Learning and herding using case-based decisions with local interactions

Andreas Krause

doi:10.1109/tsmca.2009.2014542

Learning and herding using case-based decisions with local interactions

Andreas Krause

Research output: Contribution to journal › Article › peer-review

13 Citations (SciVal)

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Abstract

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.

Original language	English
Pages (from-to)	662-669
Journal	IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans
Volume	39
Issue number	3
Early online date	24 Feb 2009
DOIs	https://doi.org/10.1109/tsmca.2009.2014542
Publication status	Published - May 2009

Keywords

Decision making
economics
simulation

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krause-39-3-2009.pdf
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Cite this

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Learning and herding using case-based decisions with local interactions

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Keywords

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