Author Archives: Kuncoro Wastuwibowo

AI technologies are primarily developed by advancements in machine learning, particularly deep learning and natural language processing. An example is ChatGPT, which is built on the Transformer architecture, and employs deep neural networks with attention mechanisms to process and generate human-like text. While the architecture of these models is inherently complex, characterised by vast parameters and intricate layers, they do not rely heavily on Complexity Science as a core framework in their design or functionality.

There are actually some indirect connections between AI and Complexity Science. Deep neural networks, for instance, can be conceptualised as complex systems where simple components (neurons) interact to produce emergent behaviours, such as understanding and generating language. While Complexity Science provides valuable insights into such emergent phenomena, these principles are not the primary foundation for AI model development. Complexity Science concepts are also applied in training optimisation, where researchers study high-dimensional optimisation landscapes, convergence properties, and loss surface dynamics to improve the stability and efficiency of training processes. Interpretability in AI benefits from complexity-based approaches like network theory and information theory, which help uncover how information flows through neural networks. Another area of overlap is robustness and generalisation, where ideas from Complexity Science and statistical mechanics, such as phase transitions and criticality, aid in understanding why large, over-parameterised models perform well in real-world scenarios.

Despite these connections, we must acknowledge that Complexity Science has not been a major driving force in the development of AI technologies like ChatGPT. The creation of such models relies more on advances in neural network architectures, data processing, and algorithmic optimisation than on the theoretical foundations of Complexity Science.

There are some opportunities if we can enrich AI with principles from Complexity Science. It could enhance the adaptability, robustness, and interpretability of AI systems by providing them a better ways of managing dynamic, non-linear interactions and uncertainty in real-world environments. This integration could enable the creation of AI models that handle emergent behaviours more effectively, excel in predictive analytics, and exhibit greater resilience, moving the field closer to achieving general, human-like intelligence.

The challenges are on behalf of Complexity Science. One major limitation is its lack of standardised, predictive methods that can be broadly applied to complex systems. Most Complexity Science models are descriptive or exploratory, emphasising qualitative understanding over quantitative prediction. Additionally, complex systems are often highly context-dependent, making it difficult to generalise findings or develop uniform approaches. Computational intensity is another barrier; many complexity-based models, such as agent-based simulations, struggle to scale to the large datasets typical in AI. Furthermore, Complexity Science has historically focused on theoretical and simulation-based methods, while AI thrives on data-driven approaches, creating a methodological gap. Finally, the absence of a unified theoretical framework in Complexity Science makes it still challenging to translate its principles into practical, standardised tools for AI.

Complexity Science offers profound insights into the behaviour of complex systems but remains underdeveloped in areas critical for its integration with AI, such as predictive capability, scalability, and standardisation. As interdisciplinary research progresses and computational capabilities grow, these limitations may be addressed, unlocking new opportunities for AI systems to benefit from the rich, nuanced perspectives of Complexity Science.

Exploring the ideas about adaptation and emergence as a part of ecosystem (i.e. complex adaptive system — CAS) development, I think it is more exciting when we see it through the combined lenses of CAS, Schumpeter, Kuhn, Foucault, and Lyotard. Each of these perspectives explores how change does not just happen bit by bit, but instead in bold and disruptive leaps, as transformations that completely alter the playing field, whether we’re talking about economies, sciences, societies, or even our basic understanding of the world.

CAS implies that change is a matter of adaptive cycles — cycles of growth, accumulation, collapse, and renewal. An ecosystem could grow, accumulates the resources until hitting a limit. Then its whole structure becomes unsustainable, collapses, and reboots in a new way — it reorganises itself with fresh relationships and opportunities. This cycle is anything but smooth; it’s like a forest fire clearing the way for new growth, and it’s essential for resilience and long-term adaptability. This model resonates closely with Schumpeter’s idea of creative destruction in economies. Schumpeter saw capitalism as a system where innovation doesn’t build up neatly on top of the old but bulldozes it — new technologies, businesses, and products disrupt markets, toppling established companies and paving the way for the next wave of growth. For Schumpeter, entrepreneurs drive this cycle, constantly reinventing the economy and shifting the landscape in unexpected ways.

Thomas Kuhn brought a similar idea into science with his concept of paradigm shifts. In Kuhn’s view, science isn’t a smooth, cumulative process of adding one discovery to the next. Instead, it moves forward in fits and starts. Scientists work within a “paradigm” — a shared framework for understanding the world — until enough anomalies build up that the whole system starts to feel shaky. At that point, someone comes along with a radically new idea that doesn’t just tweak the existing framework but replaces it. Kuhn’s paradigm shift is a profound reimagining of the rules, kind of like Schumpeter’s creative destruction but applied to the way we think and know. It’s as if science periodically wipes the slate clean and rebuilds itself from a fresh perspective.

As a Gen-X, I must also mention Michel Foucault. Foucault offered a more historical spin on these ideas with his concept of epistemes. Foucault believed that every era has its own underlying structure of knowledge, shaping how people perceive and think about the world. These epistemes don’t evolve smoothly; they’re punctuated by abrupt shifts where the entire basis of understanding changes. Just like in a Kuhnian paradigm shift, when a new episteme takes over, it fundamentally changes what questions are even worth asking, as well as who holds power in the discourse. In Foucault’s view, knowledge isn’t just a collection of facts piling up—it’s tied to shifts in power and perspective, with each era replacing the last in a way that’s not fully compatible with what came before.

Then there’s Jean-François Lyotard, who takes the idea a step further by challenging the very idea of cumulative “progress” altogether. As a postmodernist, Lyotard argued that the grand narratives that used to make sense of history, science, and knowledge are breaking down. Instead of one single, upward trajectory, we’re left with multiple, fragmented stories that don’t fit neatly together. Knowledge, for Lyotard, is no longer a matter of moving toward some ultimate truth but an evolving patchwork of perspectives. This rejection of a single narrative echoes Schumpeter’s and Kuhn’s visions of disruption and replacement over seamless continuity. Lyotard’s work suggests that, in knowledge and culture alike, stability is always provisional, subject to the next seismic shift in understanding.

So when we look at all these thinkers together, a fascinating picture emerges. In CAS, Schumpeter’s economics, Kuhn’s science, Foucault’s history, and Lyotard’s philosophy, progress is not about slowly stacking up ideas or wealth. Instead, it’s about cycles of buildup, breakdown, and renewal — each shift leaving behind remnants of the old and bringing forth something fundamentally new. This kind of progress isn’t just unpredictable; it’s fueled by disruption, tension, and revolution. These thinkers collectively remind us that the most transformative changes come from breaking with the past, not from adding to it. Progress, in this view, is a story of radical leaps, creative destruction, paradigm shifts, and fresh starts—where each new phase is a bold departure from what came before.