Keystones, silicon individualism, alpha, beta and gamma diversity
In the previous post we discussed TechRank as a tool for understanding how widely effective a particular technology, component or contribution in general is.
Now it’s time to take a little bit deeper look at how this tool can be used.
Keystones
Keystone species are species with a disproportionately large effect on its environment based on its absence or abundance. It is a concept by the zoologist Robert T. Paine from 1969.
With TechRank we can start identifying keystones also on technical level. The most connected one with the highest TechRank value are such. We can understand:
Keystone materials
Keystone technologies
Keystone companies
Keystone national infrastructure
Companies and organisations can do the same and look for
Keystone suppliers and countries
Technical keystones maintaining the structure and integrity of the system in similar manner like the keystone in an arch keeps other stones in their place and just like keystone species do in nature.
Companies can address possible supply chain risks in various ways. Short time problems can be fixed with buffering, midterm with diversification and longer term with building anti-fragility (and first figuring out what that could be in our context).
Many infrastructure elements are keystones. For them it is essential to think of building diversity. Governments’ role is to support this.
National electricity network can be diversified by decentralised technologies like solar, wind, geothermal and small scale nuclear reactors (SMRs). Luckily to some degree this is ongoing.
Money can be diversified with crypto-currencies. This prevents for example payment networks from cutting out political rivals of current power holders
Organisational models can be diversified with smart contract based autonomous organisations (DAOs) that allow easy experimentation
Health care can be diversified with self-analytics technologies and remote diagnostics services from a multitude of commercial, non-commercial and low-profit limited liability companies
Remote heat distribution (relevant in countries like Finland) can be diversified with local geothermal heat pumps
Harbours can be diversified with pop-up harbour technologies (see
https://nextbase.fi/ as an example)
Bridges with pop-up bridge technologies.
Mobile communications technologies can be diversified with more unregulated frequencies, community managed private nets and software defined radios
And so on…
Silicon Individualism
Let’s take a deeper example to see what keystone analysis would mean.
Software will power most if not all products of future. This logic is executed in processors. We have processors in smart phones, cars, the cloud and in every day products as well in industrial equipment. All our infrastructure relies on them.
Today processor designs are (almost) monocultures and they have the same risks as all monocultures. For examples in agriculture a single disease can threaten to wipe out entire species. When all plants are identical, a disease that affects one plant can infect all. This is for example what has happened with bananas - the Gros Michael variety was earlier completely wiped out with Panama disease and now Cavendish variety is being threatened with another fungal disease.
A single security fault in processor design can render much of worlds infrastructure inoperable should a malicious actor decide to utilise it. Moreover, commercial processor vendors may in future have legal obligations to add backdoors to their designs making them intentional insecure.
For the future to be safe so that lights keep shining, it is warm inside and communications flows, it seems we need to be moving to a new dominant design. One where processor designs are open sourced and generative designs (designs performed by computers according to given design constraints) are used on massive scale to ensure that processors being produced may have identical interface (instruction sets) but their internal implementations are different.
Ideally every processor being produced is different from each other or at least every production batch being produced has some unique characteristics. These differences need to be evenly split on all aspects of the processor. This way should there be faults, the faults are only in small subset of processors and malicious actors can only have limited effect. Even better with multiple processor families designed by different teams in completely different parts of the world.
In future silicon processors need to become unique internally but share much common characteristics with each other almost as every human individual is unique, but the same.
This is silicon individualism.
Alpha, Beta and Gamma Diversity
As discussed above, some software components are popular up to keystone level and are used in many of projects while others less so. Same with physical components. Rather than looking at individual components, we can look at the whole system. How fragile it is as a whole?
We can loan concepts from other domains for this – domains like biology. From biology most of us know that monocultures have inherent risk. A single disease can wipe out entire populations. Biologists use concepts like alpha, beta and gamma diversity to gain a general understanding of system (ecosystem) level diversity.
In biology alpha-diversity tells how much variety there is in species in some specific natural environment. There are many different ways to calculate it such as looking at the number of species and their proportions – meaning that if the amounts are evenly divided, the diversity is larger than if there are just a few dominant species that have most of the individuals.
Another way is to look at what types of living things there are – trees, bugs, animals. A place with only different types of grass has less diversity than an area with lots of plants and animal species. Third one is to look at the DNA of the species. How much variety there is in the genetic material in living things in that place.
Beta diversity tells how much variety there is between two natural environments. If the species and their proportions are the same, the diversity is zero. If the species are completely different, it is at max.
Gamma-diversity tells the complete diversity in a larger region. All three types of diversities are interconnected. Gamma can be calculated for example by multiplying or adding alpha and beta diversities.
Some examples.
Let’s assume national laws would one day be coded as smart contracts. Then it becomes possible to start comparing the legal systems of two counties – how similar they are, are the clusters of countries where legal structures resemble each other, in what areas are the legal structures different in similar countries, what are the areas where there is biggest diversity in legal traditions, what are the differences in number of laws in different topics (if there are lots of laws in some part, this may be seen as very important there). Since legal structures are driven to great deal by local culture, this is also a way to look at cultural traditions in different parts of the world.
For mobile apps one can look how diverse the set of apps are that people have on their phones and compare different regions (beta diversity type of questions). How is diversity in apps evolving over time, is world becoming more concentrated or diverse in terms of services (this does not say anything about the content going through apps, just compares app diversity).
From security point of view, it is interesting to look at how much source code and common libraries applications share (alpha type questions). This works if they use the same programming language.
In software it’s very common to copy ideas between frameworks but implement them in a different language. So, it is harder to assess how different are environments like the NodeJS from the Python ecosystem in terms of some of these diversities.
The same type of questions can be asked about physical products. This requires access to data and works best when the designs are open sourced. Then it becomes easy to answer for example how much products in different industries share modules (like sensors, motors, micro-controllers, …). Is the interbreeding between industries increasing in terms of shared technology or decreasing?
Are there super-connected parts, for example pieces of code, processing elements or designs, that are used almost everywhere now?
Answering these questions tells information about meta-structure in our world. This is important especially for national security. If there are super-connected parts, they are the elements, where an adversary with minimal investment can cause maximal harm. The harm does not even have to be caused on purpose; a simple human error can cause large parts of the society to stop functioning.
These analyses tell how fragile our environment is.
These analyses also tell what domains and where the hack-in labs should focus while we wait for the sleeping beauties at national level to wake up.
Credits: The different diversities are described in Ilkka Hanski’s book “Messages from Islands: A Global Biodiversity Tour” (the book is really about Ilkka’s life’s work but the diversity descriptions are there).
Summary
TechRank is a way to calculate how central various designs are to the overall society.
Different diversity numbers (alpha, beta, gamma) can be calculated to extend this to the technology ecosystem level. How vulnerable they are in general.
If we look at the past posts on decentralised models and how it leads to open sourcing designs together: we’ve destroyed business models of most industries via open-sourced designs funded by the Schrödinger Fund, nullified the concepts of gross national product as open designs do not generate revenue to count, annihilated money (as you are free to download and use open designs) and eliminated taxes (no revenue=nothing to tax).
It’s certainly a start, but time to move on on our little journey of future.