Unveiling simplexity: A new paradigm for understanding complex adaptive systems and driving technological innovation

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Scientists have long sought frameworks to understand the complex systems that shape our world, from ecological dynamics to socio-technical networks. The concept of simplexity, intermittently explored for over 70 years, has now been redefined, offering a revolutionary perspective on complexity and simplicity across natural, social, and technological domains.¹ This breakthrough provides insights into the interplay between simplicity and complexity, unifying disciplines and driving scientific and technological progress. In this context, information management shifts from mere storage and dissemination to dynamic strategies that shape behaviors and interactions, fostering knowledge-driven activities. At the core of this research is a conceptual artifact developed through design science research (DSR) and grounded theory (GT), methodologies known for generating impactful knowledge. By reframing the traditional dichotomy between simplicity and complexity, the authors argue that these concepts are not opposites but rather interconnected elements of a unified system. This artifact, synthesizing decades of research across multiple fields, clarifies their intricate relationship and serves as a tool for understanding the complex adaptive systems (CASs) that shape science, engineering, and society.²

A concept with transformational potential

The concept of simplexity is not new, but its current incarnation offers a groundbreaking perspective on the nature of CASs, characterized by self-organization, emergence, and adaptability—properties that allow them to evolve without centralized control or external direction. As noted by McGregor³:

Generally speaking, CAS are inherently paradoxical. They are leaderless with no coordination, yet things still happen. Patterns emerge, yet no one was told or directed to make a pattern. They are governed by chance and randomness (stochastic); yet, those involved trust that something will emerge. If any element of the system is altered, the whole system reacts and adapts. What is created has none of the traits of the contributing agents; yet, they all created it.

It is important to acknowledge that while the word “complex” can be traced back to the Latin “complexus,” i.e., intertwined and non-linear, on the other hand, “simple” (from “simplus”) stands for “composed of a single element; plain and uncomplicated in nature or design.” Complexity is non-linear, meaning that elements derive their meaning from how they are connected rather than from their individual characteristics, making the whole different from the mere sum of its parts.⁴

The notions of simplexity and complixity redefine this relationship, suggesting that simplicity and complexity are not opposing forces but rather interdependent elements that coexist within every system, from the molecular to the societal scales. Simplexity describes the process by which intricate system interactions give rise to outcomes that appear simple, intuitive, and usable—without losing their underlying complexity. Complixity, in contrast, refers to the emergence of new, coherent structures when previously separate elements or systems become entangled. Together, these concepts explain how complexity can be both compressed into accessible forms and expanded through integration, offering powerful tools for designing adaptive, intelligible, and resilient systems. By studying the ways in which simple rules can generate complex patterns—and conversely, how complex systems can be interpreted under the lens of simple general behavioral patterns—the researchers offer an entirely new way to reinterpret the emergentist and reductionist perspectives of the reality of the complexity of systems across diverse scientific disciplines (Figure 1).

Figure 1.

Conceptual representation of two complementary ways to model reality

(Left) Sketch illustrating two different levels of representing reality, both based on the idea of “cones” as aggregations of units. The gray arrows indicate the opposing directions taken by reductionism (downward) and emergentism (upward): the former seeks to uncover reality by breaking it down into increasingly elementary parts and studying their behavior, while the latter emphasizes the emergence of phenomena not explainable through reductionist analysis alone. (Right) In the more elaborate model, these two perspectives are integrated, showing how, by dismantling the components, it is possible to identify “simplicities of complexity” that also hold at higher levels (e.g., basic physical laws), whereas other forms of simplicity (simplexity, S₁ and S₂) or emergent complexity (complixity, C) arise when a new context results from the interaction of multiple structural units. Although it remains a simplified model, this representation highlights the potential of combining complex adaptive systems (CASs) and/or artifacts. Adapted from Falegnami et al.²

For example, in biological sciences,⁵ the principle of simplexity could be instrumental in understanding the behavior of complex biological networks, such as protein interactions or gene regulation systems. Biological systems, despite their complexity, display emergent behaviors that can be simplified into key patterns essential for drug design, personalized medicine, and understanding disease pathways. In neuroscience, simplexity offers new perspectives on how the brain generates coherent thoughts and behaviors from intricate neural interactions, potentially leading to more effective treatments for mental health and neurological disorders. In the sciences of the mind, simplexity and complixity might represent useful tools to explain the adoption of cognition strategies to cope with internal or external stressors, e.g., information overload phenomena. In engineering and technology, simplexity can drive innovation by improving the design of resilient, efficient systems that adapt to changing environments. As smart cities, artificial intelligence (AI), and Internet of Things (IoT) expand, managing their complexity becomes essential. In socio-technical systems, where human and machine interactions converge, simplexity enhances our understanding of how organizations, technologies, and people interact to produce both predictable and emergent outcomes. By viewing simplicity and complexity as coexisting and coevolving, this research offers new strategies for managing complexity in fields like healthcare, education, and corporate management. Resilience engineering, which focuses on building systems that recover from disruptions, can particularly benefit by using simplexity and complixity to design artifacts, enabling anticipation and responsiveness in systems to make them both easy to operate and resilient to unexpected challenges.

A new language for transdisciplinarity

One of the most exciting aspects of this research is its potential to enact transdisciplinarity (sensu Nicolescu). The concepts of simplexity and complixity acquire even greater relevance when framed within a transdisciplinary paradigm—an approach that goes beyond the boundaries of academic disciplines to integrate knowledge from science, technology, government, industry, and civil society. As McGregor⁴ emphasizes, transdisciplinarity is driven by inclusive and complexity logics, embracing multiple epistemologies and levels of reality in order to make sense of the modern world’s systemic challenges. In this context, simplexity refers to the ability to navigate complexity through the generation of simple yet powerful organizing principles that do not erase the underlying intricacy but rather make it intelligible and actionable. Complixity, conversely, captures the dynamic emergence of structured relationships when diverse systems and perspectives interact, giving rise to new forms of coherence and meaning. Such a perspective reflects a shift from reductionist or siloed thinking toward a consilient worldview—one where diverse methods, perspectives, and domains of knowledge "jump together" to converge on shared truths. This notion of consilience, as originally formulated by Whewell and later expanded by Wilson, is foundational to the transdisciplinary logic: it acknowledges that complex systems are best understood when multiple independent sources of insight align. Simplexity and complixity embody this principle by synthesizing emergentist and reductionist approaches—revealing how seemingly chaotic or disjointed realities can be reorganized into coherent patterns across human and technological, cultural, and scientific domains. By adopting a transdisciplinary and consilient approach, researchers and practitioners are equipped to engage with the complexity of real-world problems not through fragmentation but through integration. This enables the design of socio-technical systems that are not only efficient and resilient but also ontologically plural. In this view, simplexity and complixity are not merely conceptual tools—they are catalysts for a new kind of knowledge creation, one that operates in, between, and beyond disciplines and strives for an encyclopedic and inclusive understanding of the evolving human condition.

Implications for technological innovation and societal challenges

As we continue to advance technologically, simplexity provides a powerful framework for managing the complexities of modern systems. Consider the rapidly growing field of AI. By recognizing that simplexity exists within AI systems, researchers and engineers can design algorithms that balance the inherent complexity of machine learning with the simplicity needed for practical application. In the context of global challenges such as climate change, pandemics, and economic instability, the ability to manage complexity and predict emergent behaviors is more critical than ever. The interdisciplinary nature of simplexity makes it an ideal tool for tackling these issues. By recognizing the simplicity inherent in complex problems, scientists and policymakers can develop more effective solutions that are both adaptable and sustainable. For example, in climate modeling, simplexity could offer insights into how seemingly chaotic weather patterns can be understood through simple, underlying principles that could guide the development of more accurate climate models and mitigation strategies.

From DSR to application: Advancing complex simplicities across contexts

The present work serves as a commentary and extension of the research presented in the article “Defining conceptual artefacts to manage and design simplicities in complex adaptive systems.”² That study laid the foundation for a new theoretical framework designed to clarify the relationship between simplicity and complexity within CASs, introducing the key constructs of simplexity, complixity, and complexity compression as both conceptual and operational tools. This framework is the result of a rigorous application of DSR, supported by GT as an embedded method of theory building. Through DSR, we adopted an iterative, artifact-centered approach focused on producing a conceptual artifact capable of supporting understanding and design in complex domains. GT was employed to build a theory of complexity simplicities by systematically coding a corpus of several academic documents from different scientific fields. This theory introduces a new paradigm for CAS behavior interpretation and the design of intended functions of socio-technical systems. This conceptual contribution marks a meaningful shift toward a transdisciplinary and consilient understanding of CASs—one that supports the development of systems that are not only resilient and adaptable but also coherent, communicable, and grounded across diverse fields of application. The research on simplexity marks a watershed moment in complexity science. It offers a powerful new conceptual artifact that promises to accelerate progress across a wide range of scientific disciplines and technological fields. With complexity simplicities, we are not just simplifying complexity—we are uncovering the profound patterns that lie at the heart of all complex systems, offering a new path forward for science, technology, and society.

Funding and acknowledgments

This research received funding under the program Horizon Europe: Digital, Industry and Space grant agreement ID 101070658. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Declaration of interests

The authors declare that they have no competing interests.

Published Online: May 15, 2025