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Three Bounding Questions, Two and One
Theories in Opposition, and the Role of
Network Persistence, Artificial Intelligence,
and Machine Learning for Domination in
Whatsoever Type of Competition
Alex Alaniz, PhD
27 February 2022
Abstract
This article explores how the work in dynamics systems theory by Prigogine and Nicolis favors the
domination of democracies over autocracies, capitalist economies over socialist command economies,
and free markets over interventionist state-controlled markets, in full concordance with the so-called
knowledge problem of Nobel Prize economist A. F. Hayek. (The knowledge problem stipulates that there
is never sufficient information for central planning governments to endure, e.g., the collapse of the
Soviet Union). Also explored are the recent arguments by Gueorguiev countering Hayek’s knowledge
problem. Thanks to the internet and the internet of things (IoT), Gueorguiev contends that China is near
having the means for overcoming the knowledge problem. Lastly, the article explores the roles that
network topology, artificial intelligence (AI), and machine learning (ML) can play for continued,
persistent domination in whatsoever type of topological competition between nation states and their
respective political, governmental, and economic systems, down to operational and tactical level
conflict.
Executive Summary
“If socialists understood economics, they wouldn't be socialists.”
― Friedrich Hayek
This article explores the domination in whatsoever type of topological conflict through three bounding
questions, two and one theories in opposition, and the role of artificial intelligence (AI) and machine
learning (ML) in competitions based on Intents/Constraints Observe, Orient, Decide, Act (IC-OODA)
predictor-corrector (PC) loops. By topological, it is meant the evolving networks linking nodes together:
nations through treaties, markets through supply chains, and individual people through social media,
bloodlines, etcetera.
Background: Using dynamic systems theory in 1979, Prigogine and Nicolis informed us that it is more
likely impossible for centralized planning bodies to globally optimize the evolution of human institutions
and human wellbeing. The equations describing the complex adaptive dynamics of human institutions
are inherently chaotic, subject to bifurcation, and constantly undergo spontaneous symmetry breaking.
Stabilities here and there are ephemeral, overcome by the emergence of new, ineluctable dynamics:
wise, benevolent, or malefactor leaders die while new replacements are born. New, unpredictable
technologies spontaneously emerge from underlying technologies, new music styles spontaneously
emerge from older music styles, and new forms of government spontaneously evolve from older forms
of government: from imperial Rome to feudalism to today’s bipolar world between the United States
and its powerful allies and China with its limited friends and partners such as North Korea and Russia.
A Prigogine and Nicolis case in point: The emergence of the internet was predictable by extrapolation of
the underlying and rapidly advancing technologies that were being promoted by the US government
through the Advanced Research Projects Agency Network (ARPANET) in the 1970s. The simple actions of
individuals using the internet across the globe, however, produced complex and unpredictable results. If
people expected world-wide-web conviviality, they got, rather, grassroot fragmentation into myriad,
post-reality conspiracy theory factions, flat-Earthers, anti-vaxxer movements, white supremacists,
nation state botnet disinformation campaigns, deep fakes, dark webs, cybercrime and cyberwarfare, as
well as financial system disruption through the emergence of decoupled digital currencies, ad hoc social
media network-orchestrated short squeeze attacks, and ransomware attacks to name but a few of
today’s information age virological-like emergent phenomenology.
The three bounding questions
1. Given global competition comprised of multiply overlapping, non-linearly interacting, hierarchical
geopolitical competitive processes between and within nation states, their forms of government, and
the treaties between themselves, is perpetual domination of one type of government, economic
framework, and market dynamics possible?
2. More specifically, can democracy within the framework of capitalism (owners and workers) and free
trading markets perpetually dominate over autocratic socialism, state controlled command economics
and interventionist state regulated markets [Kroenig, 2020]?
3. What roles, if any, will machine learning AI/ML play in such hierarchical Intents/Constraints
[Seidenberger, 2022] OODA loop (or IC-OODA PC loop) competitions?
The two and one theories in opposition
1. In 1945, economics Nobel laureate Friedrich Hayek theorized that centrally controlled governments
(autocracies/communist states) would always fail to endure as there would always be insufficient
information available to these types of central governments for the task of central government planning
and execution. Hayek argued that information is decentralized – that knowledge is unevenly dispersed
among different members of society – and that as a result, decisions are best made by those with local
knowledge rather than by a central authority. This is known as the knowledge problem [Hayek, 1945].
2. In opposition, Associate Professor of Political Science at the Maxwell School of Citizenship and Public
Affairs at and Director of Chinese Studies at Syracuse University, Dimitar Gueorguiev, countered Hayek’s
argument in January 2022 with respect to, specifically, China. Gueorguiev theorizes that Chinese
autocratic communism, thanks to the internet and the internet of things (IoT), finally produces sufficient
information for China to succeed under centralized government planning and execution. With the
internet and IoT, that is, China can overcome the Hayek knowledge problem.
3. Ilya Prigogine, the 1977 Nobel Prize laureate in chemistry for his work in non-equilibrium statistical
physics, along with collaborator Grégoire Nicolis, used dynamic systems theory to theoretically
demonstrate the unlikelihood of centralized planning bodies to globally optimize the evolution of human
institutions and human wellbeing thanks to the manifold inherent nonlinear feedbacks that power
economic chaos and catastrophes in the sense of mathematical catastrophe theory [Prigogine and
Nicolis, 1977; Gilmore, 1981].
Topological Network Persistence in Competition
In topological relationships and competition (the ongoing and historical processes of global
treaty/trade/conflict-based nation state dynamics, down to our local personal histories, the networks of
relationships with our friends, families, neighborhoods, and sports teams) some networks remain
persistent over relatively long timescales, often surviving major paradigm shifts, while others fade
quickly away. After the collapse of the Soviet Union, the world ceased being bipolar. The current
hegemony of the United States is currently giving way to a tripolar world with China and a reemergent
Russia leading to a new trivalent cold war. After leaving Brooklyn in 1957, the year of Sputnik nearer the
beginning of the Cold War, the Los Angeles Dodgers have remained steadfastly in Los Angeles.
The role of AI and Machine Learning (ML) in IC-OODA PC loop competitions
With respect to topological IC-OODA PC loop competition, the nature of a network typically drives its
dynamics. Many of humanity’s networks are so-called small world networks with underlying power law
distributions, e.g., wealth distributions. In capitalistic democracies, wealth distributions are not normal,
or bell-shaped distributions, but rather power-lawed long-tailed distributions with few billionaires and
the rest of us falling off into the tails. In China, billionaires can be disappeared, quickly flattening their
nation’s wealth distribution towards a more Mao Zedong-like uniformity.
The characteristic timescale on which a nonlinear, dynamical system becomes chaotic, e.g., the time at
which smooth cigarette smoke devolves into chaotic flow, is the Lyapunov time. The Lyapunov time
characterizes the limits of the predictability of a chaotic system (of differential equations). Recent work
using machine learning model-free prediction in nonlinear systems has led to effective machine learning
predictors of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension
to large multiples of the Lyapunov time [Pathak et al, 2018]. In plain speak, AI/ML methods allow for
prediction of chaotic flows for significant periods of time after the transition to chaos. IC-OODA PC loop
competitors with the better nonlinear system predictors would dominate almost always.
The Arab Spring, toppling four (topologically related) dictators and sparking (topologically related)
popular uprisings in several nearby countries, was triggered by a Tunisian man burning himself alive in a
powder keg of pent up correlation risk of topologically correlated angry youth and unions across
neighboring countries over decades. The amplification of such topological correlation risk to fuel social
division and discord by AI/ML IC-OODA PC loop social media weapons would likely extend naturally from
the marketing departments of the giant social media companies. IC-OODA PC loop topological
competitors (in some given dynamical geopolitical network market structures, kill webs, etcetera) armed
with the best IC-OODA PC loop AI/ML predictors could, in principle, hack democracies and/or
autocracies by fomenting new Tiananmen Square-like uprisings or 6 January 2021-like attacks on the US
Capitol.
A good starting point for AI/ML methods to hack nation states and their allies is through the use of
perturbative post-Darwinian IC-OODA PC loop exploration of systems of systems operating in heat (or
entropy) baths. The attainment of artificial general intelligence (AGI) may, or may not, be requisite for
driving sustained IC-OODA PC loop domination.
The resolution in brief
After conjoining Hayek with Prigogine and Nicolis to Hidalgo [Hidalgo, 2015], it is argued in this paper
that the best economic solutions, found under conditions of non-equilibrium, emerge when local
competitors are given the free market freedom to explore the entropy of evolutionary solutions using
reservoir machine learning methods designed to predict future states in complex adaptive systems
[Pathak et al, 2018]. There are many ways to generate heavier than air flight for different ends and
purposes: flies, birds, bats, locusts, and, after the respective emergences of hominid intelligence,
language, civilization, science and engineering, B-52 BUFFs, F-16 Fighting Falcons, F-15 Eagles, and F-22
Raptors. Democracy, with its reduced frictions, outraced autocracy to the moon during the Old Cold
War. On the way to Mars, the private enterprise, SpaceX, appears to be outrunning China, with its
somewhat free market system for unfree people. While autocratic China might get to Mars first, not
unlike when autocratic Russia orbited the first human being in space, the free market power of the
United States eventually overtook it.
The necessary background to deal with the three bounding questions, the two and one opposing
theories, and the role of AI/ML is provided in the body of the work in small, self-contained blocks before
addressing the questions, theories, and the roles of AI/ML for domination in whatsoever form of
topological competition.
References
F. A. Hayek, The Use of Knowledge in Society, The American Economic Review, Volume Thirty-Five,
Number Four, September 1945
C. Hidalgo, Why Information Grows: The Evolution of Order, from Atoms to Economies, Basic Books;
Illustrated edition, 2 June 2015
D. Gueorguiev, China’s Surveillance State Will Test The West – Digital surveillance could allow China’s
leaders to succeed where previous authoritarian regimes have failed, NOĒMA, January 2022
R. Gilmore, Catastrophe Theory for Scientists and Engineers, Wiley Interscience, 1981
M. Kroenig, The Return of Great Power Rivalry Democracy versus Autocracy from the Ancient World to
the U.S. and China, Oxford University Press, 2020
G. Nicolis and I. Prigogine, Self-Organization in Nonequilibrium Systems From Dissipative Structures to
Order through Fluctuations, John Wiley & Sons, 1977
Jaideep Pathak, Brian Hunt, Michelle Girvan, Zhixin Lu, and Edward Ott, Model-Free Prediction of Large
Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, Phys. Rev. Lett. 120,
024102 – Published 12 January 2018
Capt S. Seidenberger, USAF, Personal Communication on adding Intents and Constraints to the OODA PC
loop, 2022
Limits of Historical Dynamics
Many historical processes are mathematically dynamical: the growth and decline of populations,
territorial expansion and contraction of empires, trends in political centralization or decentralization, the
spread of world religions, diseases, technologies, and cultures to name but a few examples. In Historical
Dynamics Peter Turchin introduces methods of Asabiya (a concept of social solidarity with an emphasis
on unity, group consciousness, and a sense of shared purpose and social cohesion) to his differential
equation-based empirically powered stochastic models of the rise and fall of European empires between
the collapse of Rome and the beginning of the Industrial Revolution [1].
For a highly simplified model of the rise and fall of empires, consider a model with the following minimal
dynamics: Success in war leads to an expansion in territorial size. In turn, a larger territory leads to more
resources, which give an advantage in future wars. But a large empire has a longer border to defend,
and big territories are more difficult to govern. Marc Artzrouni and John Komlos simulated the co-
evolution of computer generated ‘states’ driven by such historical forces. During 3,900 iterations, virtual
states expanded, contracted, and disappeared on a Europe-shaped map. Surprisingly, a number of
simulations resulted in a configuration that resembles that of modern Europe A.D. 1800: Spain, Italy,
and Britain are clearly visible.
Beyond the Industrial Revolution, the accelerating pace of industrial innovation overwhelms such
simplified modeling methods. Without the assistance of AI/ML methods, such simplistic statistical
modeling methods cannot handle the effects of exponential technological advancement that enabled
the emergence of world wars in the previous century.
Hayek’s information problem in brief
In, “The Use of Knowledge in Society”, Nobel Laureate Friedrich Hayek argued in 1945 that information is
decentralized – that knowledge is unevenly dispersed among different members of society – and that as
a result, decisions are best made by those with local knowledge rather than by a central authority.
Hayek's article argues against the establishment of a Central Pricing Board by highlighting the dynamic
and organic nature of market price-fluctuations, and the benefits of this phenomenon. Hayek asserts
that a centrally planned economy will never be able to match the efficiency of open markets because
what is known by a single agent is only a small fraction of the sum total of knowledge held by all
members of society. A decentralized economy thus complements the dispersed nature of information
spread throughout society. In Hayek's words, "The marvel is that in a case like that of a scarcity of one
raw material, without an order being issued, without more than perhaps a handful of people knowing
the cause, tens of thousands of people whose identity could not be ascertained by months of
investigation, are made to use the material or its products more sparingly; that is, they move in the right
direction."
Arguments Countering the Knowledge Problem of Hayek Inspired from
‘China’s Surveillance State Will Test the West’, Gueorguiev 2022
Heterodox economists like Friedrich Hayek and Ludwig von Mises have long argued that control regimes
are bound to stumble due to their inability to collect all the information necessary for making globally
optimal decisions, and much less process the information if all the necessary information were available
(the problem of computational complexity). If one reviews the trajectories of democracies over
autocracies over the millennia, only liberal societies using free markets have been able to efficiently
solve the knowledge problem and the problem of computational complexity. The difference being that
control regimes work at computing global solutions, whereas liberal societies using free markets, come
across local solutions much the same way Darwinian evolution derives local solutions for local problems.
Polar bears are not endemic to equatorial latitudes.
Nobel laureate Amartya Sen observes that authoritarian regimes lack the incentive or the infrastructure
necessary for “enhancing the hearing that people get in expressing and supporting their claims,” the
absence of which prevents government from acting in a responsive or accountable manner. The hearing
of the people is a noisy, messy problem for democracies, but debate and compromise often produce
better solutions than some rigidly, autocratically imposed “right” solution from dear leader.
Paradoxically, China’s one-party government often comes across as hypersensitive to public opinion,
citizen complaints, and social media sentiment. As the Australian political scientist John Keane noted,
China’s leaders are so fearful of the loss of control that comes along with democracy that, ironically,
they act like elected officials in the West who constantly plumb the public mood in the hope of winning
the proverbial ballot.
Despite the internet and IoT, current Chinese leaders must still struggle with information hoarding
across the vast administrative structures of the country. Even in areas where micromanagement is
strong, anecdotal evidence suggests attention remains on loyalty, not efficiency.
Bottom up Information, Digital Currency, and Digital Infrastructure for Top Down Control
from the Bottom Up
In recent years, Chinese authorities have overcome some top-down information gaps by enlisting the
help of average citizens who volunteer bottom-up input, investing in centralized databases, and rapidly
expanding artificial intelligence. Retrofitting China’s analog system of control with technologies that
facilitate both information aggregation and information processing could well allow it to avoid the
pitfalls that made the Leninist model fail so miserably during the Soviet industrial age – and could place
China ahead of the curve in strategic competition with the West.
Digital Currency: China and US
China has by and large chosen its path, piloting state-backed digital currency while laying waste to
mining and finance activities tied to decentralized cryptocurrencies like Bitcoin. Unlike paper RMB, e-
RMB operates around an immutable distributed ledger, making it both infinitely easier to track and
impossible to corrupt, thus offering the CCP its preferred blend of capacity and control. Importantly,
digital RMB will likely be highly attractive to China’s tech-savvy population – an e-RMB wallet app is
already available in the Apple and Android stores. (RMB is the digital version of the people’s renminbi,
the coins and bills of China’s official currency, while e-CNY (or just CNY) is the digital version of the Yuan,
the unit of account of the country’s economic and financial system.)
The ongoing introduction of electronic currency will give China’s government unprecedented ability to
track individual and institutional transactions from the bottom up. In the not-so-distant future, China’s
government will not only have insight into how the people spend scarce resources. China will also have
the vision to decide who deserves more and who will settle for less. Digitization makes it easier for
China’s planners to optimize capital disbursements and micromanage transactions at home. When the
Chinese government injected consumer stimulus in response to the large-scale COVID lockdown in the
spring of 2020, it did so with time-sensitive digital vouchers that had to be spent on local goods and
services. Soon, large amounts of digital transaction data and blockchain strategies will make it possible
to train probabilistic models for selective stimulus to residents, targeted investment in public and
private directed firms and even for pricing complex market externalities like carbon emissions.
Digital currency also offers safer vehicles for challenging the U.S. dollar abroad, such as by offering
preferential financing to other countries using China’s Interbank Cross-Border Payment System (a
competitor of the Belgian-based SWIFT system) or by invoicing trade via e-RMB smart contracts. To the
extent that such a system gains traction, China’s central planners will see domestic fiscal control and
international monetary influence grow in tandem.
In the U.S., pressure is also growing on the Fed, Treasury and regulators to jumpstart plans for a digital
dollar, and there is growing indication that the powers that be are taking note. For some, like Fed
Governor Christopher Waller, a dollar-based CBDC is unlikely, given the diffuse nature of American
banking at the federal, commercial and regulatory levels.
Digital infrastructure
Thanks in part to the advent of digital technologies, China’s leaders are reaching a point where they
believe they have sufficient tools to overcome and move past the computational challenge of managing
ever more complexity by deepening control through connectivity. Digital control in China operates as a
dual-use technology – repressive in a security sense but progressive from a socialist one. On the one
hand, it serves a conventional coercive function by keeping tabs on 1.4 billion people and letting them
know it. On the other, it facilitates public polling, responsiveness, oversight, and probabilistic forecasting
enabled by massive caches of aggregated data on individual and group-level behavior.
Smartphones and facial recognition, for instance, make it near impossible for dissidents or protesters to
organize. These ubiquitous technologies also make it easier to fine jaywalkers or redirect traffic in case
of a jam. Public complaints about corruption can be weaponized for political purges, but they are also a
tool against self-serving officials who embezzle or waste public funds. Social credit scores help the state
coerce a preferred form of citizenship, but they also help alleviate mistrust and risk in China’s unruly
consumer market.
Chinese management through increased monitoring and machine learning-based analysis of digital
information is hardly yet a perfect system. The information generated through public polls or online
complaints is bound by perceptions about what the state deems appropriate, rightful, and legitimate.
Even the passive surveillance provided by hidden algorithms will be biased by the conscious or
subconscious self-censorship of their unwitting victims. Social credit scores can and will be exploited.
The Chinese system cares less about nuance and more about deviations of any kind that would escape
the cosseted design of society imposed by the Party’s vision. Why the public prefers one policy over
another or whether a dangerous dissident was in fact a constructive critic does not matter. In other
words, the CCP operates in a system of echoes in which the data points of individual behavior, crude as
they may be, add up in large numbers and offer the regime a remarkably detailed picture from which to
exert control – or what it calls “stability.” (Stability is a fleeting phenomenon in nonlinear dynamics.)
When the global financial crisis struck in 2007, rigid, information-based capital controls shielded
financial institutions from some of the volatility. Most recently, monitoring many streams of online data,
China has proven almost uniquely adept at controlling the spread of the coronavirus, despite the risks
posed by its large and densely packed urban centers. These triumphs have emboldened the regime to
pursue further “global” controls, digital and analog, to micromanage life across more and more
platforms, from restrictions on entertainment and education to regulatory crackdowns on national
champions in the corporate sector.
Emergence of chaotic corruption through micromanagement
The computational complexity of micromanaging a political economy, however, presents the autocrat
with a predicament. Autocrats can constrain economic actors, hobble their administrations, and
suppress the voice of citizens in efforts to simplify their computational problems. The more a regime
attempts to coerce down economic and administrative actors, the more corrupted the system becomes.
Coercion contributes to the loss of control that coercion was meant to tame, e.g.,
• The 2020 – 2022 Chinese property sector crisis is a current financial crisis sparked by the
financial difficulties of Evergrande Group and other Chinese property developers in the wake of
new Chinese regulations on these companies' debt limits
• The 1920s bootlegging in the US during prohibition
After prohibition was repealed, the sophisticated black-market business schemes and money-laundering
tactics of organized crime remained. The biggest gangs shifted their operations away from alcohol and
into secondary businesses like drugs, gambling, and prostitution. China’s top anti-corruption officials
have launched a nationwide audit of major financial firms and regulators to eliminate risk in the sector,
following debt crises at state-owned financial conglomerate Huarong and private property developer
China Evergrande.
The problem that tighter micromanagement leads to increased corruption, increases the logistic
feedback that drives the inescapable existence of nonlinear dynamics with the full spectrum of nonlinear
behaviors: chaotic transitions between strange attractors such as market crashes and the emergence of
entirely new behaviors. Paradoxically, then, only in the limit of no micromanagement, or complete
laissez faire, would causes for corruption disappear. “Corruption free”, however, is not how history
characterize laisse faire systems.
Taking a middle ground approach, if an authoritarian regime relinquishes some control to a market
system, it frees itself from complex computational challenges such as setting prices. While this may
result in a more vibrant economy, as it has for China, it comes at the cost of governance challenges by
proliferating nodes of power outside the control capacity of the regime, e.g., Chinese billionaires and
celebrities currently being quietly disappeared [2, 3].
The Costs of Control Above and Beyond Corruption
Time and again, the Chinese Communist Party (CCP) has proven remarkably willing to let the economy
and citizens absorb the costs of control. Such developments should be seen not so much as
miscalculations on the part of administrators but as a testament to their tolerance for inflicting damage
on their own economy in the service of control. Recent regulatory crackdowns, for instance, have wiped
more than 1 trillion dollars in market value from the books of leading Chinese tech companies. Most
recently (2021 – 2022), large swaths of the country have fallen under electricity blackouts due in part to
national efforts at reducing emissions in China’s exceedingly dirty energy sector.
The CCP may be increasingly inclined to impose costly controls because it feels powerful enough to reap
the political benefits while socializing the economic costs. Some of this attitude is on display in the form
of old-fashioned coercive redistribution, e.g., the new “Common Prosperity” campaign, obliging China’s
rich and powerful to give away portions of their fortune to the poor and up-and-coming.
These costs of control are not trivial. State censorship over China’s media, for instance, has undercut the
country’s soft power abroad. The China Global Television Network (CGTN), China’s flagship international
network, commands only a fraction of the audience enjoyed by Qatar’s Al Jazeera English. It is only
reasonable to expect that the CCP’s increased assault on scholars and entrepreneurs will have a similar
effect on China’s research and innovation potential.
Reduction in Degrees of Freedom
Markets, of course, are not perfect. As recent supply chain failures and the compounding tragedy of
climate change make clear, markets have a tough time absorbing shocks and pricing externalities. Even
in the best of times, market transactions suffer the inefficiencies of unwanted variability, or what the
Nobel prize winning economist Daniel Kahneman refers to simply as “noise.” The computational
problem for centralized, control-based governments dealing with innate system complexity comes to
the fore here. The empirical record continues to show that state planning consistently underperforms
market forces. China, nevertheless, feels competent enough to trade off the costs of control against
potential efficiency gains on offer from free market dynamics, e.g., recent anti-monopoly controls by
China against its “private” big-tech sector – a drama also playing out in the United States and Europe.
A big risk to China, as well as the US and Europe, is that government intervention will deter private
investment and innovation and, in China’s case, cede opportunities to less efficient state-run firms. The
question for China is if the tech-enhanced state can direct capital into productive state ventures on-par
with or better than private hands, an unprecedented achievement in the historical record. Given that
the best economic, technological, policy and other types of solutions are more typically found under
conditions of non-equilibrium, emerging when competitors are given the free market freedom to
explore the entropy of evolutionary solutions, China’s censorship, regulatory crackdowns, and rigid
policy planning of picking winners and losers among their state owned enterprises (SOEs), handicaps the
ability of Chinese communism to dominate over global democracies. Democracies, even with their
sometimes successful and sometimes useful anti-monopolistic campaigns, can explore spaces with
larger, freer degrees of freedom than can China under its system of communism. Directing Chinese
capital into productive state ventures on-par with or better than private hands is hobbled by having
access to fewer degrees of freedom to explore.
Unpredictability of (Meta)Humanity according to Prigogine and Nicolis
People don’t wake beside random spouses, let out a random dog from a random house filled with
random kids running about a random neighborhood, eat a random breakfast, and report to a random
job at a random address. Grocery stores remain at fixed locations over decades. Wikipedia links, while
subject to potential corruption, are not stochastic. History (and Wikipedia) steadfastly record that Albert
Einstein’s relativity displaced Newtonian physics and that Lise Meitner first calculated the yield of
splitting the uranium atom. While dynamic in nature, our families, our friends, our towns and cities, as
well as our governments persist for some periods of time, but wheretofore our incalculable tomorrows
slip to, between imperceptible shifts and jarring shocks, are the realms of fathomless evanescent
butterflies dancing in chaotic fogs [4].
Getting into the weeds of Nobel Laureate Ilya Prigogine and coauthor Grégoire Nicolis, consider, for a
population at large, two options and with two preference levels and , and two evolving sub-
populations and that may choose either or . The feedback dynamics, to first order, follow the
balance equations:
where . When generalizing to such partitions with transition coefficients
the equations become highly nonlinear, suffering multiple solutions branching over complicated
bifurcation phenomena. Different initial conditions result in different basins of attraction producing
different evolutions and different future histories.
Recording a particular history among myriad possible histories doesn’t represent the actions of rational
global planners attempting to optimize some overall utility function, but simply captures a particular
pattern with some level of stability. Depending on time and location, perturbations towards new
metastable states may take, grow, and stabilize, or oscillate periodically, or oscillate chaotically, or
simply fade away unnoticed. The adaptive possibilities of societies are the main source allowing them to
survive long term, to innovate of themselves, and to produce originality. Innovations and innovators
rewrite the equations, and more so in democracies than in autocracies [5].
Genocidal autocratic dear leaders with their alternative facts, red books, gulags, gallows, and gas
chambers, their Big Brother secret police and social credits, supported by eternally aggrieved
brownshirts and pseudo religious warriors, prefer a steady state:
where is a constant.
Strikingly, the non-too dissimilar differential equations of gene networks are anything but chaotic,
driving, rather, homeostatic processes [6]:
Perhaps more stable methods of government and commerce may yet be inspired by homeostatic
biological systems.
Human and nature dynamics (HANDY) model, a Hayek, Prigogine and Nicolis case in point
In the HANDY model [7]:
stands for commoners and for elites, is the birthrate, is the death rate, and are the
consumption of elites and commoners, and is subsistence wage per capita times commoners’
population. The are taken to be functions of , where stands for accumulated
wealth. The modeling of natural resources is an amalgamation of nonrenewable and renewable
resources with regeneration term and depletion term . The regeneration term is
written in a logistic equation form (which is subject to chaos and bifurcation) with a regeneration factor,
, with exponential regrowth for low values of , and saturation when approaches , where stands
for the level of natural resources.
Many equilibria and non-equilibria dynamics are possible in the HANDY model. Below is a case for an
approach to equilibrium:
In the figure below, with different parameters, is a case for boom/bust cycles:
Thanks to the presence of the regeneration term (a term of feedback following the logistic equation) it is
possible to find a large set of initial conditions leading to chaotic dynamics.
Review of Rabbit Population Logistic Equation Dynamics, Bifurcation, Period Doubling, and Chaos
The logistic equation (or map) is: , where is the growth rate and is the ratio of
existing bunny population to the maximum possible population. Chaotic behavior arise from this simple
non-linear dynamical equation as varies.
With , the reproduction rate between 0 and 1, the population eventually dies independent of the initial
population. With between 1 and 2, the population quickly approaches the value
,
independent of the initial population. With residing between 2 and 3, the population will also
eventually approach the same value as in the previous sentence, but it will fluctuate around that value
for some time before asymptotically settling down. With between 3 and , from almost all initial
conditions the population will approach permanent oscillations between two values – the population,
that is, bifurcates, oscillating one year from the low value to the high value and back again – period
doubling. With between 3.44949 and 3.54409 (approximately), from almost all initial conditions the
population will approach permanent oscillations among four values, the next bifurcation and period
doubling, and so on.
In dynamical systems theory, a period-doubling bifurcation occurs when a slight change in a system's
parameters causes a new periodic trajectory to emerge from an existing periodic trajectory—the new
one having double the period of the original. As can be seen in the figure above, as continues to grow,
the period doubling phenomenology explodes chaotically (and effectively unpredictably).
The cause of logistic equation bifurcation is its recursion. The logistic map falls along the center of the
three-dimensional Mandelbrot set! (See Veritasium YouTube.) Recursion, therefore, is a a necessary
condition for chaos, and chaos ensues from almost all initial conditions (ICs). Some ICs lead to stability.
Background on the OODA loop of Col Boyd, USAF
The predictor-corrector method developed by Col John Boyd in the 1950s: the Observe, Orient, Decide,
and Act (OODA) loop is a concise tool for navigating the networks of reality in conflict situations to win
fights. OODA loop theory was initially developed for fighter pilots. In a dogfight, the pilot who can run
through the OODA loop faster forces the opposing pilot to react – ditto for boxers or tennis players.
Beyond dogfights, Boyd eventually theorized that corporations, governments, and militaries, all
connected over myriad networks (communication channels, geopolitical relationships, geographical
relationships…) possess hierarchical OODA loops at the tactical, operational, and strategic levels. The
most effective organizations, Boyd believed, are those with decentralized chains of command utilizing
objective-driven orders rather than method-driven orders to harness the creative abilities of
individual leaders at each level. Method-driven orders restrict degrees of freedom and ignore local,
decentralized ground truth to better inform objective-driven goals. Accordingly, OODA loop theory
aligns well with Hayek as a tool for finding locally optimal solutions in complex, adaptive systems built
from networks of systems of systems (SoSs).
In competitive interactions of whatsoever nature between intelligent agents, the agent who observes
the surrounding networks and their dynamics in relation to the opposing agent with quicker and greater
accuracy, can orient itself more quickly, decide on a course of action more quickly, and take action more
quickly over its slower, less situationally aware adversary. In this sense, Boyd’s OODA loop put substance
to one of Sun Tzu’s aphorisms: the successful general dictates the nature of battlefield combat,
compelling the opposing general to fight on his or her ground and his or her terms.
Implicit in the OODA loop framework is the intent of the competitor (or warfighter) and the constraints
of the problem space. Explicitly calling out intents and constraints in the OODA loop framework, as in,
IC-OODA loops, forces planners, logisticians, weaponeers, and related support teams to avoid under (or
over) specifying battlespaces.
To dominate in a fog of nonlinear war, topological IC-OODA loop-based fighting methods developed
from constructive modeling and/or virtual wargaming studies employing predictor-corrector methods
should, therefore, be explored to predict, create, and hone advantageous IC-OODA PC loop-based
tactics, techniques, and procedures. Literature with key words: OODA loop AND control theory, is to be
found online across a spectrum of real-world (military) problems [8-13].
Topological Networks and Dynamics – A Ubiquitous Battlespace for IC-
OODA PC loop Competitions
Many kinds of networks exist in nature or have been engineered by human genius. Among the most
common networks in both natural and human-based networks is the ubiquitous small world network.
Small World Networks – A Common Battlespace
While many distinct types of network topologies exist, small world networks underlie much of the
dynamics of (meta)humanity and potentially explain how our brains can change subjects so quickly [14].
A small-world network is a type of mathematical graph in which most nodes are not neighbors of one
another, but the neighbors of any given node are likely to be neighbors of each other and most nodes
can be reached from every other node by a small number of hops or steps, e.g., any two persons in the
world are at most six degrees of separation from each other (really far less). The author’s spouse worked
for an eventual Texas Supreme Court Justice, for whom, in a third degree of separation, President
George W. Bush campaigned, and from George W. Bush it’s only one step to the Pope and so forth to
almost anyone on the planet.
More specifically, a small-world network is defined to be a network where the typical distance, ,
between two randomly chosen nodes (the number of steps required) grows proportionally to the
logarithm of the number of nodes, in the network, . Some small world nodes (such as
popular YouTubers) are highly connected, but most nodes have few connections. Small world networks
such as Instagram allow for videos from unknown small time YouTubers to pop up and go viral from
time to time. Going viral exemplifies a catastrophe in the mathematical sense.
Small world networks have many interesting and exploitable properties. Most Tweets and YouTube
posts quickly fade away. Occasionally, a Tweet produces a massive shock (it goes viral). Research based
on Fundamental Interpersonal Relations Orientation (FIRO) theory [15] suggests that to increase the
odds of creating a viral shock, one should target netizens who seek to be individualistic, altruistic, and
are content hungry [16]. These people will pass your message on. Meanwhile, avoid netizens seeking
personal growth [17]. Knowing who is better at spreading information, and hence who to target for
information campaigns, reference [18] includes a case study of successful internet weaponization for
fake news, [19] an elaboration on ways to weaponize artificial general intelligences (AGIs) for social
engineering, and [20] a study of the algebraic topology of scientific collaboration networks. Researchers
should seek methods to automate these approaches to hacking small world internet battlespace
networks using IC-OODA PC loop to promote division and distrust in adversary nation states.
Nothing new under the sun: Weaponization of the science of fake news with mathematical
feedback metrics
Many studies going back decades demonstrate that Fake News is a viable weapon. One early study in
the 1970s tested the phrase “French horn players get cash bonuses to stay in the U.S. Army” [22]. The
study concluded that the more a lie (or a truth) is repeated, the more believable it becomes. Other work
has shown “fake news” travels faster [23]. Thanks to the pervasiveness of the Internet of Things (IoT),
big data, and social media, it is possible to design machine learning mathematical feedback metrics to
tune Fake News campaigns [24-27] towards herding (correlation) or catastrophic events, e.g., fomenting
crowds towards precipitous violence by increasing correlation risk within generally unpredictable
complex adaptive systems. Reference 27 introduces the synchronizability matrix to characterize the
maximum synchronizability of a network. Several new concepts, such as sensitive edge and robust edge
methods, are being proposed for analyzing the robustness and fragility of synchronization of a network.
Small world networks in China
There is work regarding small world Chinese shipping port networks [28] and airports with respect to
spreading disease [29], a topic of high value after the COVID-19 pandemic. It should not surprising that
the Chinese are interested in small world networks [30], with applications to cultural dynamics not being
ignored [31]. The literature, however, is poor regarding the characterization of China’s social media. An
open question is whether Chinese social media platforms assume small world structure and dynamics.
Research is also being carried out on the nature of Chinese propaganda being conducted within China
through social media [32]. Perhaps this work by the Chinese is aimed at better control of internal social
media networks from insider threats, and/or perhaps to obtain greater political resiliency against
external threats from other types of government.
Part of the answer to above question comes in comparing the advantages of democracies over
autocracies.
In Democracy versus Autocracy: United States versus China and Russia And a Framework for
Machiavellian General Artificial Intelligence Project, Matthew Kroenig’s 2020 book, The Return of Great
Power Rivalry, Democracy versus Autocracy from the Ancient World to the U.S. and China, is reviewed.
Reviewing the historical record, Kroenig makes a convincing case that throughout the history of
democracies from Athens, Republican Rome, the Venetian Republic, the Dutch Republic, Great Britain,
the United Kingdom, and of late the United States, the best frameworks for adaptability, innovativity,
and originality are well informed, law abiding democracies with well-motivated populations. For citizens
of democracies there is reward for risk. For citizens of autocracies, identifying solutions to problems is
often too risky. In Stalinist Russia, do-gooders often got tagged as non-conformists by bosses who did
not want to look bad, and were often shipped off to some gulag or shot in a ditch.
Power Laws (or Scale Free Laws)
A network is normally distributed when each node has roughly the same number of links to other nodes.
Networks where the majority of nodes have few links to other nodes, while a minority of nodes have a
large number of links to other nodes, e.g., Twitter and Facebook, have long-tailed distributions (see
figure below). These popular social media sites exemplify long-tailed small world networks, which are of
intense interest across many fields such as cancer genetics, the internet, languages, wars, etc.
Significantly, small scale networks bear the stamp of natural selection by survival and propagation of the
fittest.
Comparison of a normally distributed network versus a long-tailed network
Small world networks follow power law distributions and are otherwise synonymous with so-called scale
free networks.
For an example power law, consider earthquake statistics. Earthquakes of magnitude eight on the
Richter scale occur roughly once per year (see figure below). Earthquakes of magnitude seven occur
about ten times per year. Smaller tremors of magnitude four occur about ten-thousand times per year,
and so forth. Thus, with rounding for the sake of clarity, the number of earthquake events per year, N,
for a given magnitude of earthquake, M, follow the power law: N = 9.510 M-10. A log-log plot of number
of earthquakes by magnitude, with linear slope, is given in the figure below.
The distribution is said to have a long tail because earthquake magnitudes vary across a large scale from
tiny tremors to giant quakes. Adult male weight is approximately normally distributed (see figure below
on the left hand side).
Comparison of normal distribution versus a long-tailed distribution
The occurrence of power law statistics does not guarantee the existence of an underlying network. Small
tremors due to fracking in Oklahoma are not related to small tremors in Tibet. Conversely, power laws
often indicate the presence of the kinds of scale free networks that can be hacked to create large shocks
through social engineering methodologies with bots and trolls on the internet, wars by propaganda, and
so forth.
Of key interest to physicists [33], power laws indicate that small world networks organize themselves so
that they teeter on the brink between order and chaos, e.g., a crowd synching up that might readily turn
into a mob at the slightest provocation [34]. The sudden (catastrophic) change from orderly crowd
behavior to uncontrolled mob behavior exemplifies a phase transition, e.g., water heated into steam.
With respect to shaping the battlespace of angry peoples, history offers suggestions for attack by IC-
OODA PC loop attacks. On 20 January 2001, President Joseph Estrada of the Philippines became the first
head of state in history toppled by a smart mob [1]. More than one-million Manila residents mobilized
and coordinated by waves of text messages to assemble at the site of the 1985 ‘People Power’ peaceful
demonstration that had toppled the Marcos regime. A few years later, mob-like behavior was
repeatedly precipitated on smaller scales during and after the 2016 American presidential election
season through Russian Facebook attacks [35-37].
Need for IC-OODA PC loop doctrine for social media warfare – hacking trust
Much as the world’s militaries began developing air power doctrine during and after WW I, military and
intelligence departments might do well to begin formalizing doctrinal processes for IC-OODA PC loop
social media warfare. Social networks are based on three “hackable” ideas:
1. Social foci – Think links created by people sharing social foci: workmates, schoolmates, or AI
social media avatars
2. Triadic closure – Links formed among people who share friends, or AI social media avatars
3. Homophily – Referring to the stickiness of links, e.g., people who like opera.
Trust is key to the development of large social networks. With respect to economies, the presence of
industries of different sizes indicates the presence of trust; see Trust: The Social Virtues and The Creation
of Prosperity by Fukuyama 1995 [38]. Countries that have vigorous private nonprofit organizations such
as schools and hospitals are also likely to develop strong private economic institutions. Trust enables
networks and conversely. Trust between diverse races, cultures, and religions is more necessary in cities
than in isolated, homogeneous rural areas.
Finally, trust is a form of social capital. Silicon Valley had (has) dense social networks and open labor
markets that encourage entrepreneurship. Its former competitor along Route 128 had secretive firms
that made employees sign nondisclosure agreements. Silicon Valley obliterated Route 128 out of
competition. Trust is why employers tend to hire referrals as referrals are less likely to quit. Building
trusting relationships is a slow process, hence why the military moves its people around to forge
networks.
The antithesis of trust is division, the gift from social media giants, and fomenting division is highly
algorithmic, as Cambridge Analytica can attest to [39]. IC-OODA PC loop methods can likely be
engineered to improve on Cambridge Analytica methods.
Self-organized criticality (SOC) and learning in small world networks
Infectious diseases, computer viruses, ideas, and rumors, among many other types of information
shocks, can spread like wildfires in small world networks where a minority of nodes have lots of links,
e.g., the spread of viral pandemics may be enabled by airports such as John F. Kennedy which link the
globe together. (See Forest-Fire Model.) The process of self-organization leading towards behavioral
criticality (where phase transitions occur) is of growing interest. In neuroscience, SOC is informing the
study of self-organizing recurrent neural networks (SORNs) that in turn inform how biological brains
learn [40, 41]. Many experiments have suggested that the brain operates close to a critical state, based
on signatures of criticality such as power-law distributed neuronal avalanches. SOC has become a
general framework for the study of machine and human cognition [42].
With an eye towards IC-OODA PC loop weaponization, an excellent discussion on SOC and Fake news
appeared in Scientific American on 14 July 2017 [42]. There were three key takeaways. The first was
that, even in a perfect world where everyone wants to share real news and is capable of evaluating the
veracity of every claim, some fake news still reaches thousands (or millions) of people thanks to
information overload. The second was that there is not nearly enough time to verify everything online
before sharing links. Finally, the third takeaway was that the internet is, not surprisingly, a collection of
small world networks. Similar conclusions have been reached in other studies [43].
Catastrophes in small world networks (see Appendix on Catastrophe Theory)
In dynamical systems theory, catastrophe connotes a sudden, irreversible change in the behavior of a
dynamical system, e.g., stock market crashes and giant avalanches of snow. Many causes underlie
societal catastrophes. All have one thing in common: strong correlation and are subject to SOC:
• In October 1929, news broke out that public utility holding companies would be regulated. A
sell-off cascaded through the system as overleveraged (highly correlated) margin investors
became forced sellers.
• On 7 December 1941, Imperial Japan bombed Pearl Harbor, sealing its doom as America aligned
into an angry, determined correlated people (anticorrelated to Japanese people).
• On 17 December 2010, Tarek el-Tayeb Mohamed Bouazizi set himself afire, precipitating the
Arab Spring in a region long on dictators and highly correlated aggrieved citizenries.
The process of self-organization makes contact with the world of dynamical systems theory and
complexity theory [2]. Reference 2 details important concepts such as bifurcation, order parameters,
nonlinearity, and symmetry breaking. Such is spontaneous symmetry breaking, so that snowflakes, made
from symmetrical droplets, take on many shapes. One of the aims of statistical physics is the
characterization of critical points and order parameters that lead to instability, bifurcation, changes of
phase, symmetry breaking, and new network topology [21]. This field of study should be integral in the
designers of warfare by IC-OODA PC loop warfare.
Successful hacks of ‘FundMe’ and Support_Political_Issue websites
The two examples of successful hacking of various kinds of campaign below are only two of many such
examples from physicist Albert-László Barabási in ‘The Formula: The Universal Laws of Success’, Little,
Brown and Company, 6 November 2018. The experiments are based on using preferential attachment as
a network hacking tool. The design of algorithms on complex networks, such as routing, ranking or
recommendation algorithms, requires a detailed understanding of the growth characteristics of the
networks of interest, such as the Internet, the web graph, social networks, or online communities. To
this end, preferential attachment, in which the popularity (or relevance) of a node is determined by its
degree, is a well-known and appealing random graph model, whose predictions are in accordance with
experiments on the web graph and several social networks. Linear preferential attachment network
processes follow the discrete Yule-Simon probability distribution: for integer
, , and is the beta function.
Hacking Change.org
Change.org is an American petition website and a San Francisco-based business with the same name.
Change.org has over 400 million users, offering the public the ability to promote the petitions they care
about to potential signers in 196 countries to promote change in their communities. Curious how a
Change.org campaign gains visibility, sociologist Arnout van de Rijt created an experiment by selecting
two hundred early-stage campaigns and granting a dozen signatures to a hundred randomly selected
ones [44]. The causes Arnout randomly supported, thanks to preferential attachment, were far more
likely to accrue further signatures than those he snubbed. That means that even when we are presented
with an issue of potentially deep political or ethical importance, an issue about justice, say, a potential
unjust hacking mechanism may come into play. We may think of these petitions as a way to further
democracy, but what Arnout discovered is that while all causes are created equal, it is previous success,
not a cause’s moral urgency, that drives its later success.
Hacking Kickstart
Kickstarter is an American public benefit corporation from Brooklyn, New York, that maintains a global
crowdfunding platform focused on creativity. In a similar experiment hacking Change.org, van de Rijt
hacked Kickstart campaigns by giving small amounts of funding at random to half of a group of funding
campaigns to study the effects of preferential attachment. The funding campaigns that received small
amounts of seed money pulled away from those funding campaigns that received nothing from van de
Rijt’s experiment. In the next experiment, van de Rijt tested how multiple random donations to a single
source fared. In cases where he withheld funding altogether, 68 percent of projects failed, not attracting
other donations. By contrast, only 26 percent floundered when they received one of his blind donations.
But when he went as far as offering four blind donations, only 13 percent of the projects failed. In other
words, more initial support virtually guaranteed success. Additional donations, however, resulted in
progressively smaller returns. A single initial donor attracted 4.3 additional donors. But the three
following donors attracted only 1.7 more donors apiece. In monetary terms, van de Rijt’s initial
investment, which averaged $24.52, returned a remarkable $191.00 on average. But the return on his
three subsequent investments only yielded half of that—$89.57 each. Simply put, the push provided at
the get-go by the first donor made a much more significant impact than caused by subsequent donors.
Using preferential attachment (wherein success begets success) as a tool to hack social networks of very
diverse natures can likely be weaponized through machine learning and artificial intelligence IC-OODA
PC methods.
Persistent success for domination over networks
Persistence, shaped by the willingness to try repeatedly for a breakthrough, eventually pays off.
Suppose we can express the success, , of a product (a research paper, a startup, an information
campaign…) according to:
where refers to one’s capability to turn one’s idea into success and is the merit of the idea. Opening
a fast food joint in a crowded mall’s food court is assigned an value near zero.
Research has shown that one’s individual value does not really improve over time; rather, it remains
relatively stable throughout our lifetimes [45]. One strategy to improve is to collaborate with networks
of others (local partners, coworkers, and/or distributed teams) such that
This approach, of course, extends naturally to firms, and quite likely to nations states, and even to
nation state alliances such as the North Atlantic Treaty Organization (NATO). As a leader in some
organization, a formulation for everyone’s success would be:
This would extend to intelligent agents employing IC-OODA PC loops to:
Following in the next, final section is a specific example of how machine learning and artificial
intelligence methods can extend the predictive temporal horizon significantly further than had
previously been thought possible in complex, nonlinear systems represented by systems of (higher
order) differential equations.
Dynamical systems, complex adaptive systems, and powerful predictive
machine learning technologies for IC-OODA PC-based superiority
A complex adaptive system is a system in which a perfect understanding of the individual parts does not
automatically convey a perfect understanding of the whole system's behavior [46]. Weather systems
and stock markets are complex adaptive systems. Interactions among system elements (or agents) are
nonlinear. Small changes due to interactions or external stimuli can effect significant changes in system
behavior. Hence the horizon of prediction is generally small. Two infinitesimally near particles of
cigarette smoke rising up smoothly near the end of a lit cigarette eventually undergo rapid, turbulent
separation, exceeding the prediction power of any supercomputer. Too much error gets integrated far
too quickly (see Lyapunov exponent [47]).
Reservoir computing is a framework for computation derived from recurrent neural network theory that
maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-
linear system called a reservoir. Recent work using reservoir computing in nonlinear systems has led to
shockingly effective machine learning predictors of spatiotemporally chaotic systems. The figure below
compares actual versus reservoir machine learning computing prediction of the Kuramoto-Sivashinsky
(KS) equation, a fourth-order nonlinear partial differential equation to model the diffusive instabilities in
a laminar flame front [48].
Panel (a) contains the actual data from the KS model. Panel (b) has the reservoir computing prediction.
Panel (c) contains the Error [panel (b) minus panel (a)] in the reservoir prediction.
Conclusion
Finite games have winners and losers, the rules of the game are known to both sides, the boundaries of
the playing field are well-defined, a scoreboard keeps track of the game’s activity, and at the end of a
prescribed period of time, or when options run out, a winner is determined. In infinite games, on the
other hand, there no winners or losers. Rules often do not exist, and if they do, they are fuzzy and open
to interpretation. The playing field is undefined, and progress is hard to measure. Opponents change
frequently, as does the game itself. There are no clear winners or losers in the infinite game.
Competitors drop out of the infinite game when they lose the will or resources to stop playing. The goal
is to outlast your competition.
Using these definitions, Simon Sinek, author of The Infinite Game, asks a compelling question regarding
the Vietnam war: how do you decimate your enemy, win all the crucial the battles and still lose the war?
Sinek boils down the answer to the fact that the United States was playing a finite game, aimed at short-
term victory, while the North Vietnamese were playing an infinite game, with the goal of outlasting the
enemy. Eventually, the Americans simply ran out of the resources and the willpower to stay in the game.
In the potentially infinite game between democracies and autocracies, Russia is making a finite game
move by invading Ukraine late this February 2022. China is likely gauging the invasion for lessons
learned regarding its ambition to invade Taiwan.
In the infinite game, competitions between nation states, between free market economies and
command economies, between democracies and autocracies in complex adaptive networked
battlespaces, domination will go to those with superior data collection and information processing for
finding sufficiently good, local Hayek solutions, an ironic conclusion for autocracies with command
economies chasing after global solutions should they ever defeat their free market foes, e.g., China’s use
of free markets on their path to world domination through communist centralized government. That
more degrees of freedom lead to better local solutions will be hard to overcome.
For winning the peace
IC-OODA PC agent-based reservoir computing machines, or the like, potentially running on quantum
computers, should form an initial basis for predicting human preferences relations over all hierarchical
spatial and temporal scales
The predictor systems that most accurately predict the future (in the same sense of the best hurricane
prediction models at predicting the paths of hurricanes) should be used for handling large scale Monte
Carlo simulations out to some reasonable future period of time (most likely between a few days or
weeks to at most a few months) to seek out the best plays for increased domination.
For winning war
If the predominant mode of human prediction at the speed of war is linearized Taylor-like prediction,
human combatants will soon require battle management aids for handling faster, more maneuverable,
and more numerous threats in battlespaces subject to nonlinear complex adaptive behavior. At some
point, only superior IC-OODA PC agent-based reservoir computing machines will be capable of
dominating competitions in complex adaptive battlespaces by looking as far ahead in Lyapunov time
scales as possible. The need for artificial general intelligence for domination is an open question.
Appendix on Catastrophe theory
Catastrophe theory is a branch of bifurcation theory in the study of dynamical systems theory. In the
most general sense, it concerns itself with the search for solutions for a
system of integro-differential equations (or worse) defined over a space whose coordinates are
:
where
In thermodynamics, catastrophe theory is interesting in the study of:
• Phase transitions (first and second order)
• Critical points and Triple points
• Fluctuations
Can the universe be modeled as object oriented programming (OOP) IC-OODA PC agents in SoSs through
reservoir (quantum) computing methods? An agent can be a tree, a forest, Google, or NATO. Where
NATO has intents and constraints, a tree does not, and similarly for prediction correction methods.
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Published 12 January 2018
