Machine Economies Peeref

A. K. M. Bahalul Haque, Najmul Islam, Patrick Mikalef Lukas Gruetzner, David Voss, Michael H Breitner Frederik Moeller, Ilka Jussen, Virginia Springer, Anna Giess, Julia Christina Schweihoff, Joshua Gelhaar, Tobias Guggenberger, Boris Otto

Chia-Ying Li, Chien-Hsiang Liao, Yu-Hui Fang Konrad Degen, Rick Lutzens, Paul Beschorner, Ulrike Lucke You have full access to this open access article This fundamentals article discusses efficient machine economies in which non-human agents can autonomously exchange information and value. We first identify criteria for achieving Pareto efficiency in such economies by drawing on the Coase Theorem. We then translate these economic criteria to technical requirements before developing a framework that characterizes four types of machine economies.

We discuss real-life examples for each type to highlight key challenges in achieving Pareto efficiency. In particular, we highlight that machine economies with human involvement in economic interactions and governance face significant challenges regarding perfect information, rationality, and transaction costs. Machine economies without human involvement, in turn, promise a high degree of Pareto efficiency, but there are still many open questions, particularly regarding machine-enforced governance. We conclude with opportunities for future research on the interactions and governance in machine economies. Avoid common mistakes on your manuscript. Digital technologies continuously evolve, transform, and merge to create innovative ways of economic interaction, not only between machines and humans but also among machines themselves.

As a result, “interconnected machines, software and [digital] processes" (Arthur , 2017, p. 3) are increasingly facilitating and shifting value exchange into virtual economies. Algorithmic trading is one of many examples. It relies on software agents that autonomously observe market movements, automatically make decisions, and submit and execute orders. In effect, these software agents are fully-fledged market participants. In many instances, algorithmic trading agents have become so relevant that they account for most of the trading volume and liquidity provided on several exchanges (Hendershott et al., 2021; Moriyasu et al., 2018).

These developments are not exclusive to financial services. Autonomous agents also play an important role as value creators and contributors on digital platforms (Hein et al., 2020). For instance, the Amazon AWS IoT platform allows machines to share wear and tear data and to automatically order new parts (Amazon Web Services, 2022). Moreover, the recent improvements in artificial intelligence may lead to an increasing number of business decisions being made by software agents with little or no human oversight (Berente et al., 2021). In these and many other cases, machines engage in economic interactions, creating what can be described as a machine economy. Working Papers Journal Articles Books and Chapters Software Components

EconPapers FAQ Archive maintainers FAQ Cookies at EconPapers The RePEc blog The RePEc plagiarism page Eduard Hartwich (Obfuscate( 'uni.lu', 'eduard.hartwich' )), Alexander Rieger (Obfuscate( 'uni.lu', 'alexander.rieger' )), Johannes Sedlmeir (Obfuscate( 'uni.lu', 'johannes.sedlmeir' )), Dominik Jurek (Obfuscate( 'berkeley.edu', 'dominik_jurek' )) and Gilbert Fridgen (Obfuscate( 'uni.lu', 'gilbert.fridgen' )) Additional contact information... Electronic Markets, 2023, vol. 33, issue 1, No 36, 13 pages Peeref publishes scientific posters from all research disciplines.

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