1,720,999 research outputs found
The coordinating power of social norms
Full text linkA popular empirical technique to measure norms uses coordination games to elicit what subjects in an experiment consider appropriate behavior in a given situation (Krupka and Weber, 2013). The Krupka-Weber method works under the assumption that subjects use their normative expectations to solve the coordination game. However, subjects might use alternative focal points to coordinate, in which case the method may deliver distorted measurements of the social norm. We test the vulnerability of the Krupka-Weber method to the presence of alternative salient focal points in two series of experiments with more than 3000 subjects. We find that the method is robust, especially when there are clear normative expectations about what constitutes appropriate behavior
Inductive probabilistic taxonomy learning using singular value decomposition
Get PDFCapturing word meaning is one of the challenges of natural language processing (NLP).
Formal models of meaning, such as networks of words or concepts, are knowledge repositories
used in a variety of applications. To be effectively used, these networks have to be large or, at
least, adapted to specific domains. Learning word meaning from texts is then an active area
of research. Lexico-syntactic pattern methods are one of the possible solutions. Yet, these
models do not use structural properties of target semantic relations, e.g. transitivity, during
learning. In this paper, we propose a novel lexico-syntactic pattern probabilistic method
for learning taxonomies that explicitly models transitivity and naturally exploits vector space
model techniques for reducing space dimensions. We define two probabilistic models: the
direct probabilistic model and the induced probabilistic model. The first is directly estimated
on observations over text collections. The second uses transitivity on the direct probabilistic
model to induce probabilities of derived events. Within our probabilistic model, we also
propose a novel way of using singular value decomposition as unsupervised method for
feature selection in estimating direct probabilities. We empirically show that the induced
probabilistic taxonomy learning model outperforms state-of-the-art probabilistic models and
our unsupervised feature selection method improves performance
Identifying types in contest experiments
No full textWe apply the classifier-Lasso (Su et al. 2016) to detect the presence of latent types in
two data sets of previous contest experiments, one that keeps the grouping of contestants fixed over the experiment and one that randomly regroups contestants after each round. Our results suggest that there exist three distinct types of players in both contest regimes. The majority of contestants in fixed groups behaves reciprocal to opponents’ previous choices. A higher share of reciprocators per group is associated to lower
average overspending which hints at cooperative attempts. For experiments in which
contestants are regrouped, we find a significantly lower share of ‘reciprocators’ and
no significant association between the share of reciprocators and average efforts
Dis-cover ai minds to preserve human knowledge
No full textModern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. The structure of symbols—the operations by which the intellectual solution is realized—and the search for strategic reference points evoke important issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped ahead with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we would investigate the syntax as possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning, and human knowledge
KERMITviz: Visualizing Neural Network Activations on Syntactic Trees
No full textThe study of symbolic syntactic interpretations has been the cornerstone of natural language understanding for many years. Today, modern artificial neural networks are widely searched to assess their syntactic ability, through several probing tasks. In this paper, we propose a neural network system that explicitly includes syntactic interpretations: Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees Visualizer (KERMITviz). The most important result is that KERMITviz allows to visualize how syntax is used in inference. This system can be used in combination with transformer architectures like BERT, XLNet and clarifies the use of symbolic syntactic interpretations in specific neural networks making the black-box neural network neural networks explainable, interpretable and clear
Learning and dropout in contests: an experimental approach
No full textWe design an experiment to study investment behavior in different repeated contest settings, varying the uncertainty of the outcomes and the number of participants in contests. We find decreasing over-expenditures and a higher rate of ‘dropout’ in contests with high uncertainty over outcomes (winner-take-all contests), while we detect a quick convergence toward equilibrium predictions and a near to full participation when this type of uncertainty vanishes (proportional-prize contests). These results are robust to changes in the number of contestants. A learning parameter estimation using the experience-weighted attraction (EWA) model suggests that subjects adopt different learning modes across different contest structures and helps to explain expenditure patterns deviating from theoretical predictions
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