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DTSTART:20200308T030000
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BEGIN:VEVENT
DTSTAMP:20200625T231627Z
UID:D9786068-D847-46FD-96C1-0DD492F21E23
DTSTART;TZID=US/Eastern:20200625T173000
DTEND;TZID=US/Eastern:20200625T190000
DESCRIPTION:Opinion dynamics models aim at capturing the phenomenon of soci
 al learning through public discourse. While a functioning society should c
 onverge towards common answers\, the reality often is characterized by div
 isions and polarization. This talk reviews the key models that capture soc
 ial learning and its vulnerabilities. In particular\, we review models tha
 t explain the effect of bounded confidence and social pressure from zealot
 s (i.e. fake new sources) and show how very simple models can explain the 
 trends observed when social learning is subject to these phenomena. We the
 ir influence exposes trust different agents place on each other and introd
 uce new learning algorithms that can estimate how agents influence each ot
 her.\n\nSpeaker(s): Dr. Anna Scaglione\, \n\nAgenda: \nTechnical support s
 et-up: 5:30pm EST\nIntroductions 6pm-6:05pm EST\nDL: 6:05pm-6:50pm EST\nQ&
 A: 6:50pm-7pm EST\n\nZoom Virtual Meeting\, Arizona\, United States
LOCATION:Zoom Virtual Meeting\, Arizona\, United States
ORGANIZER:Signal@ieee.li
SEQUENCE:10
SUMMARY:Modeling and learning social influence from opinion dynamics under 
 attack
URL;VALUE=URI:https://events.vtools.ieee.org/m/230271
X-ALT-DESC:Description: <br /><p>Opinion dynamics models aim at capturing t
 he phenomenon of social learning through public discourse. While a&nbsp\;f
 unctioning society should converge towards common answers\, the reality of
 ten is characterized by divisions and&nbsp\;polarization. This talk review
 s the key models that capture social learning and its vulnerabilities. In 
 particular\, we review&nbsp\;models that explain the effect of bounded con
 fidence and social pressure from zealots (i.e. fake new sources) and show&
 nbsp\;how very simple models can explain the trends observed when social l
 earning is subject to these phenomena. We &nbsp\;their&nbsp\;influence exp
 oses &nbsp\;trust different agents place on each other and introduce new l
 earning algorithms that can estimate&nbsp\;how agents influence each other
 .&nbsp\;</p><br /><br />Agenda: <br /><p>Technical support set-up: 5:30pm 
 EST<br />Introductions 6pm-6:05pm EST<br />DL: 6:05pm-6:50pm EST<br />Q&am
 p\;A: 6:50pm-7pm EST</p>
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