Abstract
Integrating information gained by observing others via Social Bayesian Learning can be beneficial for an agent’s performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent population.
We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social
Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant information
We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social
Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant information
| Original language | English |
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| Title of host publication | ALIFE 14 |
| Subtitle of host publication | Procs of the 14th Int Conf on the Synthesis and Simulation of Living Systems |
| Publisher | MIT Press |
| Pages | 837-844 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | ALIFE 2014 - New York, United States Duration: 30 Jul 2014 → 2 Aug 2014 |
Conference
| Conference | ALIFE 2014 |
|---|---|
| Country/Territory | United States |
| City | New York |
| Period | 30/07/14 → 2/08/14 |