Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The nature of mathematical modeling
The nature of mathematical modeling
IEEE Transactions on Knowledge and Data Engineering
Artifacts in the A&A meta-model for multi-agent systems
Autonomous Agents and Multi-Agent Systems
Emergent songs by social robots
Journal of Experimental & Theoretical Artificial Intelligence
Operating System Battle in the Ecosystem of Smartphone Industry
IEEC '09 Proceedings of the 2009 International Symposium on Information Engineering and Electronic Commerce
Platforms and services: understanding the resurgence of Apple
Communications of the ACM
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Insights into layout patterns of mobile user interfaces by an automatic analysis of android apps
Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems
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App developers are constantly competing against each other to win more downloads for their apps. With hundreds of thousands of apps in these online stores, what strategy should a developer use to be successful? Should they innovate, make many similar apps, optimise their own apps or just copy the apps of others? Looking more deeply, how does a complex app ecosystem perform when developers choose to use different strategies? This paper investigates these questions using AppEco, the first Artificial Life model of mobile application ecosystems. In AppEco, developer agents build and upload apps to the app store; user agents browse the store and download the apps. A distinguishing feature of AppEco is the explicit modelling of apps as artefacts. In this work we use AppEco to simulate Apple's iOS app ecosystem and investigate common developer strategies, evaluating them in terms of downloads received, app diversity, and adoption rate.