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Contents

  1. Understanding the Dynamics of Typical People by Richard Bents
  2. Popular Posts
  3. Managing team dynamics: what every manager needs to know about building a successful team
  4. Surface-level characteristics

They then assimilate, sort, and link all of this information in a process that may be as much unconsciously as consciously directed.


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This process, like digestion, takes its own time, until suddenly everything comes together in a highly detailed, systemic understanding of a situation, plan of action, or product. They are not really slow at all, but rather thorough. Their organic process takes time, but they are typically able to assimilate and synthesize more data and comprehend and handle more complex situations than people of any other personality dynamic.

Physical-emotional people typically have a prodigious capacity to remember data. They can recollect events from even the distant past in which they were fully engaged in extraordinary sensory detail. The personality dynamics that we have identified are not equally prevalent or evenly distributed. Of the five dynamics that we have described, mental-physical people are encountered most rarely — they seem to constitute no more than about 3 percent of the population.

Anyone of any personality dynamic may be more or less intelligent, more or less compassionate, more or less contributive, more or less gifted. It has been fascinating for us to experience over the years that in the Western cultures in which we, or the facilitators we have trained, have worked extensively such as North America, Europe, South America, and Israel , we have found a slight majority of people to be emotionally centered and the rest to be physically centered.

In Eastern countries in which we have worked, such as Malaysia, China, Singapore, and Japan, we have found by far the great majority of the people to be physically centered. These findings apply even to people of Asian descent whose families have lived in the West for many generations.

Understanding the Dynamics of Typical People by Richard Bents

We have no explanation for the fact that the two physically centered personality dynamics seem to predominate in the East and the two emotionally centered ones in the West. We simply offer our findings. However, we emphasize that no value judgments adhere to this observation. All of the personality dynamics are equal in value. Indeed, we can say that each needs the others for results that are optimal and whole.

We have often been asked if we think that this distribution of personality dynamics could be the result of cultural influence. Our experience has led us to believe otherwise, for a number of reasons:. The cultural explanation does not account for the many physically centered people in the West or the emotionally centered people in the East. If culture created the personality dynamics, then one would expect the many people of Asian background who have been assimilated over generations into Western cultures, and who are not part of an Asian community within the larger community, to show the characteristic processes of the majority in their adoptive cultures.

However, we have not observed this to be the case. Although many Asian Americans, for instance, may have adopted more characteristically Western values, their foundational processes of handling information, learning, problem-solving, and so on remain those characteristic of physical centering. We have come up with the same findings in following infants adopted from the East into families in the West in which both of the adoptive parents were emotionally centered.

It may influence what one thinks or learns, but not how one naturally thinks or learns.

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We are led to assume, therefore, that the distinctions we have identified are inherent and genetically determined. This conclusion is reinforced by our findings that people almost always identify at least one parent as having the same personality dynamic as themselves or, if not, a grandparent. These different ways of being and functioning are represented wherever people live, learn, and work together. It has been said that 90 percent of the difficulties that organizations face can be attributed to dysfunctional relationships among people.


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When people develop awareness and understanding of the different personality dynamics, much interpersonal misunderstanding and conflict is avoided. Also a groundwork is laid for developing optimal communication, teamwork, coaching, mentoring, and training. For example, at the beginning of the school year, students discuss with the teacher-facilitators and their parents what they will learn during the year.

Then each day students decide individually how they will learn. This may be followed by a period of conventional group instruction. Then students are free to follow their own self-study plans. They work alone, in pairs, or in groups as they wish, and move from one learning environment to another as meets their needs. They also have close relationships with the parents, who are involved in the planning process and attend Human Dynamics presentations. A conscious goal of this facilitative approach is that students become aware of and value their own processes including learning and their associated gifts, capacities, affinities, and developmental needs.

Not only do they feel highly affirmed, but they become equipped with fundamental self-knowledge that will serve them throughout their lives. They also learn how to support and complement the processes of other students. As a result, these classrooms have become highly motivated, conscious, deeply respectful, and mutually supportive learning communities, in which each student participates and functions in accordance with his or her natural design. Nevertheless, just on the basis of this article, you may find it beneficial to:. Sandra Seagal and David Horne are the founders and directors of Human Dynamics International, an organization that disseminates unique training programs in the fields of organizational development, education, healthcare, and cross-cultural bridge-building.

They are also the founders and directors of the Human Dynamics Institute, which is engaged in original research into the personal, interpersonal, and transpersonal functioning and development of people. Sandra and David are coauthors of Human Dynamics: A New Framework for Understanding People and Realizing the Potential in Our Organizations Pegasus Communications, and are working on a new book directed toward parents, teachers, and all who care about children. For more information, go to www. Consider any complex, potentially volatile issue — Arab-Israeli relations; the problems between the Serbs, Croats, and Bosnians; the ….

During the mids, a large high-technology company launched a project to begin thinking more systemically about strategy.

Managing team dynamics: what every manager needs to know about building a successful team

What Is Human Dynamics? The Three Principles Let us first briefly explain what we mean when we refer to each of the three principles — mental, emotional, and physical. As a result, many teachers use pedagogical approaches that exemplify learning by facilitation rather than instruction and have designed methodologies and learning environments to meet the needs of all the personality dynamics. Nevertheless, just on the basis of this article, you may find it beneficial to: Discuss with other team or family members why you think you might be a certain personality dynamic.

Comparing modularity maximization for weighted graphs to our method in Fig. The lack of a transition region destabilizes modularity maximization and leads to merging groups of different topics that should be separated in our analysis. Depicted is an example, where the hashtags summer and winter get assigned to the same group by modularity maximization, while they are separated by the transition region gray with our method. Values of two local network measures and their effects on community detection: a the local clustering coefficients in one network snapshot and b the degrees of the nodes.

The darker the color and the larger the radius of a node, the higher the value of a measure.

Surface-level characteristics

The two pictures show the same network, with fixed node positions. The described method has two advantages: 1 It can be customized to our needs for the characteristic underlying network structure, by modifying the RW process. It is outside the scope of this work to compare our method to other community detection methods, in part because our method is designed to infer fundamentally different topological structures.

The remaining parts of this work are independent of the community detection on the static snapshots, allowing for a customized solution as the one presented above. The fashion world underlies strong seasonal and trend-driven changes, which lead to alterations in the hashtag landscape.

In Fig. It can be observed that the community structure varies largely between the two seasons. Understanding the dynamics of these developments requires a method to quantitatively capture the communities over time. We propose a meta-algorithm that solves the bipartite matching problem, which arises from connecting previously obtained partitions of every snapshot network.

Human Behavior: Passive-Aggressive Dynamics

It is important to note that this method is independent from the choice of the algorithm used for the static community detection on the individual snapshots. Generally, the class of matching-based methods for temporal community detection [ 8 , 9 , 10 , 11 , 14 ] offers a big advantage, by allowing us to choose a static detection method for the specific data structure and question. Two representative snapshots, with clustering: a the resulting communities on a snapshot from August, b the results for a week in December.

To measure properties like stability, and the rise and descent or the lifetime of communities, we track their history through the snapshot networks. In contrast to an event-based approach [ 9 ], our goal is to find long-term developments and re-identify forgotten trends rather than observe behavioral patterns of various events. This directly suggests maximizing the sum of pairwise similarity measures for adjacent timesteps. Using the above, we construct a weighted bipartite graph with hashtag communities as vertices and weighted edges with the Jaccard index as schematically drawn in Fig.

In order to track the groups over time, we face a matching or coloring problem on that graph, which can be solved by the Hungarian method in polynomial time [ 31 ].