<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"></head><body dir="auto"><div dir="auto">Stephen, </div><div dir="auto"><br></div><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">https://en.wikipedia.org/wiki/Forgetting_curve</div><div dir="auto"><div dir="auto"><div dir="auto"><img id="HEV1591646109767" src="cid:storage_emulated_0_Download_ExponentialDecay_png_1591646109767" name="storage_emulated_0_Download_ExponentialDecay_png_1591646109767" width="192" height="128" onmouseover="imageMousePointerUpdate(true)" onmouseout="imageMousePointerUpdate(false)" style=""></div><div dir="auto"><br></div><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">https://en.wikipedia.org/wiki/Half-life</div><div dir="auto"><img id="HEV1591646061775" src="cid:storage_emulated_0_Download_FlockSize_png_1591646061775" name="storage_emulated_0_Download_FlockSize_png_1591646061775" width="192" height="128" style="" onmouseover="imageMousePointerUpdate(true)" onmouseout="imageMousePointerUpdate(false)"></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto">-Jochen</div><div id="composer_signature" dir="auto"><div style="font-size:85%;color:#575757" dir="auto"><br></div></div><div dir="auto"><br></div></div></div><div><br></div><div style="font-size:100%;color:#000000" dir="auto"><!-- originalMessage --><div>-------- Original message --------</div><div>From: Stephen Guerin <stephen.guerin@simtable.com> </div><div>Date: 6/6/20 23:24 (GMT+01:00) </div><div>To: The Friday Morning Applied Complexity Coffee Group <friam@redfish.com> </div><div>Subject: Re: [FRIAM] Oblivion resistant swarm </div><div><br></div></div><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">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">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" width="562" height="266" onmouseover="imageMousePointerUpdate(true)" onmouseout="imageMousePointerUpdate(false)" name="com_samsung_android_email_attachmentprovider_1_8202_RAW_1591646021540"><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">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">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" width="375" height="299" style="margin-right: 0px;" onmouseover="imageMousePointerUpdate(true)" onmouseout="imageMousePointerUpdate(false)" name="com_samsung_android_email_attachmentprovider_1_8201_RAW_1591646021541"><br></div><br><br><br>Or download Netlogo and search in the netlogo library.<br><br><div><br><br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr">_______________________________________________________________________<br><a href="mailto:stephen.guerin@simtable.com" target="_blank">Stephen.Guerin@Simtable.com</a><div>CEO, Simtable <a href="http://www.simtable.com/" target="_blank">http://www.simtable.com</a><br><div>1600 Lena St #D1, Santa Fe, NM 87505<div><div>office: (505)995-0206 <span style="font-size:12.8px">mobile: (505)577-5828</span></div><div><span style="font-size:12.8px">twitter: @simtable</span></div><div><span style="font-size:12.8px"><a href="http://zoom.com/j/5055775828" target="_blank">zoom.com/j/5055775828</a></span></div><div></div></div></div></div></div></div></div></div></div></div></div><br></div></div></div></div><br><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">jofr@cas-group.net</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><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></div><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"></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><div dir="auto"><br></div><div dir="auto">-J.</div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><br></div><div id="gmail-m_1800171517110602160composer_signature" dir="auto"><div style="font-size:85%;color:rgb(87,87,87)" dir="auto"><br></div></div><div dir="auto"><br></div></div>- .... . -..-. . ...- --- .-.. ..- - .. --- -. -..-. .-- .. .-.. .-.. -..-. -... . -..-. .-.. .. ...- . -..-. ... - .-. . .- -- . -..<br>
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