Strategic pre acquisition
Hayek identified price (in a market economy) as a distributed information processing system. The price of elements in the chain adding up to produce the overall cost; and each of these elements subject to pressures reflecting factors of supply and demand.
This system has been used and abused to value goods and services for millennia. The abuses come from a litany of different structural problems, cost externalisation, immediacy of need and negotiating power, economies of scale and the monopoly tendency. The uses from a equally wide array of structural advantages, a changeable scale of value, a means of organising co-operation among long flexible chains, a testing and garbage collection system for economic innovation.
Price is an implicit information aggregation system, the question arises, what affect on the economy will the capacity to explicitly de-aggregate and process the same information have?
The capacity clearly exists, through blockchains, barcodes, RFID combining in the IoT, to track entire supply chains. This could allow the inclusion of many externalities within price. This would require enlightened legislative change, in global economy, this change might have to be agreed across many jurisdictions.
More realistically, it will create better information along supply chains, and will, tautologically, favour organisations and individuals who process this information effectively.
One application of this new suite of technologies is in the explicit decomposition of price. That is, the capacity to analyse every element of a supply chain, in Amazon’s case, down to the number of footsteps each worker uses between a shelf and a conveyor. Traditionally these analytical processes have been used to improve industrial efficiency.
Increased computing power provides the capacity to combine supply chain production models with explicit price decomposition of other elements in the supply chain. Parallel computing, and soon plausibly, ternary parallel computing through synthetic biology, makes it possible to calculate multiple probably pathways for how the external, or uncontrolled elements may change in value over time. These can be combined into probable future price predictions.
Whilst always theoretical possible, the number of data points, and the declining cost (while Moore’s law still holds, the infrastructure that makes the power accessible is cloud computing) means that theories are testable, and testable by a far wider range of actors.
The implications for the production and capture of value in the economy are clearly seismic.
The most obvious implication is that competing futures models will try and acquire resources or companies that believe to be presently undervalued. Ever has it been thus. The differences is likely to be an ability to identify both human and material resources relevant to a production process ahead of time.
The feedback effect on price is likely to make a complex system even more complex. Strategic competition over future inputs, largely driven by the insights of AI systems would appear to create an economy of competing AI gamblers. For doubtless a layer of financialisation will include direct and indirect bets on the profitability of competing divination systems.
There is every chance that these algorithms will be actively competing to bias the decisions of others and of course every chance one or two will emerge to dominate large swathes of the global economy as BlackRock and State Street or Amazon do today.
Of course the capital that flows on the back of the assumptions will have biasing effect on outcomes in the real world, such that with enough corporate will, defects in the assumptions of any given projections can be overcome (“throw money at the problem”). This would appear to be SoftBank’s strategy on occasion, and the basic assumption of the Silicon Valley model.
So we have a picture, not only of competing parallel prediction systems attempting to strategically capture future economic advantage, but in doing so, actively biasing those futures through their actions. As of course is implicit in the word “enterprise”. However, the degree to which human intelligence can fathom these chess moves will be an interesting dimension.
Another obvious application will be in providing the processing power for economic management of a planned economy, with the nation treated like an uber-firm.
If price can be rationally decomposed into its elements, then economic direction can take place without exchange being facilitated by currency. A data driven understanding of productivity and efficiency can be used to direct resource flow. This is unlikely to happen in a market economy but seems a likely direction for Communist states.
A predictive approach likely lends itself to the production of modular components with flexibility. Smart materials that can be assembled into different forms closer to the point of use. This will involve innovative inputs to existing 3D printers and biological molecular components for RNA printers. Thus building value early in manufacturing chain. The share of the economy taken by “pre-manufacture” component makers should expand and the sophistication of the material science means they are likely to have pricing power.
So we will move into a world where market prices can be explicitly decomposed and this is likely to increase volatility in prices as competing models seek arbitrage and by their very action change the price equation.
There is of course the promise of a more rational economic management of resources, including factors that are known to be valuable but are commonly excluded due to human cognitive bias or poorly constructed property rights. Further, there is the possibility of organisation of resource distribution through explicit information management. But that would be Christmas and the turkeys that brought us this status quo have a strong franchise.
svendesai 