scholarly journals Remedies for algorithmic tacit collusion

2020 ◽  
Author(s):  
Francisco Beneke ◽  
Mark-Oliver Mackenrodt
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Competition Law ◽  
Tacit Collusion ◽  
Learning Technology ◽  
Social Harm ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Social ◽  
Use Efficiency

Abstract There is growing evidence that tacit collusion can be autonomously achieved by machine learning technology, at least in some real-life examples identified in the literature and experimental settings. Although more work needs to be done to assess the competitive risks of widespread adoption of autonomous pricing agents, this is still an appropriate time to examine which possible remedies can be used in case competition law shifts towards the prohibition of tacit collusion. This is because outlawing such conduct is pointless unless there are suitable remedies that can be used to address the social harm. This article explores how fines and structural and behavioural remedies can serve to discourage collusive results while preserving the incentives to use efficiency-enhancing algorithms. We find that this could be achieved if fines and remedies can target structural conditions that facilitate collusion. In addition, the problem of unfeasibility of injunctions to remedy traditional price coordination changes with the use of pricing software, which in theory can be programmed to avoid collusive outcomes. Finally, machine-learning methods can be used by the authorities themselves as a tool to test the effects of any given combination of remedies and to estimate a more accurate competitive benchmark for the calculation of the appropriate fine.

scholarly journals Examination of ‘Interests’ and ‘Activities’ of Social Network users

2019 ◽  
Vol 9 (2) ◽  
pp. 2688-2693
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
General Population ◽  
Social Media Marketing ◽  
Family Status ◽  
Learning Methods ◽  
Attribute Data ◽  
Machine Learning Methods ◽  
The Social

The present study relates to the analysis of attribute data related to users of the social network VK. The general population N = 52,614 users is the intersection of audiences from two communities for social media marketing. Based on the collected statistics on the “interests” attribute, one can compile a generalized portrait of an IT specialist and online marketer: this is a man aged about 30 years old, not married, or who defines his family status as “everything is complicated”. He speaks an average of two languages, works for an organization, or studies at a university. He has about 370 followers on VK. The result based on the data from the field 'activities' is very close to the data from the field 'interests', and gives a similar picture of the generalized portrait of a specialist. As part of the study, the authors have learned how to segment users into the users that identify themselves as „IT specialists or online marketers‟, and „other‟ users, using machine learning methods

scholarly journals Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Jacob Tryon ◽  
Ana Luisa Trejos
Keyword(s):  
Machine Learning ◽  
Elbow Flexion ◽  
Relevant Information ◽  
Classification Model ◽  
Learning Technology ◽  
Learning Methods ◽  
Time Frequency ◽  
Machine Learning Methods ◽  
Signal Representations ◽  
Flexion Extension

Wearable robotic exoskeletons have emerged as an exciting new treatment tool for disorders affecting mobility; however, the human–machine interface, used by the patient for device control, requires further improvement before robotic assistance and rehabilitation can be widely adopted. One method, made possible through advancements in machine learning technology, is the use of bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to classify the user's actions and intentions. While classification using these signals has been demonstrated for many relevant control tasks, such as motion intention detection and gesture recognition, challenges in decoding the bioelectrical signals have caused researchers to seek methods for improving the accuracy of these models. One such method is the use of EEG–EMG fusion, creating a classification model that decodes information from both EEG and EMG signals simultaneously to increase the amount of available information. So far, EEG–EMG fusion has been implemented using traditional machine learning methods that rely on manual feature extraction; however, new machine learning methods have emerged that can automatically extract relevant information from a dataset, which may prove beneficial during EEG–EMG fusion. In this study, Convolutional Neural Network (CNN) models were developed using combined EEG–EMG inputs to determine if they have potential as a method of EEG–EMG fusion that automatically extracts relevant information from both signals simultaneously. EEG and EMG signals were recorded during elbow flexion–extension and used to develop CNN models based on time–frequency (spectrogram) and time (filtered signal) domain image inputs. The results show a mean accuracy of 80.51 ± 8.07% for a three-class output (33.33% chance level), with an F-score of 80.74%, using time–frequency domain-based models. This work demonstrates the viability of CNNs as a new method of EEG–EMG fusion and evaluates different signal representations to determine the best implementation of a combined EEG–EMG CNN. It leverages modern machine learning methods to advance EEG–EMG fusion, which will ultimately lead to improvements in the usability of wearable robotic exoskeletons.

A Review on the Importance of Machine Learning and Artificial Intelligence in Real Life Problem Solving

2020 ◽  
Vol 17 (9) ◽  
pp. 4336-4339
Author(s):  
D. S. V. Suma Priya ◽  
D. Esther Rani ◽  
A. Pavan Shankar Sai ◽  
A. Konda Babu ◽  
Durgesh Nandan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Concept Learning ◽  
Real Life ◽  
Learning System ◽  
Level System ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Life Problem

This paper clearly explains the concept, importance and main aim of machine learning and construction of the machine learning system. There are several ideas regarding this machine learning which are formed by a number of strategies. This effort leads to introduce many machine learning methods such as learning by commands, concept, learning by comparison, and learning by some algorithms. This article provides information about the main purpose of machine learning and its development. Machine learning is the primary aspect that promotes any system to have intelligence. One of its main applications is artificial intelligence. Machine learning is highly suited for complex level system representation. There are a number of machine learning concepts that leads to the integration of number of networks.

scholarly journals Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3597 ◽  
Author(s):  
Qitao Zhang ◽  
Chenji Wei ◽  
Yuhe Wang ◽  
Shuyi Du ◽  
Yuanchun Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reservoir Characterization ◽  
Short Term Memory ◽  
Water Saturation ◽  
Petroleum Industry ◽  
Learning Technology ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Saturation Distribution ◽  
Model Training

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

Public Sentiments Analysis Based on Fuzzy Logic for Text

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1341-1360 ◽  
Author(s):  
Xinzhi Wang ◽  
Hui Zhang ◽  
Zheng Xu
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Web Mining ◽  
Experimental Studies ◽  
Learning Technology ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Public Sentiment ◽  
Similar Accuracy

Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

scholarly journals Prediction of Foodborne Diseases Pathogens: A Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Hanxue Wang ◽  
Wenjuan Cui ◽  
Yunchang Guo ◽  
Yi Du ◽  
Yuanchun Zhou
Keyword(s):  
Machine Learning ◽  
Foodborne Pathogens ◽  
Pathogenic Bacteria ◽  
Pathogen Detection ◽  
Real Life ◽  
Foodborne Diseases ◽  
Foodborne Disease ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Relationship

BACKGROUND Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases. However, foodborne diseases caused by different pathogens lack specificity in the clinical features, then there is a low proportion of clinically actual pathogen detection in real life. OBJECTIVE Analyzing the data of foodborne disease cases, selecting appropriate features based on the analysis results, and using machine learning methods to classify foodborne disease pathogens, so as to predict the pathogens of foodborne diseases which have not been tested. METHODS Extracting features such as space, time, and food exposure from the data of foodborne disease cases, analyzing the relationship between these features and the pathogens of foodborne diseases, using a variety of machine learning methods to classify the pathogens of foodborne diseases, and comparing the results to obtain the optimal pathogen prediction model with the highest accuracy. RESULTS By comparing the results of four models we used, the GBDT model obtains the highest accuracy, which is almost 69% in identifying four pathogenic bacteria including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. And by evaluating the importance of features, we find that the time of illness, geographical longitude and latitude, diarrhea frequency and so on, play important roles in classifying the foodborne disease pathogens. CONCLUSIONS Related data analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

scholarly journals Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

2021 ◽  
Vol 13 (14) ◽  
pp. 7662
Author(s):  
Jingyi Zhang ◽  
Jiaxin Liu ◽  
Yaqi Chen ◽  
Xiaochun Feng ◽  
Zilai Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Agricultural Management ◽  
Agricultural Science ◽  
Learning Technology ◽  
Learning Approaches ◽  
Product Price ◽  
Learning Methods ◽  
Machine Learning Methods

With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.

scholarly journals Machine Learning for Social Science: An Agnostic Approach

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Justin Grimmer ◽  
Margaret E. Roberts ◽  
Brandon M. Stewart
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Social Science ◽  
Scientific Data ◽  
Annual Review ◽  
Publication Date ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Social ◽  
Wide Range

Social scientists are now in an era of data abundance, and machine learning tools are increasingly used to extract meaning from data sets both massive and small. We explain how the inclusion of machine learning in the social sciences requires us to rethink not only applications of machine learning methods but also best practices in the social sciences. In contrast to the traditional tasks for machine learning in computer science and statistics, when machine learning is applied to social scientific data, it is used to discover new concepts, measure the prevalence of those concepts, assess causal effects, and make predictions. The abundance of data and resources facilitates the move away from a deductive social science to a more sequential, interactive, and ultimately inductive approach to inference. We explain how an agnostic approach to machine learning methods focused on the social science tasks facilitates progress across a wide range of questions. Expected final online publication date for the Annual Review of Political Science, Volume 24 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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