About Possible and Impossible Tasks

More about the constructor theory through the lens of an innovation marketer.

Universal systems are programmable

The side of the constructor theory that interests me most: there's no such thing as an abstract program - it needs matter and a universal system to run at. A system is universal if it is solid enough to store...and fluid enough to transform…Computers, obviously able to store data and transform signals are the prototypes of universal systems.

But, IMO, also our money system is universal: it's able to store values and manage economic transformations…

Think it, build it, market it

You can have great products on your "drawing boards", great process recipes, operation plans...but do they exist, before they are built?  No, but even more, there are no goods, before they do not materialize as prices at a concrete market in a concrete sales contract.

Technical and economic feasibility

To asses whether a task is technically or economic feasible, we make usually predictions. But in the sense of constructor theory, we need to switch to the mode of explanation - the task is possible or impossible. And my plea is: go beyond the technical explanation, try to verify the economic feasibility.

Tasks, objects, information, reality

Provided, you seek perfection of a rolling process at your hot rolling mill. You've developed a great predictive model of your rolling process, based on elasto-plastic and thermo-dynamic theories, deep metallurgical transformation rules...you've recipes, know the ingredients…you've calibrated your models to concrete historic rolling data and the fit was so well…but the result is suddenly out of your ambitious tolerances.

The reason? You may have rolled a material with unexpected elaso-plastic properties thats concrete parameter values can be only acquired during the process. You need another information to derive them  (a kind of verified soft sensing)…in order to re-calibrate your models adaptive.

The same things happen usually to economic processes, where the models are not dominantly physical…but stochastic. Even more, the unexpected behavior is systemic.

and marketability

In the rolling example, you might need to ride the material property waves and in an investment example price and volume waves.

What we can learn from the constructor theory: switch from prediction to explanation.

Remember, the constructor of your new robot generation lies in its robot control…try to explain their fitness for purpose, coverage, precision, robustness…in depth and try to explain to yourself what the optimal market risk is.

Explanations are easier, if your knowledge is computational, your programming contains symbolic computation techniques, you combine analytical, statistical and data driven methods intelligently…