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Strategy in StarCraft 2 learning curves

Resource type
Thesis type
(Thesis) M.A.
Date created
2022-08-19
Authors/Contributors
Abstract
The power law of practice has been a long-standing theory of the learning curve: how skill improves with experience. Despite the general agreement that learning fits a smooth curved pattern, there have been specific areas (piecewise learning curves, improper aggregation, and plateaus) that have refuted the existence of single smooth learning curves for a given task. The present study attempts to generalize strategy-specific learning curves to a large longitudinal dataset of games in StarCraft 2, a highly complex task with refined measures of skill. Using novel methodology to balance error across high dimensional measures, the ubiquity of the power law of practice is not supported, yet shifts in strategy do not account for the lack of power law learning curves. The existence of hobbyists (people who do not want to become experts) is considered as a possible explanation. Implications of the findings and directions for further research are discussed.
Document
Extent
44 pages.
Identifier
etd22078
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Blair, Mark
Language
English
Member of collection
Download file Size
etd22078.pdf 1007.34 KB

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