Using the Myers-Briggs Type Indicator (MBTI) for Modeling Multiagent Systems

The formation of high-performance teams has been a constant challenge for organizations, which despite considering human capital as one of the most important resources, it still lacks the means to allow them to have a better understanding of several factors that influence the formation of these teams. In this sense, studies also demonstrate that teamwork has a significant impact on the results presented by organizations, in which human behavior is highlighted as one of the main aspects to be considered in the building of work teams. The Myers-Briggs Type Indicator seeks to classify the behavioral preferences of individuals around eight characteristics, which grouped as dichotomies, describe different psychological types. With it, researchers have sought to expand the ability to understand the human factor, using strategies with multiagent systems that, through experiments and simulations, using computer resources, enable the development of artificial agents that simulate human actions. In this work, we present an overview of the research approaches that use MBTI to model agents, aiming at providing a better knowledge of human behavior. Additionally, we make a preliminary discussion of how these results could be explored in order to advance the studies of psychological factors' influence in organizations' work teams formation.

Prediction Of Myers-Briggs Type Indicator Personality Using Long Short-Term Memory

Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.

Myers-briggs Personality Prediction and Sentiment Analysis of Twitter using Machine Learning Classifiers and BERT

Twitter being one of the most sophisticated social networking platforms whose users base is growing exponentially, terabytes of data is being generated every day. Technology Giants invest billions of dollars in drawing insights from these tweets. The huge amount of data is still going underutilized. The main of this paper is to solve two tasks. Firstly, to build a sentiment analysis model using BERT (Bidirectional Encoder Representations from Transformers) which analyses the tweets and predicts the sentiments of the users. Secondly to build a personality prediction model using various machine learning classifiers under the umbrella of Myers-Briggs Personality Type Indicator. MBTI is one of the most widely used psychological instruments in the world. Using this we intend to predict the traits and qualities of people based on their posts and interactions in Twitter. The model succeeds to predict the personality traits and qualities on twitter users. We intend to use the analyzed results in various applications like market research, recruitment, psychological tests, consulting, etc, in future.

Building an Entrepreneurial Spirit Based on Model, Brain Color and Myers Briggs Type Indicator (MBTI)

One of the characteristics of an entrepreneur is that his thoughts and insights are oriented towards Action rather than just dreaming, wishing and talking. An entrepreneur always faces risk with uncertainty and limitations in every problem he faces. If we only speak words and do not take action, all opportunities that exist will turn into disasters and calamities in our lives. A drafter or theorist, works with data and is rarely in the field. On the other hand, an entrepreneur spends 95% of his time in the field with his employees, suppliers and customers. Because working with data, in order to be valid and scientific, a drafter must be accustomed to testing the data, building a model and doing validation. What will be a problem if a drafter does not master the situation and information in the field and can become doubtful about his decision so that he tends to repeat the cycle again. Namely collecting data that causes him to be spindly and more oriented to the mind than Action. On the other hand, an Action-oriented person is a person who has a high level of effectiveness. To study the characteristics of an Action-oriented person using the model of an effective person

PROFIL GAYA BELAJAR MAHASISWA PRODI PENDIDIKAN MATEMATIKA BERDASARKAN MODEL VARK

Berbagai model gaya belajar yang digunakan diantaranya Model Kolb, Fleming VAK, VARK, Honey dan Mumford, Indikator Tipe Myers–Briggs, Gregorc, Felder-Silverman. Setiap gaya belajar mempunyai karakteristik yang berbeda-beda. Gaya belajar memiliki peranan penting dalam proses pembelajaran. Setiap mahasiswa mempunyai cara sendiri dalam menyerap materi yang disampaikan oleh dosen. Terdapat 4 gaya belajar mahasiswa diantaranya visual, auditori, reading dan kinestetik. Penelitian ini bertujuan untuk mengetahui profil gaya belajar mahasiswa prodi Pendidikan matematika berdasarkan model VARK. Penelitian ini merupakan penelitian deskriptif kualitatif. Sampel penelitian merupakan mahasiswa prodi Pendidikan Matematika UNIMUS semester 2 sebanyak 42 mahasiswa. Triangulasi data dengan melakukan observasi, angket, dan wawancara mendalam. Berdasarkan hasil penelitian diperoleh informasi: (1) sebanyak 43% menggunakan satu gaya belajar dan 57% menggunakan lebih dari satu gaya belajar; (2) pemetaan dominasi belajar mahasiswa sebanyak 67% menggunakan gaya belajar kinestetik, 16% reading, 12% auditori dan 5% visual; (3) terdapat 11 gaya belajar yang terdiri dari gaya belajar tunggal sebanyak 3, kombinasi 2 gaya belajar sebanyak 3, kombinasi 3 gaya belajar ada 4 dan kombinasi 4 gaya belajar ada 1 gaya belajar. Berdasarkan kajian penelitian diketahui bahwa mahasiswa dapat menggunakan lebih dari satu gaya belajar. Berdasarkan hasil tersebut maka sebagai dosen harus dapat memfasilitasi mahasiswa dalam mengoptimalkan penggunaan gaya belajar dalam menempuh mata kuliah sehingga hasilnya lebih maksimal.Kata kunci: Auditori, gaya belajar VARK, kinestetik, reading, visual

Aplikasi Mobile Your Job MBTI (Myers-Briggs Indicator) Menggunakan Algoritma Fisher-Yates Shuffle

Work is something that everyone will do. This is because by working we will earn money that can make us able to fulfill our needs. But sometimes there are still quite a lot of people out there who don't even know what job is right for them. Therefore the author designed an application called Your Job based on Android, this application will provide suitable job recommendations based on the person's personality. In this study using the fisher-yates shuffle algorithm. Fisher-Yates shuffle algorithm can be applied to randomization of questions. The design of this application also uses the MBTI (Myers-Briggs Indicator) method to make it easier to determine a person's personality. After doing it to several people about this application. They gave a very good response, it is certain that the fisher-ystes shuffle algorithm runs well in randomizing the questions and using the MBTI method the accuracy level is almost 100% accurate.

IDENTIFICATION OF MBTI (MYERS-BRIGGS TYPE INDEX) HUMAN TYPE USING TEXT ON SOCIAL NETWORKS BASED MACHINE LEARNING

This study aims to create a classifier using machine learning methods that determine the psychological type of people based on the text published on social networks according to the Myers-Briggs Type Index classification. The article is based on the implementation of automation of the task of determining the personality type using machine learning, with an explanation for determining the characteristics of a person using the MBTI personality indicator. The methods of logistic regression, random forest and support vector machines were used, and a literary analysis of similar works was carried out. The article presents the progress of research work and the results of each classifier, as well as an analysis of the approaches used. In the context of the current quarantine restrictions, such studies can be of great help in the selection of personnel in companies due to the transition of people to an online format of work, since the study involves determining the personal qualities of people based on their posts in social networks. In this paper, the most effective machine learning algorithms for the Kazakh language, which are simple to use and do not require a lot of computing power, were used and, accordingly, the results of the work for each method were presented, among these methods, the accuracy and reliability of the classifier for the Kazakh language by the method of support vectors were at a good level.

Myers-Briggs Type Indicator Personality Types of Ophthalmology Residents

Objective This study attempts to use the Myers-Briggs Type Indicator (MBTI) to analyze personality types among current and recent ophthalmology residents. We aimed to evaluate the prevalence rates of each specific personality type in ophthalmology, and whether these changed by level of training, training program, or fellowship selection. The study aimed to evaluate whether certain personality types are more prevalent in ophthalmology as a unique medical specialty. This can help understand specialty choice and potentially predict trends in specialty selection. Study Design After obtaining institutional review board approval from Howard University Hospital, an electronic version of the MBTI questionnaire, form M, was sent to participants. In addition to the questionnaire, participants responded to four questions inquiring about home program, postgraduate training level, subspecialty interest, and work environment (if applicable). The anonymous responses of the surveys were automatically scored on google forms, and the results were analyzed by using StatView statistical analysis. Setting This study was conducted at Howard University, Georgetown University, George Washington University, University of Texas Medical Branch at Galveston, and Kresge Eye Institute. Participants A total of 66 current residents and recent graduates of five residency programs were involved in this study. Main Outcomes and Measures This study evaluated four-letter personality type from each participant. Results Ophthalmology residents were statistically more likely to be identified in the categories of extroversion (E) than introversion (I) (p = 0.049), thinking (T) than feeling (F) (p = 0.027), and judging (J) than perceiving (P) (p = 0.007), with no statistically significant difference between sensing (S) and intuition (N). ENTP, ESTJ, and ISTJ were the most common personality types, each comprising 13.6% of the sample population. The ratio of J:P was found to increase as training level increased, beginning with postgraduate 2nd year until graduate level. Conclusion Certain personality types are more common among ophthalmology residents in our cohort from five different training programs. It is possible that individual types change over the course of residency training and career. Understanding that these findings exist can be used as a baseline for future research in terms of potential predictors for applicants, of resident knowledge base, and personality changes over the course of one's training.

DECISION MAKING STYLES AMONG IT PROFESSIONALS

We all make decisions of varying importance every day, so the idea that decision making can be a rather sophisticated art may at first seem strange. However, studies have shown that most people are much poorer at decision making than they think. An attempt is made in the present investigation on Decision Making Styles among IT Professionals. 120 IT Professionals working in software companies in and around Hyderabad city constituted to the sample of study. The material used for this study is Decision Making Style questionnaire adapted by Myers-Briggs (1983) consists of 16 statements, it is hypothesized that there would be significant difference among IT Professionals in their Decision Making Styles. The results were analyzed and discussed by using appropriated statistical techniques such as Mean, SD and ANOVA. The results indicate that significant differences are found among IT Professionals in their Decision Making Styles. Based on the results obtained the implications of the findings are discussed.