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Networks that provide agents with access to a common database of the agents' actions enable an agent to easily learn by observing the actions of others, but are also susceptible to manipulation by “fake” agents. Prior work has studied a model for the impact of such fake agents on ordinary (rational) agents in a sequential Bayesian observational learning framework. That model assumes that ordinary agents do not have an ex-ante bias in their actions and that they follow their private information in case of an ex-post tie between actions. This paper builds on that work to study the effect of fake agents on the welfare obtained by ordinary agents under different ex-ante biases and different tie-breaking rules. We show that varying either of these can lead to cases where, unlike in the prior work, the addition of fake agents leads to a gain in welfare. This implies that in such cases, if fake agents are absent or are not adequately present, an altruistic platform could artificially introduce fake actions to effect improved learning.
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Observational Learning with Fake Agents
It is common in online markets for agents to learn from other's actions. Such observational learning can lead to herding or information cascades in which agents eventually "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that choose pay-off optimal actions. In this paper, we additionally consider the presence of fake agents that seek to influence other agents into taking one particular action. To that end, these agents take a fixed action in order to influence the subsequent agents towards their preferred action. We characterize how the fraction of such fake agents impacts behavior of the remaining agents and show that in certain scenarios, an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome.
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- PAR ID:
- 10199488
- Date Published:
- Journal Name:
- 2020 IEEE International Symposium on Information Theory (ISIT)
- Page Range / eLocation ID:
- 1373 to 1378
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISIT44484.2020.9174217
