25 lines
2.6 KiB
Plaintext
25 lines
2.6 KiB
Plaintext
---
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title: "Emergent Social Dynamics"
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tags: [artificial-life, complex-systems, neural-networks, self-organization, emergent-behavior, predictive-coding, artificial-chemistry, social-interaction]
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excerpt: "Artificial chemistry networks develop predictive models via surprise minimization."
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teaser: "/figures/18_surprised_soup_teaser.jpg"
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venue: "ALIFE 2023"
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---
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This research extends the study of **artificial chemistry** systems populated by neural network "particles" <Cite bibtexKey="gabor2019self" />, focusing on the emergence of complex behaviors driven by **social interaction** rather than explicit programming. Building on systems where particles may exhibit self-replication, we introduce interactions based on principles of **predictive processing and surprise minimization** (akin to the Free Energy Principle).
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<FloatingImage src="/figures/18_surprised_soup_teaser.jpg" alt="A stylized depiction of particles interacting in a soup, representing the core concept of the system." width={1200} height={800} float="right" caption="A stylized depiction of particles interacting in a soup, representing the core concept of the system." />
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Specifically, particles are equipped with mechanisms enabling them to **recognize and build predictive models of their peers' behavior**. 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."
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Key observations from this setup include:
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* The emergence of **stable behavioral patterns and population dynamics** 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.
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* The introduction of a unique **"catalyst" particle** designed to exert evolutionary pressure on the system, demonstrating how external influences or specialized agents can shape the collective dynamics.
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<div className="my-6 text-center">
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<CenteredImage src="/figures/18_surprised_soup_trajec.jpg" alt="Trajectories or state space visualization of the particle population dynamics over time" width={1200} height={800} caption="Visualization of particle trajectories or population dynamics within the 'social soup'." />
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</div>
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This study <Cite bibtexKey="zorn23surprise" /> 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. |