Landscape thinking models adaptation in turbulent environments

A computational study proposes a new way to analyze how organizations adapt in unpredictable environments marked by tightly coupled interdependencies. Wenwen Liu and Wanzheng Zhao introduce “landscape thinking” as an adaptive mindset for exploring multidimensional possibility spaces, then use NK-landscape simulations to examine how the structure of those spaces can shape adaptive outcomes. The framework is grounded in evolutionary biology and built around three constructs.

The first is multivariable and diverse possibility: interacting variables help determine the structure and ruggedness of a possibility landscape. The second is initial divergence, describing how early positions and search heuristics can generate distinct adaptive trajectories. The third is dynamic and adjacent possibility, which concerns how new opportunities emerge as an explorer continues interacting with the environment.

To illustrate how these constructs operate together, the researchers developed computational models based on NK landscapes. Their simulations consider incremental fine-tuning and irregular oscillation as mechanisms for changing position over time. Across the models, the authors examine the effects of variable interdependencies, initial positions and dynamic neighborhood updates under conditions of turbulence.

The simulations show that these features shape adaptive outcomes in different ways. Interdependencies influence the structure and ruggedness of the possibility space, while initial positions and search heuristics determine how exploration begins and where trajectories diverge. Dynamic neighborhood updates affect which options remain adjacent to an explorer’s current position as interaction with the environment continues.

Initial divergence gives the model a time-dependent component. An organization does not enter the modeled landscape without a starting point, and its early position can influence the adaptive path that follows. The simulations therefore distinguish the effects of where exploration starts from the effects of subsequent changes in the surrounding neighborhood.

Dynamic and adjacent possibility adds another layer by treating opportunities as something that can emerge during continued interaction. Rather than relying only on the options visible at the outset, the model captures how relationships among variables and available neighboring possibilities evolve. This permits the simulations to represent adaptation in an environment whose option structure changes over time.

The contribution is principally theoretical and computational. By defining the landscape metaphor through multivariable possibilities, initial divergence and dynamic adjacent possibilities, the study provides a formal vocabulary for analyzing organizational adaptation in complex environments. Its simulations illustrate how combinations of interdependencies, starting positions and neighborhood updates can produce different adaptive outcomes under turbulence.

The results are limited to the researchers’ NK-landscape models and do not by themselves establish performance effects in operating organizations. They instead advance a framework for studying strategic flexibility: adaptation is analyzed as exploration across a structured possibility space whose features depend on many interacting variables, early choices and ongoing environmental engagement. The authors argue that this framework can guide thinking about organizational responses to complex, evolving environments.

Subject of Research: Computational modeling of organizational adaptation through landscape thinking

Article Title: The future seems wide open with possibilities: landscape thinking in the midst of turbulence

Article References: Liu, W., & Zhao, W. (2026). The future seems wide open with possibilities: landscape thinking in the midst of turbulence. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09091-x

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09091-x

Keywords: organizational adaptation, landscape thinking, NK landscapes, strategic flexibility, complex systems, computational modeling, interdependencies, turbulence, evolutionary biology, adaptive trajectories, future, seems