OnbuildingaForexvisualizer (1)
- 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/