the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Learning from conceptual models – a study of emergence of cooperation towards resource protection in a social-ecological system
Abstract. Engaging ecological resource users in intervention to protect the resource is challenging for governments due to self-interest of users and uncertainty about intervention consequences. Focusing on a case of forest insect infestations, we addressed questions of resource protection and environmentally responsible behavior promotion with a conceptual model. We coupled a forest infestation model with a social model in which a governing agent applies a mechanism for recognition and promotion of environmentally responsible behavior among several user agents. We ran the coupled model in various scenarios with a Reinforcement Learning algorithm for the governing agent as well as best-case, worst-case, and random baselines. Results showed that a proper recognition policy facilitates emergence of environmentally responsible behavior. However, ecosystem health may deteriorate due to temporal differences between the social and ecological systems. Our work shows it is possible to gain insight about complexities of social-ecological systems with conceptual models through scenario analysis.
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Status: open (until 31 Jul 2024)
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RC1: 'Comment on gmd-2024-57', Anonymous Referee #1, 16 Jul 2024
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General comments
The manuscript is very well-structured, presenting a coherent flow from introduction to conclusion. The role of conceptual models in research is effectively discussed and well-situated within the existing literature. The results, discussion and conclusion are well explained and make the gained insights on the social-ecological model clear. The paper provides a relevant contribution to understanding the emergent dynamics in coupled social-ecological systems.
However, there are areas where the manuscript can be improved. The manuscript would benefit from more empirical evidence to justify the choices made in the social model. Furthermore, a clearer and more detailed description of the social model in the methods section would be beneficial for the reader’s understanding. Some modelling details are only mentioned in the discussion section. Including these details in the methods section would provide a comprehensive understanding of the model from the outset and improve the overall clarity of the manuscript.
Specific comments
1. Empirical evidence for modelling choices
- It would be beneficial to include some empirical evidence that granting recognition for promoting environmentally responsible behaviour can be a successful governmental strategy that effectively influences the behaviour of logging companies.
- Other socio-economic factors e.g. environmental awareness of the user agents may be relevant in the considered resource protection problem that are not considered in the presented social model. Therefore, it may be beneficial to explain in the paper why focusing on a simple social model that examines only one aspect (social recognition and norms) is still a powerful tool which helps in understanding the underlying mechanisms.
2. Clearer description of the social model
- Clearly state that “principal agent” and “governing agent” refers to the same entity
- Explain the decision-making process of the user agents, which is based on a simple if-statement without learning capabilities. Clarify how utility is calculated for the user agents, including the calculation of uniqueness and value.
- Describe the economic part of the model in the method section. Explain how the user’s profit is calculated: “In the example of this study, total harvest from the forest comprises market supply, which influences market price and sales quantity, which in turn influence users’ profit and hence individual decisions on harvest in the next time step”
3. Move model description from the discussion section to methods section
Examples:
- Explanation of how the RL algorithm works: “The governing agent’s RL algorithm uses a policy for making decisions, and updates that policy based on the rewards it receives as a result of its decisions”
- Decision-making of user agents
- Real-world context of the ecological model and background information on modeling choices for both the ecological and forestry models.
4. Explain in more detail what happens during preparation time and relate it to the mechanisms of the social model
Discuss why preparation time is beneficial even for random runs where the government is not learning. Can the observation that user agents are more willing to cooperate with the government after a preparation period be explained by the decision-making process of the user agents (uniqueness and value)?
5. A lot of repetition in the results part
Technical corrections
“Figures 5 and 6 show the mean proportions of study area that are covered by healthy and infested forest, as well as the area that is cut, in each time step.” P.11
Only figure 6 shows this, not figure 5.
Citation: https://doi.org/10.5194/gmd-2024-57-RC1 -
RC2: 'Comment on gmd-2024-57', Anonymous Referee #2, 25 Jul 2024
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General Comments
The manuscript presents a social-ecological model examining the emergence of cooperation between logging company agents and a governance agent in the context of preventing forest insect infestation. It is well-structured and clearly written, with established theories and literature effectively supporting the conceptual validity of the model. Numerical experiments have been conducted to compare different scenarios under various governance settings. However, several areas need improvement to enhance the manuscript. These include the modelling of governance agents, the presentation of the research's novelty, and improvements to the text and figures.
Specific Comments
- Modelling governance agents:
One of the critical challenges in modelling governance agents is capturing their autonomous, adaptive decision-making processes. While reinforcement learning (RL) is a technically feasible approach, it does not capture the essence of real-world policymaking, as policymakers do not have access to data-intensive training opportunities to optimize their strategies. Therefore, it would be beneficial to include a discussion on how the RL algorithm aligns with or diverges from the nature of real-world policymaking behaviours.
- Presenting novelty:
The novelty of the research is a major concern. The model is based on a set of hypothetical arguments, and while it is not expected to produce quantitatively accurate results for real-world policymaking, it should ideally reveal novel trends or patterns within the social-ecological system. The potential novel patterns would also support the use of the RL algorithm. However, the manuscript does not clearly demonstrate that the model has captured new patterns. The effectiveness of "responsible user labels" is a key factor influencing the efficacy of scenarios with policy interventions (RL and random) compared to those without. If the benefit parameters are fixed, the numerical experiments mainly test the RL algorithm against random interventions, which may not yield novel insights. A stronger presentation of the model's novelty is required.
While one innovation of the paper is the coupling of two models, much of the groundwork appears to have been done in the authors’ previous publications. The current model represents a new application of these two models connected through a simple (but effective) communication protocol. Therefore, a major improvement for this manuscript to address might be clearly articulating the novelty of this research.
- The method section:
The initial paragraph of the Methods section repeats content from the introduction. Additionally, it may not be necessary to argue for the use of modelling approaches to explore policy impacts on complex social-ecological systems. This paragraph could be shortened or removed to improve the overall fluency of the manuscript.
- Flowchart (Figure 3):
The flowchart in Figure 3 could be improved for clarity. Including "Start" and "End" components would help, and the arrows could be less confusing. Typically, if two arrows originate from the same block, that block should be a conditional block with True/False branches. Even though one model always listens to the other for new messages, organizing the flowchart in a sequential order would better reflect the model's logical flow. Alternatively, using different types of arrows to indicate message direction might be beneficial.
Addressing these points will strengthen the manuscript and better communicate its contributions to the field.
Technical corrections:
In Figure 5, the use of black solid and dashed lines as legends is confusing because they do not appear in the figure. It would be more straightforward to present six legends for the six lines, adding only one more legend than currently used.
Citation: https://doi.org/10.5194/gmd-2024-57-RC2
Data sets
Datasets for model flipflopSEM Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas https://doi.org/10.17605/OSF.IO/URJQ8
Model code and software
flipflopSEM Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas https://doi.org/10.5281/zenodo.11245520
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