BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Claremont Center for the Mathematical Sciences - ECPv6.15.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://colleges.claremont.edu/ccms
X-WR-CALDESC:Events for Claremont Center for the Mathematical Sciences
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20190310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20191103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20200308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20201101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20210314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20211107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200212T161500
DTEND;TZID=America/Los_Angeles:20200212T171500
DTSTAMP:20260418T070157
CREATED:20190830T174207Z
LAST-MODIFIED:20200210T182301Z
UID:1436-1581524100-1581527700@colleges.claremont.edu
SUMMARY:Applications of Markov Chains to Swarm Robotics and Political Redistricting
DESCRIPTION:What do swarm robotics and political redistricting have in common? One answer is Markov chains\, which have recently been used in very different ways to address problems in both these areas. To get a large swarm to exhibit a desired behavior\, one solution is to make each individual in the swarm fairly intelligent; another is to make the individuals simple\, but to let the desired behavior emerge as a result of their interactions. My collaborators and I recently used Markov chains and ideas from statistical physics to develop distributed algorithms that follow this second paradigm.  We also worked with physicists to create a physical robot system where each individual cannot compute anything\, but the system as a whole can still accomplish complex tasks. For political redistricting\, the main mathematical technique developed in the last few years for detecting gerrymandering is to compare a proposed plan to the space of all possible alternative plans; if the proposed plan is an outlier\, that’s an indicator it might be gerrymandered. However\, the space of all possible districting plans is far too large to ever be studied in its entirety.  Instead\, Markov chains are used to generate random samples of alternative plans\, where the hope is that the sampled plans are reasonably representative of all possible plans. This approach has already been used successfully in court cases around the country\, though questions still remain about what mathematical guarantees we can give about the randomly sampled districting plans.
URL:https://colleges.claremont.edu/ccms/event/tba-16/
LOCATION:Freeberg Forum\, LC 62\, Kravis Center\, CMC
CATEGORIES:Colloquium
ORGANIZER;CN="Blerta Shtylla":MAILTO:shtyllab@pomona.edu
END:VEVENT
END:VCALENDAR