Discovering GeoDB 7. The GEO token

“Three eras of currencies: commodity based, government based, and math based.”
Chris Dixon, 2015 [1]

Today we conclude the Discovering GeoDB post series. In this last publication we’ll explain the mechanism by which we seek to reward users for providing their private location data, but first, let’s remember everything we’ve talked about so far:

• In [2], The power of place, we’ve analyzed the great value of private location data.
• In [3], Game theory, we’ve reviewed how in a free and competitive market, the price will be dictated by both sellers and buyers.
• In [4], Blockchain 101, we’ve summarized the pillars of blockchain technology and we’ve indicated how we aspire to use it to work with private location data.
• In [5], Modular Blockchain Architectures, we’ve explained our concept of a modular blockchain architecture and why we believe that the interconnection between blockchains and therefore, this type of architecture, will be the predominant in the coming years.
• In [6], Measuring size and cost, we’ve shown the results of some studies that we’ve carried out about the estimated size of our big data and about the cost of storing this information using blockchain technology.
• In [7], Interconnection, we’ve talked about our interconnection model, which we’ve designed in order to minimize costs.

In our previous post we’ve indicated that we’re going to use an hybrid stack in which we’ll use i) a token defined using Ethereum to manage the economic value of location data and ii) open source blockchain technology for our infrastructure.

When we talk about creating a new token we’re aware that many readers will ask themselves, is this necessary? This doubt will usually come to those who observe the blockchain technology from a competitive point of view, many of whom consider that a fierce battle is currently being waged among hundreds of cryptoactives until, finally, only a handful of them will be victorious.

For us, the reality is a little less romantic, although no less epic.

As we’ve indicated in previous posts, for us there are no silver bullets in this area. Our vision is that blockchain technology is destined to transform our social and commercial relationships in a myriad of areas. Currently even the most visionary can not imagine everything that it’ll allow us to do.

Nobody knows in what direction this technology will evolve in the next decades. There’s only agreement at one point among all the experts in the area, it’s here to stay. We know that many people have earned a lot of money thanks to being pioneers in the field. Whether due to technological ignorance or speculative interests, many of them still defend the competitive vision among solutions, but gradually it’s reaching a consensus situation under which the discourse is that the coexistence of blockchain solutions adapted to different scenarios will be common in the future. It’s just common sense. The real world is heterogenous, each problem is unique, and each of them requires a customized solution.

The irruption of blockchain technology has brought about a paradigm shift in how to approach economic relations. Frankly, is anyone able to imagine a better mechanism for the transmission of economic value than one built under a decentralized trust scheme? While for a time many people have not been able to see beyond the pure economic utility of the technology, in recent years the collective vision has been transformed to the point that today, the most common discourse is that blockchain technology is much more than a tool for the transmission of the economic value.

Blockchain technology is a tool to build solutions in scenarios in which the decentralization of trust allows the emergence of new possibilities. For example, from the perspective of the use of private location information for big data analysis, the use of this technology makes it possible to build a solution that guarantees that [4] i) the locations were captured by whoever claims to have captured them, ii) the locations were captured at a given moment, iii) history isn’t mutable or iv) the results are verifiable. A scenario of this type, which is built on the pillars of trust and decentralization is, in addition, the best option for the transmission of the economic value of the commercial operations that occur within it.

In a world in which specific blockchains solutions coexist, some of them designed in scenarios in which, in addition, the same blockchain solution is the best way to transmit the economic value in the scenario, it may be convenient or even necessary to define a digital asset to capture and facilitate the transmission of the economic value in the commercial operations carried out in it.

As we’ve indicated above, we don’t support the opinion that there’s a competition in which a cryptoactive will triumph over the rest, except obviously in those cases in which there are several cryptoactives created to solve an identical problem. For us, these digital assets are the ideal mechanisms to transfer the economic value in the commercial operations carried out in a scenario, and their total capitalization will go in proportion to the market size in which they’ve been defined.

From this perspective, we propose the definition of a specific token for GeoDB, the GEO token.

GeoDB token

GeoDB is building a structure in which the exchange of value is based on the creation of its own token, the GEO token. GEO token represents the market value of big data in GeoDB’s marketplace. To achieve this it’s vital to accurately design two aspects, the i) cost of data acquisition and the ii) incentive system.

Cost of data acquisition

We must carefully model the cost of data acquisition in order to maintain an equilibrium between benefits for users and costs for buyers at all times. We believe that the best way to do this is to link the cost of data acquisition to the diffusion rate expected for GeoDB, establishing reduced prices during first years to encourage purchases and progressively increasing them until reaching the equilibrium point.

To estimate the diffusion of GeoDB we embrace the theory of the diffusion of innovations [8]. Professor Everett Rogers [9] popularized in his book of 1962, Diffusion of Innovations, the theory with the same name which seeks to explain how, why, and at what rate new ideas and technology spread.

The concept of diffusion on which Everett’s theory is developed was studied in 1890 by Gabriel Tarde in The Laws of Imitation [10]. He identifies 3 main stages through which innovations spread: 1) Difficult beginnings, during which the idea has to struggle within a hostile environment; 2) Exponential take-off of the idea; 3) Logarithmic stage, corresponds to the time when the impulse of the idea gradually slows down while, simultaneously new opponent ideas appear. The ensuing situation stabilizes the progress of the innovation, which approaches an asymptote [11]. This diffusion model is usually modeled using sigmoidal functions [12], also known as S-curves, which are widely used in fields such as artificial neural networks, biology, biomathematics, chemistry, demography, economics, geoscience, mathematical psychology, probability, sociology, political science, linguistics, and statistics.

The cost curve that we have defined for GeoDB will be the following:

• M, the maximum cost.
• m_0, the minimum cost.
• b, the depth of the current block in GeoDB big data ledger.
• B, the number of blocks to generate before reaching M.
• f_c, an adjustment factor of the steepness of the curve.

We have established the following values:

• M = 10.000.
• m_0 = 1.
• B = 2.207.520.
• f_c = 13.

So we can substitute in the previous function to obtain the cost function of GeoDB:

Its graphic representation can be seen below:

We’ve talked about the cost in a generic way without specifying the units used to measure it. Our approach is to combine several aspects such as the amount of data, the number of blocks explored or the complexity of the query, to generate a cost metric that we’ll call Proof-of-Analytics, or PoA, as a clear nod to other similar ideas in this area such as PoW or PoS. PoA must be a verificable value obtained from the aggregation of a demonstrable amount of analysis work. It’ll be used in a cost function in which this value, the current diffusion of the protocol, and other intrinsic factors of the data such as its origin or its demand, will allow to set the price for the dataset.

The functions to calculate PoA and cost will be proposed in the technical white paper of GeoDB protocol, which will be available in a couple of months.

Taking into account the origin of the data or its demand to compute the cost of a dataset allows us to simultaneously create the mechanisms to reward users depending on the expected demand for their data and thus maximize the heterogeneity of the information.

Incentive system

The correct setting up of the incentive system is one of the most delicate issues for the economic sustainability of GeoDB’s protocol in the long-term. Any incentive distribution could be adequate in the first days of protocol operation, in which there will be an abundance of resources for all, but an inequitable distribution will propitiate that those participants who feel that the distribution is not fair, turn their backs on the project, which would be disastrous for its sustainability. Being aware of this, we have designed an equitable incentive system adjusted to the technological penetration that we expected in order to guarantee the interests of all the parties involved.

We’ve built a fixed token supply curve based on time, that establishes reward token process over the first 21 years of the system operations from preassigned 300.000.000 GEO tokens to our complete supply of 1.000.000.000 GEO tokens. Further data and our complete model can be accessed upon request.

To distribute the rewards we have defined a decremental logarithmic model. This model will be repeated in cycles of 21 years at the rate of a block every five minutes, giving a total of 2.207.520 blocks per cycle.

The cumulative reward curve that we have defined for GeoDB will be the following:

• T, the number of tokens to be rewarded.
• b, the depth of the current block in GeoDB big data ledger.
• B, the number of blocks to generate before reaching M.
• f_r, an adjustment factor of the steepness of the curve.

We have established the following values:

• T = 700.000.000.
• B = 2.207.520.
• f_r = 10.

So we can substitute in the previous function to obtain the cumulative reward function of GeoDB:

Its graphic representation can be seen below:

And with this we conclude our GeoDB discovery post series. Our hope is that it’ll serve to familiarize you with our proposal, but do not fear if you still have many doubts, we still have many things to tell you and many things to explain. Stay tuned! ;)

References

1. https://twitter.com/cdixon/status/563223999543382017
2. https://medium.com/@GeoDataBlock/discovering-geodb-1-the-power-of-place-fb97a935b3d9
3. https://medium.com/@GeoDataBlock/discovering-geodb-2-game-theory-c6dc5c6985c9
4. https://medium.com/@GeoDataBlock/discovering-geodb-3-blockhain-101-bfd719ddac38
5. https://medium.com/@GeoDataBlock/discovering-geodb-4-modular-blockchain-architectures-63aaf83c263
6. https://medium.com/@GeoDataBlock/discovering-geodb-5-measuring-size-and-cost-de59f3aed416
7. https://medium.com/@GeoDataBlock/discovering-geodb-6-interconnection-a9bb197d7ed9
8. Rogers, Everett (16 August 2003). Diffusion of Innovations, 5th Edition. Simon and Schuster. ISBN 978–0–7432–5823–4
9. https://en.wikipedia.org/wiki/Everett_Rogers
10. https://en.wikipedia.org/wiki/Gabriel_Tarde
11. https://en.wikipedia.org/wiki/Logistic_function
12. https://en.wikipedia.org/wiki/Sigmoid_function