Learning in a changing environment

Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants

Foreign Taleovers and Wages: Theory and Evidence from Hungary

This study discriminates FDI technology spillover from learning effects. Whenever learning takes time, our model predicts that foreign investors deduct the economic value of learning from wages of inexperienced workers and add it to experienced ones to prevent them from moving to local competitors. Hence, the national wage bill is unaffected by foreign takeovers. In contrast to learning, technology spillover effects occur whenever a worker with MNE experience contributes more to local firms’ than to MNEs’ productivity. In this case, experienced MNE workers are hired by local firms and the host country obtains a welfare gain. We investigate empirically wages, productivity, and worker turnover during the course of foreign takeovers on employee-employer matched data of Hungary and find evidence consistent with learning, but not with FDI technology spillovers.FDI, foreign takeover, cross-border M&A, wage regression, employee-employer matched data, propensity score matching, FDI technology spillover

Neural End-to-End Learning for Computational Argumentation Mining

We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.Comment: To be published at ACL 201

Yes, Actually Subjugation Is A Vocabulary Word

Abstract. Teaching today is driven by calls for accountability in the form of high-stakes testing and relies on a standardized, de-contextualized approach to education. The results are one size fits all curriculum that ignore local contexts of students\u27 lived experience, discourage student engagement and ultimately work against deep understanding of the content. In contrast, a praxis of ethical caring and place-based education that includes a radical-democratic approach, recognizes teaching as political and utilizes the students’ stories, local knowledge, culture, language and community as an integrating context for learning

FDI Technology Spillovers and Wages

This study distinguishes multinational firm (MNE) technology-spillover from learning effects. Whenever learning takes time, the model predicts that foreign investors deduct the economic value of learning from wages of inexperienced workers and add it to experienced ones to prevent them from moving to local competitors. Hence, the national wage bill is unaffected by the presence of MNEs. In contrast to learning, technology spillover effects occur whenever a worker with MNE experience contributes more to local firms’ than to MNEs’ productivity. In this case, experienced MNE workers are hired by indigenous firms and the host country obtains a welfare gain from the presence of MNEs. Implications of this model for the empirical findings of the MNE wage premium and the empirical FDI technology spillover literature are also discussed.FDI, foreign takeover, cross-border M&A, FDI technology spillover