The study by Francesco Gagliardi published in AI & Society highlights a significant historical convergence in the scientific methods of cognitive science and statistical physics, focusing on the emergence of the synthetic method and artificial modeling. Gagliardi revisits the notion of the “Discovery of the Artificial” articulated by historian Roberto Cordeschi, which posits that complex phenomena in both disciplines can be better understood through the creation and observation of artificial models.
Key Points:
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Historical Context:
- In the mid-20th century, behavioral scientists began creating machines that modeled intelligent behavior, while physicists were simplifying complex systems, exemplified by the Lenz-Ising model of ferromagnetism.
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Synthetic Method:
- This method contrasts with traditional analytical approaches. Instead of breaking systems down to understand their components, the synthetic method builds models from simple parts and observes emergent behaviors.
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Complexity and Intractability:
- Both fields faced challenges in analyzing complexity. For instance, while classic physics could derive laws from particle behavior, the Ising model and cognitive models often yield results that can only be approximated through simulation due to computational limits (i.e., NP-hard problems).
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Computational Irreducibility:
- Gagliardi emphasizes that for complex systems, the dynamics must be allowed to unfold over time, highlighting that simulation is necessary for understanding rather than mere convenience.
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Philosophical Implications:
- This convergence challenges traditional views of scientific explanation based purely on mathematical derivation. It suggests that models and simulations have become essential tools for understanding complex systems.
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Contemporary Relevance:
- The intertwining of cognitive science and statistical physics is reflected in modern developments in artificial intelligence, where model-building approaches are increasingly seen as foundational in both fields.
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Methodological Unity:
- Despite remaining distinct, cognitive science and statistical physics share a deeper epistemic bond; both disciplines have evolved to rely on the construction of artificial models and the insights gained through their behavior, marking a shift in what constitutes scientific explanation.
Conclusion:
Gagliardi’s work illustrates a new era in scientific inquiry where artificial models play a crucial role in understanding complexity, ultimately redefining traditional notions of explanation in modern science.
