Strategic behavior is inherent to multi-actor decision-making, allowing actors to see their goals reflected in the process and outcome, which is needed for achieving satisfaction. Traditional decision-making often favors those with the loudest voices, which can lead to decisions that reflect only the preferences of a few rather than a balanced consideration of all stakeholders' interests. While strategic behavior is necessary, its associated risks such as stagnation, perceived unfair distribution of benefits, and suboptimal solutions are well-documented. These can negatively impact the decision quality. These risks are tied to the strategic approach employed, whether competitive or collaborative, and are influenced by how the decision environment is structured. The literature indicates that a dominant reliance on competitive strategies within such environments often leads to the issues mentioned earlier.
While Decision Support System literature acknowledges the risks of strategic behavior, a lack of behavioral insight has been identified as a limitation. Understanding how decision support systems influence behaviors is crucial for improving their effectiveness. Emphasizing the importance of learning from real-life manifestations of decision-makers' behavior. It is assumed that the limited understanding how strategic behavior unfolds in practice within decision support system (DSS) supported decision-making environments, supports the understanding how these environments influence such behavior. This limited understanding hinders the development of effective decision support systems as well as current decision making practices. This research addressed this gap by employed a behavioral informed approach, used qualitative research methods such as interviewing and directly observing participants behavior with the use of a Decision Support Systems (DSS). Providing an understanding of how decision environments, shape strategic behaviors.
Through a qualitative analysis using the Preference-Based Accommodation Strategy (PAS) approach as a research instrument, this study explored how this design and decision support system facilitates a decision-making environment and influences the use of strategic behavior. Through analyzing the occurrence of these different approaches, collaborative or competitive, within specific decision environments, patterns were identified. These patterns revealed which environmental factors encourage or diminish certain strategies employed. For instance, collaborative patterns increase under conditions of transparency and inclusivity, while competitive patterns decrease as these address the underlying causes that facilitate such behaviors. These were found consistent in both literature, current decision making and the design and decision making process supported and facilitated by the PAS approach. This research provided insight into how the structure of a decision environment, such as the one created by the PAS approach, influences the nature and prevalence of strategic behaviors.
The PAS approach integrates features such as transparent information, transparent modeling and analysis, interdependency of goals, the overall preference score, and open dialogue. These elements create a transparent and inclusive environment where collaborative strategic behavior is naturally incentivized and is both effective and rewarding, reducing the reliance and effectiveness of competitive strategies. The research shows that PAS addresses limitations in current decision-making environments by fostering a transition from competitive to collaborative strategic behavior, through a transparent and inclusive environment.
By moving beyond assumptions, this research provides a clear understanding of how the PAS approach shapes and influences strategic behavior, offering insights into the design of decision support systems and current decision making practices that promote collaborative strategic behaviors over competitive strategic behavior, enhancing the potential to mitigate the risks mentioned earlier.

The research question addressed in this study is ”To what extent does the presence of a cloud forest canopy impact the Mestelá River catchment and how will this affect the various involved stakeholders?”. The study aims to investigate the importance of cloud forests in the Mestelá River catchment, Alta Verapaz, Guatemala, related to water security and the social impact of cloud forest conservation and management. The research methods used in this study were a combination of quantitative and qualitative methods.

Cloud forests play a vital role in regulating water flow in catchments. The Mestelá River catchment, where the NGO Community Cloud Forest Conservation (CCFC) is situated, is the focus of this research. The project’s primary aim was to establish a long-term canopy setup, ensuring future data collection. The project’s scope encompasses a range of methodologies, including the installation of a long-term measurement station in the canopy, computation of the Mestelá River discharge, the development of a rating curve, and the utilisation of a FLEX-Topo model to simulate the hydrological cycle in the catchment. Additionally, a stakeholder management analysis was conducted to understand the complex impact of cloud forests (conservation) on various stakeholders.

The study did not explicitly formulate any hypotheses, but the findings provide evidence for the impact of cloud forest canopies on river catchments and discharge. The study also has limitations, including the small sample size and the lack of long-term data. However, the study provides valuable insights into the importance of cloud forest ecosystems for water security and the social impact of cloud forest conservation and management. The stakeholder analysis reveals that for CCFC two methods of advocacy can be used. Whilst the CCFC is effective in bottom-up engagement with the community, in addition, a strip for small children was constructed. For top-down advocacy, using the FLEX-Topo
model for visualising water security in combination with cloud forest protection holds promise.

The implications of this work are substantial for cloud forest conservation and associated ecosystems. The findings offer valuable insights for developing effective conservation strategies that consider the canopy’s impact on the catchment and its stakeholders. It is important to note that the FLEX-Topo model is currently conceptual and requires further refinement and detail for the Mestelá River catchment. Nevertheless, this study contributes significantly to the understanding of cloud forest ecosystems and offers practical and theoretical applications for future research and conservation efforts.