Marek Laskowski’s research while affiliated with New York University and other places

Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, the authors propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models – a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.

The study of setting up cryptocurrency incentive mechanisms and operationalizing governance is called token economics. Given the US$250 billion market cap for cryptocurrencies, there is compelling need to investigate it. In this article, we present facets of the token engineering process for a Swiss blockchain startup.

Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, the authors propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models – a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.

In spring 2016, the Distributed Autonomous Organization (The DAO) was created on Ethereum. As with Bitcoin, Ethereum uses a P2P network, where distributed ledgers are implemented as daisy-chained blocks of data. Ethereum's native cryptocurrency, Ethers are spent to execute pieces of code called smart contracts. Investors paid their Ethers for the DAO to operate and received the opportunity to vote on and become investors in venture projects proposed by Ethereum-based startups. Transactions and settlements between investors and startups are executed autonomously. The DAO experiment failed shortly after inception as an anonymous hacker stole over $50M USD worth of Ethers out of the$168M invested. The Ethereum community voted to return (or fork) the state of the network to one prior to the hack, returning Ethers back to investors and shuttering the DAO. However, this action arguably represented as a bailout—ironically, Bitcoin was conceived as a reaction against the 2008 bailout of US banks—and violated the ledger immutability and “code is law” ethos of the blockchain community.

Blockchains can be public, permissionless networks implementing novel cryptocurrency-based technology features or permissioned, interorganizational networks championed by industry consortia. Some ventures operationalize a hybrid of these two network types to enhance adoption of their blockchain platforms by broadening their base of stakeholders or facilitating interoperability between heterogeneous blockchains. In this article, we synthesize literature and industry writings to identify four hybrid blockchain architectures: hybrid blockchain approach, connected hybrid blockchain, interoperable blockchain architecture, and hard-forked blockchain for enterprise use. We then analyze these architectures along dimensions of semantic modeling support between private and public networks, data connectivity between networks, syntactic interoperability support between networks with heterogeneous codebases, governance model, and technical features. We find that hybrid blockchain ventures make trade-offs: support API's, tools, and customized development so that a codebase is useful for private and public networks or provide such support for interoperation between heterogeneous codebases. We then conduct a case study of an exemplar for a hybrid blockchain approach, the startup Insolar. We identify characteristics that have led Insolar to be idiosyncratically agile and effective in its blockchain development, which together with our architecture analysis may be timely and prescriptive as enterprises grow interested in addressing blockchain hybridity and interoperability.

We present a methodology for evidence based design of cryptoeconomic systems, and elucidate a real-world example of how this methodology was used in the design of a blockchain network. This work provides a rare insight into the application of Data Science and Stochastic Simulation and Modelling to Token Engineering. We demonstrate how the described process has the ability to uncover previously unexpected system level behaviors. Furthermore, it is observed that the process itself creates opportunities for the discovery of new knowledge and business understanding while developing the system from a high level specification to one precise enough to be executed as a computational model. Discovery of performance issues during design time can spare costly emergency interventions that would be necessary if issues instead became apparent in a production network. For this reason, network designers are increasingly adopting evidence-based design practices, such as the one described herein.

The cryptocurrency market is valued in excess of $250 billion. Virtually all these ventures employ cryptocurrencies to incentivize adoption and use of their blockchain-based platforms. For Bitcoin, miners are incentivized to mitigate the double spend problem; for Ethereum, to operate a distributed application development platform. The study of how to set up these incentive mechanisms and to operationalize governance is token economics. Given the size of the crypto market, we believe that there is still great opportunity to research this novel topic. In this paper, we present facets of the token engineering process for a real life 80 person Swiss blockchain startup, Insolar. We show how Insolar used systems modeling and simulation combined with cryptocurrency expertise to design a mechanism to incentivize enterprises and individual users, and in particular through the use of subsidy pools, application developers, to help adoption of their new MainNet public blockchain network. For example, the study showed that the pool can be too small to properly incentivize developers, but it also can be so large as to signal largesse to greedy developers. For a startup like Insolar, their success will hinge upon how well their model incentivizes various stakeholders to participate on their MainNet network versus that of numerous alternatives.