scholarly journals Artificial Intelligence and Its Application in Optimization under Uncertainty

2021 ◽  
Author(s):  
Saeid Sadeghi ◽  
Maghsoud Amiri ◽  
Farzaneh Mansoori Mooseloo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Optimization Under Uncertainty ◽  
Data Driven ◽  
Future Research ◽  
Intelligent Decision Support Systems ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Optimization Methodology ◽  
Conventional Optimization

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

scholarly journals The cognitive and perceptual correlates of ideological attitudes: a data-driven approach

2021 ◽  
Vol 376 (1822) ◽  
pp. 20200424 ◽  
Author(s):  
Leor Zmigrod ◽  
Ian W. Eisenberg ◽  
Patrick G. Bissett ◽  
Trevor W. Robbins ◽  
Russell A. Poldrack
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Sensation Seeking ◽  
Social Dominance Orientation ◽  
Data Driven ◽  
Future Research ◽  
Perceptual Decision Making ◽  
Psychological Traits ◽  
Data Driven Approach ◽  
Ideological Attitudes

Although human existence is enveloped by ideologies, remarkably little is understood about the relationships between ideological attitudes and psychological traits. Even less is known about how cognitive dispositions—individual differences in how information is perceived and processed— sculpt individuals' ideological worldviews, proclivities for extremist beliefs and resistance (or receptivity) to evidence. Using an unprecedented number of cognitive tasks ( n = 37) and personality surveys ( n = 22), along with data-driven analyses including drift-diffusion and Bayesian modelling, we uncovered the specific psychological signatures of political, nationalistic, religious and dogmatic beliefs. Cognitive and personality assessments consistently outperformed demographic predictors in accounting for individual differences in ideological preferences by 4 to 15-fold. Furthermore, data-driven analyses revealed that individuals’ ideological attitudes mirrored their cognitive decision-making strategies. Conservatism and nationalism were related to greater caution in perceptual decision-making tasks and to reduced strategic information processing, while dogmatism was associated with slower evidence accumulation and impulsive tendencies. Religiosity was implicated in heightened agreeableness and risk perception. Extreme pro-group attitudes, including violence endorsement against outgroups, were linked to poorer working memory, slower perceptual strategies, and tendencies towards impulsivity and sensation-seeking—reflecting overlaps with the psychological profiles of conservatism and dogmatism. Cognitive and personality signatures were also generated for ideologies such as authoritarianism, system justification, social dominance orientation, patriotism and receptivity to evidence or alternative viewpoints; elucidating their underpinnings and highlighting avenues for future research. Together these findings suggest that ideological worldviews may be reflective of low-level perceptual and cognitive functions. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.

scholarly journals A data-driven approach to measuring epidemiological susceptibility risk around the world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Bitetto ◽  
Paola Cerchiello ◽  
Charilaos Mertzanis
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Data Driven ◽  
Dynamic Factor ◽  
Country Level ◽  
Epidemiological Risk ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Total Variability ◽  
Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

scholarly journals An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 182 ◽  
Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Topsis ◽  
Future Research ◽  
Negative Effects ◽  
Green Supplier Selection ◽  
Managerial Implications ◽  
Interval Type

Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today’s organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.

scholarly journals A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

2020 ◽  
Vol 279 ◽  
pp. 115834
Author(s):  
Usman Ali ◽  
Mohammad Haris Shamsi ◽  
Mark Bohacek ◽  
Karl Purcell ◽  
Cathal Hoare ◽  
...  
Keyword(s):  
Decision Making ◽  
Building Energy ◽  
Energy Modeling ◽  
Data Driven ◽  
Building Energy Modeling ◽  
Multi Scale ◽  
Data Driven Approach ◽  
Support Decision Making

Technology Focus: Field Development (September 2021)

2021 ◽  
Vol 73 (09) ◽  
pp. 43-43
Author(s):  
Reza Garmeh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Development Strategies ◽  
Digital Transformation ◽  
Data Driven ◽  
Development Work ◽  
Field Development

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

Will the relevance of review language and destination attractions be helpful? A data-driven approach

2020 ◽  
pp. 135676672095035
Author(s):  
Sunyoung Hlee ◽  
Hyunae Lee ◽  
Chulmo Koo ◽  
Namho Chung
Keyword(s):  
Online Reviews ◽  
Data Driven ◽  
Online Review ◽  
Cognitive Fit ◽  
Theoretical Contribution ◽  
Language Style ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Tourism Destinations ◽  
The Impact

Because tourism destinations are difficult to assess in certain standard aspects, the factors that contribute to the helpfulness of reviews remain largely unknown. Moreover, the helpfulness of online reviews has not been explored in terms of the interaction between language style (high- vs. low-cognitive) and attraction type (hedonic vs. utilitarian). Hence, this study examines the impact of language style on the helpfulness of an online review of an attraction, depending on the type of attraction and the meaning of the destination. This study’s data included 8,032 reviews of four attractions (2 hedonic x 2 utilitarian), drawn from TripAdvisor in two different meanings of destinations. Specifically, our findings indicate that when a reviewer posts a utilitarian attraction of the destination, high-cognitive language is perceived to be more helpful. First, we discuss the theoretical contribution of our study using cognitive fit theory, and then provide the study’s managerial implications.

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