Tahra Eissa, PhD, Postdoctoral Fellow at University of Colorado Boulder, presents: “Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence”.
 
Solutions to challenging inference problems are often subject to a fundamental trade-off between: 1) bias (being systematically wrong) that is minimized with complex inference strategies, and 2) variance (being oversensitive to uncertain observations) that is minimized with simple inference strategies. However, this trade-off is based on the assumption that the strategies being considered are optimal for their given complexity and thus has unclear relevance to forms of inference based on suboptimal strategies. We examined inference problems applied to rare, asymmetrically available evidence, which a large population of human subjects solved using a diverse set of strategies that were suboptimal relative to the Bayesian ideal observer. We found that these suboptimal strategies reflected an inversion of the classic bias-variance trade-off: subjects who used more complex, but imperfect, Bayesian-like strategies tended to have lower variance but higher bias because of incorrect tuning to latent task features, whereas subjects who used simpler heuristic strategies tended to have higher variance because they operated more directly on the observed samples but lower, near-normative bias. Our results yield new insights into the principles that govern individual differences in behavior that depends on rare-event inference and, more generally, about the information-processing trade-offs that can be sensitive to not just the complexity, but also the optimality, of the inference process.
Hosts: Catherine Elorette, PhD and Angélica Torres-Berrío, PhD
 
- HYBRID EVENT–
Location: Hess 9-101 and via Zoom
Meeting ID: 853 9573 5482
Passcode: 809842
 
The Twitter hashtag for this event is #MSNseminars.