Exploration through NetLogo| Starting from 17th August, 2021

Hello everyone!!!
This is a pleasure to announce that we will be exploring “NetLogo”, a multi-agent programmable modelling environment, from the upcoming week.

  • About Netlogo:

NetLogo is a multi-agent programmable modelling environment. It is used by many hundreds of thousands of students, teachers, and researchers worldwide. It is authored by Uri Wilensky and developed at the CCL. You can download it free of charge. You can also try it online through NetLogo Web. (Souce: https://ccl.northwestern.edu/netlogo/)

The computational interface enables us to explore interesting questions such as the following,

  1. How creatures can aggregate into clusters without the control of a “leader” or “pacemaker” cell? For example, “slime mold cells were organizing themselves without the need of a leader” - is this statement true?

  2. What is osmotic pressure? How to illustrate the colligative nature of osmotic pressure?

  3. What governs the movement of gases in a closed chamber? How to obtain the different properties (like average speed or average energy etc.) from the initial data we have?

  4. Suppose we divide a stick in three. Then replace the middle one with two sticks so that the old one and two new ones form an equilateral triangle. Now we have 4 sticks. Do the same for these 4 sticks. Continue the process. What will you get at the end?

We will attempt to explore the phenomena and concepts from the empirical data generated by the interface without using any formulae or experiments. NetLogo has a rich library of many such simulations to answer these kinds of questions and we will explore new aspects of those during our weekly discussions.

To have an idea about the sessions you can look at the following example:

The sessions will be conversational where everyone can participate and share their thoughts without hesitation. To get an idea of the conversation, do check out the recorded videos related to physics and chemistry-related simulations session earlier along with their schedule. Here is the invitational post link. Click Here

Sounds interesting? Please fill in the expression of interest (EOI) here

Here are the details,

Meeting Link: Here

Time: Every Tuesday | 3:00 pm - 4:00 pm (* starting from 17th Aug 2021)

Kindly note, the sessions will be recorded and will be released as an Open Educational Resource.

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Could not connect to the 17th Aug meeting as your webserver found an incompatible browser on my raspberry-pi. Suggested to switch to Chrome or Firefox. But the Chromium-browser on raspberry-pi is a Chrome. I have been able to hear and watch many talks from CUBE on my Chromium-browser. Please consider it as compatible.
I have installed and run NetLogo-6.2.0 on the Java 9-Raspbian on raspberry-pi… Happy to show you the wshot of Wolf-Sheep-Grass.
wolf-sheep-grass-on-raspberry-pi

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Finally after updating the chromium browser on raspberry pi, could attend the netlogo talk today.
The wolf-sheep-grass ecosystem is seen in the Yellowstone national park of US as (gray wolf)-(elk, coyote, hares, beaver)-(willows, cottonwoods, aspen). The park extirpated (exterminated?) the gray wolf by 1926. The elk (large deer with branched horns) population increased, elks reduced willow trees, the beavers reduced as the willows were gone, coyotes increased and attacked hares etc. The park condition ‘declined drastically’.
The reintroduction of the gray wolf was started in 1995. The wolves reduced elk and coyote population, and more beavers built their ‘dams’ as willows came back.
History of wolves in Yellowstone

Why gray wolf story? See the Annual status of Wolves in Yellowstone (as of December). The gray wolf population appears to fluctuate between 110 and 80 with a cyclic time of 4 years! We saw this wave behaviour in our Netlogo simulation.

Invite students to simulate Yellowstone National Park to explain the gray wolf population wave.

Just now found that Yellowstone ecosystem and Netlogo have been linked! See elk (Cervus elaphus) migration patterns in Yellowstone National Park Bennett & Tang

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Think before you Compute

or Apply Science before you Simulate

A person from Kolkotta(?) saw that rocks roll down a hill and often bang against trees or walls to break them. The rolling rocks could even kill animals trying to run away. Thinking ahead, the person ‘invented’ a free-running rickshaw that needed no peddling. The driver would sit on a slopping seat. To prevent falling off the seat, the driver has to hold on to the handle of the rickshaw, or push against the handle. A forward force is exerted on the rickshaw on the handle and it will run forward! No need to push feet laboriously against the foot pedal.

Now look at science. Newton had long back come up with the principle or third law “ for every action (force) in nature there is an equal and opposite reaction “. If there is a force on the handle there is an opposite and equal force on the driver. We may think that the reverse force is only on the driver, not the rickshaw. But driver sits on the rickshaw seat and passes on the reverse force to the rickshaw.

Calculating position from Force and resultant acceleration is complicated. Easier to use the energy conservation law. Let us listen to:

‘Conservation’ (the conservation law) means this … that there is a number, which you can calculate, at one moment-and as nature undergoes its multitude of changes, this number doesn’t change. That is, if you calculate again, this quantity, it’ll be the same as it was before. An example is the conservation of energy: there’s a quantity that you can calculate according to a certain rule, and it comes out the same answer after, no matter what happens, happens.

Richard P. Feynman

In the Wolf-Sheep-Grass simulation check whether the sheep get more energy than what the grass generates from the sun. And the wolf …

We know that the energy flows from the sun to grass to sheep to wolf. Where does it go? The energy could be stored as chemical energy in the dead grass, sheep or wolf. Where and how did the energy in the petrol or diesel come?

2021 Physics Nobel for Complex Systems like Global Climate
E60EB68A-18CF-43D5-AB38C11FD083926D_source

In 2021 the Nobel Prize in Physics was awarded to Syukuro Manabe, Klaus Hasselmann and Giorgio Parisi “for groundbreaking contributions to our understanding of complex systems.” Credit: Niklas Elmehed © Nobel Prize Outreach

“There is no clear definition of complex systems,” says Kunihiko Kaneko, a physicist at the University of Tokyo. “But roughly speaking, there are many interacting elements, and they often show chaotic or dynamic behavior.”

Let us NetLogo!