TY - JOUR
T1 - Learning and herding using case-based decisions with local interactions
AU - Krause, Andreas
PY - 2009/5
Y1 - 2009/5
N2 - We evaluate repeated decisions of individuals using a variant of the case-based decision theory (CBDT), where individuals base their decisions on their own past experience and the experience of neighboring individuals. Looking at a range of scenarios to determine the successful outcome of a decision, we find that for learning to occur, agents must have a sufficient number of neighbors to learn from and access to sufficiently independent information. If these conditions are not fulfilled, we can easily observe herding in cases where no best decision exists.
AB - We evaluate repeated decisions of individuals using a variant of the case-based decision theory (CBDT), where individuals base their decisions on their own past experience and the experience of neighboring individuals. Looking at a range of scenarios to determine the successful outcome of a decision, we find that for learning to occur, agents must have a sufficient number of neighbors to learn from and access to sufficiently independent information. If these conditions are not fulfilled, we can easily observe herding in cases where no best decision exists.
KW - Decision making
KW - economics
KW - simulation
UR - http://www.scopus.com/inward/record.url?scp=67349278451&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1109/tsmca.2009.2014542
U2 - 10.1109/tsmca.2009.2014542
DO - 10.1109/tsmca.2009.2014542
M3 - Article
SN - 1083-4427
VL - 39
SP - 662
EP - 669
JO - IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans
JF - IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans
IS - 3
ER -