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CREATED:20230605T101716Z
LAST-MODIFIED:20230605T101716Z
DTSTAMP:20261008T002740Z
UID:1791408460@tuc.gr
SUMMARY:Ομιλία της Κωνσταντίνας Βαλογιάννη (
 IE Univ, Madrid) | 07.06.2023, 15:00
LOCATION:Λ - Κτίριο Επιστημών/ΗΜΜΥ, 141Π-98
DESCRIPTION:https://www.ece.tuc.gr/el/katalogos-
 ekdiloseon?tx_tucevents2_tuceventsdi
 splay%5Baction%5D=show&tx_tucevents2
 _tuceventsdisplay%5Bcontroller%5D=Ev
 ent&tx_tucevents2_tuceventsdisplay%5
 Bevent%5D=6227&cHash=03c9a3d15586fbc
 9e4c46be903ec7d51\nΤίτλος\n Causal A
 BM: A Methodology for Learning Plaus
 ible Causal Models using Agent-Based
  Modeling\n  \n Περίληψη\n We presen
 t Causal ABM, a methodology to deriv
 e causal structures describing compl
 ex underlying behavioral phenomena. 
 Agent-based models (ABMs) have power
 ful advantages for causal modeling t
 hat have not been explored sufficien
 tly. Unlike traditional causal estim
 ation approaches which often result 
 in "one best" causal structure that 
 is learned, two properties of ABMs -
  equifinality (the ability of differ
 ent sets of conditions or model repr
 esentations to yield the same outcom
 e) and mutlifinality (the same ABM m
 ight yield different outcomes) - can
  be exploited to learn multiple dive
 rse "plausible causal models" from d
 ata. Using an illustrative example o
 f news sharing on social networks we
  show how this idea can be applied t
 o learn such causal sets. We also sh
 ow how genetic algorithms can be use
 d as a estimation technique to learn
  multiple plausible causal models fr
 om data due to their parallel search
  structure. However, significant com
 putational challenges remain before 
 this can be generally applied, and w
 e, therefore, highlight specific key
  issues that need to be addressed in
  future work.\n \n Βιογραφικό\n Kons
 tantina Valogianni is an Assistant P
 rofessor of Information Systems at I
 E University. She has received her P
 hD from Rotterdam School of Manageme
 nt, Erasmus University Rotterdam (20
 16). Her research focuses on using M
 achine Learning to enable sustainabl
 e societies. Her main line of resear
 ch focuses on designing intelligent 
 algorithms to facilitate a better el
 ectric mobility integration in curre
 nt smart grids. Her work has appeare
 d in journals, such as Information S
 ystems Research, Production and Oper
 ations Management, Information &
  Management, Decision Support System
 s, Energy Policy, as well as confere
 nces such as the International Confe
 rence on Information Systems (ICIS),
  AAAI Conference on Artificial Intel
 ligence (AAAI), the International Co
 nference on Autonomous Agents and Mu
 ltiagent Systems (AAMAS). She is tea
 ching technology and innovation mana
 gement and machine learning courses 
 at the Masters and Executive levels,
  whereas she also teaches PhD course
 s on information systems.\n
STATUS:CONFIRMED
ORGANIZER;RSVP=FALSE;CN=TUC;CUTYPE=TUC:mailto:webmaster@tuc.gr
DTSTART:20230607T150000
DTEND:20230607T160000
TRANSP:OPAQUE
CLASS:DEFAULT
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