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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20261012T210910Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST306@linklings.com
SUMMARY:Biased Error Attribution in Human–Multi-Agent Systems Under Delaye
 d Feedback
DESCRIPTION:Teerthaa Parakh (Georgia Institute of Technology)\n\nHuman dec
 ision-making is often influenced by cognitive biases, especially in scenar
 ios involving uncertainty and risk. Prior work has mainly studied biases i
 n single-step decision environments with immediate outcomes or in human in
 teraction with a single AI agent. However, less attention has been given t
 o (1) cascaded decision-making settings with delayed outcomes or feedback,
  where decisions at one step influence future system states and (2) human 
 interaction with multiple AI agents. To study this setting, we designed a 
 naval battle strategy game in which participants act as commanders and int
 eract with two AI advisors. We analyze decision-making following positive 
 and negative outcomes. We find asymmetric responses to gains and losses, w
 ith greater changes after negative outcomes. Moreover, participants fail t
 o identify the action steps responsible for failures and instead misattrib
 ute responsibility across AI agents. This leads them to modify decisions a
 t steps weakly related to the true causes of poor performance. We refer to
  this behavior as bias in error attribution. Our findings show that cognit
 ive biases can emerge in human-AI teams involving cascaded decision-making
 , delayed outcomes, and multiple autonomous agents, underscoring the need 
 for decision-support systems that facilitate causal reasoning about action
 s and outcomes.\n\n
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