Building the CEIS
Document Description
The following document "Building the CEIS" explains our proposal for forming an Economic Intelligence Network (EIN) that facilitates a decentralized cybernetic marketplace.
Table of Contents
List of Appendices
Appendix A. Data Monetization Protocol
Appendix B. Production CSF
Appendix C. Robo-advisory
Appendix D. Smart e-Business Revenue
Appendix E. Network Performance Evaluation (Case Study)
List of Acronyms
List of Tables
Table 1. The VSM’s 5 subsystems and their processes
List of Figures
Figure 1. Depicts how consumer payments as a means of capital investment into their own e-store.
Figure 2. Human vs. Agent system of mutual exchange: data monetization
Figure 3. Human vs. Agent system of mutual exchange: e-business income
Figure 4. Optimization: shared goal between Human vs. Agent
Figure 5. A high-level depiction of how network intelligence is capitalized by marketplace payments
Figure 6. Stafford Beer’s Viable System Model (VSM)
Figure 7. Javier Livas Cantu’s depiction of the free-market VSM
Figure 8. The CEIS economy as depicted through the Viable System Model
Figure 9. CSF fractionalized payments between Consumer e-businesses
Figure 10. Margin split between network stakeholders of a Product CSF
Figure 11. Margin split between network stakeholders of a Lending CSF
Figure 12. Demonstrates how transactions are validated using a DPoA staking method
Figure 13. The Phillips curve model
Figure 14. The CEIS’ inversed Phillips curve model
Figure 15. Agents maintain a symmetrical (efficient) distribution of wealth within the network by selecting optimal investment ratios between both sides of the income distribution curve
Figure 16. Executing left and right investment ratios within a CSF
Figure 17. Depicts the CEIS’ ranges of wealth
Figure 18. Production CSF Protocol
Figure 19. The VSM/CEIS’ method for measuring performance
1. Introduction
In real terms, wages in the USA have been stagnant for the last half century while the nation’s productivity almost tripled over the same period. Advancements in automation have brought about an explosion in productivity and wealth, but correlate negatively to the wage and household wealth of low-mid skilled workers. This is in contrast to highly trained professionals whose wages have been increasing, as capital continues to centralize toward tech and other highly-skilled sectors. The result is an accelerating wage and wealth gap throughout the nation, a by-product of entrenched market failures that narrow economic opportunities for low-mid skilled workers. These conditions are projected to worsen, as innovations in production and consumer technologies persist.
1.1. Proposal
To solve this, we propose forming an Economic Intelligence Network (EIN) that facilitates a decentralized cybernetic marketplace. A cybernetic economy in a shared virtual space. In this space, AI-powered robo-advisors, or intelligent agents conduct digital commerce on behalf of their human owner’s e-business (Smart e-business). A decentralized cybernetic marketplace would leverage blockchain technology to enable the peer-to-peer exchange of goods and services. The platform operates through a network of distributed user e-business nodes that function as self-regulating systems, using feedback loops and other mechanisms to monitor and adjust real-time market factors, such as pricing, demand, and inventory levels.
Today’s e-commerce software helps entrepreneurs and small businesses digitize and automate their operational processes, but fulfill little to no function augmenting their business intelligence. Consequently, today’s most prominent e-commerce software’s can only boast a client success rate of 5%. On the other hand, a decentralized cybernetic marketplace combines the principles of decentralization and cybernetics, using advanced technologies such as blockchain, artificial intelligence, and other mechanisms to optimize and automate various aspects of the transactional processes. An EIN is a unified and emergent economic intelligence that allows the platform to analyze user behavior and preferences, as well as execute business decisions on behalf of its owner.
The combination of decentralization and cybernetics in a marketplace could unravel a whole new dimension of possibilities in production efficiency economic transparency, individual economic autonomy, and wealth creation. Decentralization helps reduce transaction fees, increases transparency, and facilitates more flexible transactional arrangements, like De-Fi, while cybernetics provides a self-regulating system that can adapt to changing market conditions and intertemporal user requirements and preferences. Users connect to the marketplace through intelligent agents capable of operating 24/7, learning and solving for complex market dynamics, while executing business decisions faster and more accurately than any human. By distributing intelligent agents to the public, we may begin to reverse the causal factors of accelerating wealth disparity, while augmenting our individual and productive capacities by orders of magnitude.
1.2. Conceptual framework
In this paper, we intend to demonstrate a conceptual framework for a decentralized cybernetic marketplace, or cybernetic economy that merges human activity, data, and data technology to form a system of mutual exchange between humans and cybernetic systems. In this exchange, humans and their personal intelligent agents would trade data for productive outputs, and in so doing, augment the human’s productive capacity to that of their agent’s. This symbiosis reforms the adversarial relationship between technological advancements in productive technologies and their negative externalities, such as unemployment, wage stagnation, and widening wealth and income inequality by distributing the economic benefits of their emergent capabilities, directly to the public. More specifically, the human vs agent system of mutual exchange in combination with a unique, network-wide economic framework aims to leverage data technology to produce more efficient supply chain systems and transactions that find a way to re-distribute its derivative profits in accordance with the human user’s contribution to the system’s intelligence. Here, we put forth a new model for e-commerce transactions that would convert payments from consumer purchases into capital investment for their own e-store.
Figure 1: Depicts the proposed framework, whereby payments from consumer purchases are converted into investment capital investment for their own e-store.
2. Intelligent Agents
Intelligent Agents combine RPA and AI/ML to learn, manage, and operate their owner’s decentralized eBusiness (Smart e-business). Together, they form a network that exchanges market intelligence, while utilizing game theory tactics to execute and achieve win-win transactions between parties. This new type of e-commerce is called "Robo-commerce".
For Agents to act intelligently, they must possess four key characteristics:
Autonomous behavior
The ability to sense their environment and other AIAs
The ability to act upon their environment, and
Be goal-driven
With these four characteristics, Agents can intelligently
Identify demand
Source products,
Establish contracts,
Manage logistics, and
Request payments
This creates a publicly distributed Economic Intelligence Network composed of user-owned intelligent agents capable of conducting profitable transactions through their own market logic, mapping and executing their decisions, as well as observing feedback and adjusting accordingly.
2.1. Human vs. Agent System of Mutual Exchange
Human owners can choose between a spectrum of settings that actively and/or passively train their agents. They can modify their level of human intervention to allow for a complete manual override, however, by default Agents will learn passively, via data transfer. Agents feed off the data their human owners produce when interacting with the marketplace and, via a data monetization protocol, compensate their owner in the currency they can use for purchases and further investment into their business. That data is shared between the user’s agent and the network’s central intelligence where it's converted into market intelligence and utilized for learning and managerial purposes. Human owners and their Agents maintain a symbiotic relationship that merges their separate motivations (humans vs. technology) under a shared goal, optimization.
Figure 2. Human vs. Agent system of mutual exchange: data monetization
Figure 3. Human vs. Agent system of mutual exchange: e-business income
Figure 4. Optimization: shared goal between Human vs. Agent
3. Data Monetization
The Data Monetization Protocol (DMP) is a decentralized data marketplace that compensates users for their data. It uses a method of data capitalization that quantifies a consumer's data relative to its current productive value. The DMP facilitates peer-to-peer transactions where users collectively pay for each other's data to feed the network's central intelligence.
The protocol determines the productive value of a unit of data by correlating user data engagement with marketplace sales. Each user's data engagement is measured and associated with data credits, which are capitalized at the point of a product purchase. The protocol compensates users by distributing profits according to their data engagement relative to the total data engagement of the network.
Figure 5. A high-level depiction of how network intelligence is capitalized by marketplace payments
It promotes transparency and decentralization, which enhances the security and reliability of the system. By facilitating a decentralized data marketplace, the DMP provides users with greater control over their data and ensures that the economic benefits of their data are distributed more fairly. Appendix A provides a high-level outline of the DMP framework.
4. Internal Currency
The use of an internal blockchain currency in the cybernetic marketplace offers several advantages, including:
Lowering Transaction Costs: By using an internal blockchain currency, transaction costs can be reduced as there is no need to convert currencies or pay fees to intermediaries. This can result in faster and more cost-effective transactions, particularly beneficial for microtransactions.
Enhancing Security: The use of an internal blockchain currency can enhance the security of the network by providing a tamper-proof record of transactions. Transactions are recorded on the blockchain, making them immutable and resistant to alteration without the consensus of the network.
Decentralization: The use of an internal blockchain currency supports the decentralized nature of the system by reducing the risk of a single point of failure. Transactions between network participants can be conducted in a decentralized manner, promoting resilience and reducing dependency on a central authority.
Overall, the use of an internal blockchain currency in the cybernetic marketplace can improve transactional efficiency, enhance security, and promote decentralization, contributing to a more efficient and resilient marketplace for the exchange of data and productive outputs between humans and cybernetic systems.
5. Smart e-Businesses
Smart e-businesses empower individuals to maintain control over their e-commerce activities by using intelligent agents that develop and manage a personalized data profile of their human owners. The agents then use this profile to tailor the e-business operations to the unique requirements of each individual.
Given the varied and ever-changing conditions of each user, the intelligent agents prioritize learning about their human owners to determine the optimal investment strategies to satisfy their intertemporal needs. To achieve this, the agents follow a 4-stage protocol that enables them to collect and analyze data about their owners, develop customized investment strategies, and continuously learn and adapt to changing market conditions.
To develop an optimal investment strategy for their human owners, intelligent agents follow a 4-stage protocol:
UIPF Establishment: The agents create a unique User Investment Profile Fit (UIPF) for each human owner based on their consumption and investment habits, as well as their specific requirements.
Model Selection: The agents select an appropriate investment model that aligns with the UIPF of their human owner from a variety of available models.
Product Identity Awareness: Agents source investment products that match the investment characteristics of their human owners’ UIPF.
Distribution: The agents execute investments by allocating funds in accordance with the UIPF's investment model.
By following this protocol, intelligent agents can effectively manage the e-business operations of their human owners and create customized investment strategies that meet their unique needs and preferences.
6. Account Types
Account types in the Smart e-business ecosystem are divided into two categories: Consumer and Supplier.
6.1. Consumer accounts/e-business
Consumer accounts/e-business are designed to merge the consumer's shopping behavior with De-Fi (Decentralized Finance) to facilitate peer-to-peer transactions. Each Consumer Smart e-business operates as a node within the network, informing the entire marketplace of its operational requirements. Collectively, Consumer Smart e-businesses engage in operational activities such as sourcing, supplying, fulfilling product orders, and other relevant tasks, generating income for their Human owners.
In addition to facilitating transactions, Consumer accounts also offer other features such as peer-to-peer wallet transfers, product inventory analytics, portfolio analytics, e-commerce advisory services, and network engagement features. These features enable consumers to manage their transactions, track their product inventory and portfolio performance, receive e-commerce advice, and engage with the network for enhanced user experience and operational efficiency.
6.2. Supplier accounts/e-business
The supplier accounts/e-business in the cybernetic marketplace are designed for product producers and manufacturers who want to promote their products to potential customers. They can register their supplier accounts and onboard their products to the platform’s Inventory Management System. The agents managing the supplier e-businesses can then source and promote these products to potential customers.
The agents can also manage customer relations by addressing user questions, comments, and concerns. They can use real-time market data to propose business and product suggestions to the manufacturers. The agents are intelligent enough to strategize tactics for managing the products and can help manufacturers achieve their target sets.
The NLP framework of the agents allows manufacturers to query the demand for a certain product within the network and develop customized reports and implementation requirements. Appendix C provides a high-level schematic demonstration of the 3 human vs agent dialogue states.
In addition to the features mentioned above, the supplier accounts also have peer-to-peer wallet transfers, product inventory analytics, survey applications, portfolio analytics, e-commerce advisory, and client engagement features.
7. The Cybernetic Economic Intelligence System
The Cybernetic Economic Intelligence System (CEIS) is a decentralized information system that powers the network operations of the cybernetic marketplace. It consists of three layers: the economic intelligence center, the network of intelligent agents, and marketplace activities. The CEIS is designed based on Stafford Beer's Viable System Model (VSM), which illustrates the universal principles of communication between sub-systems of a self-sustaining entity.
The VSM is used to maintain homeostasis and viability of the system despite changes in the marketplace. According to Ashby's Law of Requisite Variety, a system must contain enough variety to maintain desirable states. The VSM ensures that information is distributed within the network, allowing agents to maintain dynamic intelligence and respond to market shifts while considering the interests of the human owners.
7.1. Design Architecture: The Viable System Model (VSM)
The VSM design helps create mutual coherence between parties on the network, accurately defining target states. It merges data outputs from the internal marketplace to form a global intelligence that instructs strategic responses to the agent network, in order to keep outcomes within the desired target sets.
Each layer of the CEIS can be associated with one of the three states of the VSM. The three states of the VSM are as follows:
State 1: Environment/Marketplace - This state corresponds to the first layer of the CEIS, which is responsible for facilitating market transactions within the cybernetic marketplace.
State 2: Operations - This state corresponds to the second layer of the CEIS, which is responsible for applying operational processes that ensure the continued viability of the system. This includes the DMP, CSF, and Inventory management systems.
State 3: Governance/Management/Central Intelligence - This state corresponds to the third layer of the CEIS, which is responsible for analyzing market feedback and devising or optimizing strategies for future viability. This layer acts as the central intelligence of the system and oversees the functioning of the other two layers.
Figure 6. Stafford Beer’s Viable System Model (VSM)
Within these 3 states are 5 subsystems whose functioning combine to maintain variety, sustainability, and viability. These are expressed in the table below:
Table 1. The VSM’s 5 subsystems and their processes
7.2. The Free-Market System Represented Through the VSM
The principle of variety characterizes the benefits of the free-market system. Distinguished from its theoretical counterparts, the free-market system fuels a competitive landscape that fosters innovation through individual agency (variety). Here is Javier Cantu’s depiction of the VSM, as it applies to the free-market economy.
In this context, the VSM can be used to represent the free-market system, which relies on the principle of variety to foster competition and drive innovation through individual agency. By applying the VSM to the free-market economy, it becomes possible to identify and address the various sub-systems that make up the larger economic system, including market transactions, operational processes, and central intelligence.
Through the VSM, the free-market system can be viewed as a self-sustaining entity that is capable of adapting to changes in the environment, such as market fluctuations or shifts in consumer demand. This is achieved through the maintenance of homeostasis, which is enabled by the system's ability to self-regulate and maintain a dynamic intelligence that can respond to changes in the marketplace. The VSM provides a useful framework for understanding and optimizing the free-market system, and can help to ensure that the system remains viable and effective in the face of changing conditions.
Figure 7. Javier Livas Cantu’s depiction of the free-market VSM
The CEIS' design architecture is influenced by Javier Livas Cantu's depiction of the Viable System Model (VSM), specifically as it applies to the free-market system. The VSM is recursive, meaning that its operations can be applied to multiple sectors of the economy. In the case of Moneetize's cybernetic economy, the initial focus is on simulating the "Commerce" sector, specifically trading in consumer goods. The diagram illustrates the operational process for the "Commerce" sector, as observed from its respective layer of abstraction.
Figure 8. The CEIS economy as depicted through the Viable System Model
8. De-Fi: Community-Sourced Funds
In order to securely enable multi-party transactions between Consumers and Product producer agents, the CEIS utilizes smart contracts called "Consumer Sourced Funds" (CSF) that are recorded on the blockchain. These smart contracts are designed to facilitate transactions without the need for trust, ensuring security and transparency in the exchange of goods and services.
8.1. CSF Transactions: Merging e-commerce and De-Fi
Community Sourced Funds (CSFs) are De-Fi instruments that aim to lower the barrier to entry for e-business investments. Traditional businesses, whether online or brick-and-mortar, are typically required to purchase a Minimum Order Quantity (MOQ) from suppliers, which can be costly. However, users of CSFs can pool funds together with other users who have similar e-business interests, allowing them to develop a diverse portfolio of consumer products without having to achieve MOQ on their own and at a fraction of the cost. This aligns with the principles of the Viable System Model (VSM), as CSFs enable users to fulfill MOQ demands, dilute risk, and lower the barrier to asset acquisition. When a product is sold within the marketplace, the CSF distributes the yield to each holder according to their share of the investment, providing a mechanism for sharing profits among investors.
Figure 9. CSF fractionalized payments between Consumer e-businesses
8.2 CSF Variations
CSFs come in three different variations, each of which plays a critical role in the maintenance of the cybernetic economy:
Product CSFs: This type of CSF enables consumers to purchase inventory for their smart e-businesses. By pooling funds with other users, consumers can achieve the MOQ required to purchase inventory at a lower cost and with less risk.
Figure 10. Margin split between network stakeholders of a Product CSF
Lending CSFs: This variation allows peers on the network to provide working capital to one another through peer-to-peer lending. This allows users to access the capital they need to grow their businesses while also generating returns for the lenders.
Figure 11. Margin split between network stakeholders of a Lending CSF
Insurance CSFs: This type of CSF leverages a DPoA staking method to validate key marketplace transactions, including logistics and fulfillment. By staking tokens, users can participate in the validation process and receive rewards for their contributions to the network.
Figure 12. Demonstrates how transactions are validated using a DPoA staking method
9. The CEIS’s Economic Framework
9.1. Goal
The ultimate mission of CEIS is to use automation to reverse the negative effects of technology on low to mid-income workers. These negative effects are primarily rooted in the impact of automation on labor dependency. As automation becomes more widespread, corporations rely less on labor, leading to increased profits due to reduced labor scarcity, higher productivity, and depressed wages.
CEIS aims to reverse this trend by ensuring that future advancements in production technologies also benefit the public financially. This is referred to as "Inversing the Phillips curve", which is an economic concept illustrating the inverse relationship between inflation and employment.
Figure 1". The Phillips curve model
The goal is to prevent additional wealth on the platform from being mitigated by inflation and instead translate cost savings from increased production efficiencies into additional income for consumers. The synthetic environment of CEIS adjusts the Phillips curve model by replacing the x-axis (unemployment rate) with the rate of passive income growth, which measures the network's productivity. The y-axis has also been modified to demonstrate the correlation between passive income growth and deflation in production costs.
Figure 14. The CEIS’ inversed Phillips curve model
9.2. Methodology
CEIS utilizes two primary methods to achieve its objective of reversing the negative effects of automation on low to mid-income workers:
Re-orienting the pricing mechanism: CEIS aims to ensure stable or increasing profit margins for consumer accounts. By re-orienting the pricing mechanism, CEIS strives to create a pricing structure that enables consumers to generate profits consistently or even increase their profit margins. This approach is intended to provide financial stability and growth opportunities for consumers within the system.
Efficient revenue/profit distribution: CEIS is designed to distribute revenue and profits efficiently throughout the network to facilitate individual and network-wide growth. This means that the benefits of increased productivity and cost savings from automation are shared among the users of the platform in a way that promotes overall growth and prosperity. This approach is aimed at creating a fair and equitable distribution of wealth within the system, ensuring that the financial benefits of the platform are not concentrated in the hands of a few, but are spread across the network for the benefit of all participants.
Methodology #1: Re-orienting the pricing mechanism
By placing retail pricing controls in the hands of product producers, their pricing incentives shift. If producers lower their product's retail prices too low, relative to their product competitors in the same pricing class, this reduces the margin received by consumer e-businesses that profit from the distributive margin of the product sale. It also reduces the retail margin amount that funds the data monetization protocol, which negatively impacts the wealth of the entire system. Conversely, if product producers increase their product retail price too high, relative to their product competitors in the same pricing class, then the chances of their product being purchased will decrease, which also negatively impacts overall profits.
To achieve a balance, the pricing mechanism is designed to accomplish three things:
Maintain sufficiently high product margins so that marketplace stakeholders can generate sufficient returns, mitigating against any deflationary force on prices. This is important because if product margins are too low, there may not be enough profits to go around to all stakeholders, which could lead to decreased investment and growth.
Maintain sufficiently low product margins so that marketplace prices are approximate to market equilibrium, mitigating against any inflationary force on prices above the wealth demand. This is important because if prices are too high, consumers may not be able to afford products, which can lead to decreased demand and decreased profits for all stakeholders.
Re-orient the deflationary pressure to the product producer. This is because any decrease in their margin relative to stable prices generates increasing returns for their marketplace stakeholders (consumers and consumer e-businesses). This incentivizes producers to focus on improving production efficiencies to increase consumer profits while also maintaining stable prices to mitigate price volatility.
Methodology #2: Enforcing A Network-Wide Policy for the Efficient Distribution of Wealth
In order to address the negative impacts of automation, the cybernetic economy is designed to maintain a normal (Gaussian) distribution of income and wealth, promoting a fairer and more sustainable distribution of resources. This is achieved through the Monee Tree Protocol (MTP), which is implemented by the CEIS.
The MTP establishes "target sets" of investment goals for each user, which reflect their desired economic position within the distribution curve. Users are categorized based on their position on the distribution curve, with those on the far left having high consumption requirements and a low propensity to invest, and those on the far right having low consumption requirements and a high propensity to invest.
To optimize investment returns for all users, the MTP matches individuals from different target sets based on their specific investment needs. This heterogeneous investment mechanism optimizes the investment capacity from each respective economic position. Those with a lot of wealth help those with little wealth catalyze their product supply requirements, catalyzing returns. While those with a lot of wealth gain from those catalyzed and non-diminished returns. They also benefit from a wealthier network that has an increasing capacity to spend and invest.
Figure 15. Agents maintain a symmetrical (efficient) distribution of wealth within the network by selecting optimal investment ratios between both sides of the income distribution curve
For example, a user in the +4th standard deviation of the distribution with low consumption requirements may be matched with an investment pool containing ten users in the -4th standard deviation of the distribution with high consumption requirements. By pooling their investment capacities and leveraging their respective investment goals, users from different target sets can optimize returns according to their specific investment needs.
Figure 16. Executing left and right investment ratios within a CSF
Each user account aims to increase their economic status by at least 1 standard deviation, which is represented by a "Monee Station" within the distribution. A Monee Station is a range of wealth that corresponds to a standard deviation within the population's wealth distribution. The system has a theoretically limitless number of Monee Stations, as it can divide the population into different ranges of wealth and add more ranges proportionally as wealth increases on the platform.
Figure 17. Depicts the CEIS’ ranges of wealth
9.3. Performance Evaluation
The economic performance of the platform is evaluated using five performance metrics: actuality, capability, potentiality, productivity, and latency.
Actuality refers to the platform's current performance, taking into account existing constraints.
Capability measures the platform's potential performance, also considering existing constraints.
Potentiality represents the platform's performance if certain constraints are removed.
Productivity is the ratio of actuality and capability, while latency is the ratio of capability and potentiality.
To determine the overall performance of the platform, the ratio of actuality and potentiality is calculated. These performance metrics are recursive and apply to individual accounts as well as the entire platform economy.
To monitor the impact of any action modification, the system sends algedonic alerts and measures the effect on productivity, latency, and performance ratios. Based on the results, the system can adopt good techniques, take corrective actions, and optimize its performance to remain viable.
Figure 18. Demonstrates how performance measurements are sent to the management state as algedonic alerts
9.4. Outcome: Flywheel Effect
The CEIS’ economic paradigm, also known as the “Flywheel Effect,” creates a positive cycle of growth sustained by real economic productivity. Each transaction on the platform incentivizes further engagement and stimulates the network’s economic growth and productivity. This can be seen through various stages of the cycle:
More purchases lead to more data monetization
More data monetization leads to more investments
More investments lead to more product selections
More investments and product selections lead to more traffic
More traffic leads to more income
More income leads to even more purchases
This positive cycle creates a virtuous circle of growth where the increased transaction activity leads to a higher purchasing power for the network, thereby stimulating even further economic activity.
Figure 19. A depiction of the CEIS’ economic flywheel
10. Conclusion
With technology advancing rapidly, there is a risk of massive economic disproportions arising between those with the resources to exploit the benefits of technology and those who don't. To address this issue, we propose a decentralized cybernetic marketplace powered by an emergent economic intelligence network. Human owners can form a symbiotic relationship with their agent, which privately and securely exchanges data for money, and then uses that data to conduct profitable commercial interests on their behalf.
The platform's integrated economic system regulates inflationary and deflationary forces, implementing controls that facilitate decentralized investments and maintain a normative distribution of income and wealth. Additionally, the CEIS has the potential to expand and become a Decentralized Cyber-Physical System, a virtual workspace that integrates hardware and software tools, enabling users to fulfill end-to-end supply chain activities.
In conclusion, the CEIS offers a sustainable and equitable solution to the challenge of automation by providing an open and emergent economic intelligence network that promotes a virtuous growth cycle sustained by real economic productivity. The platform incentivizes further engagement and stimulates the network's economic growth and productivity, leading to even more transactions and increasing the purchasing power of the network.
11. Appendices
Appendix A. Data Monetization Protocol
The following is a high-level depiction of the Data Monetization Protocol (DMP):
▪ Profit margin extraction: The profit margin extraction step takes place after the purchase of a product. The profit margin from each item purchased is added together to initiate the data monetization protocol.
▪ Reserve balance sorting: Margins in fiat are exchanged for an internal exchange currency via the ‘Token Reserve System’ (TRS) and deposited into a reserve balance. Reserve balances are classified by ‘product category’, therefore ‘Product category A (purchase) = Product category A (escrow deposit)’.
▪ Product category performance ranking: The system calculates the sum of product categories and divides them into x number of classifications that rank according to performance. A product category performance is defined by the revenue of extracted margin deposited within a given product category reserve balance.
▪ Allocation part 1 – Tier classifications: This step determines the way an internal exchange currency is distributed to each user. Each user’s distribution method is dependent on their respective wallet balance.
▪ Allocation part 2 – Distributing according to user wallet performance: The user’s account balance and the distribution of their points (by percentage) determine the performance tier class their wallet falls under. This distribution method is termed the ‘Monee point distribution spread’.
▪ Data Valuation – Determining the real-time market value of user data: Once the Monee point distribution spread has been determined for a respective user, the system will calculate the data valuation per user.
▪ Yielding – Liquidating user data: A default setting allows the user’s Monee agent to liquidate their data at the best valuation for them. However, users can manually interfere to liquidate their data valuation at any time before the 48-hour time limit. The data valuation is displayed on the users’ dashboard where they can view their valuation in real-time and execute liquidation at their convenience.
Appendix B. Production CSF
After a thorough demand analysis, Production Community-Sourced Funds select a basket of products listed by manufacturers within the e-investment department of the Moneetize marketplace. Once a product within the portfolio is sold in the marketplace, each CSF holder of that product will be distributed their respective share of the profit.
The Production CSF procedures and protocols are the following:
▪ Product sourcing: Triptolemus will deploy a Management Model (TMM) that will source product inventory and determine the best CSF investments for its user. This process can be manually interfered with by the users themselves, through the settings feature of their account. The user account ID will be stored in the derivative list of yield beneficiaries.
▪ Minimum Order Quantity (MOQ) Calculator - MOQ threshold: As a given user account submits x amount of capital into a CSF, the system determines whether the most recent capital input of has met or surpassed the MOQ threshold. The MOQ will remain in a holding state until the threshold has been met or surpassed.
▪ Processing product order: After the minimum order confirmation, the system can initiate the order from the supplier. Once the order has been processed, the purchase order invoice will be sent to the system validator and initiate the logistics and assurance protocol described below.
▪ Production CSF yield calculator: At this point in the protocol, both the distribution and retail margins have been separated. A yield calculator accounts for the yield (per unit sale) of every investor within the fund.
▪ Production CSF yield distribution: The final stage of the protocol distributes the yield produced from a product purchase. If necessary, fiat dollars will be exchanged into the required platform currency and transferred to the appropriate user wallets.
Appendix C. Robo-advisory
The following is a crude high-level schematic demonstration of a dialogue between a supplier account and its robo-advisor using 3 dialogue states.
dialogue state: Greeting
James Hirschorn: Why is my revenue decreasing?
sentiment analysis: Negative intent detection: Problem Inquiry
dialogue state: Problem Identification
Robo-advisor: Data shows that there’s been an increase in competition in your category. Over the last 7 months, 8 new suppliers have listed products similar to yours. For that reason, my analysis indicates that the market demand for your product has decreased by 11.4%.
Robo-advisor: However, there are multiple ways that you can still continue to grow your business. Are you interested in hearing more?
action: Request confirmation
dialogue state: User confirmation
James Hirschorn: Yes. context: User seeks advice on business growth
action: Generate report
dialogue state: Report generation
Robo-advisor: Great. First, let me generate a real-time report on your business. action: Obtain financial report for user James Hirschorn’s business from the Knowledge Base
context: add report to the context
dialogue state: Present findings
Robo-advisor: It shows here that you’ve been experiencing a slow decline in returns for the last 12 months. Our report also demonstrates significant latency issues. Your inventory deliveries were late 24% of the time and reduced your rating by 1 star.
Robo-advisor: Customers have also been complaining about product quality. Out of 124 deliveries, your product has been returned 28 times due to defects. Also lowering your ranking on the system.
action: Offer strategic solutions
dialogue state: Present suggested solutions
Robo-advisor: Given you want to continue selling Product X, I’ve generated a few strategic solutions for you in my strategic plan report . . .
Appendix D. Smart e-business revenue (Case Study)
Julia’s annual passive income from the Monee marketplace
After purchasing $200.00 worth of items, Julia receives $50.00 compensation for her data.
Julia’s robo-advisor uses her $50.00 worth of investment capital to build her portfolio in the following manner:
where,
a = the capital invested in the CSF r = the rate of return from the CSF n = the number of times per year a quantity of stock within a CSF has been sold
▪ 1st capital investment - Production CSFs
$25.00 is allocated toward investments in Production CSFs, where the average yield rate is 15% and the turnover rate is 24 times within a 12-month period.
a1 = $25.00
r1 = 15% avg
n1 = 24
▪ 2nd capital investment - Logistics & Assurance CSFs
$15.00 is allocated toward supply chain insurance, where the average yield rate is 10% and the turnover rate is 20 times within a 12-month period.
a2 = $15.00
r2 = 10% avg
n2 = 20
▪ 3rd capital investment - Credit Liquidity CSFs
$10.00 is allocated toward lending, where the average yield rate is 5% and turnover rate is 5 times within a 12-month period.
a3 = $10.00
r3 = 5% avg
n3 = 5
Therefore,
ROI (12) = [25 × (1− (1.1524)/1 − 1.15)] + [15 × (1 − (1.120)/1 − 1.1)] + [10 × (1 − (1.055)/1 − 1.05)]
ROI (12) = $715.63 + $100.91 + $12.76
ROI (12) = $829.30
Thus, Julia’s annual ROI based on a single $50.00 capital investment in the various CSFs is $829.30 or $16.59 for every $1 invested.
That means Julia’s initial yield of $50 via data monetization will eventually earn her an income of $829.30 for the entire year.
Now, let us also assume that rather than a single order of $200.00, Julia spends an average of $200.00 USD every month for 12 months, totaling $2,400.00 per annum (approximately the average annual amount US consumers spend shopping online). Let us also assume the investment variables illustrated above represent an average and remain consistent throughout the year, including her data monetization rate of $50 every month.
We can observe her returns in the chart below:
To summarize, Julia's annual ROI based on a single $50.00 capital investment in the various CSFs is $5,543.26 or $110.86 for every $1 invested.
Notes:
Please note that for the purposes of this demonstration, these calculations assume the given investment variables remain constant throughout the year.
It also assumes that each month’s revenue is 100% re-invested in the following month.
Appendix E. Network Performance Evaluation (Case Study)
Let us suppose our synthetic economy realized $1,350,000 in sales within the month of February. Let us also suppose that our calculations demonstrate it was capable of achieving $2,000,000 in sales, given the determined aggregate demand + available funds on the platform. Cancellations that month were disproportionately high and maintaining the average would have increased sales to $2,500,000.
Therefore,
▪ Actuality = $1,350,000
▪ Capability = $2,000,000
▪ Potentiality = $2,500,000
thus,
▪ Productivity = $1,350,000/$2,000,000 = 0.675 or 67.5%
▪ Latency = $2,000,000/$2,500,000 = 0.8 or 80% efficient and 20% latent
▪ Performance = $1,350,000/$2,500,000 = 0.54 or 54%
Figure 20. The VSM/CEIS method for measuring performance
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