1. On Building a Forex Visualizer
By Muizz Salami 12/18/2016
In my previous paper I touched on a graphical
nested graph based system for visualizing and
modeling economic activity and human
exchange.
I would now like to dive deeper into
how one would go about building such a
system and gathering the information needed
to make the model stable and hopefully
accurate.
The term data swamp has been thrown
around a number of times in discussions and
as the proliferation of self-driving cars and
Internet of things capable devices increases so
too will the amount of information that is
stored in the various forms of data swamp.
The obvious solution to filling a graph
of this nature is a web crawler a program
designed to move through the data and
somehow identify the relevant information
and place that data within the model as the
probabilistic distribution of life parameters.1
Of course it'd be infinitely easier if the
data structure used to store the data was of
the sort of nested graph I'd described but since
we currently have the issue of massive data
swamps of unlearned information we must
find a way to convert that data into
information.
We must first dive into the structuring
of the graph. This model will by its nature have
1
​Najork, Marc. "Web crawler architecture."
Encyclopedia of Database Systems​ . Springer
US, 2009. 3462-3465.
to in any visual form be navigable by GPS and
as such by the nearest individual to given GPS.
The easiest linear traversal will be time
stepping people by birth order and death rates
randomly pruning people as ailments are
distributed by demographic and estimated
lifestyles.
Beyond setting the graph up by region
and subregions we must add within all regions
basic economic metrics easily pulled from
normal census data i.Ce. population, family
sizes distributions (including pets), income
distributions, scaling up to gross national
incomes and so on.
There is a need to gather information
on how well tied the immigrant population
within each sector is tied to immigrants in
other sectors. If for instance a bond is strong
and one family member is in a stronger
economy the strength of those connections
will drive funds towards the weaker economy.2
Here we start to see how money mirrors
energy as a potentiality flowing through
economies created by human effort.
Within each region and subregion, we
must also map relative distributions for the
uses of goods and services within about 20-30
or so broad categories of industries.
With three broad categories of people
tiered by net worth/income scaling against
the regional averages with fractional, scalar(
2
​Alonso, Fernando Gil. "Can the rising pension
burden in Europe be mitigated by immigration?
Modelling the effects of selected demographic
and socio-economic factors on ageing in the
European Union, 2008-2050." ​Vienna Yearbook
of Population Research​ (2009): 123-147.

2. <10) and exponential(10-100+) income ratios
based on a common income distribution with
behaviors determined by heuristic random
distributions based on regional research in
spending data swamps.
We then distribute ages over the
masses according to the collected
demographic data, we distribute the
populations by industry (skewed to by the
demographics we have tagged the individuals
with) with banking and service tiers splitting
further into the varying scales of incomes and
services available.
So we are now left with a tiered
structure of incomes distributed by income
and demographics. On a per individual basis
we also distribute debts and savings and
investments scaling with the ratio of income to
the local averages, meaning the greater the
yearly income the greater the range of
potential savings, debts and investments aka
hoarders vs liquid assets.
Assuming we've done all the above
steps properly we can now make an attempt at
modeling the spending patterns of these large
crowds.
With spending in each category scaling
with income logarithmically leaving out high
net worth individuals(hnwis) defined as people
with incomes greater than integer scalars
more than the regional averages.
Ultra hnwis or people with high
exponential income ratios 100+ will of course
have to be treated very differently because
there is only so much one can spend sensibly
before reinvesting or hiding funds in
something like foreign shell corporations.3
Fractional to low scalar income
people's spending is quite easy to model
especially in poorer regions as the necessities
of life are universal and disposable income
remain scarce. Of course modeling their4
economies leaves no room to measure of the
depth and profundity of the lives they live and
we are doomed to random probing in this
regard.
As disposable income increases we
first see spending increase in the directions of
the base life needs tied with improved food5
and clothing quality, smaller luxury goods,6
eventually we reach benchmarks for housing
mortgages that amount to multiples of 5-10
times the yearly income ,and rentals ideally
kept at 40-60 times yearly divided over the
course of 10-30 year mortgages with a
distribution of rate styles mirroring fixed and
variable interest rates that will be tied to
activity we'll get to in the banking sectors, high
art also starts to make appearance here but
3
​Furnham, Adrian. "Why do people save?
Attitudes to, and habits of saving money in
Britain." ​Journal of Applied Social Psychology
15.5 (1985): 354-373.
4
​Meyer, Bruce D., and James X. Sullivan.
Measuring the well-being of the poor using
income and consumption​ . No. w9760. National
Bureau of Economic Research, 2003.
5
​Du, Shufa, et al. "Rapid income growth
adversely affects diet quality in
China—particularly for the poor!." ​Social science
& medicine​ 59.7 (2004): 1505-1515.
6
​Hayhoe, Celia Ray, et al. "Differences in
spending habits and credit use of college
students." ​Journal of Consumer Affairs​ 34.1
(2000): 113-133.

3. that will of course be mostly relegated to the
highest of income brackets.
The spending patterns of the merely
affluent as such being a large portion of the
economy will be trickier to predict since their
spending and saving habits will be more likely
to fluctuate based on concerns espoused by
the media.
The apparently small financial margin
for error in this brackets means that although
there will be access to higher order goods
most won't go all out on upgrading all they can
simply because it's fiscally irresponsible, Again
the research for these models would be
ridiculous.
As such we will have to source
information on the spending habits of these
brackets by region, as some places will likely
encourage more spending in specific
industries as we may have more cars in rural
texas, with a similarly bracketed family in new
york city rather investing in the stock market
buying art or theater tickets and art.
The regional differences here will also mean
that migration of people from region to region
will most likely mean a hybridization of
regional spending strategies.
While in the lowest tax brackets
people will spend comparatively more of what
they have just to scrape by on what they have7
and often incurring relatively small amounts of
debt that is often harder to recover as we get
to the higher brackets it's more the
7
​Caplovitz, David. ​The poor pay more:
Consumer practices of low-income families. Free
Press, 1968.
maintenance of maximal living standards that
leads people and families into overspending
and poorer fiscal planning creating the bulk of
the most viable debts from the working
classes.We can now track debts by tax
brackets this will come in useful later.
Now we can consider the highest of
the income brackets more popularly referred
to as the top 2-1%. With these percentiles we
see a scaling of home ownership and the
associated goods that go with maintaining
multiple homes, these families also tend to get
larger than the national average leading to
increased familial spending on food, clothing,
education, transportation etc.8
Within these higher percentiles we see
that they are often small business owners and
industry leaders meaning that as the total
values associated with a given economic
subgroup thos pegged within these higher tax
brackets will have the advantage of a larger
share of the profits as they are likely invested
or receiving some share of profits.
It's easiest to use a generic random
assignment of trading strategies for simplicities
sake in modeling behaviors of meta-layers like
banking and some services. Market research9
into the meta layers of banking will likely help
show variances between the values assigned
by banking institutions and some or of real
valuation of tangible assets like commodities
8
​Shiller, Robert J. ​Understanding recent trends
in house prices and home ownership. No.
w13553. National Bureau of Economic Research,
2007.
9
​Gray, Dale F. "Modeling banking, sovereign, and
macro risk in a CCA global VAR." (2013).

4. that would then help correct for more
abstracted trades like stocks and bonds.10
While in the lower brackets health care
cost would largely triggered by emergencies
here we can expect to see increased spending
on preventative measures even going as far as
considering plastic surgery a mental health
activity we see a dramatic increase in health
care spending.11
Here the majority of high art gets
distributed while again we also get a few
people existing in these brackets as effective
retirees living off accruing interest from having
multiple accounts distributed around the
world adding the first obvious strong
international links of resource flows.
As these higher income have greater
access to quality resources we see a
generational latency of of income potential
skewing dramatically in favor of the offspring
of the mega rich living in an adjacent income
brackets most probabilistically skewed
towards the lower end with most grand
fortunes largely dissolving in inheritances and
debts within 3 generations. i.e. the great
robber baron fortunes that have to be
maintained by the incomes of the subsequent
generations.
10
​Kane, Edward J., and Haluk Unal. "Modeling
structural and temporal variation in the market's
valuation of banking firms." ​The Journal of
Finance 45.1 (1990): 113-136.
11
​Su, Tin Tin, Bocar Kouyaté, and Steffen Flessa.
"Catastrophic household expenditure for health
care in a low-income society: a study from Nouna
District, Burkina Faso." ​Bulletin of the World
Health Organization 84.1 (2006): 21-27.
Since the top 2% behavior is largely
similar to the behavior of those below we can
scale their spending patterns accordingly with
larger ratios of income exposable to savings
and investments as the base living costs stay
consistent and one can only eat so much
before killing oneself.
Now we get to the top .01% and above
here a disproportionate amount of wealth and
debt means that individuals in these spending
classes can send massive ripples through the
financial markets on whims. These fortunes
are often primarily tied to the industries in
which they are made so such erratic behavior
has not been too frequent, of course this
doesn't mean that one won't occasionally see
a billionaire buy millions dollars of art on an
American Express Black Card or buy a private
jet as an anniversary gift.
The fortunes of the billionaire classes
are so massive their wealth and strategies for
maintaining and managing said wealth means
that they are far more connected to the
general masses in a graphical sense by the
movement of their funds with or without their
knowledge.
A billionaire with a 12 million dollar
account at a chase or Capital One that rarely
gets any activity may have the 12 million
dollars on their account page when in reality
the "funds" are being moved to cover budget
deficits caused by risky/irresponsible
investments on the banks part.
As such if the bank were to get some
bad press, the hnwis would be more likely to
decide to arbitrarily move that account to

5. another group creating potential budget
shortfalls whereby the banks no longer have
access to enough idle funds to provide the
liquidity their customers need, creating a
domino effect and leading to the collapse of
the financial institution unless they get a
bailout or take out loans from other banks
effectively diffusing the risk across the planet.
but at once setting of ripples of instability that
unless diffused by gifting and charity creates a
buildup of social tension lack of true charity is
created by fear.12
It is here that we are forced to
confront a way to model the behavior of the
media, zeitgeist and public opinion as it
impacts banks companies and individuals on all
scales of spending.
Public opinion is notably incredibly
fickle with the 2016 presidential election being
a perfect reminder of how complicated and
often difficult it can be to accurately model
behavior based on professed opinions. Or even
to be fooled by simple misinformation tactics
i.e. the russian email hacks.13
Much of the issue here lies in trying to
assume people behave rationally or with a
general penchant for equanimity or cruelty,
the crowds complexity can only be
approximated by randomness skewed towards
12
​Schulze, William S., Michael H. Lubatkin, and
Richard N. Dino. "Toward a theory of agency and
altruism in family firms." ​Journal of business
venturing18.4 (2003): 473-490.
13
​Stout, Christopher T., and Reuben Kline. "I’m
not voting for her: Polling discrepancies and
female candidates." ​Political Behavior 33.3
(2011): 479-503.
self serving optimality if we cannot actually14
understand what is happening , we will have to
probe in the dark, so into our distributions we
probe randomly, much like fate.
I offer again a more complex
differential model for explaining some of the
issues of the last election and future
excursions in modeling mass human behavior.I
start with dunbar's number the suggested
maximal number of people an average
individual is able to seriously care for.15
Here it's easiest to model care along a
logarithmic distribution of value and love
about a social circle progressively moving
down an assumed social network.16
I offer a new way of interpreting dunbar's
number as; a measured limit of institutions,
people and hnwis a person can viscerally care
about.
With a scalar multiple representing
interest and an exponentiation of dunbar's
number representing awareness i.e. facebook
friends, twitter followers, and other relatively
ephemeral online connections. We all ideally
behave in ways to maximize the potentialities
of those around us or at the very least those
within our immediate dunbar range.
14
​Camerer, Colin F., and Ernst Fehr. "When
does" economic man" dominate social
behavior?." ​Science 311.5757 (2006): 47-52.
15
De Ruiter, Jan, Gavin Weston, and Stephen M.
Lyon. "Dunbar's number: group size and brain
physiology in humans reexamined." ​American
anthropologist 113.4 (2011): 557-568.
16
​McRaney, David. ​You are not so smart.
Virginia D. Persons, 2011.

6. Of course we'll have to make
allowances for nihilists and people that are self
destructive since they would essentially do the
opposite for those around them meaning you
get an inversion of the effective care value the
closer into the person's inner circle you come.
I'd also like to offer a hater distribution
throughout the dunbar ranges meaning
sometimes people hate other people and
would work to diminish the available
potentialities of those high and randomly
distributed in their hater index.For example, I
don't like apple so not only do I not buy apple
products but I also speak ill of apple products
to those around me diminishing the likelihood
of their buying apple products as well, here i
may spend more to avoid using an apple
product and as such the negativity is
distributed over the dunbar ranges and
influences those around them.
We can assign other personality traits
to members in this system but in fear of
further over complication I stop at base care as
it gives us a probabilistic distribution of where
we are most likely to share our wealth with or
spend money to harm.
This means that even the
self-destructives would also be modeled well
in such a system and it offers us a way to
model the connectivity of high value
individuals through the now logarithmic ranges
of people and entities they care for meaning
we can now better model the probabilistic
flow of funds throughout the entire economy
as a highly interconnected complex recursively
nested network. With each layer being
navigable by relative hot cold measured
relationships.
This all of course privileges the use of a
model like this to institutions capable of
compiling the necessary info to model the
behaviors of such high and low income ration
individuals unless we are working in a
different economic environment where the
movement of funds can be tracked
electronically as we see in some forms of
cryptocurrency.17 18
This means that this sort of tool if it
doesn't exist will exist in the hands of govts,
powerful banking institutions, and skilled
hackers.The fact that it will get to the hands of
hackers will mean that in time it will be
diffused amongst the public, I suggest that
instead of hoarding the tech within banks and
governmental spheres it be shared and used as
a teaching tool to answer questions,
essentially an electronic potentiality oracle.
With the ranges of people now
relatively modelable with market volatility
being driven most by the bottom and top tax
brackets by mass actions influenced by
changes in media, artistic sentiments, political
movements and price fluctuations in housing
17
​Plümper, Thomas, and Christian W. Martin.
"Democracy, government spending, and
economic growth: A political-economic
explanation of the Barro-effect." ​Public choice
117.1-2 (2003): 27-50.
18
​Gjermundrød, Harald, and Ioanna Dionysiou.
"Recirculating Lost Coins in Cryptocurrency
Systems." ​International Conference on Business
Information Systems. Springer International
Publishing, 2014.

7. (due to interest rate shifts and modified utility
costs).19
We can plug in scenarios and see
probabilistic ways that the results play out and
ripple through the market as people try to
make ends meet or keep to their desired
standard of living .
As we'd be trying to model what would
effectively be millions to billions of people
going about simplified models of their lives
we'll have to make a few allowances for
simplification and isolation lest we develop
quantum computers capable of handling the
mess of computations needed to run this in
real time.
This sort of model is far too dynamic
and sensitive to butterfly effect propagation to
offer great long term view resolutions past a
months or year (depending on the and
resolution) and too unwieldy to offer much in
the way of high frequency trading positions
but offers placement points for mass economic
indicators of mass market movements.20
Where this sort of model does shine
through is in the projection of major events
and changes in pricing of commodities like
wheat, corn wood and oil and oil that would
affect the masses in a stable predictable way.
It also offers a testing platform for
economic ideas to show who would be most
19
​Albizzati, Marie-Odile, and Helyette Geman.
"Interest rate risk management and valuation of
the surrender option in life insurance policies."
Journal of Risk and Insurance (1994): 616-637.
20
​Beinhocker, Eric D., Diana Farrell, and Adil S.
Zainulbhai. "Tracking the growth of India's middle
class." ​McKinsey Quarterly 3 (2007): 50.
likely to benefit from policy changes without
having to depend on so called "natural
experiments." We can see how policy decision
will potentially trickle down and test policies
meant to affect specific outcomes like helping
curb homelessness or increasing access to
educational opportunities or even universal
income spending habits with elimination of the
economics needed to drive people to work to
fuel the economy.21
We could also model how general
behavioral changes would influence the
economy and predict the effects of things like
protests, boycotts and economic sanctions on
the general wellbeings of a given populace as
measured by relative death rates.22
Now to processing data swamps
After creating the aforementioned deeply
nested interconnecting graph (most easily
done by constructing dedicated hard coded
physical quantum computing system, (my little
secret), akin to a field programmable gate
array (FPGA), (doable I'm working on this ask
me more I'm happy to share.) )23
In traversing through the data swamp
one can keep track of how a given account is
21
​Klein, Elise. "Universal basic income." ​Arena
Magazine (Fitzroy, Vic) 142 (2016): 6.
22
​Freeman, John, Glenn R. Carroll, and Michael
T. Hannan. "The liability of newness: Age
dependence in organizational death rates."
American sociological review (1983): 692-710.
23
​Anguita, Davide, Andrea Boni, and Sandro
Ridella. "A digital architecture for support vector
machines: theory, algorithm, and FPGA
implementation." ​IEEE Transactions on Neural
Networks 14.5 (2003): 993-1009.

8. related to an individual and place out who else
they are connected to via where the money
flows and for what purposes as is easily
classifiable and use the values extracted by
traversing through multiple deep data swamps
of varying types to fill out the variables needed
to make this model stable.
Social media processing techniques like
tweet2vec and facebook graph traversals24
means we can fill out dunbar relations partially
beyond the immediate families.
Telecom data and data from self-driving cars,
toll booths and public transit data means we
can track migrations.Utilities data is easy to
model and their material cost dependencies
are pretty transparent.
With each industry or layer a modified
version of the data interpreter will be needed
but that should not discourage its creation
since we are trying to model reality it must be
expected that the tools used to create such a
model would have to mirror the reduced
complexity of the system its modeling. As such
“pure language” or an understanding of how
humans name things will be necessary to make
educated guesses about significances in forms.
25
Within this framework understanding
data swamps becomes a complex
semi-supervised algorithmic learning problem
24
​Vosoughi, Soroush, Prashanth Vijayaraghavan,
and Deb Roy. "Tweet2Vec: Learning Tweet
Embeddings Using Character-level CNN-LSTM
Encoder-Decoder."
25
​Hanley, J. Richard, and Janice Kay. "Reading
speed in pure alexia." ​Neuropsychologia 34.12
(1996): 1165-1174.
necessitating a way to make the network
transparent so that its biases can be
understood and effectively quantified.26 27
It'll be easy to tell how well the
learning process has been moving forward by
how closely local values are mirrored in reality
by taking time steps backwards in time.
Time stepping in the model/real world
would mean that parts of the system may fall
out of sync with reality so there will be a
constant need to reground the system by
traversing through time-series data swamps
and making calibration predictions.
With this system as the economy
grows we are offered an easy logical way for
the system to grow and develop by imitating
the biological processes involved in
neurogenesis whereby new neurons develop
and are later pruned in the development of
new habits passions knowledge and skills.28
Connectivity is first established to the
nearby cells and follows connections to the
most common connecting partners of
neighboring signals creating a self-reinforcing
but also adaptable system for creating and
generating stimuli especially when considering
the differentially myelinated pathways signals
26
​Belkin, Mikhail, Irina Matveeva, and Partha
Niyogi. "Regularization and semi-supervised
learning on large graphs." ​International
Conference on Computational Learning Theory.
Springer Berlin Heidelberg, 2004.
27
​Belkin, Mikhail, and Partha Niyogi.
"Semi-supervised learning on Riemannian
manifolds." ​Machine learning 56.1-3 (2004):
209-239.
28
​Eriksson, Peter S., et al. "Neurogenesis in the
adult human hippocampus." ​Nature medicine
4.11 (1998): 1313-1317.

9. can traverse to carry signals through the brain
in order to create unified cohort firings for
specialized activities, categorizations, and
higher order thought processes.
It is here that we must start to
consider the relationship between the
biological sphere and economic systems. As29
logical as it can be said that the biological
sphere is a substrata of the economic sphere
as it relates to human energy exchange it is
further true that the rules governing activity
and exchange economically must have been in
part generated by biological limitations.30
We are forced to confront our
definitions of unitarity, where we draw lines
between bodies and delineate one from other.
Though our system obviously shows that the
nested complication would lead us to consider
everything interior to as subsisting of that unit
we must also consider a difference and
distance from self figuration that would help
us recognize that families, friend, and niche
community units transcend borders and
traditional economic barriers.
As such our model as all models breaks
down in capturing the minutiae left
unmeasured. The limitless abstraction
afforded to the mind and economies as
systems of minds thus become unitary, we are
left to assign values as dictated by the market
29
​McNeely, Jeffrey A. ​Economics and biological
diversity: developing and using economic
incentives to conserve biological resources.
Iucn, 1988.
30
​Mohammdian, Mansour. ​Bioeconomics
Biological Economics: Interdisciplinary Study of
Biology, Economics and Education. Entrelineas
Editores, 2003.
which is the system the model seeks to
emulate and fill itself, a serpent consuming its
tail, or better yet an distributedly self aware
economy now capable of working in unison for
an estimable greatest good for the most/
totality.31
Words fail models fail markets fail
societies fall but rarely all at once and it is in
measuring the differentials and velocity of
directions that we are left with the best
estimates as possible to be afforded by any
modeled system.We are lazy, we are stupid,
we are endlessly creative, ibid.
There's a reason the engineers in
Asimov's foundation sought a way to better
navigate the fall of their civilization, the worry
is of course the espionage based
information-"war" that would as a result begin
to occur, in many ways and end of history.32
Charity and philanthropy isn't the
answer to market stabilization as
embezzlement is rampant, perhaps a33
universal basic income fueled in part by mass
philanthropic donations or a system34
31
​Wicklund, Robert A. "The influence of
self-awareness on human behavior: The person
who becomes self-aware is more likely to act
consistently, be faithful to societal norms, and
give accurate reports about himself." ​American
Scientist 67.2 (1979): 187-193.
32
​Kren, George M., and Leon H. Rappoport.
"Varieties of psychohistory." (1976).
33
​Baber, William R., Andrea Alston Roberts, and
Gnanakumar Visvanathan. "Charitable
organizations' strategies and program-spending
ratios." ​Accounting Horizons 15.4 (2001):
329-343.
34
​Porter, Michael E., and Mark R. Kramer. "The
competitive advantage of corporate philanthropy."
Harvard business review 80.12 (2002): 56-68.

10. whereby everything but food for those older
than a certain age and high tech/art is free.
Some people need utility or rather as the
generational shift comes on those that will
lose their utility to the economy will harbor
increased rates of depression and other
economic drawdowns.35
Layers:
Transportation
Utilities
Food
Clothing
Housing
Electronics
Medical Care
Psychological Care -includes drugs, therapy
and self medication-
Regular Luxury Goods
Electronics -hardware
Coders- software
Media
Nightlife (bars , clubs etc.)
High Art
Services i.e. general maintainance, babysitting
,security, etc - essentially a meta category
Banking -another meta category due to debt
distributions with saving strategies
Sex
Education- increased spending on education
channeled in favor of each individual offers a
logarithmic increase in average income and
35
http://www.philanthropyroundtable.org/alm
anac/statistics/
also increases the likelihood of reaching
higher/exponential tax brackets.
Weapons
Judiciary
Defense
Unemployed
DIsabled
Retired
Construction
Shipping
legislature
Intelligentsia
Charity
Philanthropy36
36
https://www.nptrust.org/philanthropic-reso
urces/charitable-giving-statistics/