TY - CHAP
T1 - Behavioral Aspects of Learning in Social Networks
T2 - An Experimental Study
AU - Choi, Syngjoo
AU - Gale, Douglas
AU - Kariv, Shachar
N1 - Funding Information:
We are grateful to John Morgan for his comments and suggestions. This paper has benefited from suggestions by the participants of SITE 2004 Summer Workshop, the New and Alternative Directions for Learning Workshop at Carnegie Mellon University, and seminars at several universities. For financial support, Gale acknowledges the National Science Foundation Grant No. SBR-0095109) and the C. V. Starr Center for Applied Economics at New York University, and Kariv thanks University of California Berkeley (COR Grant).
PY - 2005
Y1 - 2005
N2 - Networks are natural tools for understanding social and economic phenomena. For example, all markets are characterized by agents connected by complex, multilateral information networks, and the network structure influences economic outcomes. In an earlier study, we undertook an experimental investigation of learning in various three-person networks, each of which gives rise to its own learning patterns. In the laboratory, learning in networks is challenging and the difficulty of solving the decision problem is sometimes massive even in the case of three persons. We found that the theory can account surprisingly well for the behavior observed in the laboratory. The aim of the present paper is to investigate important and interesting questions about individual and group behavior, including comparisons across networks and information treatments. We find that in order to explain subjects' behavior, it is necessary to take into account the details of the network architecture as well as the information structure. We also identify some "black spots" where the theory does least well in interpreting the data.
AB - Networks are natural tools for understanding social and economic phenomena. For example, all markets are characterized by agents connected by complex, multilateral information networks, and the network structure influences economic outcomes. In an earlier study, we undertook an experimental investigation of learning in various three-person networks, each of which gives rise to its own learning patterns. In the laboratory, learning in networks is challenging and the difficulty of solving the decision problem is sometimes massive even in the case of three persons. We found that the theory can account surprisingly well for the behavior observed in the laboratory. The aim of the present paper is to investigate important and interesting questions about individual and group behavior, including comparisons across networks and information treatments. We find that in order to explain subjects' behavior, it is necessary to take into account the details of the network architecture as well as the information structure. We also identify some "black spots" where the theory does least well in interpreting the data.
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U2 - 10.1016/S0278-0984(05)13002-8
DO - 10.1016/S0278-0984(05)13002-8
M3 - Chapter
AN - SCOPUS:33645920658
SN - 0762311940
SN - 9780762311941
T3 - Advances in Applied Microeconomics
SP - 25
EP - 61
BT - Experimental and Behavorial Economics
A2 - Morgan, John
ER -