More Like this

1. Prediction with expert advice applied to the problem of prediction with expert advice

   https://doi.org/10.1007/s11229-022-03809-5  

   Herrmann, Daniel A. ( July 2022 , Synthese)

   We often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice (PWEA) framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well.

    

   more » « less
2. A collaboratively derived international research agenda on legislative science advice

   https://doi.org/10.1057/s41599-019-0318-6  

   Akerlof, Karen ; Tyler, Chris ; Foxen, Sarah Elizabeth ; Heath, Erin ; Gual Soler, Marga ; Allegra, Alessandro ; Cloyd, Emily T. ; Hird, John A. ; Nelson, Selena M. ; Nguyen, Christina T. ; et al ( December 2019 , Palgrave Communications)
3. Lived Experiences of Native American STEM Faculty in Academia: Our Stories, Insights, and Advice

   https://doi.org/10.1353/jaie.2022.a912063  

   Thomas, Aaron ; Hoagland, Serra J. ; Kills, Ruth Plenty ; LaPier, Rosalyn ; Grant, Anne ; Wu, Ke ; Belcourt, Annie ( January 2022 , Journal of American Indian Education)

   Abstract: Native American faculty in science, technology, engineering, and mathematics (NAF-STEM) are severely under-represented. NAF-STEM often have greater responsibilities and challenges in academia leading to lower retention and promotion rates, which can also have an impact on career satisfaction and individual success. A group of Native scholars and diverse scholars at two tribal colleges and one state university collaborated on a culturally grounded project with NAF-STEM in higher education to examine their collective lived experiences in academia. Qualitative analyses were guided by Indigenous Research Methodologies using a collaborative autoethnography approach. The results of these shared experiences are intended to aid in the creation and maintenance of equitable and successful models to recruit and retain NAF-STEM. Advice aimed at guiding educational equity for Indigenous scholars is shared.

    

   more » « less
4. Single-Leg Revenue Management with Advice

   https://doi.org/10.1145/3580507.3597704  

   Balseiro, Santiago ; Kroer, Christian ; Kumar, Rachitesh ( July 2023 , EC '23: Proceedings of the 24th ACM Conference on Economics and Computation)
5. Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

   https://doi.org/10.18653/v1/N19-1052  

   Fu, Liye ; Chang, Jonathan P. ; Danescu-Niculescu-Mizil, Cristian ( January 2019 , Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)

   People often share personal narratives in order to seek advice from others. To properly infer the narrator’s intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the adviceseeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative. The main challenge in constructing this task is finding pairs of semantically plausible adviceseeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator. 

   more » « less