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Title: Maximum Viable Economic Planning Author: Emmi Bevensee Date: July 30th, 2020 Language: en Topics: planning, economics Source: Retrieved on 7th June 2021 from https://c4ss.org/content/53166 Notes: This essay is part of the C4SS Mutual Exchange Symposium: “Decentralization and Economic Coordination.” It is written in reply to The Problem of Scale in Anarchism and the Case for Cybernetic Communism by Aurora Apolito.
Even in his devastating critiques of high-modernist central planning,
James C Scott acknowledges the benefits to planning and the levels at
which it can occur with relative safety. The author M Black also
challenges us not to fetishize decentralization in such a way as to
ignore the benefits of non-coercive degrees of centralization. So some
degree of planning should exist. There seems to also be general
agreement between all of the authors that some complexity and scale
based obstacles exist to central planning, even in less centralized
forms. From that point we can debate where these lines are.
I will continue to advocate that we intentionally build out multiple
competing/cooperating social welfare planning measures up to and no
farther than our limits and simultaneously explore the problem space of
different value signal feedback loops such as markets. This approach of
testing a wide range of planning and value signal coordination
approaches follows the line of thinking in Kevin’s sentiment of “Let
one-hundred flowers bloom.” As Aurora’s essay is the one most directly
opposed to my approach I will focus on challenging its claims and
incorporating its advancements in the theoretical development of a
Maximum Viable Economic Planning measure. Comprehensive integration of
this limit should form the basis of any model for a new economy or array
of overlapping new economies.
Beautifully integrating and generating novel insights from the fields of
complexity science, network science, information theory, and
neuroscience Aurora offers what I am not shy to say is one of the most
substantive advancements in mathematical anarchist (communist) thought.
It faces boldly the problem of scale in non-hierarchical systems in ways
that few others have even attempted. It must be read by
anarcho-communist theorists and must be seriously contended with by
people in P2P spaces, libertarianism, social ecology, and other
decentralized economics as well as being of interest to mathematicians,
computer scientists, and economists more broadly. It’s fascinating and a
joy to read. However, while its contributions are substantive, it
suffers from several critical failures and other weaknesses which could
be strengthened through future work. The contributions it does offer
though help to elucidate a more robust measure of Maximum Viable
Economic Planning which should be the basis for any conversation about
planning, decentralization, and economic coordination.
The basic premise of the piece is that the optimization of economic
coordination through the profit mechanism in markets should be replaced
by an optimization of complexity through cooperation. Aurora parses
several of the major advancements in related fields to settle upon a
proposal that optimizes for “integrated complexity” utilizing an
effective complexity measure built into a network analysis. One should
take a moment to truly consider how beautiful that is on its face. It
offers much to the problem of coordination, a shared metric for
optimization of ideal quantities in a supply chain, which is a major
area of contention in the calculation debate.
While this is a deeply intriguing view of societal evolution in general,
and decentralized economic coordination in particular, it absolutely
does not replace or solve against markets in the way the author assumes
it does. The critical failures are as follows:
The substantive open problem of revealed preference and discovery in
economics directly undercuts the viability of this proposal for large
scale economic coordination. This issue was covered in some depth by the
essays of myself, Gillis, and Miroslav as well as in great depth, if
from a more liberal perspective, by Don Lavoie in many of his books but
notably in Rivalry and Central Planning. There are also interesting
parallel spaces of exploration using technology such as Holochain, as
mentioned in the article by Sthalekar.
Relatedly, this essay does not actually deal with any of the practical
issues of economic coordination such as, centrally, supply and demand.
It claims to supplant Linear Programming but does not accomplish the
basic feat that LP does. It does something more interesting but it does
not solve supply chain optimization. The algorithm proposed would be
better suited for analyzing possible modes of societal and economic
evolution rather than serving as a practical replacement to markets at
the material levels. However, optimizing such evolution is also a task
that markets freed from capitalism and monopoly rents can accomplish, as
shown extensively in the various works most commonly associated with
C4SS as an institution. This proposal could be thought of as a value
vector creator for which something like Linear Programming could then
optimize the ideal proportions of labor for. If that is the case though,
all the critiques and limits of Linear Programming to central planning
still apply to this model.
The claim that this algorithm replaces the need for subjective value
measures overall is completely unsubstantiated with some disturbing
possible implications. Even capturing the raw input measurements for
maximized integrated effective complexity does not skirt the problem of
accurate input information unless the author (which I doubt) proposes
some form of massive surveillance architecture to capture the
information needed for this form of cybernetic coordination.
While I will not go into it in-depth here, the author takes a very naive
view of markets as automatically generating capitalism, exploitation,
and massive unequal accumulation. She does not adequately address the
wide arrange of known and unknown spaces of exploration around exchange
such as but not limited to, left-market anarchism, mutualism, Georgism,
and value-signal employing P2P systems. The author does not show a depth
of understanding of the critiques of these and other schools of thought
that are anti-capitalist but pro-market. Most importantly, she does not
understand the types of countervailing and centrifugal forces that C4SS
has long labored to explore in the process of resisting the formation of
capitalism while utilizing some of the benefits of exchange. Her
knee-jerk response to markets as automatically leading to capitalism is
a common one because it makes some sense at the surface level (coming,
as I did, from the left it was a hard pill for me to swallow). However,
as a wide range of subsidies and artificial economies of scale have
distorted and made myopic our visions of what is possible, it’s the duty
of the anarcho in anarcho-communism to bravely facedown groupthink in
the pursuit of root dynamics and mapping the wide space of possibility.
These issues are extremely nontrivial. They do not, however, minimize
the overall contribution of this work, but rather call into question
some of its central premises about what it can and cannot accomplish.
This all being said, the contributions of this essay are also extremely
non-trivial, even to, and this may dismay the author, the study of mixed
market and planned decentralized economies. Indeed it offers a great
jumping off point to further develop a theory of Maximum Viable Economic
Planning.
The transition involved in realizing a new ideal economics will involve
a central conflict between those efforts devoted to expanding the
non-market spaces of mutual-aid and social welfare and those innovating
through the various internally competing and cooperating exchange
nexuses. While this space of contestation will be dynamic and complex,
as it already is, with constant new innovations blossoming in the
cracks, we can build some structure now in order to reduce harm while we
explore the problem space. So while Belinsky discusses Minimum Viable
Economic Planning, I argue that one form of harm reduction for
exploration is developing a sense of the Maximum Viable Economic
Planning Limit.
The basic point to understand is that our ability to plan should be
greater than or equal to how much we are currently relying on planning.
A high level and deeply simplified overview would look like the
following:
Model rate of complexity processing >= Required rate of complexity
processing
However to engage with non-math audiences as much as possible we can
break this down further:
model = complexity / time
Think of this as a rate like miles per hour. It’s essentially a rate of
computation within some constraints. An example would be model = 10 bits
per millisecond or something like that. The model can be anything from
linear programming on a certain array of computers or a direct
democratic system of federated councils.
actual model >target model
The actual model is what we’re currently capable of doing. This idea is
agnostic to how you’re solving the planning (ie linear programming, deep
learning, councils, or whatever). This is saying if we use this type of
algorithm to solve this problem we have right now, this is what our rate
of solving it will look like.
The target model is what rate of solving the problem we need to have.
For example, how many linear programming variables we need to compute in
a certain amount of time to make sure a million people don’t die from
not getting a vaccine.
The actual model must be greater than the target model or it will be
failing to reach the demands placed on it.
actual model = target model
When we set it up this way you can solve for either time or complexity
in the actual model side by making the other static and making it an
equality. So say:
target model = 1 bit / 5 milliseconds
actual model = 1 bit/ 10 milliseconds
Clearly we need to either double the amount of bits we can process or
half the amount of time we can process it in, if we want to produce the
type of robust social economic coordination plan we need to thrive.
This simplified model of rate of computations compared to what we need
to ensure everyone gets fed makes the problem of scale more stark. We
can reduce the amount of complexity we need to produce or increase our
computational methods or infrastructure. The major contribution of
Aurora’s work is to help us define a compelling measure for economic
(“integrated”) complexity that we could incorporate into an MVEP
calculation in order to face soberly our computational limits. Though
this does not solve the other issues related to her proposition, it
opens the door for a whole new field of inquiry building on both this
and her work. For example, teasing out what this MVEP inequality would
look like with more robust measures on complexity, could help us gain a
more nuanced view of the possibilities inherent to our given model, and,
as Aurora mentioned, optimize towards more complexly interconnected and
sustainable societies.
Once we’ve established this basic theoretical grounding it starts to get
even more complex. The alternative to tankie style central economic
planning is what’s called local knowledge which is a way of
decentralizing and parallelizing the problem by relying on individuals
to make the best decisions they can about their own domains and then
things roughly maintain a dynamic (dis)equilibrium.
My suspicion is that as you move closer to local(decentralized)
knowledge your target rate of computation decreases because you can
parallelize. But if the Austrians are correct, and I imagine they are
about this even if their conclusions are weird, then that is not a
linear descent. A locally embedded human mind can solve exponentially
more than a broken down super-computer. This means that local knowledge
has more computational power overall by parallelizing the problem. This
is explored to some extent in Bilateral Trade and Small World Networks
by Wilhite where he looks at different nested scales of trade networks.
Through agent based modeling he shows how: global trade networks require
high search resources but are able to find an optimum, local trade
networks require low search resources but are not able to find an
optimum, and hybrid networks allow for some leveraging of both local and
global coordination knowledge. This could suggest that some planning can
help a hybrid model allocate resources most optimally while leveraging
local knowledge at the same time. While planning and even direct
democratic consensus have complexity limits, this does not eliminate its
utility in total. What’s more, there are situations in which the high
context information provided by deliberation, as opposed to the stripped
signals of prices, can be more beneficial. An unintended hypothetical
proof of the hybrid model is how a locally planned social safety net can
be locally optimal if not globally optimal, but nonetheless can help
provide the basic needs of a community to better prepare them to engage
in complex global coordination ie. If you aren’t starving to death you
are more likely to be excited to build pro-social supra-local
collaboration.
(technical section) This idea can be expanded by looking at how
computation actually happens in a computer as well. The following
picture is an AMD microchip. Most of what you see in this microchip is
actually memory caches and connections. The logical computation is
essentially free. What is expensive are all the interconnects required
to move data around. In this way, even the computer that is expected to
solve our coordination problems faces similar computationally expensive
dilemmas of mitigating Shannon entropy of communicating preferences at
different scales. This is why when trying to write high-performance
software, the first thing to do is to maximize data locality and
minimize communication. This logic also applies to all methods of
coordinating an economy, not just those that rely on a microchip. [1]
Looking into the technicalities of applying super-computation to
problems of (decentralized) economic coordination will help us to more
accurately model what is possible and gauge our risk-taking
proportionately. Similarly it allows us to break down the problem into
more computable chunks or incorporate innovative overlaps with
non-decentrally planned networks of cooperative exchange.
Linear Programming
Most of the non-market based models including among the decentralists,
knowingly or unknowingly, rely on the contributions of Cockshott and
Cottrell as proof of the calculability of economic planning and
coordination through Linear Programming. There is much to be said about
the nature of their models overall but suffice it to say that the actual
code that people think solves all of these hard problems is a messy old
Java repo with multiple years old unresolved pull requests and an open
issue declaring “there is no bread”:
Cockshott’s assumptions in this regard can be seen in the way he teaches
this topic in that he, like Aurora, claims that cybernetics and the
internet solve these problems:
problem of dispersed information – Hayek’s key objection
Mises objection
transferable labour credits
This, of course, similarly fails to address problems of discovery and
revealed preference, while also relying on problematically simple
notions of a labor theory of value which he describes at more depth in
“Calculation, Complexity, and Planning.” It is no surprise then, that he
is also anti-sex worker, as he sees the whole world through this
simplified view of labor that is not even universally accepted among
Marxists. Similarly, the issues of computation I have raised in this and
my initial paper further challenge his hand-waving magical thinking
about Big Data and Supercomputers. It is with an odd parallel to Hayek’s
absurd insistence that Pinochet’s authoritarianism did not violate his
principles of local knowledge, that Cockshott also claims that direct
democracy will be able to transmit high enough information at scale to
satisfyingly solve virtually all major decisions needed by a global
society. Cockshott’s model’s deserve to be one of the one hundred
flowers we let blossom in testing, but they are wonky and ill-suited to
replace a global economy in the ways that he believes they will, most
notably, because they sidestep issues of complexity, local knowledge,
and revealed preference by artificially constraining the real world
difficulty of these problems especially at scale. Determining the
realistic limits to these and related approaches with independent
outside auditors and real-world testing could help prevent us from
damning ourselves with over-reliance and directing us towards much
needed modernizations and pivots towards functional sustainability. His
last bullet point is telling as well. His electronic payment cards would
of course create a centralized super surveillance network, required for
most central planning initiatives, wherein the ableist and workerist
value system of an individual’s worth is their labor, replaces the
grotesque capitalist notion that an individual’s worth is their wealth.
M Black states, “The problem for inter-firm coordination within a market
is simply that there is no mechanism which enables firms to actually
coordinate their plans together and make mutual adjustments as
necessary. The ideal market lacks not only a mechanism for coordination
(as could exist in, e.g., a cooperative federation or a cartel) but also
inhibits cooperation from the start because the competitively stable
strategy within a competitive market is always non-cooperation.” as if
this were a fundamental truth of markets rather than a myopic view of
how they (sort of) exist now. Indeed, though regional confederations do
already make complex decisions about various aspects of markets and
production in large-scale co-ops and networks of co-ops, similar
interventions are another space for experimentation in a hybrid economy.
What does it look like for markets and direct democracies and consensual
partial centralizations of coordination look like? No doubt, authors
like Prytchiko and Lavoie would react in horror at the undermining of
the perfect Laws of Profit, but we can work on different models that
accept a degree of negative externalities of one kind (inefficient
incentives) in favor of positive externalities of another kind
(elimination of perverse accumulation). It seems likely that these
forces would naturally compete and vie for legitimacy in the social will
through proving themselves in action.
In a Twitter thread, a YPG veteran called Joshua Bailey discussed how
Rojava is similarly gradually introducing various collectivizations,
resisting or dismantling monopolies, and utilizing currencies amidst a
living experiment in Social Ecology that resembles much of what
mutualists have advocated for centuries only modernized for the new era.
Imperfect as it is real, they are also very much attempting to put into
practice more ecological and solarpunk principles while defending
themselves against fascist takeover from many directions at once.
Solarpunk is the blending of high-tech, sustainable green innovation
with accessibility, and traditional forms of low-tech DIY wisdom. I
think it provides a vision for what a modern economic mesh of
decentralized coordination could strive for. We must build from the
thriving of those most vulnerable in not creating a new capitalist
hell-hole of ableism and exploitation. Through this form of sensitive
local knowledge, in which we build from the complex needs and
preferences of individuals, while constantly seeding spaces of
innovation, we can start to practice the new economy with the tools of
what is in front of us. Building towards our liberation will look
different than any of us can plan, because we are limited in our
knowledge of not just the future, but also of each other. But using some
version of a Maximum Viable Economic Planning measure we can tease out
what strategies are most viable and most worth the risks of testing with
our scarce resources. We can bootstrap some proofs of concept and
revisit our prior MVEP measures with the new information we gained as a
result. As such this measure forms the basis of a networked mesh of new
economies.
The problem is inherently complex and, as Aurora notes, complexity
itself is a meaningful goal when it stands in for the depth of vibrant
choices available to people and societies. Utilizing every form of
complexity maximizer available to us, including both mediums of exchange
and large-scale decentralized social planning, we increase our chances
of feeding the solarpunk future, already sprouting around us in the
heart of this massive and violent collapse of the old order.
[1] Thanks to @hdvalence for helping me think all of this through! Here
is further explanation from him: In that picture there are 8 cores in a
2Ă—4 layout, each of which has a bunch of processing logic (the more
organic-looking blobby area) and its own cache (the solar-panel looking
area). Zooming in to one of the cores you can see that fully half of the
area is spent on the big data cache, which is used to avoid having to
communicate with the main memory. Then zooming in to the other part of
the core you can see even more caching layers (the regular patterned
areas, laid out in tiles) fit in with the actual processing logic (the
blobby areas, laid out algorithmically). Zooming all the way out,
there’s a second chip the same size as this entire unit that’s dedicated
to the main memory.