scholarly journals Sequential Voting with Confirmation Network

2019 ◽  
Vol 297 ◽  
pp. 19-34
Author(s):  
Yakov Babichenko ◽  
Oren Dean ◽  
Moshe Tennenholtz
Keyword(s):  
2021 ◽  
Author(s):  
Harisu Abdullahi Shehu ◽  
William Browne ◽  
Hedwig Eisenbarth

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.


2019 ◽  
Vol 33 (1-2) ◽  
pp. 159-191 ◽  
Author(s):  
Cristina Cornelio ◽  
Maria Silvia Pini ◽  
Francesca Rossi ◽  
Kristen Brent Venable

Author(s):  
Lirong Xia ◽  
Jérôme Lang ◽  
Mingsheng Ying

2017 ◽  
Vol 90 ◽  
pp. 141-144 ◽  
Author(s):  
Natalia M. Novikova ◽  
Irina I. Pospelova

Author(s):  
Adiel Teixeira de Almeida ◽  
Danielle Costa Morais ◽  
Hannu Nurmi

2017 ◽  
Vol 107 (6) ◽  
pp. 1477-1506 ◽  
Author(s):  
Andreas Kleiner ◽  
Benny Moldovanu

We analyze sequential, binary voting schemes in settings where several privately informed agents have single-peaked preferences over a finite set of alternatives, and we focus on robust equilibria that do not depend on assumptions about the players' beliefs about each other. Our main results identify two intuitive conditions on binary voting trees, ensuring that sincere voting at each stage forms an ex post perfect equilibrium. In particular, we uncover a strong rationale for content-based agendas: if the outcome should not be sensitive to beliefs about others, nor to the deployment of strategic skills, the agenda needs to be built “from the extremes to the middle” so that more extreme alternatives are both more difficult to adopt, and are put to vote before other, more moderate options. An important corollary is that, under simple majority, the equilibrium outcome of the incomplete information game is always the Condorcet winner. Finally, we aim to guide the practical design of schemes that are widely used by legislatures and committees and we illustrate our findings with several case studies. (JEL D71, D72, I10, J16, J32, K10)


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