- .... . -..-. . ...- --- .-.. ..- - .. --- -. -..-. .-- .. .-.. .-.. -..-. -... . -..-. .-.. .. ...- . -..-. ... - .-. . .- -- . -..I would not go so far as saying analytically, but certainly if the simulation is computationally shallow – doesn’t evolve opportunity costs that need to unfold in time and space -- I would think mixed integer linear programming or constraint/logic optimization would give a more comprehensive result.
From: Friam <[hidden email]> on behalf of Frank Wimberly <[hidden email]>
Reply-To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Date: Saturday, June 6, 2020 at 1:17 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] Oblivion resistant swarm
I thought it worked well. I don't know what Ford did with it if anythinng. There was at least one person that claimed he could reach the same conclusions analytically. But he wouldn't have been able to model the social interaction of customers, the effects of advertising, and the effects of social media for instance. I'll bet there are Ford Ranger Facebook groups. Ford wanted the results in order to determine which option sets to manufacture.
Frank
On Sat, Jun 6, 2020 at 2:12 PM Barry MacKichan <[hidden email]> wrote:
How did it work out? Did anyone at Ford run with it?
On 6 Jun 2020, at 16:08, Frank Wimberly wrote:
I developed some ABMs at BiosGroup. That looks interesting and it would be fun to develop. The "Ford Model" that I implemented took more than two weeks and it was written in Java. If had to do with modeling purchaser behavior in relation to Ford Ranger pickups and the desirability of options sets. We were anticipating modeling the influence customers have on each other among other things.
Frank
On Sat, Jun 6, 2020 at 2:03 PM Jochen Fromm <[hidden email]> wrote:
Looks interesting but complicated, I was hoping that Stephen or Owen might have seen something similar because they have done a lot of agent-based modeling as far as I know.
-J.
-------- Original message --------
From: Marcus Daniels <[hidden email]>
Date: 6/6/20 21:56 (GMT+01:00)
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] Oblivion resistant swarm
This approach might be useful to understand such phase transitions. Imagine the agents have a pairwise influence network that attract or repel one another, and further any subset of agents can be biased left or right as a function of time (like from a political convention), or to uncertain states (superposition).
https://science.sciencemag.org/content/361/6398/162
From: Friam <[hidden email]> on behalf of Jochen Fromm <[hidden email]>
Reply-To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Date: Saturday, June 6, 2020 at 12:27 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: [FRIAM] Oblivion resistant swarm
I would like to add an agent-based model for the last chapter of my book. The idea is to use a classic swarm as a model for a religious or political movement (since the basic rules like global attraction and local repulsion are isomorphic, as I argue in earlier chapters).
The new thing is an "oblivion" factor which causes agents to forget the classic Boids swarm rules step by step. In order to keep the swarm from dissolving the model reinforces the rules every T timesteps, which simulates a rally, convention or congregation for the movement. Therefore the name "Oblivion Resistant Swarm" (ORS model) :-)
As T varies, I expect to find some kind of phase transition in simulations where the swarm forms or dissolves. If T is too large, the swarm forgets the rules and is unable to maintain the form. If T is very small we get the classic Boids model and the swarm is able to form. Does that make any sense? Two more questions:
1. Is two weeks a reasonable timespan for the time we need to learn new rules in general?
2. Do you know any existing ABMs which are similar?
-J.
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Frank Wimberly
140 Calle Ojo Feliz
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140 Calle Ojo Feliz
Santa Fe, NM 87505
505 670-9918
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