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DTSTAMP:20260421T230925Z
UID:https://www.econ.mpg.de/events/15658/13692
DTSTART:20180319T160000Z
CLASS:PUBLIC
CREATED:20181008T083345Z
DESCRIPTION:Revisiting the longstanding debate about contract remedies and 
 "efficient breach\," we study experimentally whether expectation damages o
 r specific performance better promote renegotiation of the contract when i
 t matters-when the seller cannot be sure whether performing his obligation
  is efficient. We hypothesize that giving the buyer a right to specific pe
 rformance enables her to disclose more private information about her valua
 tion of the good\, facilitating agreement between the parties. We test the
  hypothesis in a first experiment with one-sided asymmetric information: t
 he seller's cost of performance is commonly known but the buyer's valuatio
 n is private information. The experimental design aims at insulating the i
 ncentive effect\, stripping away the contractual context to neutralize the
  players' normative preconceptions. The results lend some support to the a
 dvantages of specific performance. They also suggest that those advantages
  will become more prominent under two-sided asymmetric information\, which
  we intend to test in a second experiment.\nSpeaker: Andreas Engert
LAST-MODIFIED:20181008T083524Z
LOCATION:MPI\, Room: Ground Floor
SUMMARY:The inefficiency of efficient breach: Contract renegotiation under 
 asymmetric Information 
URL;VALUE=URI:https://www.econ.mpg.de/events/15658/13692
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260421T230925Z
UID:https://www.econ.mpg.de/events/15657/13692
DTSTART:20180312T160000Z
CLASS:PUBLIC
CREATED:20181008T082245Z
DESCRIPTION:We investigate subnational disparities in preliminary reference
  activity by locating national courts on maps of the EU territory. Spatial
  visualization reveals that involvement in the preliminary ruling procedur
 e tends to be concentrated in a relatively small subset of regions within 
 member states. Using a machine learning approach\, we explore a wide range
  of possible predictors and the relations among them. Our data-driven anal
 ysis shows that regions that are the seat of a peak court and have a large
  cargo port are associated with higher referral rates. So too are regions 
 that are the seat of the country's capital and regions exhibiting greater 
 economic dynamism. Our ndings directly inform the theoretical discussion a
 nd suggest ways to reconcile varying strands of research on trade\, courts
 \, litigation and institutional change in the EU context.\nSpeaker: Arthur
  Dyevre
LAST-MODIFIED:20181008T083334Z
LOCATION:MPI\, Room: Ground Floor
SUMMARY:The Geography of Legal Integration in Europe: Mapping and Predictin
 g Subnational Disparities in Referral Activity 
URL;VALUE=URI:https://www.econ.mpg.de/events/15657/13692
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260421T230925Z
UID:https://www.econ.mpg.de/events/15656/13692
DTSTART:20180219T160000Z
CLASS:PUBLIC
CREATED:20181008T081946Z
DESCRIPTION:Speaker: Stefan Magen
LAST-MODIFIED:20181008T082506Z
LOCATION:MPI\, Room: Ground Floor
SUMMARY:On Experimental Legal Philosophy 
URL;VALUE=URI:https://www.econ.mpg.de/events/15656/13692
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260421T230925Z
UID:https://www.econ.mpg.de/events/15655/13692
DTSTART:20180129T160000Z
CLASS:PUBLIC
CREATED:20181008T080816Z
DESCRIPTION:Machine (data-driven learning-based) decision making is increas
 ingly being used to assist or replace human decision making in a variety o
 f domains ranging from banking (rating user credit) and recruiting (rankin
 g applicants) to judiciary (profiling criminals) and journalism (recommend
 ing news-stories). Recently concerns have been raised about the potential 
 for discrimination and unfairness in such machine decisions. Against this 
 background\, in this talk\, I will pose and attempt to answer the followin
 g high-level questions: (a) How do machines learn to make discriminatory o
 r unfair decisions? (b) How can we quantify unfairness in machine decision
  making? (c) How can we control machine unfairness? i.e.\, can we design l
 earning mechanisms that avoid unfair decision making? (d) Is there a cost 
 to fair decision making?\nSpeaker: Krishna Gummadi
LAST-MODIFIED:20181008T082207Z
LOCATION:MPI\, Room: Ground Floor
SUMMARY:Fairness in Machine Decision Making 
URL;VALUE=URI:https://www.econ.mpg.de/events/15655/13692
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260421T230925Z
UID:https://www.econ.mpg.de/events/15653/13692
DTSTART:20180115T160000Z
CLASS:PUBLIC
CREATED:20181008T080439Z
DESCRIPTION:Speaker: Axel Ockenfels
LAST-MODIFIED:20181008T080640Z
LOCATION:MPI\, Room: Ground Floor
SUMMARY:Current Challenges in Market Design 
URL;VALUE=URI:https://www.econ.mpg.de/events/15653/13692
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