There are roughly 1028 H2O molecules in a 300 liter bathtub. Pull the plug, and a little drama takes place. They all try to move down the drain at once. Gravity acts on each molecule. We witness entropy; it is easier for the molecules to go down than it is to stay where they are. So they push and rub against each other, causing friction and static electricity that influence each other’s thermodynamics. A pattern of movement appears, a beautiful spiral. This is the easiest and fastest way for the molecules to flow out of the tub.
The phenomenon of water molecules spiraling when flowing down a drain is commonly observed in sinks, bathtubs, or other draining systems. This spiraling motion, often referred to as the “Coriolis effect,” is a result of a combination of factors, including the initial motion of the water and the rotation of the Earth. However, in the case of water draining in sinks or bathtubs, the Coriolis effect is relatively negligible compared to other dominant factors that influence the flow pattern.
Contrary to popular belief, the Coriolis effect is not responsible for the direction of water flow in a draining system on a small scale, such as a sink. The Coriolis effect is more significant in large-scale phenomena, such as the rotation of weather systems or ocean currents, where the distances and time scales are much larger.
On a smaller scale, the spiraling motion observed when water drains in a sink is primarily influenced by other factors, such as the shape of the sink or bathtub, the structure of the drain, and the initial motion of the water. These factors, coupled with surface tension effects and the flow dynamics near the drain, contribute to the formation of a vortex or spiral pattern.
As water begins to flow down a drain, it initially encounters a flat or concave surface, causing the water to circulate around the drain in a circular motion. This circular motion is driven by the conservation of angular momentum. As the water moves closer to the drain, the decreasing radius of the circular flow causes an increase in the velocity of the water, resulting in the formation of a whirlpool or vortex. This vortex causes the water to spiral as it flows down the drain.
It is important to note that the direction of the spiral is influenced by various factors, including the shape and design of the draining system, as well as any residual motion imparted to the water during its entry into the sink or bathtub. While the Coriolis effect does exist and influences large-scale phenomena, it is not a significant factor in determining the direction of water flow in a typical household draining system.
Theories of self organization: https://www.sciencedirect.com/topics/social-sciences/self-organization#:~:text=The%20term%20self%2Dorganization%20refers,through%20external%20intervention%20or%20instruction.
Definitions self organization, emergence, complexity, adaptivity
A further concept often overlaid onto self-organization is adaptivity. A self-organized system that is also considered adaptive is termed a complex adaptive system. Such systems are widely considered to be the paradigmatic objects of interest in complexity theory.
Examples – Small to big
quantum physics, physics, chemistry, microbiology
biology macrobiology — sciences of complexity (Cowan et al. 1994). The field focuses on the emergent properties of complex systems during the course of development and evolution (Solé and Goodwin 2000). —- The theory of self-organization now provides the possibility of recasting evolutionary principles in consistently dynamic terms, resolving the tensions that have existed between development and evolution, ontogeny and phylogeny.
Why does it work better in some contexts than others?
Title: Exploring Types of Self-Organizing Phenomena: A Fascinating Journey into Complexity
Self-organizing phenomena are prevalent in various natural and artificial systems, ranging from biological networks to social structures and technological advancements. This essay aims to provide an in-depth exploration of different types of self-organizing phenomena, elucidating their characteristics and significance. Through a comprehensive examination of examples and visual illustrations, we will delve into the realms of emergence, synchronization, pattern formation, and swarm intelligence. By understanding these captivating phenomena, we can gain insights into the underlying principles that govern complex systems.
Self-organization refers to the spontaneous emergence of order, structure, or coordinated behavior in a system without the need for external control or central authority. It is a fascinating aspect of complex systems, where intricate patterns arise from simple interactions between the system’s components. This essay will explore four prominent types of self-organizing phenomena: emergence, synchronization, pattern formation, and swarm intelligence.
Emergence is the phenomenon where collective behavior or properties emerge from the interactions of individual components in a system. Examples of emergence can be found in various domains, such as ant colonies, traffic flow, and neural networks. A visual example is the formation of ant trails, where individual ants communicate through pheromones, resulting in the emergence of organized trails leading to food sources.
Synchronization refers to the coordination of rhythms or oscillations between multiple entities in a system. It is observed in a wide range of systems, including fireflies, pacemaker cells in the heart, and coupled mechanical pendulums. A visual example of synchronization is the phenomenon of firefly flashing, where fireflies synchronize their bioluminescent signals, resulting in a mesmerizing display of rhythmic flashes.
- Pattern Formation
Pattern formation involves the spontaneous creation of regular or intricate patterns from initially random or uniform states. This phenomenon is observed in diverse systems, such as convection patterns in fluids, animal coat patterns, and the formation of snowflakes. A visual example is the Belousov-Zhabotinsky reaction, a chemical system that exhibits colorful pattern formation due to the interaction of various reactants.
- Swarm Intelligence
Swarm intelligence refers to the collective behavior of a decentralized group that exhibits sophisticated problem-solving abilities. Examples include the foraging behavior of ants, bird flocking, and the optimization algorithms inspired by social insects. A visual example is the simulation of bird flocking, where individual agents follow simple rules, leading to the emergence of complex flocking patterns.
- Significance and Applications
Understanding self-organizing phenomena has profound implications in various fields. In biology, it helps us comprehend the behavior of social insects, cellular processes, and ecosystem dynamics. In physics, self-organization is essential for understanding the behavior of complex systems, such as fluid dynamics and phase transitions. Furthermore, self-organization finds applications in engineering, optimization algorithms, and the development of robust and adaptive systems.
Self-organizing phenomena are captivating manifestations of complexity that occur in diverse systems. Through the exploration of emergence, synchronization, pattern formation, and swarm intelligence, we have gained insights into the inherent order and organization that can arise from simple interactions. Visual examples, such as ant trails, firefly flashing, patterned chemical reactions, and bird flocking, have provided a tangible representation of these phenomena. As we continue to unravel the principles underlying self-organization, we unlock new possibilities for understanding and harnessing the power of complex systems.
In conclusion, self-organizing phenomena are an integral part of nature and our technologically driven world. By studying and comprehending these phenomena, we can appreciate the beauty and intricacy of the natural world, while also leveraging them to design
Certainly! Here are some examples of self-organizing phenomena in different scientific disciplines:
- Rayleigh-Bénard convection: When a fluid is heated from below and cooled from above, it can undergo a self-organizing process where convection cells form. These cells transport heat and give rise to intricate patterns.
- Spin systems: Magnetic materials exhibit a phenomenon called spontaneous magnetization, where individual magnetic spins align collectively without any external influence, resulting in the emergence of magnetic domains.
- Morphogenesis: The development of complex body structures in organisms is an example of self-organization. For instance, the formation of branching patterns in blood vessels or the growth of an embryo involves the emergence of ordered structures from initially undifferentiated cells.
- Swarm behavior: Social insects, such as ants, termites, and bees, exhibit self-organizing behavior in their foraging, nest construction, and defense strategies. They collectively coordinate their actions without any central authority, leading to efficient and adaptive outcomes.
- Turing patterns: Chemical reactions can give rise to self-organizing spatial patterns known as Turing patterns. For instance, the reaction-diffusion system can produce patterns like stripes, spots, or labyrinthine structures, as observed in the Belousov-Zhabotinsky reaction.
- Self-assembled monolayers: In materials chemistry, self-assembly processes can result in the formation of ordered layers of molecules on surfaces. The molecules spontaneously organize themselves into regular patterns due to intermolecular forces and surface interactions.
- Social Sciences:
- Opinion dynamics: In social networks, individuals’ opinions can self-organize into clusters or consensus due to their interactions with others. This phenomenon is observed in opinion formation, voting patterns, and the spread of information or rumors.
- Market dynamics: Financial markets exhibit self-organizing behavior through the emergence of price patterns, herding behavior, and market bubbles. These phenomena arise from the collective actions and interactions of traders and investors.
- Computer Science:
- Artificial neural networks: Neural networks can self-organize by adjusting their connection strengths through learning algorithms. This self-organization enables them to learn and recognize patterns, make predictions, and perform complex tasks.
- Swarm robotics: Groups of robots can exhibit self-organizing behavior to achieve collective goals. They can coordinate their actions, communicate, and distribute tasks efficiently without centralized control, mimicking swarm behavior in nature.
These examples demonstrate how self-organizing phenomena are present across various scientific disciplines, highlighting the ubiquity and significance of such phenomena in understanding and explaining complex systems.
Certainly! Here are three examples of self-organizing phenomena in each of biology, chemistry, physics, sociology, and communication:
- Flock Behavior: Birds and other animals exhibit self-organizing flock behavior. Each individual in the group follows simple rules, such as maintaining a minimum distance from neighbors or aligning their direction with the average heading of nearby individuals. This results in the emergence of coordinated flocking patterns.
- Slime Mold Aggregation: Slime molds are single-celled organisms that can aggregate and form complex multicellular structures. Through chemical signaling and sensing mechanisms, individual cells come together to form fruiting bodies or intricate branching patterns, demonstrating self-organization at the microscopic level.
- Social Insect Nest Construction: Social insects, such as ants or termites, construct complex nests without any central authority. Individual insects follow simple rules, such as depositing or removing materials based on local cues, resulting in the emergence of intricate and functional structures.
- Self-Assembled Nanoparticles: Nanoparticles can self-organize into ordered structures through processes like self-assembly. For example, gold nanoparticles can arrange themselves into highly regular patterns due to attractive forces between their surfaces, leading to the formation of plasmonic crystals.
- Micelle Formation: Amphiphilic molecules in a solution can self-organize into micelles, which are spherical structures with hydrophobic tails facing inward and hydrophilic heads facing outward. This phenomenon arises due to the hydrophobic effect and enables the solubilization of hydrophobic substances in water.
- Pattern Formation in Bénard Cells: When a fluid is heated from below, it can exhibit self-organized pattern formation known as Bénard cells. Convective heat transfer causes the fluid to form regular cells or rolls, with hot fluid rising in the center and cold fluid descending along the sides.
- Bifurcation and Chaos: Systems with nonlinear dynamics can exhibit self-organizing behavior in the form of bifurcations and chaos. Small changes in parameters can lead to dramatic shifts in the system’s behavior, resulting in the emergence of complex and unpredictable patterns.
- Magnetic Domain Formation: Ferromagnetic materials undergo self-organization as magnetic domains spontaneously form due to the alignment of atomic magnetic moments. This results in regions of uniform magnetization separated by domain walls.
- Spinodal Decomposition: In certain materials undergoing phase separation, such as binary alloys, self-organization occurs through spinodal decomposition. Random fluctuations in the composition can lead to the formation of interconnected regions with different compositions, giving rise to intricate microstructures.
- Opinion Dynamics: Individuals in social networks can self-organize their opinions through interactions with others. This can lead to the emergence of opinion clusters, consensus, or polarization, depending on the network structure and dynamics of influence.
- Crowd Behavior: Crowds or gatherings of people often exhibit self-organizing behavior. Individuals adjust their movements and behaviors based on local cues from their surroundings, resulting in phenomena like crowd flow, collective decision-making, or emergent leadership.
- Social Norms and Conventions: Social norms and conventions emerge through self-organization in societies. They are the result of individuals’ interactions, where certain behaviors or rules become widely accepted and followed, shaping social behavior and group dynamics.
- Spontaneous Language Development: In communities isolated from external language influences, a self-organizing process can lead to the emergence of a new language, known as a creole or a pidgin. Initially rudimentary communication systems can gradually evolve and become more complex through collective language development.
- Information Cascades: In online social networks, information cascades can occur when individuals observe others
‘ actions or opinions and adopt them, leading to a domino effect. This self-organizing phenomenon shapes the spread of information, trends, and virality in online communities.
- Synchronization of Firefly Flashing: Fireflies communicate and synchronize their bioluminescent flashes to attract mates. Through self-organization, individual fireflies adjust their flashing patterns to achieve synchrony, resulting in mesmerizing displays of coordinated light emission.
These examples showcase the diverse range of self-organizing phenomena observed in biology, chemistry, physics, sociology, and communication, highlighting the inherent complexity and order that can arise from simple interactions and local rules.
Certainly! Here are six examples of self-organizing phenomena in the context of education:
- Peer-to-peer learning: In a classroom setting, students often engage in self-organizing behavior when they collaborate and learn from their peers. Students can form study groups, exchange ideas, explain concepts to each other, and collectively navigate through the learning process, leading to enhanced understanding and knowledge acquisition.
- Spontaneous group formation: During project-based learning or collaborative activities, students may self-organize into groups based on shared interests, complementary skills, or personal affinities. These self-formed groups can effectively distribute tasks, support each other, and collectively work towards achieving shared learning goals.
- Informal learning networks: Outside the formal classroom setting, self-organizing phenomena can occur in the form of informal learning networks. Students might naturally form study circles, online communities, or discussion forums where they share resources, seek assistance, and engage in peer-to-peer learning beyond the confines of traditional educational structures.
- Student-driven inquiry: When students are given the autonomy to pursue their own research questions or explore topics of personal interest, self-organizing phenomena often emerge. Students take ownership of their learning, conduct independent investigations, seek relevant resources, and construct their knowledge, fostering a sense of curiosity, motivation, and self-directed learning.
- Adaptive learning systems: With the advancement of technology, adaptive learning systems have emerged as self-organizing phenomena in education. These systems use algorithms and data analytics to adapt the learning experience based on individual student needs, preferences, and performance, providing personalized instruction and feedback.
- Classroom management and self-regulation: Within the classroom, self-organizing phenomena can be observed in students’ self-regulatory behaviors. Students develop self-discipline, time-management skills, and the ability to set goals, organize their tasks, and monitor their progress independently. This self-regulation fosters a positive learning environment and empowers students to take ownership of their academic journey.
These examples highlight how self-organizing phenomena manifest in educational settings, promoting collaboration, autonomy, and individual agency in the learning process. By recognizing and harnessing these phenomena, educators can facilitate student-centered approaches and create conducive environments for effective and engaging learning experiences.