English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper considers online reputation and polling systems where individualsmake recommendations based on their private observations and recommendations offriends. Such interaction of individuals and their social influence is modelledas social learning on a directed acyclic graph. Data incest (misinformationpropagation) occurs due to unintentional re-use of identical actions in thefor- mation of public belief in social learning; the information gathered byeach agent is mistakenly considered to be independent. This results inoverconfidence and bias in estimates of the state. Necessary and sufficientconditions are given on the structure of information exchange graph to mitigatedata incest. Incest removal algorithms are presented. Experimental results onhuman subjects are presented to illustrate the effect of social influence anddata incest on decision making. These experimental results indicate that sociallearning protocols require careful design to handle and mitigate data incest.The incest removal algorithms are illustrated in an expectation polling systemwhere participants in a poll respond with a summary of their friends' beliefs.Finally, the principle of revealed preferences arising in micro-economicstheory is used to parse Twitter datasets to determine if social sensors areutility maximizers and then determine their utility functions.

Online Reputation and Polling Systems: Data Incest, Social Learning and Revealed Preferences