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<p>Jochen -</p>
<p>I'm glad I didn't jump in earlier and let some of this play out.
I hope I'm not still jumping in too early (<a
moz-do-not-send="true"
href="https://www.youtube.com/watch?v=O2tXIm1OHvk">'you move too
soon'</a>) here... but as with Tom's question, I'm not sure
what you are trying to model with "forgetting"? Is this adding
thermal noise to the rules on principle (annealing) or does it
model something like a loss/fading of allegiance to an affinity
group over time?</p>
<p>I did a half-ass search for the pre-thread when you talked about
your book-in-progress to see what I might have missed there.</p>
<p>More background would be interesting to me.</p>
<p>- Steve<br>
</p>
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<div dir="auto">Stephen, </div>
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<div dir="auto">here are some first simulation results. I took a
classic Boids model and made the Boids forgetful. They lose the
memory of the rules, and I have simply used the classic "curve
of forgetting". The "curve of forgetting" describes the
exponential rate at which something is forgotten after it is
initially learned. Using Python and Matplotlib it looks like
this and describes the memory loss of an agent</div>
<div dir="auto"><a class="moz-txt-link-freetext" href="https://en.wikipedia.org/wiki/Forgetting_curve">https://en.wikipedia.org/wiki/Forgetting_curve</a></div>
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<div dir="auto"><img id="HEV1591646109767"
src="cid:part2.FEE96DD0.6D183505@swcp.com"
name="storage_emulated_0_Download_ExponentialDecay_png_1591646109767"
onmouseover="imageMousePointerUpdate(true)"
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<div dir="auto">To counteract the memory loss the agents are
taught the rules again at regular "teaching" intervals.
After a teaching event the agents start to forget again. If
this teaching interval exceeds the half life time of the
curve of forgetting, the swarm starts to disintegrate as
expected.</div>
<div dir="auto"><a class="moz-txt-link-freetext" href="https://en.wikipedia.org/wiki/Half-life">https://en.wikipedia.org/wiki/Half-life</a></div>
<div dir="auto"><img id="HEV1591646061775"
src="cid:part3.361A0603.2CA35A68@swcp.com"
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style="" onmouseover="imageMousePointerUpdate(true)"
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<div dir="auto">-Jochen</div>
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<div>-------- Original message --------</div>
<div>From: Stephen Guerin <a class="moz-txt-link-rfc2396E" href="mailto:stephen.guerin@simtable.com"><stephen.guerin@simtable.com></a> </div>
<div>Date: 6/6/20 23:24 (GMT+01:00) </div>
<div>To: The Friday Morning Applied Complexity Coffee Group
<a class="moz-txt-link-rfc2396E" href="mailto:friam@redfish.com"><friam@redfish.com></a> </div>
<div>Subject: Re: [FRIAM] Oblivion resistant swarm </div>
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<div dir="ltr">Jochen,
<div><br>
</div>
<div>Here's a video recording I made this afternoon for
you using Josh Thorp's Processing flocking model for a student
lesson for 6-12th graders in the NM Supercomputing Challenge
that shows this kind of manipulation of the control parameter
to move the flocking through its phase transition<br>
</div>
<div> <a href="https://bit.ly/FlockingPhaseTransition"
moz-do-not-send="true">https://bit.ly/FlockingPhaseTransition</a>
(turn on the audio for narration) <br>
<br>
To make an interactive example to run on line, you could use
Owen's flocking model in Agentscript using a 3D View:<br>
<a href="http://backspaces.github.io/as-app3d/models/?flock"
moz-do-not-send="true">http://backspaces.github.io/as-app3d/models/?flock</a> <br>
<br>
<div><img
src="content://com.samsung.android.email.attachmentprovider/1/8202/RAW"
alt="image.png"
onmouseover="imageMousePointerUpdate(true)"
onmouseout="imageMousePointerUpdate(false)"
name="com_samsung_android_email_attachmentprovider_1_8202_RAW_1591646021540"
moz-do-not-send="true" width="562" height="266"><br>
</div>
<div><br>
</div>
<div>or add a UI to the 2D version:<br>
<a
href="https://backspaces.github.io/agentscript/models2/flock.html"
moz-do-not-send="true">https://backspaces.github.io/agentscript/models2/flock.html</a> <br>
<br>
Either could be modified to add an interface to manipulate
the micro rules to move the system through the phase
transition of "flocking / no flocking" like I was doing in
the movie. I would operationalize that with an order
parameter of an entropy on the collective heading or a kind
of "linear momentum". <br>
<br>
Also, definitely check out the Netlogo Web option as there's
some very nice "alternative visualization" approaches:<br>
<a
href="https://www.netlogoweb.org/launch#https://www.netlogoweb.org/assets/modelslib/Alternative%20Visualizations/Flocking%20-%20Alternative%20Visualizations.nlogo"
moz-do-not-send="true">https://www.netlogoweb.org/launch#https://www.netlogoweb.org/assets/modelslib/Alternative%20Visualizations/Flocking%20-%20Alternative%20Visualizations.nlogo</a> </div>
<div><br>
</div>
<div>In the top search bar: type in "flock" to see
alternatives. <br>
<div><img
src="content://com.samsung.android.email.attachmentprovider/1/8201/RAW"
alt="image.png" style="margin-right: 0px;"
onmouseover="imageMousePointerUpdate(true)"
onmouseout="imageMousePointerUpdate(false)"
name="com_samsung_android_email_attachmentprovider_1_8201_RAW_1591646021541"
moz-do-not-send="true" width="375" height="299"><br>
</div>
<br>
<br>
<br>
Or download Netlogo and search in the netlogo library.<br>
<br>
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<div dir="ltr">_______________________________________________________________________<br>
<a href="mailto:stephen.guerin@simtable.com"
target="_blank" moz-do-not-send="true">Stephen.Guerin@Simtable.com</a>
<div>CEO, Simtable <a
href="http://www.simtable.com/"
target="_blank" moz-do-not-send="true">http://www.simtable.com</a><br>
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<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">On Sat, Jun 6, 2020 at 1:27 PM
Jochen Fromm <<a href="mailto:jofr@cas-group.net"
moz-do-not-send="true">jofr@cas-group.net</a>> wrote:<br>
</div>
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0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<div dir="auto">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). </div>
<div dir="auto"><br>
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<div dir="auto">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) :-)</div>
<div dir="auto"><br>
</div>
<div dir="auto">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:</div>
<div dir="auto"><br>
</div>
<div dir="auto">1. Is two weeks a reasonable timespan for
the time we need to learn new rules in general? </div>
<div dir="auto"><br>
</div>
<div dir="auto">2. Do you know any existing ABMs which are
similar? </div>
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<div dir="auto">-J.</div>
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