A major challenge in the development of computational models of collective behavior is the empirical validation. Experimental data from a spatially explicit dynamic commons dilemma experiment is used to empirically ground an agent-based model. Three distinct patterns are identified in the data. Two naïve models, random walk and greedy agents, do not produce data that match the patterns. A more comprehensive model is presented that explains how participants make movement and harvest decisions. Using pattern-oriented modeling the parameter space is explored to identify the parameter combinations that meet the three identified patterns. Less than 0.1% of the parameter combinations meet all the patterns. These parameter settings were used to successfully predict the patterns of a new set of experiments.
The last few years have seen a rapid increase in the number of Free/Libre Open Source Software (FLOSS) projects. Some of these projects, such as Linux and the Apache web server, have become phenomenally successful. However, for every successful FLOSS project there are dozens of FLOSS projects which never succeed. These projects fail to attract developers and/or consumers and, as a result, never get off the ground. The aim of this research is to better understand why some FLOSS projects flourish while others wither and die. This article presents a simple agent-based model that is calibrated on key patterns of data from SourceForge, the largest online site hosting open source projects. The calibrated model provides insight into the conditions necessary for FLOSS success and might be used for scenario analysis of future developments of FLOSS.
The last few years have seen a rapid increase in the number of Free/Libre Open Source Software (FLOSS) projects. Some of these projects, such as Linux and the Apache web server, have become phenomenally successful. However, for every successful FLOSS project there are dozens of FLOSS projects which never succeed. These projects fail to attract developers and/or consumers and, as a result, never get off the ground. The aim of this research is to better understand why some FLOSS projects flourish while others wither and die. This article presents a simple agent-based model that is calibrated on key patterns of data from SourceForge, the largest online site hosting open source projects. The calibrated model provides insight into the conditions necessary for FLOSS success and might be used for scenario analysis of future developments of FLOSS.
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