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Summary on interaction
Topic: Interaction
by Xiuhua, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
The result of interactions within nature cause the formation of complex patterns. The mechanism by which these patterns form and how the pattern that results is caused by the nature of those said interactions, whereby simple rules of interactions can cause differentiation and remarkable variation within patterns. A real life example would be the formation of animal skin patterns individual biological (skin) cells interact with each other by emitting chemicals into fluid between them, thus influencing chemical activity of other cells and resulting in biological pattern formation.
There are two possible types of interactions- activating interactions and inhibiting interactions. An activating interaction causes cells to behave in the same way for example a cell that produces pigment releases a chemical that causes other cells to also produce pigment. An inhibiting interaction on the other hand, would cause cells to behave in the opposite way e.g a cell that produce pigment by it own but releases a chemical that causes other cells to not produce
Using interaction as a behaviour model, it is important to emphasise the existence of transitions in collective patterns of behaviour. With humans as an example, it is understanding the difference within interactions between people, conditions under which people are interacting and the triggering influence (if any). Using the panic model as an example to show transitions in collective patterns of behaviour, we demonstrate the spread of panic in a crowded room of people. As time goes on, it is assumed people will spread according to numerous factors and cluster together, thus leaving regions in the room where there are fewer people and more space over intervals of time until eventually stabilising. Another real life example could be social cliques within teenagers at high school.
Different models of interaction include local activation and long range inhibition models, building upon the previous concept of interaction types. Local activation models are caused when there is a slow moving chemical that is activating, whereas long range inhibition models are caused by faster moving chemicals that are inhibiting. Using spotted or striped patterns as a classic working example: activating interaction as a result cause formation of patches or spots, and inhibiting interaction limits the size or edge of patches formed, until a stable and complex pattern is reached. The differences come from a bias that makes cells have greater inclination to being light or dark. The bias can be changed upon preference or can be equal for light or dark, thus resulting in striped patterns. Within this, these simple rules of interaction can cause differentiation and result in the creation of remarkable patterns.
These models give context for many different types of natural patterns eg clouds, ocean waves, traffic jams and heartbeats.
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If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
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Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
The result of interactions within nature cause the formation of complex patterns. The mechanism by which these patterns form and how the pattern that results is caused by the nature of those said interactions, whereby simple rules of interactions can cause differentiation and remarkable variation within patterns. A real life example would be the formation of animal skin patterns individual biological (skin) cells interact with each other by emitting chemicals into fluid between them, thus influencing chemical activity of other cells and resulting in biological pattern formation.
There are two possible types of interactions- activating interactions and inhibiting interactions. An activating interaction causes cells to behave in the same way for example a cell that produces pigment releases a chemical that causes other cells to also produce pigment. An inhibiting interaction on the other hand, would cause cells to behave in the opposite way e.g a cell that produce pigment by it own but releases a chemical that causes other cells to not produce
Using interaction as a behaviour model, it is important to emphasise the existence of transitions in collective patterns of behaviour. With humans as an example, it is understanding the difference within interactions between people, conditions under which people are interacting and the triggering influence (if any). Using the panic model as an example to show transitions in collective patterns of behaviour, we demonstrate the spread of panic in a crowded room of people. As time goes on, it is assumed people will spread according to numerous factors and cluster together, thus leaving regions in the room where there are fewer people and more space over intervals of time until eventually stabilising. Another real life example could be social cliques within teenagers at high school.
Different models of interaction include local activation and long range inhibition models, building upon the previous concept of interaction types. Local activation models are caused when there is a slow moving chemical that is activating, whereas long range inhibition models are caused by faster moving chemicals that are inhibiting. Using spotted or striped patterns as a classic working example: activating interaction as a result cause formation of patches or spots, and inhibiting interaction limits the size or edge of patches formed, until a stable and complex pattern is reached. The differences come from a bias that makes cells have greater inclination to being light or dark. The bias can be changed upon preference or can be equal for light or dark, thus resulting in striped patterns. Within this, these simple rules of interaction can cause differentiation and result in the creation of remarkable patterns.
These models give context for many different types of natural patterns eg clouds, ocean waves, traffic jams and heartbeats.