Decision Making - Science topic

I want to write a psychological package about decision on marital reconciliation.

Publisher:

Emerald Publishing

Book Title:

Data Science for Decision Makers: Leveraging Business Analytics, Intelligence, and AI for Organizational Success

Editors:

· Dr. Miltiadis D. Lytras, The American College of Greece, Greece

· Dr. Lily Popova Zhuhadar, Western Kentucky University, USA

Book Description

As the digital landscape evolves, the integration of Business Analytics (BA), Business Intelligence (BI), and Artificial Intelligence (AI) is revolutionizing Decision-Making processes across industries. Data Science for Decision Makers serves as a comprehensive resource, exploring these fields' convergence to optimize organizational success. With the continuous advancements in AI and data science, this book is both timely and essential for business leaders, managers, and academics looking to harness these technologies for enhanced Decision-Making and strategic growth. This book combines theoretical insights with practical applications, addressing current and future challenges and providing actionable guidance. It aims to bridge the gap between advanced analytical theories and their applications in real-world business scenarios, featuring contributions from global experts and detailed case studies from various industries.

Book Sections and Chapter Topics

Section 1: Foundations of Business Analytics and Intelligence

· The evolution of business analytics and intelligence

· Key concepts and definitions in BA and BI

· Data management and governance

· Analytical methods and tools

· The role of descriptive, predictive, and prescriptive analytics

Section 2: Artificial Intelligence in Business

· Overview of AI technologies in business

· AI for data mining and pattern recognition

· Machine learning algorithms for predictive analytics

· Natural language processing for business intelligence

· AI-driven decision support systems

Section 3: Integrating AI with Business Analytics and Intelligence

· Strategic integration of AI in business systems

· Case studies on AI and BI synergies

· Overcoming challenges in AI adoption

· The impact of AI on business reporting and visualization

· Best practices for AI and BI integration

Section 4: Advanced Analytics Techniques

· Advanced statistical models for business analytics

· Deep learning applications in BI

· Sentiment analysis and consumer behavior

· Realtime analytics and streaming data

· Predictive and prescriptive analytics case studies

Section 5: Ethical, Legal, and Social Implications

· Data privacy and security in AI and BI

· Ethical considerations in data use

· Regulatory compliance and standards

· Social implications of AI in business

· Building trust and transparency in analytics

Section 6: Future Trends and Directions

· The future of AI in business analytics

· Emerging technologies and their potential impact

· Evolving business models driven by AI and analytics

· The role of AI in sustainable business practices

· Preparing for the next wave of digital transformation

Objectives of the Book

· Provide a deep understanding of AI’s role in transforming business analytics and intelligence.

· Present strategies for integrating AI to enhance Decision-Making and operational efficiency.

· Address ethical and regulatory considerations in data analytics.

· Serve as a practical guide for executives, data scientists, and academics in a data-driven economy.

Important Dates

· Chapter Proposal Submission Deadline: 25 November 2024

· Full Chapter Submission Deadline: 31 January 2025

· Revisions Due: 4 April 2025

· Submission to Publisher: 1 May 2025

· Anticipated Publication: Winter 2025

Target Audience

· Business Professionals and Executives: Seeking insights to improve Decision-Making.

· Data Scientists and Business Analysts: Expanding their toolkit with AI and analytics techniques.

· Academic Researchers and Educators: Using it as a resource for teaching and research.

· IT and MIS Professionals: Enhancing their understanding of BI systems and data management.

· Policy Makers and Regulatory Bodies: Understanding the social and regulatory impacts of AI and analytics.

Keywords

· Artificial Intelligence

· Business Analytics

· Business Intelligence

· Data Science

· Decision-Making

Submission Guidelines

We invite chapter proposals that align with the outlined sections and objectives. Proposals should include:

· Title

· Authors and affiliations

· Abstract (200-250 words)

· Keywords

Contact Information

Dr. Miltiadis D. Lytras: miltiadis.lytras@gmail.com

Dr. Lily Popova Zhuhadar: lily.popova.zhuhadar@wku.edu

One article is listed twice. How can I merge them?

The correct author information:

Vörösmarty, G., & Dobos, I. (2023). Management applications and methodology developments in DEA-an overview of literature reviews. International Journal of Management and Decision Making, 22(4), 472-491.

Can you help me with it?

Thanks

Gyöngyi Vörösmarty

# 160

Dear Mahammad Nuriyev, Aziz Nuriyev, and Jeyhun Mammadov

I read your paper:

Renewable Energy Transition Task Solution for the Oil Countries Using Scenario-Driven Fuzzy Multiple-Criteria Decision-Making Models: The Case of Azerbaijan

My comments:

1- I agree with you in a 100% when you speak of an ambivalent situation: On one side you depend of oil because it is your main export, and on the other, you are constrained because the universal policy of complete decarbonization from oil, which with no doubt will hurt your country economy.

2- In page 2 you say “The transition to a renewable-based energy system is not a one-step process, especially for countries with a high share of oil and gas in their GDP”.

You are right and it is not either a short one. I am working in energy transition and reckon that the zero decarbonization of electricity generation takes many steps and decades. The objective of having zero CO2by 2050 is, is in may humble opinion, an illusion in most countries that will benefit economically by no importing oil, but in your country is worse. There is not a dichotomy here, because it is not a 1 to 0 game, but in reaching gradually that condition, i.e., achieving an equilibrium.

For instance, in my research I propose a long-term plan to be executed in periods, lasting five or six-years each, until 2050, to reach an acceptable compromise, because in those 26 years period, oil contaminated plants must be shutdown, but at the same time being replaced by new technologies, that are not built overnight. Therefore, oil will be continued in use still for years to come.

In your case, you would need not only to build renewable energy plants, but also, to find another uses for your oil. My wrighting follows he same pace as reading your paper, and for that reason, later on, reading the whole paper several pages from here, I learned that you also have gas, and that is a big difference.

Needless to say, I agree with what you say regarding MCDM, however, I do not think that fuzzy can help on this. This scenario is not a matter of using exact numbers but in following right procedures and policies. It is not a matter of only mathematics, but rather involving in a very large extent government, exports, environment, developing of products oil based like plastics, hydrogen, fertilizers, etc.

3- In page 4 you refer to SAW as a fuzzy method. Not in my opinion. If you refer to the fact that weights are needed, and I agree, the problem is to determine how these weights are generated. If you are talking about subjective weights, have you wondered what is the purpose of using fuzzy logic on invented weights, that can change if another DM computes them? Don’t you think weird that the solution of a problem may be valid, ONLY considering what a group of people decide?

Of course, fuzzy can be used to find average values and determining DM coherence in crisp values, and have near transitivity or ‘consistency’. And what is that good for, if there is no guarantee that results can be applied to a problem in the real world? Because, as far as I know there is not a mathematical axiom or theorem that supports that assumption. Convenient of course, but also false

4- Page 4 “The above-mentioned papers demonstrate the effectiveness of the fuzzy approach in formalizing uncertainty in decision making within the energy sector”

Could you please inform the reader how that demonstration could prove effectiveness if you do not have any yardstick to compare?

5- Page 5 “Expert evaluation of the importance of weights and each alternative with respect to each criterion”

Weights are useless to evaluate alternatives, since even if for a criterion you multiply each performance value by the criterion weight, it affects all values equally, i.e., the proportion or distances between performance values does not change with the multiplication. It only provokes that the corresponding criterion line displaces parallel to itself.

6- Since you are using experts estimates it does not make sense to use fuzzy, because you are certainly decreasing uncertainty, but on the subjective opinion of a DM or a group of them. There is no mathematical support for this, although it is extensively used. What if another group thinks differently, which group will you choose? This is an over simplification of the problem, not by you, but by 99 % of MCDM methods. Why this happens? Because many people believe that a MCDM method consists in filling a matrix, without analysing the sequence and reason of each step. Since there is no way to know the reality, any result is accepted and heralded as a success. Who is going to check? Not the reviewers certainly.

7- Page 11 “Rising domestic and foreign demand for electricity will be offset by renewables. There are significant differences in the capacity of the available renewables in the country”

Are you sure? How will you replace an oil-fired power plant generating say 600 MWh, and working 24/7 with renewables, especially solar and wind, that can only work a couple of hours per day, and assuming that there is wind and enough solar irradiation? As you can see the problem is not that simple, and regarding hydro, assuming that river flows are constant.

8- In page 11 you detail the eight criteria and I think that it is a very good set, although incomplete. For instance, in my opinion, you should add ‘Job generation’, ‘Land use’, ‘Site selection’, ‘Necessary investment’, ‘Return’, etc

.

9- As a final result you say that A7 is the best, followed by A9. Obviously, the main actor in both is gas, which in my opinion is quasi mandatory, but this result is lacking realism because:

First: You use different MCDM methods, compare their results, which is useless, since you do not have a yardstick for comparison, and in any case, you get a set of solutions instead of only one.

Second: In all methods a criterion is considered in isolation, when all criteria should be taken into account and simultaneously This is another false procedure used by 99 % of MCDM methods. Why do I say this?

Because all criteria and alternatives constitute a system, and as that, normally all of them are interrelated. For instance, you cannot consider cost per se, because any increase or decrease may affect say resources; as an example, a decrease in capital investment may reduce the availability of resources for education, and at the same time, increase noxious emissions.

This multi cross analysis cannot be made my hand, but only by an adequate MCDM method

Third: In addition, since all criteria are direct or indirectly related, you cannot use AHP to compute weights because this method works only under the condition that criteria are independent. By the way, and explained by Saaty himself, AHP should no be uses with fuzzy as in FAHP, because it is already fuzzy.

These are my comments, and I hope they can help

Nolberto Munier

In your experience, how can we balance the need for evidence-based practices with the rapidly changing demands of modern workplaces? Are there areas where intuition or experience should guide decisions when evidence is still catching up?

I would like to invite researchers and professionals (industry experts) to share insights on a current dilemma in workplace psychology, organizational development, work culture, employee performance, organizational change, workplace training, and relevant areas engage in a discussion around the role of research, intuition, and evolving work environments. I would like both experienced practitioners and researchers to weigh in with personal experiences and scientific perspectives.

I am trying to solve the Wi-Fi offloading decision making problem using classification and clustering of known and unknown traffic respectively in a given mobile network using bi-flows of packets in the network

I'm looking for sport-specific tests that assess decision-making abilities in football players. Ideally, I'm interested in validated tests that use visual stimuli relevant to the sport (e.g. match videos). Tests using a temporal occlusion paradigm would be valuable, such as OASSIS test (Belling et al., 2015), which unfortunately is no longer retrievable.

Moreover, sport-specific tests assessing visuo-spatial working memory abilities are also of interest.

Any suggestions or references would be greatly appreciated.

Thank you!

How to incorporate the latest technology for analysis

Good evening,

I am looking for a method or approaches in multi-criteria decision making (MCDM) that deal with incomplete decision matrices. This means that no values or intervals can be assigned to a single performance measure. How can these be calculated? If there are no existing methods, what ideas do you have?

Thank you and best regards.

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN.

source: Review Article

Free Access

Artificial intelligence enabled software-defined networking: a comprehensive overview

Majd Latah, Levent Toker

First published: 01 March 2019

Artificial intelligence enabled software‐defined networking: a comprehensive overview - Latah - 2019 - IET Networks - Wiley Online Library

Decision is an important concept in social network. If any one have website link , please give link.

to what extend it is good to encourage the youth to act as environmental activists in order to have climate corps in the future at decision making level.

I have tried using the Analytical Hierarchical Process (AHP) for MCDM to evaluate the career preferences of graduates. However, I would like to know more about other best options.

Which Multi-Criteria Decision Making (MCDM) is appropriate to use for farm Machinery/technology selection?

Is there any tool which consider both the technical and economic factor?

Recently, the technology of generative artificial intelligence, which is taught certain activities, skills previously performed only by humans, has been developing rapidly. In the process of learning, artificial neural network technologies built on the likeness of human neurons are used, as well as deep learning technology. In this way, intelligent chatbots are created, which can converse with people in such a way that it can be increasingly difficult to diagnose, to distinguish whether we are talking to a human or an intelligent chatbot, a tool. Chatbots are taught to converse with the involvement of digital big data and information, and the process of conversation, including answering questions and executing specific commands is perfected through guided conversations. Besides, tools available on the Internet based on generative artificial intelligence are also able to create graphics, photos and videos according to given commands. Intelligent systems are also being created that specialize in solving specific tasks and are becoming more and more helpful to humans in solving increasingly complex problems. The number of new applications for specially created tools equipped with generative artificial intelligence is growing rapidly. However, on the other hand, there are not only positive aspects associated with the development of artificial intelligence. There are more and more examples of negative applications of artificial intelligence, through which, for example, fake news is created in social media, disinformation is generated on the Internet. There are emerging possibilities for the use of artificial intelligence in cybercrime and in deliberately shaping the general social awareness of Internet users on specific topics. In addition, for several decades there have been films in the genre of science fiction, in which futuristic visions of the future were presented, in which intelligent robots, equipped with artificial intelligence autonomous cyborgs (e.g. Terminator) or artificial intelligence systems managing the flight of a ship of an interplanetary manned mission (e.g. 2001 Space Odyssey), artificial intelligence systems and intelligent robots transformed humanity from a source of electricity to their needs (e.g. Matrix trilogy) and thus instead of helping people, they rebelled against humanity. This topic has become topical again. There are attempts to create autonomous human cyborgs equipped with artificial intelligence systems, robots able to converse with humans and carry out certain commands. Research work is being undertaken to create something that will imitate human consciousness, or what is referred to as artificial consciousness, as part of the improvement of generative artificial intelligence systems. There are many indications that humans are striving to create a thinking generative artificial intelligence. It cannot be ruled out that such a machine could independently make decisions contrary to human expectations which could lead to the annihilation of mankind. In view of the above, in the conditions of dynamic development of generative artificial intelligence technology, considerations about the potential dangers to humanity that may arise in the future from the development of generative artificial intelligence technology have once again returned to relevance.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Hello,

I would like to pursue my PhD in marketing about the relation of AI and how it affects the consumer behavior & decision making. I would appreciate if you could suggest any topics.

Thanks,

Farnaz

As the world is advancing, new and advance technologies are being invented. Artificial intelligence (AI) is the ability of computers to carry out operations that ordinarily requires human intelligence, such as problem-solving, visual perception, decision making, and communication. In the clinical laboratory, Artificial Intelligence may increase the value of laboratory services and promote patience’s satisfaction and improve the results by using AI’s wide range of possible application. However, factors in technical, ethical, regulatory, and human concerns are some of the difficult and constraints that Artificial Intelligence must overcome before being implemented in the clinical laboratories. Therefore, this discussion aims to give knowledge about the risks in involving and applying the use of artificial intelligence in the clinical laboratory especially when obtaining the results.

Generative artificial intelligence finds application in various spheres of commercial banking, including banking offered to customers remotely through online and mobile banking. In addition to improving remote channels of marketing communication and remote access of customers to their bank accounts, tools based on generative AI are being developed, used to increase the scale of efficiency, automation, intelligent processing of large sets of data and information on various processes carried out inside the bank. Increasingly, generative AI technologies learned in deep learning processes and the application of artificial neural network technologies to perform complex, multi-faceted, multi-criteria data processing on Big Data Analytics platforms, including data and information from the bank's environment, online databases, online information portals and internal information systems operating within the bank. Increasingly, generative AI technologies are being used to automate analytical processes carried out as part of the lending business, including, first and foremost, the automation of creditworthiness analysis processes, processes carried out on computerized Big Data Analytics and/or Business Intelligence platforms, in which multicriteria, intelligent processing is carried out on increasingly large sets of data and information on potential borrowers and their market, competitive, industry, business and macroeconomic environment, etc. However, still the banking system supervisory institutions do not allow changes in banking procedures in which generative artificial intelligence in the credit departments of commercial banks will not only carry out the entire process of analyzing the creditworthiness of a potential borrower but will also make a decision on whether to grant a loan. Banking supervisory institutions still do not allow this kind of solution or precisely it is not defined in the legal norms defining the functioning of commercial banking. This raises the question of whether the technological advances taking place and the growing scale of applications of generative artificial intelligence technology in banking will not force changes in this area of banking as well. Perhaps, the growing scale of implementation of generative AI into various spheres of banking will contribute to the continuation of the processes of automation of lending activities which may result in the future in generative artificial intelligence making a decision on whether or not to extend credit.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion about it?

What do you think about this topic?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

Currently working on comparative education studies, the educational policy area. The question above will help me understand the decision - making educational policy factors.

Appreciate your contributions.

AHP in detail

If i have got a matrix of 16x12 and i want to create 3 classes.Is there any machine learning technique which can identify the lower and upper boundary levels for each of the classes.

Shared decision-making (SDM) has been recognized as the preferred model to help patients understand their treatment options, and make informed decisions that align with their values and preferences. How can shared decision making help CKD patients?

Dear reader,

I am about to embark on a PhD in political science focusing on left wing authoritarianism.

To what extent do left wing politics go too far in informing decision making in Western higher education?

Any and all faculty member's experiences are welcome.

Note: discussion on this thread is not for data gathering purposes.

help me please

I am planning to research the relationship between framing effects and gestures in the field of cognitive psychology. I want to know about those feld research links or something like that.

I am creating a AHP model to compare the best business model which is the criteria and parameters affecting each criteria (sub- criteria). Is it possible to create a model to compare the criteria and sub criteria without the alternatives?

I’m looking for some advice on experiment design for some decision making research. I’m interested in investigating whether artificial intelligence is introducing bias in clinicians when reviewing medical imaging. I have used Think Aloud in the past and am familiar with eye tracking. What other methods are out there?

Thanks in advance

Mark

Hello!

Multiple Criteria Decision-Making (MCDM) methods are applied in many fields of science, as a result, many scientific publications related to the application of these methods have been prepared.

Some of the most popular MCDM methods or MADM (Multiple Attribute Decision-Making) are TOPSIS, SAW, AHP, etc. In describing these methods, some authors use the term "criteria", and others use the term "attribute". I would like to know your opinion on which term should be used.

Some references:

Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: an introduction. Sage publications.

Triantaphyllou, E. (2000). Introduction to Multi-Criteria Decision Making. In: Multi-criteria Decision Making Methods: A Comparative Study. Applied Optimization, vol 44. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3157-6_1

Thank you!

Dear Friends

I hope you also have submitted the paper in the Springer's Famous Journal for Wireless Personal Communication. I have submitted in December 2017 and still the Journal have not found the appropriate reviewers.

Don't you think such Journal Must assure the policy of standard reveiwing and decision making within in proper time. Else such journal must be removed so the researcher won't face problems.

Please suggest and comment your personal expericenes.

I feel that, in water resource management activities (specially flood management), most of the developed software tools are not widely or continuously used. The reason may be either the decision makers work independently from project to project or fully /partially automate the required processes unique to the project.

I would like to know your experiences as well as comments on the utilization of the software tools to assist flood management decisions.

What are the whys and dos and don'ts of artificial intelligence in an essay with ethics?

Artificial intelligence and the use of new machine weapons, what are the moral dos and don'ts in this field?

Decision making and decision-making of human ethics and its interference in artificial intelligence?

What requirements should metaverse and artificial intelligence have in ethics?

Keivan reasipoorashraf asks you and is interested in learning from your opinions.

Please join me in answering these questions and let's have research cooperation together.

#Metaverse_Keivan

#moral_intelligence

#Morali_artificial intelligence

#Metaverse_Ethics

#Ethic_metaverse

ٍKivan_Metaverse

#Cosmos_tomorrow

#Keivan_Reisi

#future_universe

@keyhanefarda

@keivan.reisipourashraf

http://keyhanefarda.blogfa.com/?

Please share a paper/report/thesis how monte carlo simulation can be applied to Multi criteria decision making.

Please share a report/paper in which calculation of ELECTRE III OR ELECTRE TRI MCDM are explained.

Suppose there is a network of problems (causes and consequences) and you need to prioritise some of them to be fully analysed afterwards. You need to prioritise because you don't have time to analysed all of them. Such causes and consequences are not independent but interdependent. How would you prioritise some of them?

I have read a lot of papers/reports on fuzzy Bayesian network and most of the authors are using the same procedure to find the probabilities. Please recommend a unique technique or out of the box approach to find the probabilities in the fuzzy Bayesian network.

Could any expert try to examine the new interesting methodology for multi-objective optimization?

A brand new conception of preferable probability and its evaluation were created, the book was entitled "Probability - based multi - objective optimization for material selection", and published by Springer, which opens a new way for multi-objective orthogonal experimental design, uniform experimental design, respose surface design, and robust design, etc.

It is a rational approch without personal or other subjective coefficients, and available at https://link.springer.com/book/9789811933509,

DOI: 10.1007/978-981-19-3351-6.

Best regards.

Yours

M. Zheng

How can data-driven decision-making be used to optimize environmental management programs?

Servicescape is of great essential because it affects the overall business service experience which is human emotions likes feel of comfort, happiness, safe, healthy, pleasure, etc.

Do you think the organization needs servicescape upgrading? and do you think measuring the return on the intangible impact of servicescape upgrading investment is important and needed? Can you share your opinion?

In present era of globalization “governance” and “good governance” are being increasingly getting implemented and practiced. Bad governance is considered as one of the major root causes of all irregularities and evil in the society and has a deep impact on economic environment.

International financial body like World Bank and IMF are now facilitating loan and advances to the nations on the conditions that they are practicing “Governance” and “good governance”.

•It is also defined as “the manner in which power is exercised in the management of a country’s social and economic resources for development”.

•Governance means “the process of decision making and the process by which decisions are implemented or not implemented.

•It can be viewed as the exercise of economic, political and administrative authority to manage a country’s affairs.

•It is also been defined as “the manner in which power is exercised in the management of a country’s economic and social resources for development.

•It is used in several contexts among them most widely used as corporate governance, national, international and local governance.

•It is well defined mechanism, a establish process and institutions which is been used by the citizens and groups to articulate their interest, exercise their legal rights, meet their obligations and mediate their differences.

Servicescape is of great essential because it affects the overall business service experience which is human emotions likes feel of comfort, happiness, safe, healthy, pleasure, etc.

Do you think the organisation needs servicescape upgrading?

and do you think measuring the return on the intangible impact of servicescape upgrading investment is important and needed? Can you share your opinion?

There are many styles of leadership. We will focus here on two opposite styles: Autocratic leaders who tell their employees what they want them to do, and Democratic leaders who let members of the group take a more participative role in the decision-making process.

Other types, such as, Laissez-faire leaders encourage employees to make their own decisions.

I'm trying to solve multi criteria case ,so i need any references or source how to use fuzzy ANP using Excel

It has been suggested by the researchers that servicescape elements give an impact on the individual (users) thoughts and feelings (emotions) that ultimately influence the employees' and customers' perceptions and experiences.

To upgrade the servicescape elements in a building, the organisation is required to make a decision to determine the return on investment (ROI). This decision is very crucial to convince the top or upper management about the return it is receiving on its servicescape elements investment.

Do you have ideas on how to calculate the intangible to make tangible outcomes? what method or measurement can use or practice? Thank you

It has been suggested by the researchers that servicescape elements give an impact on the individual (users) thoughts and feelings (emotions) that ultimately influence the employees' and customers' perceptions and experiences.

To upgrade the servicescape elements in a building, the organisation is required to make a decision to determine the return on investment (ROI). This decision is very crucial to convince the top or upper management about the return it is receiving on its servicescape elements investment.

Do you have ideas on how to calculate the intangible to make tangible outcomes? what method or measurement can use or practice? Thank you

Good morning, I'm interested in writing a thesis for my corporate communication master about issue arenas. The aim is to find a research question that can investigate research gaps, developing the knowledge in practical application of this new framework of stakeholders' theory. I think that right now, the descriptive amount of papers about issue arenas needs to be matched with a more practical knowledge upgrade, useful for everyday work routine.

My key idea is to relate the achievement of the homeostais in an issue arena with game theory or complex adaptive system theory.

Do you have any suggestions to narrow down the topic?

Thanks for the support.

I am searching for the main theories or frameworks that could help to explain consumer behaviours when they face sustainability constraints in their choices. It is fact that rational choices play a central role in decision making on consumption, but what theories or frameworks would help to raise broader awareness on the danger of pure rational choices in consumption? What strategies other than research-action could make consumers change their minds when perceiving the collective and individual effects of their behaviours?

　Hello, I'm recently learning Analytic Network Process, reading Thomas L. Saaty's (2013) book Decision Making with the Analytic Network Process (https://link.springer.com/book/10.1007/978-1-4614-7279-7?noAccess=true), and I'm confused by the explanation of influence between components and elements. I would appreciate it if someone can help me.

　It's written in the book that an entry in a supermatrix (as the figure blow shows) is a block W_ij positioned where the ith component or level is connected to and influences the jth level immediately above. My understanding is that the block W₂₁ stands for the influence of the 2nd component on the 1st component. Therefore, I infer that the direction of the influence must be opposite to the direction of the arrow between the 1st and 2nd components in the structure of the supermatrix. That quite makes sense so far.

　However, it's also written that:

'Those components which no arrow enters are source components. Those from which no arrow leaves are known as sink components.'

and

'A source node is an origin of paths of influence (importance) and never a destination of such paths. A sink node is a destination of paths of influence and never an origin of such paths.'

This implies that the direction of influence is the same as that of arrows, contradicting the former description.

　I'm really wondering what is the actual direction of influence. Thank you for reading my question.

How many methods we have for multi-criteria Classification (sorting) problems? Could you please name them?

As I understood we have some methods in the below approaches:

1. Multi-Attribute decision making (ELECTRE-TRI, FlowSort, Promethee IV)

2. Multi-objective decision making

3. Goal programming

4. Linear programming (Integer programming)

5. Supervised methods (UTADIS/Decision tree)

6- Clustering (K-means/K-medoids/2steps/c-means)

Could you please name some more methods which can be applied for multi-criteria classification problems?

Thank you in advance.

Hi there,

I'm currently working on a research paper which I intend to get published in Elsevier or any other high impact research journal. I will be using PLS, CFA and reliability tests. For visualization I will be using visualization libraries in R and Python. I know how to use PowerBI as well. I'm looking for an experienced co-author to work with and learn from. I'm currently pursuing a MS Marketing program in the University of Management and Technology. I can share my model and questionnaire as well. It's a work in progress at the moment. Looking forward to collaboration!

Regards,

Syed Muhammad Ehtesham Ali

Currently, data is available in forms of text, images, audio, video and other such forms.

We are able to use mathematical and statistical modeling for identifying different patterns and trends in data which can be used through machine learning which is a A.I's subsidiary for performing different decision making tasks. The data can be visualized in variety of forms for different purposes.

Data Science is currently the ultimate state of Computing. For generating data we have hardware, software, algorithms, programming, and communication channels.

But, what could be next beyond this mere data creation and manipulation in Computing?

I am planning to examine relationship between social media usage and consumer decision - making processes

Noise is stated as the (deviation from mean) by (mean) in Kahneman et al., 2016.

Is there any work that extended this methodology further?

- Kahneman, D., Rosenfield, A. M., Gandhi, L., & Blaser, T. (2016). Noise. Harvard Bus Rev, 38-46.

Is it linear, vector or logarithmic normalization? Or some other normalization?

What is the purpose, usefulness and outcome of climatic simulations, particularly those of ecological LUC measures such as the Sahel afforestation ("Great Green Wall")?

How reliable are they and how can they have an impact on decision making? There are many simulations of the intended afforestation or restoration of the Sahel region, raising warning flags of heat waves and flooding since many years. However, we find that they are based on somewhat unrealistic, hydro-ecologically not feasible vegetational assumptions.

Now, after many years of simulations the most recent study (Camara et al, Atmosphere 2022, 13, 421) at least finds that reforestation should help to improve the climate over the reforested area. - Could scientific results have caused delays in starting badly needed restoration measures? Maybe even worse, as an indirect consequence have an affect on drought events?

Can you share a sample study over python with TOPSIS method in multi-criteria decision making?

The introduction of smart foresight in the decision making process opens new possibilities for the decision making centers by upgrading aspects such as transparency, flexibility, risk aversion,, rate of adoption in both private and public sector, etc. Please comment

With extensive research into how Digital Economy practitioners and the associated audience would like to receive information to aid their buying decisions, are there expertise approaches to matching solid digital economy needs to customers' decision making process for the ultimate goal of Users' motivation to buy more and suppliers supplying robustly more?

Let us discuss about the advantages, disadvantages, and use of powerful decomposition techniques like Bender's decomposition for large-scale optimization. I invite my esteemed colleagues and researchers to share important literature, ways of implementation, and potential application areas of decomposition algorithms, in this forum.

Fuzzy TOPSIS

Fuzzy VIKOR

Hello,

I am currently working on sensitivity analysis in the context of AHP. I use the online tool BPMSG from Goepel, maybe someone here knows it. However, I have a problem with the traceability of the results. Let's assume that there are exactly 3 criteria in the AHP (C1,C2,C3). Then I would like to know how the final value for an alternative (a1) results if one of the criteria changes in weighting, right?

I'll just say C1 decreases by x. However, the value x that is taken away from C1 must be distributed to C2 and C3. I just wonder which method is used to do this. Is x simply distributed equally to C2 and C3 or does this happen according to the share of C2 or C3 in the sum of C2 and C3?

When I do that, I get the following for the remaining two criteria:

(C1-x) = New C1

(C2 + (C2 / (C2 + C3)) * x) = New C2

(C3 + (C3 / (C2 + C3)) * x) = New C3

Unfortunately, however, I do not know if this is correct. If I multiply the criteria with the corresponding values of alternative a1 and combine the whole thing to a final value, I can calculate the same again with the other alternatives. When I compare the graphs to see how big x has to be to change the final prioritization of the alternatives, I always get the wrong values compared to the online tool. Therefore I would like to know if the redistribution of the weights is correct.

I hope someone can help me despite the long question. Thanks a lot!

Dear Madam, Please advise about post Doc supervisors in the university in the field of educational data mining and learning analytics for strengthening university decision making. I will be grateful

Quantumic made, with a demonstration of Half-Newtonian behavior!

what do you understand from this phrase? let me know or tag someone to disscuss.

Regards

Amir

Petro-Sim and similar process simulation software may be an effective tool to be used in decision making. I need to know your experience in this field. Any examples or case studies will be useful and sharing them will be highly appreciated.

I am looking for a dataset which contains the judgments of the decision-makers for the decision problem, as in the AHP method. The problem can be in any area.

Most datasets are related with classification, regression, and time series problems, such as energy, diseases etc.

Let me know. Thanks in advance.

I want to model churn prediction in the banking industry using operation research models. However, I have only secondary data obtained from a customer database.

What the Impact of Risk Management on Decisions Making?

As it is known, Artificial Intelligence, Machine Learning and Deep Learning methods are used today to produce meaningful information from big data. So how can the MCDM approach be integrated into such a structure? Is it healthy to use MCDM in big data?