Abstract
Agreements, consensuses, protocols, resource-sharing, and fairness are all examples of social and political metaphors that define and shape new computational algorithms. The thought experiments and allegories about resource-sharing or agreement between nodes played a vital role in the development of "concurrent programming" (enabling processor power-sharing and process synchronization) and still later in the development of distributed computing (facilitating data access and synchronization). These paved the way for current concepts of consensus mechanisms, smart contracts, and other descriptions of cryptocurrencies, blockchain, distributed ledger, and hashgraph technologies, paradoxically reversing the relations between metaphor and artifact. New computing concepts and algorithmic processes, such as consensus mechanisms, trustless networks, and automated smart contracts or DAOs (Distributed Autonomous Organizations), aim to disrupt social contracts and political decision-making and replace economic, social, and political institutions (e.g., law, money, voting). Rather than something that needs a metaphor, algorithms are becoming the metaphor of good governance. Current fantasies of algorithmic governance exemplify this reversal of the role played by metaphors: they reduce all concepts of governance to automation and curtail opportunities for defining new computing challenges inspired by the original allegories, thought experiments, and metaphors. Especially now, when we are still learning how best to govern the transgressions and excesses of emerging distributed ledger technologies, productive relations between software and allegory, algorithms and metaphors, code and law are possible so long as they remain transitive. Against this tyranny of algorithms and technologies as metaphors and aspirational models of governance, we propose sandboxes and environments that allow stakeholders to combine prototyping with deliberation, algorithms with metaphors, codes with regulations.
Publisher
International Association for Educators and Researchers (IAER)
Subject
Electrical and Electronic Engineering,General Computer Science
Reference33 articles.
1. E A Akkoyunlu, K Ekanadham, and R V Hubert. Some Constraints and Tradeoffs in The Design of Network Communications.
2. Lev Bromberg, Andrew Godwin, and Ian Ramsay. 2017. Fintech sandboxes: Achieving a balance between regulation and innovation. Journal of Banking and Finance Law and Practice.
3. Vitalik Buterin. 2014. An Introduction to Futarchy.
4. T. R. Colburn and G. M. Shute. 2008. Metaphor in computer science. Journal of Applied Logic 6, 4: 526–533.
5. E. W. Dijkstra. 1971. Hierarchical ordering of sequential processes. Acta Informatica 1, 2: 115–138.