Building Artificial Economies: From Aggregate Data to Experimental Microstructure. A Methodological Survey

Behavioral Heterogeneity Affects Individual Performances in Experimental and Computational Lowest Unique Integer Games

“…After having a dendrogram 9 in each LUIG, the author split them into four or five clusters and obtained the inputs of "median" agents in each cluster 10 (Figures 1-4). Table 2 summarizes how the representative agents in each cluster would play and update their propensities in round 51 11 .…”

“…Based on their findings, Mohlin et al have proposed two learning models, global cumulative imitation and similaritybased imitation, where players make use of not only their choice but also the game outcome for updating their attractions [6]. On the other hand, Rapoport et al have experimentally studied a version of LUBA/HUBA with (N, M) ∈ {(5, 4), (5,25), (10,25)} and found that only a small fraction of subjects behaved as theoretically predicted [5].…”

“…According to Duffy, its advantages are summarized as "the agent-based approach to understand results obtained from laboratory studies with human subjects" and "to understand findings from agentbased simulations with follow-up experiments involving human subjects" (p. 951) [7]. The necessity of combining two approaches have been argued and the methodology has been proposed for the last decade (e.g., [8][9][10][11]). There are a few researches which indeed employ the combined approach to computationally test the validity of experimental findings in the laboratory, implement an intensive computational experiment, and extend the experimental design by using the laboratory data [12][13][14].…”