Krause, A., 2009. Learning and herding using case-based decisions with local interactions. IEEE Transactions on Systems Man and Cybernetics Part A - Systems and Humans, 39 (3), pp. 662-669.
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.
|Item Type ||Articles|
|Uncontrolled Keywords||decision making, economics, simulation|
|Departments||School of Management|
|Publisher Statement||krause-39-3-2009.pdf: Copyright © 2009 IEEE. Reprinted from IEEE Transactions on Systems Man and Cybernetics Part A - Systems and Humans. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bath’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.|
Actions (login required)