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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="zorn23surprise"><span style="font-variant: small-caps">Zorn, M., Illium, S., Phan, T., Kaiser, T.K., Linnhoff-Popien, C., and Gabor, T.</span> 2023. Social Neural Network Soups with Surprise Minimization. MIT Press Direct.</span>
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data-bibtex-json='"@inproceedings{zorn23surprise,\n author = {Zorn, Maximilian and Illium, Steffen and Phan, Thomy and Kaiser, Tanja Katharina and Linnhoff-Popien, Claudia and Gabor, Thomas},\n title = {Social Neural Network Soups with Surprise Minimization},\n year = {2023},\n publisher = {MIT Press Direct},\n organization = {Conference on Artificial Life - Alife 2023}\n}\n"'
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<p>This research extends the study of <strong>artificial chemistry</strong> systems populated by neural network “particles,” focusing on the emergence of complex behaviors driven by <strong>social interaction</strong> rather than explicit programming. Building on systems where particles may exhibit self-replication, we introduce interactions based on principles of <strong>predictive processing and surprise minimization</strong> (akin to the Free Energy Principle).</p>
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\18_surprised_soup_schematic.jpg" alt="Schematic diagram illustrating interacting neural network particles in the 'social soup'" /></p>
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<p>Specifically, particles are equipped with mechanisms enabling them to <strong>recognize and build predictive models of their peers’ behavior</strong>. The learning process is driven by the minimization of prediction error, or “surprise,” incentivizing particles to accurately anticipate the actions or state changes of others within the “soup.”</p>
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<p>Key observations from this setup include:</p>
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<ul>
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<li>The emergence of <strong>stable behavioral patterns and population dynamics</strong> purely from these local, predictive interactions. Notably, these emergent patterns often resemble the stability observed in systems where self-replication was an explicitly trained objective.</li>
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<li>The introduction of a unique <strong>“catalyst” particle</strong> designed to exert evolutionary pressure on the system, demonstrating how external influences or specialized agents can shape the collective dynamics.</li>
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<center>
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<img src="/assets/figures/18_surprised_soup_trajec.jpg" alt="Trajectories or state space visualization of the particle population dynamics over time" style="display:block; width:90%" />
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<figcaption>Visualization of particle trajectories or population dynamics within the 'social soup'.</figcaption>
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<p>This study highlights how complex, seemingly goal-directed social behaviors and stable ecosystem structures can emerge from simple, local rules based on mutual prediction and surprise minimization among interacting agents, offering insights into the self-organization of complex adaptive systems. <a class="citation" href="/publications#zorn23surprise">[Zorn et al. 2023]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2023-05-01T00:00:00+02:00">May 1, 2023</time></p>
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<a href="/projects/server-administration/" rel="permalink">LMU DevOps Admin
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Managed LMU chair IT: Kubernetes, CI/CD, automation (2018-2023).
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<a href="/research/primate-subsegment-sorting/" rel="permalink">Primate Subsegment Sorting
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Binary subsegment presorting improves noisy primate sound classification.
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