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1. Montague Grammar Induction

   https://doi.org/DOI: 10.3765/salt.v30i0.4816  

   Kim, Gene Louis ; White, Aaron Steven ( March 2021 , Proceedings from Semantics and Linguistic Theory)

   null (Ed.)

   We propose a computational modeling framework for inducing combinatory categorial grammars from arbitrary behavioral data. This framework provides the analyst fine-grained control over the assumptions that the induced grammar should conform to: (i) what the primitive types are; (ii) how complex types are constructed; (iii) what set of combinators can be used to combine types; and (iv) whether (and to what) the types of some lexical items should be fixed. In a proof-of-concept experiment, we deploy our framework for use in distributional analysis. We focus on the relationship between s(emantic)-selection and c(ategory)-selection, using as input a lexicon-scale acceptability judgment dataset focused on English verbs’ syntactic distribution (the MegaAcceptability dataset) and enforcing standard assumptions from the semantics literature on the induced grammar. 

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2. Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

   https://doi.org/10.5194/hess-24-4641-2020  

   Teweldebrhan, Aynom T. ; Schuler, Thomas V. ; Burkhart, John F. ; Hjorth-Jensen, Morten ( January 2020 , Hydrology and Earth System Sciences)

   Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to acquire an adequate sample size, which may take from days to months – especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual-based response surfaces. Here, we apply emulators of an MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time-relaxed limits of acceptability concept (pLoA). Three machine-learning models (MLMs) were built using model parameter sets and response surfaces with a limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time-relaxed limits of acceptability approach, based on the predicted pLoA values, and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations with an R2 value of 0.7 to 0.92. Similarly, the models identified using the coupled machine-learning (ML) emulators and limits of acceptability approach have performed very well in reproducing the median streamflow prediction during the calibration and validation periods, with an average Nash–Sutcliffe efficiency value of 0.89 and 0.83, respectively. 

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3. The function of teacher dashboards depends on the amount of time pressure in the classroom situation: Results from teacher interviews and an experimental study

   https://doi.org/10.1007/s42010-022-00156-9  

   van Leeuwen, Anouschka ; Rummel, Nikol ( January 2022 , Unterrichtswissenschaft)

   Teacher dashboards are visual displays that provide information to teachers about their learners. In this article, we address teacher dashboards in the context of computer-supported student collaboration in primary education. We examine the role of different types of dashboards for the specific purpose of aiding teachers in identifying which group of collaborating students is in need of support. This question is addressed using qualitative and quantitative approaches. First, an interview study is reported in which teachers’ views (n = 10) on and perceptions of the acceptability of different types of dashboards were examined. Then, the results of an experimental vignette study are reported, which built upon on the interview study, and in which teachers (n = 35) interacted with mirroring or advising dashboards. Together, the studies revealed that the classroom situation, such as differing levels of time pressure, plays an important role regarding what type of dashboard is beneficial for a teacher to use in the classroom. The theoretical contribution of our study lies in a conceptual and empirical investigation of the relation between teachers’ need for control and their perception of different types of dashboards. Our study also points to several practical implications and directions for future research. 

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4. Social Mechanism Design: Making Maximally Acceptable Decisions

   Abramowitz, Ben ; Mattei, Nicholas ( July 2023 , 9th International Workshop on Computational Social Choice)

   Sometimes agents care not only about the outcomes of collective decisions but also about how decisions are made. Both the outcome and the procedure affect whether agents see a decision as legitimate or acceptable. We focus on incorporating agents’ preferences over decision-making processes into the process itself. Taking whole decisions, including decision rules and outcomes, to be the object of agent preferences rather than only decision outcomes, we (1) identify natural, plausible preference structures and key properties, (2) develop general mechanisms for aggregating these preferences to maximize the acceptability of decisions, and (3) analyze the performance of our acceptance-maximizing mechanisms. We apply our general approach to the setting of dichotomous choice, and compare the worst-case rates of acceptance achievable among populations of agents of different types. We include the special case of rule selection, or amendment, and show that amendment procedures proposed by Abramowitz et al. [2] achieve universal acceptance with certain agent types. 

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5. Using Health Concept Surveying to Elicit Usable Evidence: Case Studies of a Novel Evaluation Methodology

   https://doi.org/10.2196/30474  

   Mariakakis, Alex ; Karkar, Ravi ; Patel, Shwetak N ; Kientz, Julie A ; Fogarty, James ; Munson, Sean A ( January 2022 , JMIR Human Factors)

   Background Developers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs. Objective In this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community. Methods We proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables. Results We demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people’s decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition. Conclusions Beyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations. 

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