A limited possibility result for social choice under majority voting

1982 ◽  
Vol 14 (4) ◽  
pp. 361-372
Author(s):  
T. M. Fogarty
Keyword(s):  
Social Choice ◽  
Majority Voting ◽  
Possibility Result ◽  
Limited Possibility

scholarly journals Flip a coin or vote? An experiment on the implementation and efficiency of social choice mechanisms

2021 ◽  
Author(s):  
Timo Hoffmann ◽  
Sander Renes
Keyword(s):  
Social Choice ◽  
Decision Rule ◽  
Decision Rules ◽  
Majority Voting ◽  
Decision Quality ◽  
Revealed Preferences ◽  
Simple Majority ◽  
Corporate Boards ◽  
Efficient Mechanisms

AbstractCorporate boards, experts panels, parliaments, cabinets, and even nations all take important decisions as a group. Selecting an efficient decision rule to aggregate individual opinions is paramount to the decision quality of these groups. In our experiment we measure revealed preferences over and efficiency of several important decision rules. Our results show that: (1) the efficiency of the theoretically optimal rule is not as robust as simple majority voting, and efficiency rankings in the lab can differ from theory; (2) participation constraints often hinder implementation of more efficient mechanisms; (3) these constraints are relaxed if the less efficient mechanism is risky; (4) participation preferences appear to be driven by realized rather than theoretic payoffs of the decision rules. These findings highlight the difficulty of relying on theory alone to predict what mechanism is better and acceptable to the participants in practice.

Majority Voting and Social Choice

1971 ◽  
Vol 38 (2) ◽  
pp. 265 ◽  
Author(s):  
J. Craven
Keyword(s):  
Social Choice ◽  
Majority Voting

Professor Johnson on Social Choice

2000 ◽  
Vol 45 (5) ◽  
pp. 518-522
Author(s):  
Mohammed H. I. Dore
Keyword(s):  
Social Choice

Social Choice Mechanisms

2002 ◽  
Author(s):  
Vladimir I. Danilov ◽  
Alexander I. Sotskov
Keyword(s):  
Social Choice

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

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