very existence of alternative methods of enquiry.  I hope my writings can help extend the complexity economics community further into the public commons.  As Bertrand Russell once said,

Do not fear to be eccentric in opinion, for every opinion now accepted was once eccentric.

I hope you will join me in considering the possibility that our world is far more complex and beautiful than any linear, equilibrium model will ever discern.  Let's get started…