Research Stage

Wprowadzenie do nauki o złożoności

September 17, 2025 11:05 AM
Power Talk
🇵🇱 Polish
Bratysława 2

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From the recording

Talk in brief

In this WaysConf presentation, Paweł "Nowy" Nowak introduces the fundamentals of complexity science and its critical application to modern design, research, and strategy. He contrasts ordered systems—which operate like predictable machines and rely on expert analysis—with complex, self-organizing systems like cities, ecosystems, and corporate teams. Using Dave Snowden's Cynefin framework and Stuart Kauffman's concept of the "adjacent possible," Nowak explains why linear planning and rigid goal-setting often fail in complex environments, leading to unintended side effects and "inattentional blindness." Instead, he advocates for a vector-based approach to change, guided by a "North Star" direction rather than a fixed destination, and supported by cheap, safe-to-fail experiments. Ultimately, Nowak calls for a fundamental paradigm shift away from Cartesian decomposition toward systemic mindfulness to address modern global challenges.

Key takeaways

  1. 01

    Systems Are Not Just Sums

    A system is a composition of interconnected elements that behaves differently than its individual parts. Kurt Koffka's Gestalt principle highlights that the whole is a distinct entity rather than simply a greater sum, as seen in how crowd intelligence can actually be lower than individual IQ.

    Watch from 3:26
  2. 02

    The Illusion of Retrospective Coherence

    In complex systems, cause-and-effect relationships are only visible in hindsight. This retrospective coherence makes us falsely believe we can predict outcomes, when in reality these systems are driven by unpredictable interdependencies and self-organizing attractors.

    Watch from 8:54
  3. 03

    Embracing the State of Aporia

    Before rushing to solve a problem, practitioners should pause in "aporia," a state of conscious unknowing. Staying in this uncomfortable space prevents teams from applying incorrect, linear methods to complex challenges before fully exploring the problem landscape.

    Watch from 13:48
  4. 04

    The Trap of Rigid Goal-Setting

    Setting highly specific, quantified goals in complex environments causes inattentional blindness. This cognitive bias forces us to focus exclusively on data that supports our pre-defined target while ignoring critical environmental changes and side effects.

    Watch from 20:38
  5. 05

    Navigating with a North Star

    Rather than planning backward from an imagined future destination, organizations should use a vector-based approach to change. By establishing a "North Star" as a directional guide rather than a literal goal, teams can navigate unpredictable environments through continuous, safe-to-fail experimentation.

    Watch from 24:42
Read edited transcript highlights

These concise notes were edited from automatic captions and checked against the talk structure. They are not a verbatim transcript.

The True Nature of Systems

A system is defined as a collection of interconnected parts that function differently together than they do individually. While Gestalt theory is often summarized as the whole being greater than the sum of its parts, it is more accurate to say the whole is simply different. For example, a crowd's collective intelligence can actually be lower than that of its individual members. However, when we assemble components like mechanical parts into a system, they produce entirely new capabilities that cannot be achieved by looking at those parts in isolation.

Watch from 3:12

The Fragility of Machine-Like Order

Ordered systems are typically designed to establish predictability, quality, and control, often operating like machines. In these environments, we generally understand our parameters, and if a failure occurs, experts can analyze the issue and apply established best practices. The vulnerability of these closed systems is their inherent fragility. When an external stressor exceeds the system's threshold of resilience, the connections break, causing the system to collapse into chaos, which requires significant time and effort to rebuild.

Watch from 4:26

The Fallacy of Retrospective Predictability

We frequently look at historical outcomes and assume that the original creators could have anticipated them. When nineteenth-century inventors built the first automobiles, they could not have foreseen how their work would reshape cities, lead to global paving, alter family structures, or spark cultural phenomena like professional racing. It is a cognitive error to view these developments as direct, predictable results of cause and effect. Instead, the invention simply created a new landscape of possibilities that allowed these unpredictable cultural and social patterns to emerge.

Watch from 14:44

The Concept of the Adjacent Possible

In complexity science, we focus on correlations and what Stuart Kauffman calls the "adjacent possible" rather than strict cause-and-effect. This concept suggests that what can happen next is enabled and constrained by what is already real in the present. Because future states emerge organically from current conditions, we cannot calculate the mathematical probability of future events without knowing the full range of potential variables. Since reality is non-deterministic, we must remain mindful of our current conditions as the only reliable foundation for navigation.

Watch from 17:09

The North Star Metaphor for Strategy

Traditional strategic planning relies on defining a precise future destination and mapping a linear path backward to the present. This approach fails in complex systems because the target destination is merely an imagined future state, not a fixed point, and every other actor in the environment is moving simultaneously. A more effective approach is the vector theory of change, which uses the metaphor of the North Star. For thousands of years, sailors navigated by the North Star not because they wanted to travel to the star itself, but because it served as a reliable directional guide. Strategy should similarly focus on establishing a clear vector of movement rather than a rigid destination.

Watch from 24:42

Moving Beyond Cartesian Decomposition

For centuries, Cartesian thinking and the method of breaking complex problems down into smaller parts have driven immense scientific progress. However, this reductionist approach is no longer sufficient for solving systemic global crises. Many modern problems are the direct result of linear, solution-oriented thinking that merely displaces design costs onto other parts of the ecosystem. To prevent impending environmental and social catastrophes, we do not need to invent more tools or accelerate existing processes. Instead, we must undergo a fundamental paradigm shift that embraces systemic mindfulness and changes how we act.

Watch from 27:22
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