Engineering design games model decision-making activities by incorporating human participants in an entertaining platform. This article distinguishes between design decisions at operational and strategic timescales as important features of engineering design games. Operational decisions consider static and short-term dynamic decisions to establish a player’s situation awareness and initial entertainment. Strategic decisions consider longer-term dynamic decisions subject to large uncertainties to retain player engagement. However, constraints on cognitive load limit the ability to simultaneously address both lower-level operational design decisions and higher-level strategic decisions such as collaboration or sustainability. Partial automation can be introduced to reduce cognitive load for operational decisions and focus additional effort on strategic decisions. To illustrate tradeoffs between operational and strategic decisions, this paper discusses example cases for two existing games: Orbital Federates and EcoRacer. Discussion highlights the role of automation and entertainment in engaging human participants in engineering design games and makes recommendations for design of future engineering design games.
Improved Retention Analysis in Freemium Role-Playing games by Jointly Modelling Players’ Motivation, Progression and Churn
We consider user retention analytics for online freemium role-playing games (RPGs). RPGs constitute a very popular genre of computer-based games that, along with a player’s gaming actions, focus on the development of the player’s in-game virtual character through a persistent exploration of the gaming environment. Most RPGs follow the freemium business model in which the gamers can play for free but they are charged for premium add-on amenities. As with other freemium products, RPGs suffer from the curse of high dropout rates. This makes retention analysis extremely important for successful operation and survival of their gaming portals. Here, we develop a disciplined statistical framework for retention analysis by modelling multiple in-game player characteristics along with the dropout probabilities. We capture players’ motivations through engagement times, collaboration and achievement score at each level of the game, and jointly model them using a generalized linear mixed model (glmm) framework that further includes a time-to-event variable corresponding to churn. We capture the interdependencies in a player’s level-wise engagement, collaboration, achievement with dropout through a shared parameter model. We illustrate interesting changes in player behaviours as the gaming level progresses. The parameters in our joint model were estimated by a Hamiltonian Monte Carlo more »
- Award ID(s):
- 1811866
- Publication Date:
- NSF-PAR ID:
- 10400112
- Journal Name:
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Volume:
- 185
- Issue:
- 1
- Page Range or eLocation-ID:
- p. 102-133
- ISSN:
- 0964-1998
- Publisher:
- Oxford University Press
- Sponsoring Org:
- National Science Foundation
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