Heterogeneity and Convergence in Shared Data Sources
The Importance of Cognitive Coherence in Collective Decision Making
In times of the internet, it has become commonplace that individuals contribute information to shared data sources such as Wikipedia or OpenStreetMap, a major shared data source of geographical information (e.g., including streets or buildings, but also mountains or forests). Despite heterogeneity in individuals with respect to their geographical and situational contexts, convergence can often be observed, leading to a consensus in the aggregated information on the collective level. The WIN project “Shared Data Sources” will examine the effect of individual cognitive processes on this convergence on the collective level using OpenStreetMap as an example.
The first parts of the project aim at an understanding of which aspects of the process of contributing to OpenStreetMap are prone to heterogeneity. For this purpose, measures will be developed to quantify both the heterogeneity and the convergence observable in the data set. In a next step, these measures allow to empirically test a theory of cognitive coherence on the individual level. This theory assumes that individuals strive for a coherent representation of the available information, a cognitive process fostering the convergence of the data set on the group level. Overall, the project will add to our understanding of the underlying cognitive processes how individuals integrate and contribute information to shared data sources and why convergence – a crucial factor of data quality – emerges on the collective level.
FB Mocnik, C Ludwig, AY Grinberger, C Jacobs, C Klonner, and M Raifer (2019): Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques. ISPRS International Journal of Geo-Information 8(5), 242. https://doi.org/10.3390/ijgi8050242
DW Heck, E Erdfelder, and PJ Kieslich (2018): Generalized processing tree models: Jointly modeling discrete and continuous variables. Psychometrika 83(4), 893–918. https://doi.org/10.1007/s11336-018-9622-0
FB Mocnik, A Mobasheri, L Griesbaum, M Eckle, C Jacobs, and C Klonner (2018): A grounding-based ontology of data quality measures. Journal of Spatial Information Science, 16. https://doi.org/10.5311/JOSIS.2018.16.360
DW Heck and E Erdfelder (2017): Linking process and measurement models of recognition-based decisions. Psychological Review 124(4), 442–471. https://doi.org/10.1037/rev0000063
FB Mocnik, A Zipf, and M Raifer (2017): The OpenStreetMap folksonomy and its evolution. Geo-spatial Information Science 20(3), 219–230. http://doi.org/10.1080/10095020.2017.1368193
DW Heck, BE Hilbig, and M Moshagen (2017): From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. https://doi.org/10.1016/j.cogpsych.2017.05.003
Publications Funded by the Project
M Mayer, DW Heck, and FB Mocnik (2019): Shared Mental Models as a Psychological Explanation for Converging Mental Representations of Place – the Example of OpenStreetMap. Proceedings of the 2nd International Symposium on Platial Information Science (PLATIAL'19), 43–50.
DW Heck, L Seiling, and A Bröder (2020): The love of large numbers revisited: A coherence model of the popularity bias. Cognition, 195, 104069. https://doi.org/10.1016/j.cognition.2019.104069
Prof. Dr. Daniel Heck
Dr. Franz-Benjamin Mocnik
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