Introducing QPS: a Consensus model for real-world, collaborative networks.

1. Abstract.

This article is a non-formal introduction to Qube Predictive Stake (QPS), a-Qube’s consensus algorithm, and the engine behind the Qube Idea Validation process.

Qube Predictive Stake’s model was designed to support a distributed network of innovation, and it aims to be the standard in collaborative processes such as prototyping, collaborative problem solving (CPS), and more generally in co-creation networks and open(-source) innovation, intended as networks based on the collaboration between individuals, where the product of their collaboration, whether a service, an artwork, an intellectual product or other forms, grows its value along the process — thanks to peers’ exchange of experience, resources, effort, knowledge, talent or time.

In fact, as Distributed Ledger Technology (DLT) matures, we are now able to propose an open, non-deterministic system which uses game theory and collaborative autonomy to set the ground for a shift from a corrective network — trying to bend human interaction for network’s own sake (maintenance) — to a more sustainable network based on self-organization — theorized since at least the half of the 20th century — able to optimize, and self-adjust with its participants, and leave space to human error.

2. Preliminary considerations.

This is to be considered a first overview on a-Qube’s design choices, and, more loosely, different means to achieve fairness and competitive equilibrium within a multi-agent network.

Also, besides wiki-definitions, buzzwords and acronyms, it’s important to notice that this article won’t be discussing “hot 2018 topics” such as scalability, TPS, and other sort of “pissing contests” in Blockchain and DLT space. It is, in fact, our non-unbiased opinion that, despite they did an egregious job in showing the potential of decentralization for humankind’s way to exchange value autonomously, existing blockchain/DLT projects proved to have little consideration for (and, in most cases, understanding of) humankind’s forms of interaction in a self-organizing, autonomous environment. As such, we don’t define consensus merely as a way of governing blockchains, but as any mechanism that allows self-organization.

3. P2P Networks. General points.

Now, before approaching QPS as a modified PoS-type (Proof-of-Stake) of Consensus — and analyzing PoS on a more general level — let’s start with some general points of what peer-to-peer networks are.

First of all, a common myth is to consider every P2P network as decentralized in nature — which, besides being untrue for practical reasons, generated lots of confusion in the way the community is organized (or self-organized) under a social perspective.

In fact, in order to coexist, members of a community need to find an agreement between each other. This agreement can be achieved, either in a centralized environment, through an external arbiter/judge/oracle, or in a decentralized network of peers, through what we more commonly call Consensus. This term describes the mechanism allowing participants to autonomously take governance decisions, whether on a limited (local) portion of the network, or on its entirety. For our analysis, we will skip trade-offs between centralized and decentralized networks, as it is evident we lean towards the second, and we will consider consensus as necessary in any human environment in order to assign and exchange value.

4. Money, nodes and x-centric network approaches.

In order to be functional, a Network design should consider at least:

1. Monetary/financial components (Tokenomics).
2. Participants/nodes.
3. The main purpose, the very reason the network has to exist.

As it’s logical to assume in the crypto-sphere, most DLTs have been built around a currency. This generated more or less decentralized ecosystems where the native coin or token of a project has taken the dominant role as the core of the network. This currency-centric approach has been by far leading the way, and it had, and it will continue to have, a consistent, powerful use-case, allowing users to transact with each other directly, without the interference and the limitations given by a middleman.

As the industry started to reach its maturity, though, more and more approaches to decentralization started to spring, addressing new, more or less concrete use-cases. Namely, interesting ones, such as content- and agent-centric networks — where the main protagonist have been the individual actions/creations of the nodes.

5. a-Qube ecosystem, a purpose-centric approach.

More than criticizing these approaches, though, we just want to outline that they are insufficient when it comes to building a self-sustainable ecosystem — meaning a balanced infrastructure able to support not only monetary transactions and individual interests, but a collaborative, purpose-centric interaction between the participants on a larger scale. Aligning positive contributions with monetary incentives creates a purpose-driven token, which serves as a means to exchange and propagate value through the network.

In essence, in fact, P2P networks are nothing but an extended social circle, a community, which, in order to be of any value to their members, needs to contain a well-defined set of rules and incentives to reward participants for their actions, interactions, work and/or behavior with each other.

As a co-creation network, back in the beginning of 2018, we designed a-Qube’s Consensus algorithm starting with its fundamental unit of measure, ideas — implementing a concrete solution to monetize ideas, and turning them into an asset, and a medium of exchange. We developed, then, by definition, a value-based network focused on its nodes — its participants, stakeholders, users, players or however defined — and their projects, with a framework where participants’ interaction and exchange of value could progressively grow the value of the network itself, and each of its subsidiary products.

This way, rather than focusing on one of the three (3) aspects as a standalone, we tried to design a system built around a purpose, a process — be it an action, a (non-)monetary exchange of value or a project/product. What we did, accordingly, was to focus on the social/human exchange of value in peer-to-peer networks.

6. Proof-of-Stake. Definition and historical landscape.

Moving forward, we may consider Qube Predictive Stake (QPS) consensus as a modified Proof-of-Stake of some sort. Now, that’s not exactly accurate, but it gives a good reference.

As a starting point, it makes sense to notice that despite its numerous attempts and applications, PoS has yet to prove itself in a real-world distributed system. Again, this is a personal opinion, and there have been many valuable, opposite claims. The educated readers are welcome to continue their own research, shape their opinions and/or open an intelligent, constructive debate with us on this matter.

Opinions aside, PoS itself represents an interesting shift in first-generation DLTs, such as Bitcoin, where an agreement between the participants (and some degree of security) is achieved through Proof-of-Work (PoW). In fact, instead of relying on computational power to solve (useless) mathematical puzzles, PoS networks, for their maintenance, rely on a set of incentives which rewards users (stakeholders) for staking part of the tokens they own in order to validate transactions (monetary and non) within the network.

The definition may be phrased in different ways, but the major shift consists in moving from an equilibrium based on energy waste (the “amount of computational power” owned by an individual or group) to an equilibrium based on the allocation of network’s resources (“staking power”) to groups or individuals participating to the network itself. Now, again, being not interested in discussing aspects such as energy consumption and employment of (physical) resources, we may say that the “improvement” of the second model over the first stands in trying to raise the human factor in networks which, by definition, are made of peers.

7. Decentralization and Governance.

Nonetheless, as we said, the main problem in current blockchain ecosystem is not only of a technological nature, which we are not going to discuss in this introduction, but it rather lays in network design. In fact, despite blockchain’s game-changing technology is somehow able to comply with the non-deterministic nature of human behavior on an individual level — this fails in adapting to the non-deterministic nature of social dynamics. This has repercussions on both the Consensus mechanisms attempted / theorized so far and, most of all, on the governance strategies adopted by blockchain-based entities and organizations — which, after a brief, chaotic, utopian phase, are slowly reorganizing in sovereign forms of control equal to the ones they were claiming to fight.

For instance, in PoW and PoS’ scenarios described above, we see how they both can generate manipulation of, and control over, the network. In fact, on PoW side, larger and larger coalitions of groups (mining farms) may collude and jeopardize immutability and decentralization within the network — and, on PoS side, single individuals or groups owning a larger stake of currency have more decisional power in taking collective choices.

Despite valuable, accurate arguments have been made in defense of both PoW and PoS models, it is easy to see how both have an innate oligarchic nature, which, in a way or another will distribute power (and wealth) amongst a small number of people.

8. QPS — a progressive consensus for collaborative processes.

Qube Predictive Stake consensus enhances a collaborative process between participants — fostering nodes codependency in a trustless environment, where complete information is impossible by design.

We will be discussing our circular tokenomical model, and its different transitory stages, in one of the next papers. As for now, it is enough to say that, in order to monetize ideas, we needed to implement not only a fluid business model, but an entire infrastructure.

In order to do so, our Consensus model was designed around an ecosystem with clear tasks, objectives and roles, together with identifiable entry and exit points.

This is a-Qube’s Ecosystem & Implementation Process wrapped-up in a few simple steps:

Entry

1. User proposes an Idea/Challenge and selects:
2. Six (6) Implementation Slots to be completed
3. Predictive Guess
4. Idea/Challenge are validated and the Implementation process starts

Implementation Process

1. Individual Contributors propose a contribution and a Predictive Guess, slot-by-slot.
2. Individual Validators validate a slot by placing a stake and giving a Predictive Guess.
3. Slots are implemented progressively from 1 to 6. part of before being appended to the project, needs to be validated by an external validator.

Exit

1. The initial project is completed, and available to be purchased as a Digital Asset (Qube).
2. Merchants (Organizations, Startups, Entrepreneurs) can purchase a Qube.
3. Creators, Contributors and Validators receive a pay-off based on their contribution and the accuracy of their prediction.

a-Qube’s Ecosystem — Entry, Implementation Process and Exit point.

9. Improvements by design.

The major improvement over traditional, oligarchic Consensus algorithms is that participants (stakeholders) are not rewarded based on the amount of currency they own, but based on the accuracy of their prediction. This adds an additional layer to standard PoS in multi-agent networks and represents a shift from standard passive (cold) staking, to active (“hot”) staking, which incentivizes participants to be involved in projects they believe in, and to add value to them, rather than passively staking tokens on an inactive network.

This way, each node limits and counter-balances the others, generating a dynamic, competitive equilibrium across the ecosystem — as irrelevant contributions or inaccurate predictions, would diminish projects’ chances to succeed, and thus players’ chances to unlock individual benefits.

Also, it’s easy to see how this purpose-centric approach allows Contributors and Validators to act as market-makers — not only monetizing ideas, but re-establishing them as structural pattern of the market.

10. Conclusions and next steps.

In this Introduction, we gave an overview of a-Qube as an ecosystem and QPS Consensus as the engine which powers it. We’ve seen how we aim to build an open, meritocratic, collaborative network of innovation, through a process where participants are able to take part in any of the different phases of the Innovation Process. Also, we’ve seen how present technology allows us to shift to self-organizing systems made of people, and peers — towards a human layer of innovation. Giving back to the participants access to a truly decentralized Innovation Process, and the power to create, influence and ultimately challenge the Market as we know it today.

Following this introduction, we will be releasing papers analyzing ideological, tokenomical and game theoretical choices behind our Consensus (QPS) and Ecosystem (a-Qube) design.

As a closure, rather than just discarding our argumentations based on alternative wording or personal biases, we would like to invite readers to open up a constructive dialogue, as the very own nature of our society and the way human interaction evolves make it impossible (and nonsensical) to try to get to a final, long-lasting solution once and for all.

For now, June 2019, a-Qube is releasing a first, limited Proof-of-Concept of our system. Whether a success or a failure, this will be a first reality check for many of these assumptions, so we will have the pleasure to invite you to be part of it, and judge for yourself.