Discovering GeoDB 2. Game theory

“An equilibrium is not always an optimum; it might not even be good. This may be the most important discovery of game theory.”
Ivar Ekeland, 2006 [1]

In our previous entry, Discovering GeoDB 1. The power of place, we discussed about the value of private locations analyzing i) the value we give to that information and ii) the usefulness and effectiveness of this information to perform big data analysis in the business if we’re a company.

We consciously avoided setting a price, allowing the reader to draw their own conclusions. However we set two premises on top of which we build our proposal:

  1. Users consider their location as their most valuable private data. In exchange for this information they demand much more money than they currently receive.
  2. Companies are aware of the importance of localization for analysis. They know that this information allows them to connect data silos to extract knowledge from them.

We also reviewed in the previous post how companies are already obtaining our private location data, using psychological tricks with which they obtain our private information in exchange for very little money. This approach leads to an equilibrium between supply and demand, which is far from being optimal or good:

  • Users are selling their private data without being aware of it, obtaining derisory benefits in return.
  • Companies are distorting reality, making users believe that demand is low and therefore the price can not be higher.

In this entry we review, from the perspective of game theory, and specifically from the study of the economic model of supply and demand [2], how in a free and competitive market the equilibrium between supply and demand sets the price for the goods that are exchanged in it.

Applying this to the commercialization of private location data, does it mean that the user can receive as much money as he wants for his data? No, it means that he will be informed of the value of his information so that he decides if he is willing to provide it for a given price with no hidden catches.

The big cost of big data

So far we have only talked about benefits, and it’s obvious that to calculate them we should not only look at the profits, but also at the costs.

From a strictly economic perspective, the cost for users is negligible, understanding that they already have the necessary devices and that the capture and transmission of the information will be done without affecting the normal functioning of their devices.

But what about buyers? What other costs do they have? Mainly the costs of the big data infrastructure.

A key enabler for big data is the low-cost scalability. For example, a PetaByte, PB, Hadoop [3] cluster will require between 125 and 250 nodes which costs around $1.000.000 [4]. So, is it possible to store 1.048.576 GigaBytes, GB, for $1.000.000. Or what is the same, can we store 1 GB per $0,954? It’s not that easy.

In 2012, Amazon carried out a study [5] on the costs associated with data warehouses, finding expenses up to $25.000 per TeraByte, TB, annually, or $1.000.000 by 40 TB for a year ($976,56 per GB).

What is behind these costs? Setup and maintenance. While storage is more affordable every year, engineering is what lies at the heart of the issue, having to solve challenges such as i) scrubbing information, ii) maintaining security, iii) establishing compatibility with business intelligence and analytics tools and iv) ongoing data movement [6].

However, a high operational cost is not problematic if the Return On Investment, ROI, is adequate, and in this field, it is. The big data market was worth $125.000.000.000 in 2015, which provides a sense of just how much financial capital enterprises are pouring in to data operations [7].

The reader could reason, ‘the costs are very high, but the ROI is also high, so it’s worth it’. But this admits another interpretation, ‘if the implementation of the solution is inadequate, you can lose a lot of money’. If you base your big data and analytics solutions on low-quality data, you will see few ROI. Some analysis concluded that a company was losing about $81.000 per month by failing to leverage data analytics effectively [8]. Therefore, it is not enough to use an adequate infrastructure, but to fill it with quality data.

It’s true that companies make large profits by exploiting our private data, but we must understand that their operating costs are high. A company that invests in big data does so based on forecasts, the higher the costs and the lower the estimated ROI, the greater the risk it must face and the lower the probability that it’ll invest in this area.

Supply and demand

So, what is the best we can do? Tell the truth. It’s necessary to speak frankly with users and explain them that their private information is valuable and the reason for it. No more tricks. Companies must decide how much they’re willing to pay for this information and each user will decide whether to accept the offer or not, it’s that simple.

Ultimately, the difference between how much companies are willing to pay and how much users are willing to accept will result in an economic equilibrium for price, something that studies the economic model of supply and demand [2].

The model is usually represented using the Alfred Marshall’s supply and demand graph [9] in which demand and supply curves are represented, and the point at which they intersect is the economic equilibrium point that determines the price.

Understand how a free and competitive market works using this chart is extremely easy. Let’s see it applying the four basic changes in a hypothetical private localization data marketplace, that is, i) increase and ii) decrease in demand and iii) increase and decrease in supply.

Unlike other markets, ours works by accumulation, that is, each new good is added to the total. The reader might think that this increase in quantity goes hand in hand with a decrease in the price, but that is the result of a very simplistic interpretation. Historical values will be increasingly cheaper, but the most recent information, which is of greater value for big data analysis since it allows us to understand the current trends, is daily information and its economic value can be measured using this economic model.

We’ll assume that the market is described by the previous graph and the price of a Location, L, in Monetary Units, MU, is 1L = 1MU, and that this ratio is fixed.

If GeoDB’s locations data pool becomes more interesting for buyers, there will be an (i) increase in demand, which will cause an appreciation of the MU.

In the opposite case, that is, that (ii) demand decreases, that will cause a depreciation of the MU.

Regarding users, an (iii) increase in the number of users willing to sell their private locations will cause an increase in the supply and consequently a depreciation of the MU.

In the opposite case, that is, that (iv) supply decreases, that will cause an appreciation of the MU.

It’s simple, right? And what will be the price of the MU? We don’t know, we only know that it would be in this area.

However, it’s a huge improvement over the current situation, in which companies try to convince us that there is hardly any demand and consequently the graph is somewhat similar to the following:

In the next post we’ll review the technical pillars on which GeoDB is built, Distributed Ledger Technologies, DLT, which, in its best known version we usually call Blockchain.

References:

  1. The Best of All Possible Worlds: Mathematics and Destiny. Ivar Ekeland, 2006. ISBN: 9780226199948
  2. https://en.wikipedia.org/wiki/Supply_and_demand
  3. http://hadoop.apache.org/
  4. https://www.forbes.com/sites/ciocentral/2012/04/16/the-big-cost-of-big-data
  5. https://aws.amazon.com/es/blogs/aws/amazon-redshift-the-new-aws-data-warehouse/
  6. https://www.atscale.com/blog/big-data-cost
  7. https://medium.com/@GeoDataBlock/discovering-geodb-1-the-power-of-place-fb97a935b3d9
  8. http://blog.syncsort.com/2017/03/big-data/quality-data-big-data-worth/
  9. https://en.wikipedia.org/wiki/Alfred_Marshall
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