Human-Machine Networks: Towards a Typology and Profiling Framework

TL;DR

This paper proposes a typology and profiling framework for human-machine networks to improve design understanding and classification, demonstrated through case studies in crisis management and peer-to-peer reselling.

Contribution

It introduces a novel typology and profiling framework for human-machine networks, supported by empirical case studies and lessons learned.

Findings

Effective profiling aids in network classification and design discussions.

Case studies demonstrate practical application of the framework.

Identifies benefits, challenges, and future research directions.

Abstract

In this paper we outline an initial typology and framework for the purpose of profiling human-machine networks, that is, collective structures where humans and machines interact to produce synergistic effects. Profiling a human-machine network along the dimensions of the typology is intended to facilitate access to relevant design knowledge and experience. In this way the profiling of an envisioned or existing human-machine network will both facilitate relevant design discussions and, more importantly, serve to identify the network type. We present experiences and results from two case trials: a crisis management system and a peer-to-peer reselling network. Based on the lessons learnt from the case trials we suggest potential benefits and challenges, and point out needed future work.