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.

Characteristics of Work Accident to Tobacco Farmers in Indonesia

Tobacco farmers are individuals who are members of farming groups engaged in the cultivation of the agricultural sector in the form of tobacco. When carrying out tobacco cultivation activities, farmers are vulnerable to work accidents. The purpose of this research was to describe the characteristics of work accidents on tobacco farmers in Kalisat District, Jember Regency. The research design used descriptive quantitative. This research employed a purposive sampling technique, with a total of 97 tobacco farmers as respondents. The research data were collected through a demographic questionnaire and a work accident accident questionnaire. In the type indicator, it was found that 43.3% farmers often experienced incidents of being hit by immovable objects. In the object indicator, 36.1% farmers often experience accidents due to work equipment. In the indicator of the nature of the wound 26.8% farmers often experienced minor injuries and 9.3% stated that they always experienced these incidents. In the indicator of the location of the wound, farmers often experienced injuries on the hands, namely 36.1%. In the cause indicator, 66.0% farmers stated that they rarely use Personal Protective Equipment (PPE). According to the results of this study, work accidents among tobacco farmers in Kalisat District are still highly prevalent. This research will be utilized as a preventative and assessment tool for public health center in the application of UKK and for PPL to develop outreach activities in order to reduce the number of work accidents. Keywords: agricultural; tobacco farmer; work accidents; descriptive

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

Personality Types and L2 Reading: correlation between University Students MBTI Profiles and Reading Proficiency

Personality types have a considerable impact on our understanding of the outer world, perception and energy. Personality influence on language learning and proficiency, however, is controversial. This study demonstrates the relationship between the Myers-Briggs Type Indicator (MBTI) profiles and L2 reading proficiency of L1 Arab undergraduates. First, learners' reading proficiency levels were associated with their Introversion/Extraversion preferences. Then, function pairs of both introverts and extraverts that contributes to the highest score of L2 reading proficiency were determined. As most reading tests are based on choice, it is very logical to consider the MBTI function pairs as they represent the mental functions used for decision-making and information gathering. Fifty (50) Arab male students from the College of Business Administration (Saudi Arabia) were randomly selected. Two instruments were used to collect data; MBTI as a personality instrument, and TOEIC Bridge as a reading test. Findings showed a lack of statistically significant relationship between Introversion/Extraversion and L2 reading proficiency. Yet, Intuitive personalities (NT/NF) preferences achieved higher scores in the TOEIC Bridge compared to their counterparts of the Sensing personalities (ST/SF). Personality types have undoubted influence on our lifestyle and attitudes. Yet, considering personality as a weighty factor in language learning and proficiency is still debatable.

Personality of a Good Language Learner: a Case Study in EFL Context

This case study aims to explore how the personality traits of a good language learner contributed to her success in language learning. Using a case study design, this study involved one student who met the criteria of a good language learner who became a champion of English competitions. This study used the Myers-Briggs Type Indicator personality test and semi-structured interviews to collect the data. The study showed that Meta's extrovert personality was described as talkative, confident, enthusiastic, and assertive. She understood a problem by talking about it and heard others' ideas in solving problems. As a sensing personality, she was objective and made decisions based on logic and facts to decide both sides. As a thinking person, she always worked with plans, and in making plans, she analyzed the pros and cons. She was objective and consistent in doing what she had planned. Her judging personality described how she dealt with their outer world. She had a decided lifestyle that had helped her achieve her goals in language learning by planning her language learning activities.

EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model

In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is implemented. For this research, the Myers–Briggs Type Indicator (MBTI) model for predicting personality is used. There are four groups in MBTI, and each group consists of two traits versus each other; i.e., out of these two traits, every individual will have one personality trait in them. We have collected EEG data using a single NeuroSky MindWave Mobile 2 dry electrode unit. For data collection, 40 Hindi and English video clips were included in a standard database. All clips provoke various emotions, and data collection is focused on these emotions, as the clips include targeted, inductive scenes of personality. Fifty participants engaged in this research and willingly agreed to provide brain signals. We compared the performance of our deep learning DeepLSTM model with other state-of-the-art-based machine learning classifiers such as artificial neural network (ANN), K-nearest neighbors (KNN), LibSVM, and hybrid genetic programming (HGP). The analysis shows that, for the 10-fold partitioning method, the DeepLSTM model surpasses the other state-of-the-art models and offers a maximum classification accuracy of 96.94%. The proposed DeepLSTM model was also applied to the publicly available ASCERTAIN EEG dataset and showed an improvement over the state-of-the-art methods.

SI engine fuel mixture type indicator and an assessment of its suitability of an economic driving style

The article presents the structure and a principle of operation of a simple indicator of the type of a fuel-air mixture supplying a spark-ignition engine with a direct fuel injection. The designed indicator was tested, as a result of which its correct operation was verified. By using information from the indicator, it was possible to assess its usefulness for assisting the driver in an economical driving style. Preliminary studies show that thanks to the use of the developed indicator, it is possible to save about 10% of fuel as a result of the correction of the economic driving style on the route selected for the purpose of this research paper. The target of this study was to confirm a noticeable reduction in fuel consumption when supplying the engine with a stratified mixture. In order to obtain more accurate data, the research should be extended to include a greater number of routes and its division into urban and non-urban areas.

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.