Sir. I have a model. There is one green patch randomly situated. There are 10 turtles.(i can change the number of turtles by moving a slider I made). When at least one turtle comes to the cell where the green patch is , the green cell becomes red(I made a code for this and its working). So when I press setup 10 turtles and one green patch is randomly situated in the world. I want to calculate the time that takes for a turtle to reach the green patch. I.e I want to make a code such that I can count the time that it takes a green patch to become red. Also i want to do this 100 times and get the average time for the green patch to become red. I am struggling to make a code for this. Please help me. Thank you very much
Note that when Rand says "complex behavior," what he means is simple behavior, e.g. the way aimless birds or bar-hoppers behave. In complex group behavior, one sees examples of sharply differentiated actions. George Kistialowski wandering around Los Alamos with a cigarette in one hand and a bucket of TNT in the other comes to mind. Here not just Kistialowski but also everyone around him is acting with complexity. They all understand that he is addicted to tobacco and is testing the explosivity of possible thrusters for use in a bomb; the group's activity is characterized by understanding, purpose, and deliberate and highly differentiated interactions. In the flock of birds or the horde of drinkers in a bar, understanding, purpose, and deliberate differentiation of varied interactions are all missing. The action of the whole consists of much simpler actions of the parts. Somebody else will have to explain why Rand cannot tell the difference between "simple" and "complex." One would have thought it was obvious, but "obviousness' does not seem to have strong explanatory power.
I'm confused by your comment and do not think I understand what you are trying to say. The point of Agent Based Modeling is to model the interactions between people, such as the knowledge between passers-by and Kistialowski.
The individual behaviour of the agents might be very simple, but in interaction between many such agents, a qualitatively different global behaviour can emerge. It is not obvious that this would be the case and it is hard for us humans to predict what the global behaviour may look like. Conversely, it is also hard to deduce the (microscopic) rules of behaviour from which a macroscopic property emerges (this is what Uri Wilensky talked about in section 1.1 of the course). I believe this is why it is called complex and why agent-based modelling is so useful in these cases, since otherwise a simple mathematical formula would suffice to study such systems. So I am not sure if you confuse “complex individual behaviour” with complex behaviour in interactions/systems.
Agents at different levels / scales
When not to use ABM, when agents are largely special and heterogeneous
i didnt understand a thing. how do ppl understand anything.
Same
This was very helpful - thank you!
Sir. I have a model. There is one green patch randomly situated. There are 10 turtles.(i can change the number of turtles by moving a slider I made). When at least one turtle comes to the cell where the green patch is , the green cell becomes red(I made a code for this and its working). So when I press setup 10 turtles and one green patch is randomly situated in the world. I want to calculate the time that takes for a turtle to reach the green patch. I.e I want to make a code such that I can count the time that it takes a green patch to become red. Also i want to do this 100 times and get the average time for the green patch to become red. I am struggling to make a code for this. Please help me. Thank you very much
I'll describe more easily: agent based modeling is Pathfinding, decision making and sometimes machine learning
bruh, half the video on what a model is
Thx 🙏🏻
Can you make Arabic translation
Note that when Rand says "complex behavior," what he means is simple behavior, e.g. the way aimless birds or bar-hoppers behave.
In complex group behavior, one sees examples of sharply differentiated actions. George Kistialowski wandering around Los Alamos with a cigarette in one hand and a bucket of TNT in the other comes to mind. Here not just Kistialowski but also everyone around him is acting with complexity. They all understand that he is addicted to tobacco and is testing the explosivity of possible thrusters for use in a bomb; the group's activity is characterized by understanding, purpose, and deliberate and highly differentiated interactions.
In the flock of birds or the horde of drinkers in a bar, understanding, purpose, and deliberate differentiation of varied interactions are all missing. The action of the whole consists of much simpler actions of the parts.
Somebody else will have to explain why Rand cannot tell the difference between "simple" and "complex."
One would have thought it was obvious, but "obviousness' does not seem to have strong explanatory power.
I'm confused by your comment and do not think I understand what you are trying to say. The point of Agent Based Modeling is to model the interactions between people, such as the knowledge between passers-by and Kistialowski.
You sound like a massive tool and you’ve contributed nothing to the conversation.
The individual behaviour of the agents might be very simple, but in interaction between many such agents, a qualitatively different global behaviour can emerge. It is not obvious that this would be the case and it is hard for us humans to predict what the global behaviour may look like. Conversely, it is also hard to deduce the (microscopic) rules of behaviour from which a macroscopic property emerges (this is what Uri Wilensky talked about in section 1.1 of the course).
I believe this is why it is called complex and why agent-based modelling is so useful in these cases, since otherwise a simple mathematical formula would suffice to study such systems. So I am not sure if you confuse “complex individual behaviour” with complex behaviour in interactions/systems.
muito bom obrigado.
Thanks but i still don't understand🫶
muito bom obrigado.