-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.html
More file actions
614 lines (611 loc) · 30.1 KB
/
index.html
File metadata and controls
614 lines (611 loc) · 30.1 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
<!DOCTYPE html>
<html lang="en">
<head>
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-MXPNPC63XE"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-MXPNPC63XE');
</script>
<script src="https://analytics.ahrefs.com/analytics.js" data-key="XXjnJNtZV6bAICAw3Uqc6A" async></script>
<meta name="ahrefs-site-verification" content="b1f1573b895ead5763166ba77346fa86ecb9449ac31144c6786475fa1c2e1e5f">
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light">
<meta http-equiv="x-ua-compatible" content="IE=edge">
<meta name="theme-color" content="#14532d">
<meta name="geo.region" content="TW-TAO">
<meta name="geo.placename" content="Guishan, Taoyuan, Taiwan">
<meta name="geo.position" content="25.0375;121.3892">
<meta name="ICBM" content="25.0375, 121.3892">
<title>BCAT — Bounded Confidence and Adoption Threshold Model | CANS Lab</title>
<meta name="description" content="BCAT opinion dynamics and innovation diffusion simulation for the 'best game no one played' phenomenon 有界信心與採納門檻混合模擬模型。">
<meta name="author" content="Prof. Chung-Yuan Huang (黃崇源教授)">
<meta property="og:title" content="BCAT — Bounded Confidence and Adoption Threshold Model | CANS Lab">
<meta property="og:description" content="Bounded Confidence and Adoption Threshold Model — a mixed opinion dynamics and innovation diffusion simulation">
<meta property="og:url" content="https://canslab1.github.io/BCAT/">
<meta property="og:type" content="website">
<meta property="og:locale" content="en_US">
<meta property="og:locale:alternate" content="zh_TW">
<meta property="og:site_name" content="CANS Lab — Complex Adaptive Networks and Systems">
<meta property="og:image" content="https://canslab1.github.io/images/software/bcat-gui-overview.png">
<meta property="og:image:width" content="1200">
<meta property="og:image:height" content="630">
<meta property="og:image:alt" content="BCAT opinion dynamics simulation showing bounded confidence and adoption threshold model">
<meta property="og:see_also" content="https://github.com/canslab1/BCAT">
<meta property="og:see_also" content="https://canslab1.github.io/">
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:title" content="BCAT — Bounded Confidence and Adoption Threshold Model | CANS Lab">
<meta name="twitter:description" content="Bounded Confidence and Adoption Threshold Model — a mixed opinion dynamics and innovation diffusion simulation">
<meta name="twitter:image" content="https://canslab1.github.io/images/software/bcat-gui-overview.png">
<meta name="twitter:image:alt" content="BCAT opinion dynamics simulation showing bounded confidence and adoption threshold model">
<meta name="twitter:label1" content="Language">
<meta name="twitter:data1" content="Python, NetLogo">
<meta name="twitter:label2" content="License">
<meta name="twitter:data2" content="MIT">
<meta name="keywords" content="BCAT, opinion dynamics, innovation diffusion, bounded confidence, adoption threshold, agent-based model, social network simulation, best game no one played, CANS Lab, 黃崇源, 長庚大學, 意見動力學, 創新擴散, 有界信心, 採納門檻">
<meta name="robots" content="index, follow, max-image-preview:large, max-snippet:-1">
<link rel="canonical" href="https://canslab1.github.io/BCAT/">
<link rel="alternate" hreflang="en" href="https://canslab1.github.io/BCAT/">
<link rel="alternate" hreflang="x-default" href="https://canslab1.github.io/BCAT/">
<link rel="icon" href="https://canslab1.github.io/images/csie.png" type="image/png">
<link rel="apple-touch-icon" href="https://canslab1.github.io/images/icon-192.png" sizes="192x192">
<link rel="dns-prefetch" href="https://cdnjs.cloudflare.com">
<link rel="dns-prefetch" href="https://www.googletagmanager.com">
<link rel="preconnect" href="https://cdnjs.cloudflare.com" crossorigin>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.11.1/styles/github-dark.min.css">
<link rel="stylesheet" href="https://canslab1.github.io/css/project-page.css">
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "SoftwareSourceCode",
"name": "BCAT",
"description": "A mixed opinion dynamics and innovation diffusion simulation model for exploring the 'best game no one played' phenomenon.",
"codeRepository": "https://github.com/canslab1/BCAT",
"url": "https://canslab1.github.io/BCAT/",
"programmingLanguage": ["Python", "NetLogo"],
"license": "https://opensource.org/licenses/MIT",
"author": {
"@type": "Person",
"@id": "https://canslab1.github.io/#person",
"name": "Chung-Yuan Huang",
"alternateName": ["黃崇源", "黃崇源教授", "CY Huang", "C.-Y. Huang", "Huang, Chung-Yuan", "Prof. Chung-Yuan Huang", "GSCOTT"],
"url": "https://canslab1.github.io/",
"affiliation": {
"@type": "CollegeOrUniversity",
"name": "Chang Gung University",
"alternateName": "長庚大學"
},
"sameAs": [
"https://canslab1.github.io/",
"https://scholar.google.com/citations?user=0klfzfAAAAAJ&hl=en",
"https://orcid.org/0000-0002-8680-6755",
"https://github.com/canslab1",
"https://www.wikidata.org/wiki/Q138673497"
]
},
"version": "1.4.1",
"dateModified": "2026-03-29",
"keywords": ["opinion dynamics", "innovation diffusion", "agent-based model", "social network"]
},
{
"@type": "WebPage",
"name": "BCAT — README",
"url": "https://canslab1.github.io/BCAT/",
"isPartOf": {
"@type": "WebSite",
"name": "CANS Lab",
"url": "https://canslab1.github.io/"
}
}
]
}
</script>
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "BreadcrumbList",
"itemListElement": [
{ "@type": "ListItem", "position": 1, "name": "CANS Lab", "item": "https://canslab1.github.io/" },
{ "@type": "ListItem", "position": 2, "name": "BCAT", "item": "https://canslab1.github.io/BCAT/" }
]
}
</script>
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "ResearchOrganization",
"name": "Complex Adaptive Networks and Systems Laboratory",
"alternateName": ["CANS Lab", "複雜適應性網絡與系統實驗室"],
"url": "https://canslab1.github.io/",
"parentOrganization": {
"@type": "CollegeOrUniversity",
"name": "Chang Gung University",
"alternateName": "長庚大學",
"url": "https://www.cgu.edu.tw/"
},
"member": { "@type": "Person", "@id": "https://canslab1.github.io/#person" }
}
</script>
<!-- Dublin Core -->
<meta name="DC.title" content="BCAT — Bounded Confidence and Adoption Threshold Model">
<meta name="DC.creator" content="Chung-Yuan Huang (黃崇源)">
<meta name="DC.subject" content="Opinion Dynamics; Innovation Diffusion; Agent-Based Model; Social Network">
<meta name="DC.description" content="Mixed opinion dynamics and innovation diffusion simulation model">
<meta name="DC.publisher" content="CANS Lab, Chang Gung University">
<meta name="DC.type" content="Software">
<meta name="DC.format" content="text/html">
<meta name="DC.language" content="en">
<meta name="DC.identifier" content="https://canslab1.github.io/BCAT/">
<meta name="DC.date" content="2022">
<meta name="DC.rights" content="MIT License">
<meta name="DCTERMS.modified" content="2026-03-29">
<meta name="DC.coverage" content="Taiwan; International">
<meta name="DC.contributor" content="Sheng-Wen Wang">
<meta name="DC.relation" content="https://github.com/canslab1/BCAT">
<meta name="DC.relation" content="https://canslab1.github.io/">
<meta name="DCTERMS.audience" content="Researchers; Graduate Students; Social Scientists">
<!-- Highwire Press (Google Scholar) -->
<meta name="citation_author" content="Huang, Chung-Yuan">
<meta name="citation_author" content="黃崇源">
<meta name="citation_author_institution" content="Chang Gung University">
<meta name="citation_author_orcid" content="0000-0002-8680-6755">
<link rel="author" href="https://canslab1.github.io/humans.txt">
<link rel="security" type="text/plain" href="https://canslab1.github.io/security.txt">
</head>
<body>
<a href="#content" class="skip-link">Skip to content</a>
<header class="site-header">
<div class="header-inner">
<a class="header-brand" href="https://canslab1.github.io/">
<img src="https://canslab1.github.io/images/csie.png" alt="CANS Lab logo" width="32" height="32">
<span>CANS Lab</span>
</a>
<nav class="header-nav" aria-label="BCAT navigation">
<a href="https://canslab1.github.io/">Lab Home</a>
<a href="https://github.com/canslab1/BCAT" target="_blank" rel="noopener noreferrer">GitHub Repo</a>
</nav>
</div>
</header>
<main class="content-wrapper">
<article class="markdown-body" id="content" aria-live="polite">
<h1>BCAT — Bounded Confidence and Adoption Threshold Model</h1>
<!-- PRERENDER-START -->
<p><img alt="Python 3.10+" src="https://img.shields.io/badge/Python-3.10%2B-blue">
<img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-green">
<a href="https://doi.org/10.5281/zenodo.19216365"><img alt="DOI" src="https://zenodo.org/badge/DOI/10.5281/zenodo.19216365.svg"></a>
<a href="https://www.protocols.io/view/reproducing-simulation-results-for-the-bcat-model-jwrwcpd7f"><img alt="protocols.io" src="https://img.shields.io/badge/protocols.io-DOI-green"></a>
<a href="https://canslab1.github.io/"><img alt="CANS Lab" src="https://img.shields.io/badge/CANS_Lab-Homepage-orange"></a></p>
<p>A mixed opinion dynamics and innovation diffusion simulation model for exploring the "best game no one played" phenomenon.</p>
<h2 id="companion-manuscript-repository">Companion Manuscript Repository</h2>
<p>The manuscript source files, figures, and supporting information for the accompanying PLOS ONE paper are hosted separately:</p>
<ul>
<li><strong>Manuscript Repository:</strong> <a href="https://github.com/canslab1/PLOS-BCAT">github.com/canslab1/PLOS-BCAT</a></li>
</ul>
<h2 id="overview">Overview</h2>
<p>The BCAT (Bounded Confidence + Adoption Threshold) model integrates a bounded confidence-based opinion dynamics model with an adoption threshold innovation diffusion model. It simulates opinion exchanges and product acceptance behaviors across four types of theoretical social networks:</p>
<ul>
<li><strong>Regular Lattice (CA)</strong>: Toroidal 2D cellular automata with Moore neighborhood (rewiring probability = 0)</li>
<li><strong>Small-World Network (SWN)</strong>: Watts-Strogatz model (0 < rewiring probability < 1)</li>
<li><strong>Random Network (RN)</strong>: Fully rewired network (rewiring probability = 1)</li>
<li><strong>Scale-Free Network (SFN)</strong>: Barabasi-Albert preferential attachment model</li>
</ul>
<p>Each simulation consists of 400 agents connected by approximately 1,600 edges, with an average of 8 neighbors per agent.</p>
<h2 id="features">Features</h2>
<ul>
<li><strong>Mixed model</strong> — Integrates bounded confidence opinion dynamics with adoption threshold innovation diffusion in a single simulation.</li>
<li><strong>Four network topologies</strong> — Regular Lattice (CA), Small-World (SWN), Random (RN), and Scale-Free (SFN) networks.</li>
<li><strong>Interactive GUI</strong> — Tkinter-based interface with real-time visualization of attitude trajectories, social networks, adoption dynamics, and distributions.</li>
<li><strong>Batch experiments</strong> — Run multiple repetitions with automatic result aggregation.</li>
<li><strong>Reproducible scenarios</strong> — Pre-configured parameter files for reproducing all key paper figures.</li>
<li><strong>Dual implementation</strong> — Both Python 3 (with GUI) and NetLogo 4.0.5 versions producing statistically equivalent results.</li>
</ul>
<h2 id="installation">Installation</h2>
<h3 id="python-version">Python Version</h3>
<ul>
<li>Python 3.10 or later (tested on Python 3.12 and 3.13)</li>
</ul>
<h3 id="setup">Setup</h3>
<pre><code class="language-bash">git clone https://github.com/canslab1/BCAT.git
cd BCAT
pip install -r requirements.txt
</code></pre>
<h3 id="dependencies">Dependencies</h3>
<p>Install all required packages:</p>
<pre><code class="language-bash">pip install -r requirements.txt
</code></pre>
<p>Or install individually:</p>
<pre><code class="language-bash">pip install numpy>=1.24.0 networkx>=3.0 matplotlib>=3.7.0
</code></pre>
<h3 id="standard-library-modules-no-installation-needed">Standard Library Modules (no installation needed)</h3>
<p><code>tkinter</code>, <code>random</code>, <code>os</code>, <code>time</code>, <code>math</code>, <code>threading</code>, <code>pickle</code>, <code>warnings</code></p>
<h3 id="netlogo-version-for-nlogo-file">NetLogo Version (for <code>.nlogo</code> file)</h3>
<ul>
<li>NetLogo 4.0.5 or later (available at https://ccl.northwestern.edu/netlogo/)</li>
</ul>
<h2 id="usage">Usage</h2>
<h3 id="python-version_1">Python Version</h3>
<pre><code class="language-bash">python3 BCAT.py
</code></pre>
<p>This launches the GUI application with:
- <strong>Left panel</strong>: Parameter sliders, control buttons, Social Network visualization, and monitors
- Social Network with legend (red = adopter; green gradient = non-adopter attitude level)
- Monitors: Critical (critical point tick), FRI (Favorable Review Index), GSI (Good Sales Index)
- <strong>Right panel</strong>: Real-time visualization plots (4 rows, all titles in blue bold)
- Row 1: Attitude Distribution, Threshold Distribution, Degree Distribution
- Row 2: Attitude Trajectory with density colorbar legend (full-width, grid lines)
- Row 3: Adoption Dynamics (adopter vs. non-adopter counts, full-width, grid lines)
- Row 4: New Adopter Dynamics (full-width, grid lines)</p>
<h3 id="controls">Controls</h3>
<table>
<thead>
<tr>
<th>Button</th>
<th>Function</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Setup</strong></td>
<td>Initialize the network and agent population</td>
</tr>
<tr>
<td><strong>Run</strong></td>
<td>Execute a complete simulation run (max-time steps)</td>
</tr>
<tr>
<td><strong>Run Once</strong></td>
<td>Execute a single time step</td>
</tr>
<tr>
<td><strong>Experiments</strong></td>
<td>Run batch experiments (no-of-experiments repetitions)</td>
</tr>
<tr>
<td><strong>Save</strong></td>
<td>Save current model state</td>
</tr>
<tr>
<td><strong>Load</strong></td>
<td>Load a previously saved model state</td>
</tr>
</tbody>
</table>
<h3 id="netlogo-version">NetLogo Version</h3>
<ol>
<li>Open <code>English - best game no one played.nlogo</code> in NetLogo 4.0.5+</li>
<li>Adjust parameters using the interface sliders</li>
<li>Click "Setup" to initialize, then "Run once" to execute</li>
</ol>
<h2 id="model-parameters">Model Parameters</h2>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Range</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>no-of-pioneers</code></td>
<td>0 -- 100</td>
<td>5</td>
<td>Number of initial adopter agents</td>
</tr>
<tr>
<td><code>clustered-pioneers?</code></td>
<td>ON/OFF</td>
<td>ON</td>
<td>Whether pioneers are spatially clustered</td>
</tr>
<tr>
<td><code>bounded-confidence</code></td>
<td>0 -- 90</td>
<td>50</td>
<td>Attitude distance threshold for opinion exchange</td>
</tr>
<tr>
<td><code>convergence-rate</code></td>
<td>0.1 -- 1.0</td>
<td>0.1</td>
<td>Rate of attitude adjustment per exchange</td>
</tr>
<tr>
<td><code>avg-of-attitudes</code></td>
<td>10 -- 100</td>
<td>50</td>
<td>Mean of initial attitude distribution</td>
</tr>
<tr>
<td><code>std-of-attitudes</code></td>
<td>0 -- 30</td>
<td>10</td>
<td>Std. dev. of initial attitude distribution</td>
</tr>
<tr>
<td><code>avg-of-thresholds</code></td>
<td>10 -- 100</td>
<td>40</td>
<td>Mean of adoption threshold distribution</td>
</tr>
<tr>
<td><code>std-of-thresholds</code></td>
<td>0 -- 30</td>
<td>10</td>
<td>Std. dev. of adoption threshold distribution</td>
</tr>
<tr>
<td><code>network-type</code></td>
<td>SFN / SWN</td>
<td>SWN/RN/CA</td>
<td>Social network topology</td>
</tr>
<tr>
<td><code>rewiring-probability</code></td>
<td>0.00 -- 1.00</td>
<td>0.00</td>
<td>Network rewiring probability (SWN only)</td>
</tr>
<tr>
<td><code>max-time</code></td>
<td>50 -- 1000</td>
<td>300</td>
<td>Maximum simulation time steps</td>
</tr>
<tr>
<td><code>no-of-experiments</code></td>
<td>10 -- 1000</td>
<td>20</td>
<td>Number of batch experiment repetitions</td>
</tr>
</tbody>
</table>
<h2 id="reproducing-paper-results">Reproducing Paper Results</h2>
<p>Parameter configuration files for reproducing the simulation scenarios presented in the paper are provided in the <code>test_scenarios/</code> directory.</p>
<h3 id="scenario-1-favorable-review-good-sales-fig-4">Scenario 1: Favorable Review + Good Sales (Fig. 4)</h3>
<pre><code>python3 BCAT.py
</code></pre>
<p>Then set parameters: no-of-pioneers=5, clustered-pioneers=ON, bounded-confidence=50, convergence-rate=0.1, avg-of-attitudes=50, std-of-attitudes=10, avg-of-thresholds=20, std-of-thresholds=5, network-type=SWN/RN/CA, rewiring-probability=0.00, max-time=300. Click Setup, then Run.</p>
<h3 id="scenario-2-downward-compatibility-opinion-dynamics-only-fig-11">Scenario 2: Downward Compatibility -- Opinion Dynamics Only (Fig. 11)</h3>
<p>Set: avg-of-thresholds=100, std-of-thresholds=0, no-of-pioneers=0, bounded-confidence=10, convergence-rate=0.4, avg-of-attitudes=50, std-of-attitudes=20, network-type=SWN/RN/CA, rewiring-probability=0.00, max-time=300.</p>
<h3 id="scenario-3-downward-compatibility-adoption-threshold-only-fig-12">Scenario 3: Downward Compatibility -- Adoption Threshold Only (Fig. 12)</h3>
<p>Set: avg-of-attitudes=100, std-of-attitudes=0, bounded-confidence=0, avg-of-thresholds=20, std-of-thresholds=10, no-of-pioneers=3, network-type=SWN/RN/CA, rewiring-probability=0.00, max-time=50.</p>
<h2 id="model-algorithm">Model Algorithm</h2>
<p>The BCAT model operates in the following phases per time step:</p>
<ol>
<li><strong>Agent Selection</strong>: All agents are processed in random order each tick.</li>
<li><strong>Neighbor Selection</strong>: Each agent randomly selects one neighboring agent.</li>
<li><strong>Opinion Exchange</strong>: If the attitude difference is below the bounded confidence threshold, attitudes are adjusted according to four scenarios based on adoption status (see Algorithm 3 in the paper).</li>
<li><strong>Adoption Decision</strong>: A not-yet-adopted agent with a positive attitude (att > 50) adopts if the proportion of adopted neighbors exceeds its adoption threshold.</li>
</ol>
<h2 id="implementation-notes">Implementation Notes</h2>
<ul>
<li>The Python version faithfully replicates the NetLogo 4.0.5 implementation, including the sequential execution semantics of NetLogo's <code>ask-concurrent</code> (which processes agents in random order with immediate effect).</li>
<li><code>int(v + 0.5)</code> is used as an equivalent to NetLogo's <code>round()</code> for positive values.</li>
<li>NumPy arrays replace NetLogo's <code>turtles-own</code> for performance optimization.</li>
<li>NetworkX graphs replace NetLogo's native turtle/link network structure.</li>
<li>Both versions produce statistically equivalent results under identical random seeds and parameter settings.</li>
<li><strong>Attitude Trajectory rendering optimization</strong>: scatter points are grouped by color into 15 fixed PathCollection objects and updated incrementally via <code>set_offsets()</code>, reducing <code>draw_idle()</code> artist traversal from O(T×K) to O(1).</li>
<li><strong>Dual-Figure architecture</strong>: Social Network is rendered on a separate matplotlib Figure embedded in the left panel, allowing the three time-series plots (Attitude Trajectory, Adoption Dynamics, New Adopter Dynamics) to share a full-width X axis (Time) in the right panel.</li>
<li><strong>Evaluation metrics</strong>: FRI (Favorable Review Index = agents with attitude > 50 / total agents) and GSI (Good Sales Index = adopters / total agents) update in real time, displayed to 4 decimal places.</li>
<li><strong>Chart legends</strong>: Attitude Trajectory includes a 15-color density colorbar; Social Network legend shows node color meanings (adopter, attitude levels) without overlapping the graph.</li>
<li><strong>Degree Distribution alignment</strong>: bars are centered on integer ticks using <code>ax.bar()</code> instead of <code>ax.hist()</code> for accurate visual correspondence.</li>
</ul>
<h2 id="data">Data</h2>
<p>The <code>data/</code> directory contains the simulation output data underlying the tables and figures in the accompanying paper. These files constitute the minimal dataset required for replication.</p>
<h3 id="sensitivity-analysis-datasensitivity_analysis">Sensitivity Analysis (<code>data/sensitivity_analysis/</code>)</h3>
<p>Raw output from 1,000-run batch experiments across four network topologies, used to generate Table 3 and Figs 7–9 in the paper.</p>
<table>
<thead>
<tr>
<th>File</th>
<th>Network Topology</th>
<th>Format</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>sensitivity_analysis_regular_lattice.xlsx</code></td>
<td>Regular Lattice (CA)</td>
<td>Excel</td>
</tr>
<tr>
<td><code>sensitivity_analysis_small_world.xlsx</code></td>
<td>Small-World (SWN)</td>
<td>Excel</td>
</tr>
<tr>
<td><code>sensitivity_analysis_random.xlsx</code></td>
<td>Random (RN)</td>
<td>Excel</td>
</tr>
<tr>
<td><code>sensitivity_analysis_scale_free.xlsx</code></td>
<td>Scale-Free (SFN)</td>
<td>Excel</td>
</tr>
</tbody>
</table>
<p>Each workbook contains per-run records of adoption outcomes (adopter counts, critical points) across systematic parameter sweeps of the five primary model parameters.</p>
<h3 id="mechanism-decomposition-datamechanism_decomposition">Mechanism Decomposition (<code>data/mechanism_decomposition/</code>)</h3>
<p>Results from three controlled experiments designed to disentangle the opinion clustering channel and the coordination failure channel in the opinion–adoption gap (Fig 10 in the paper, 30,000 total simulation runs).</p>
<table>
<thead>
<tr>
<th>File</th>
<th>Experiment</th>
<th>Topology</th>
<th>Runs</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>md_a_lattice.csv</code></td>
<td>MD-A</td>
<td>Regular Lattice</td>
<td>7,000</td>
<td>Coordination failure isolated (FRI = 1.0 by construction)</td>
</tr>
<tr>
<td><code>md_a_smallworld.csv</code></td>
<td>MD-A</td>
<td>Small-World</td>
<td>7,000</td>
<td>Coordination failure isolated (FRI = 1.0 by construction)</td>
</tr>
<tr>
<td><code>md_b_lattice.csv</code></td>
<td>MD-B</td>
<td>Regular Lattice</td>
<td>1,000</td>
<td>Opinion clustering isolated (no pioneers, GSI = 0)</td>
</tr>
<tr>
<td><code>md_b_smallworld.csv</code></td>
<td>MD-B</td>
<td>Small-World</td>
<td>1,000</td>
<td>Opinion clustering isolated (no pioneers, GSI = 0)</td>
</tr>
<tr>
<td><code>md_c_lattice.csv</code></td>
<td>MD-C</td>
<td>Regular Lattice</td>
<td>7,000</td>
<td>Full BCAT baseline (both channels active)</td>
</tr>
<tr>
<td><code>md_c_smallworld.csv</code></td>
<td>MD-C</td>
<td>Small-World</td>
<td>7,000</td>
<td>Full BCAT baseline (both channels active)</td>
</tr>
</tbody>
</table>
<p><strong>CSV columns</strong>: <code>experiment</code> (run index), <code>fri</code> (Favorable Review Index at t=300), <code>gsi</code> (Good Sales Index at t=300), <code>adopters</code> (final adopter count), <code>N</code> (population size), <code>avg_of_thresholds</code> (threshold parameter), <code>experiment_id</code> (MD-A/MD-B/MD-C), <code>topology</code> (lattice/smallworld).</p>
<h3 id="finite-size-scaling-datafinite_size_scaling">Finite-Size Scaling (<code>data/finite_size_scaling/</code>)</h3>
<p>Results from scaling experiments at N=400, 900, 1,600, and 2,500 agents, confirming that the "best game no one played" phenomenon and the dominance of avg-of-thresholds are robust to system size.</p>
<table>
<thead>
<tr>
<th>File</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>finite_size_scaling_results.csv</code></td>
<td>Raw per-run results across all system sizes</td>
</tr>
<tr>
<td><code>summary_by_threshold_and_N.csv</code></td>
<td>Aggregated mean FRI/GSI by threshold and N</td>
</tr>
</tbody>
</table>
<h2 id="scripts">Scripts</h2>
<p>The <code>scripts/</code> directory contains Python scripts for reproducing the paper's analyses and figures:</p>
<table>
<thead>
<tr>
<th>Script</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>reproduce_table3_figs.py</code></td>
<td>Reproduce Table 3 and Figs 7–9 from sensitivity analysis data</td>
</tr>
<tr>
<td><code>generate_table3_and_figs.py</code></td>
<td>Generate Table 3 values and figure images</td>
</tr>
<tr>
<td><code>run_mechanism_decomposition.py</code></td>
<td>Run MD-A/B/C mechanism decomposition experiments</td>
</tr>
<tr>
<td><code>finite_size_scaling.py</code></td>
<td>Run finite-size scaling experiments at N=900, 1,600, 2,500</td>
</tr>
</tbody>
</table>
<h2 id="project-structure">Project Structure</h2>
<pre><code>BCAT/
├── BCAT.py # Python 3 implementation with GUI (Tkinter + matplotlib)
├── English - best game no one played.nlogo # NetLogo 4.0.5 implementation
├── requirements.txt # Python dependencies
├── pyproject.toml # Project metadata (PEP 621)
├── CITATION.cff # Citation metadata
├── CHANGELOG.md # Version history
├── CONTRIBUTING.md # Contribution guidelines
├── COMPLEXITY_ANALYSIS.md # Time/space complexity analysis
├── data/ # Simulation output data for paper replication
│ ├── sensitivity_analysis/ # 1,000-run batch results (4 network topologies)
│ ├── mechanism_decomposition/ # MD-A/B/C experiments (30,000 runs, CSV)
│ └── finite_size_scaling/ # Scaling experiments (N=400–2,500)
├── scripts/ # Analysis and experiment scripts
│ ├── reproduce_table3_figs.py # Reproduce Table 3 and Figs 7–9
│ ├── generate_table3_and_figs.py # Generate Table 3 values and figures
│ ├── run_mechanism_decomposition.py # Run MD-A/B/C experiments
│ └── finite_size_scaling.py # Run finite-size scaling experiments
├── test_scenarios/ # Parameter configs for paper reproduction
│ ├── fig4_favorable_review_good_sales.json
│ ├── fig5_favorable_review_poor_sales.json
│ ├── fig11_opinion_dynamics_only.json
│ ├── fig12_adoption_threshold_only.json
│ └── sensitivity_analysis_1000_runs.json
├── LICENSE # MIT License
├── index.html # GitHub Pages landing page
├── 404.html # Custom 404 error page
├── sitemap.xml # XML sitemap for search engines
├── robots.txt # Crawler directives
└── llms.txt # AI-readable project summary
</code></pre>
<h2 id="authors">Authors</h2>
<ul>
<li><strong>Chung-Yuan Huang</strong> (黃崇源) — Department of Computer Science and Information Engineering, Chang Gung University, Taiwan (gscott@mail.cgu.edu.tw)</li>
<li><strong>Sheng-Wen Wang</strong> (Corresponding author) — Department of Finance and Information, National Kaohsiung University of Science and Technology, Taiwan (swwang@nkust.edu.tw)</li>
</ul>
<h2 id="citation">Citation</h2>
<p>See <code>CITATION.cff</code> for machine-readable citation metadata.</p>
<h2 id="license">License</h2>
<p>This project is licensed under the MIT License. See <a href="LICENSE">LICENSE</a> for details.</p>
<!-- PRERENDER-END -->
</article>
</main>
<footer class="site-footer">
<nav class="footer-nav" aria-label="CANS Lab Software">
<a href="https://canslab1.github.io/EpiRank/">EpiRank</a>
<span class="footer-sep">·</span>
<a href="https://canslab1.github.io/MV17/">MV17</a>
<span class="footer-sep">·</span>
<a href="https://canslab1.github.io/CASMIM/">CASMIM</a>
<span class="footer-sep">·</span>
<a href="https://canslab1.github.io/HETA/">HETA</a>
<span class="footer-sep">·</span>
<a href="https://canslab1.github.io/HATA/">HATA</a>
<span class="footer-sep">·</span>
<span class="current">BCAT</span>
<span class="footer-sep">·</span>
<a href="https://canslab1.github.io/SRAC-Agent/">SRAC-Agent</a>
<span class="footer-sep">·</span>
<a href="https://canslab1.github.io/AED2/">AED2</a>
</nav>
<p>© CANS Lab, Chang Gung University ·
<a href="https://canslab1.github.io/">canslab1.github.io</a></p>
</footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/marked/16.3.0/lib/marked.umd.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.11.1/highlight.min.js"></script>
<script src="https://canslab1.github.io/js/readme-loader.js"></script>
</body>
</html>