# Trace of change: How stigmergy maps social learning loops in Companion Modeling through participatory simulation

François Vendel, Oleksandr Zaitsev, Pierre Bommel, Ibra Touré, Jean-Daniel Cesaro, Penda Diop, Marieme Fall Ba, Tamsir Mbaye, Etienne Delay

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François Vendel, Oleksandr Zaitsev, Pierre Bommel, Ibra Touré, Jean-Daniel Cesaro, et al.. Trace of change: How stigmergy maps social learning loops in Companion Modeling through participatory simulation. Journal of Environmental Management, 2025, 383, pp.125292. 10.1016/j.jenvman.2025.125292. hal-05068733

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Research article

# Trace of change: How stigmergy maps social learning loops in Companion Modeling through participatory simulation

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François Vendel<sup>a,b,●</sup>,*, Oleksandr Zaitsev<sup>a,●</sup>, Pierre Bommel<sup>a,●</sup>, Ibra Touré<sup>d,f</sup>, Jean-Daniel Cesaro<sup>c,d</sup>, Penda Diop<sup>b,g</sup>, Marieme Fall Ba<sup>b</sup>, Tamsir Mbaye<sup>b</sup>, Etienne Delay<sup>a,e</sup>

## ARTICLE INFO

### Keywords:

Companion Modeling
Social learning loops
Stigmergy
Senegal
Sylvopastoralism

## ABSTRACT

This study explored integrating stigmergy in Companion Modeling (ComMod) to enhance social learning loops (SLL) in participatory natural resource management. Taking a case study from the Ferlo region of Senegal, the research focuses on how stigmergy – a concept derived from biology involving indirect coordination through environmental traces – stimulates and documents cognitive, normative, and relational changes among stakeholders. By leaving digital traces during the iterative development of an Agent-Based Model (ABM), stigmergy support participants in co-constructing the model representing local agro-sylvo-pastoral dynamics. The paper highlights the role of stigmergy in fostering trust, knowledge sharing, and collaboration among diverse stakeholders and research team in a ComMod process, helping to refine the collective strategies aimed at sustainable land management. This research showed that stigmergic imprints can be used as a tool for monitor how learning and model development evolve during iterative modeling workshops, contributing to more adaptive and resilient resource governance strategies.

## 1. Introduction

Land degradation is acknowledged to one of the major international challenges of the 21st century, particularly affecting countries in South Asia and sub-Saharan Africa (Gisladottir and Stocking, 2005; Aronson et al., 1993; Cowie et al., 2011; Gangneron et al., 2022). Initiatives to combat land degradation have multiplied, particularly through re-forestation programs (Faye and Ribot, 2017; Nijnik and Halder, 2013; Dia and Duponnois, 2012), but they have often neglected local socio-ecological specificities (Bied-Charreton and Burger, 2012; Fassinou et al., 2024), leading to mitigated long-term results and tensions with local stakeholders (UNCCD, 2023; Mugelé, 2018; Turner et al., 2023). Developing more integrative, participatory, and adaptive approaches is recommended to take the needs and knowledge of local populations more effectively into account (Ninot et al., 2019; Turner et al., 2023; Chotte and Thibon, 2021; Delay et al., 2022), while remaining mindful of the traps associated with participation processes that can occur

in countries involved in reforestation programs (Boutinot and Diouf, 2006; Ribot et al., 2006).

We used Companion Modeling (ComMod) as a participatory modeling approach to understand and manage the complex interactions existing between social and ecological systems (Étienne, 2014; Voinov and Bousquet, 2010). By involving local stakeholders in modeling activities, a ComMod process allowed them to co-construct locally negotiated and adapted solutions (Bousquet, 2005). The flexible approach of ComMod in assumptions revision throughout the process enabled contributions to be integrated across multiple levels (Bah et al., 1999).

Our primary focus was on improving **Social Learning Loops (SLL)**, which are defined as multi-level, retroactive cycles of learning aimed at reinforcing collective action and reflection to improve resource management and environmental relations (Medema et al., 2014; Keen et al., 2005; Pahl-Wostl, 2009). These loops involve cognitive, normative,

<footnote><sup>a</sup> CIRAD, UMR SENS, MUSE, Université de Montpellier, Montpellier, 34000, France</footnote>

<footnote><sup>b</sup> Institut Sénégalais de Recherches Agricoles (ISRA), Centre National de Recherche Forestière (CNRF), Dakar, 10700, Senegal</footnote>

<footnote><sup>c</sup> CIRAD, UMR SELMET, MUSE, Université de Montpellier, INRA Institut Agro, Montpellier, 34000, France</footnote>

<footnote><sup>d</sup> Cirad, UMR SELMET, Campus International de Baillarguet, Montpellier, 34398, France</footnote>

<footnote><sup>e</sup> IRD, UMI 209 UMMISCO, Centre IRD France Nord, 32 Avenue Henri Varagnat, Bondy, 93143, France</footnote>

<footnote><sup>f</sup> Cirad, DGD-RS 37, avenue Jean XXIII BP 6189, Dakar - Etoile, 10700, Senegal</footnote>

<footnote><sup>g</sup> Université Alioune Diop de Bambey, Bambey, Senegal</footnote>

<footnote>* Corresponding author at: CIRAD, UMR SENS, MUSE, Université de Montpellier, Montpellier, 34000, France.
E-mail addresses: francois.vendel@cirad.fr (F. Vendel), oleksandr.zaitsev@cirad.fr (O. Zaitsev), bommel@cirad.fr (P. Bommel), ibra.toure@cirad.fr (I. Touré), cesaro@cirad.fr (J.-D. Cesaro), penda.diop@isra.sn (P. Diop), bfma09@yahoo.fr (M.F. Ba), tamsir.mbaye@isra.sn (T. Mbaye), etienne delays@cirad.fr (E. Delay).</footnote>
and relational changes among stakeholders. Cognitive changes refer to improvements in knowledge and practices, normative changes involve shifts in collective objectives and acceptable risks, while relational changes concern the mutual trust and understanding necessary for effective collaboration. SLL have been a key focus for numerous authors working with the ComMod approach (Bousquet et al., 2005; Le Page, 2017; Coudel et al., 2017), as they suggest that one form of social learning can retroactively influence others across different levels, allowing multiple levels of learning to occur simultaneously.

However, documenting these various forms of learning and their effects is challenging. Moreover, ComMod initiatives often fail to capture the entire process, let alone track how the model and SLL contribute to each other on each iteration. To address these challenges, our research focused on applying **stigmergy** in a ComMod process. It is a concept originating from biology and refers to a mechanism of indirect coordination through the environment, where agents leave traces that influence the actions of others (Dorigo et al., 2000). In the context of a ComMod approach, stigmergy facilitates the documentation and stimulation of social learning loops within the model's construction process by leaving digital imprints on each iteration. These imprints serve as a form of collective memory and allow for the coordination of actions among collaborators throughout the process. To the best of our knowledge, this is the first study that discusses the need to support model evolution in the ComMod process and introduces the concept of stigmergic imprints.

In our ComMod approach, stigmergic imprints were introduced through codes, simulations, and reports uncovering discussions and we explored their contribution in the co-construction of an Agent-Based Model (ABM) based on a conceptual model built using the ARDI method with local stakeholders (Voinov and Bousquet, 2010; Simon and Etienne, 2010). The ABM acts as a catalyst for exploring locally negotiated and adapted solutions (Bommel, 2019). This approach enables us to stimulate interactions within an administrative area dedicated to the local management of agro-pastoral resources and practices, and to observe the individual and collective behaviors that emerge in favor of both shared access to agro-pastoral resources and the preservation of woody resources.

This article presents our methodology, the results obtained and discusses the implications of using stigmergy in a ComMod process to improve social learning loops and related changes in support of local community resilience to environmental challenges in vulnerable areas.

## 1.1. Study area

The study was conducted in the *Ferlo* region, specifically in the south-eastern part of the *sylvo-pastoral zone* of Senegal. The region covers approximately 60,000 km² along the Sahel fringe and is characterized by a predominantly arid climate with annual rainfall ranging from 200 mm to 400 mm (Ninot et al., 2020; GAYE and NIANG, 2023). The landscape is composed of shrubby steppes and tree savannas, and the rainy season extends from mid-June to September (Sarr, 2009). The population is primarily made up of Fulani people, traditionally pastoralists or agro-pastoralists, who maintain a mobile lifestyle to adapt to the fluctuating climatic conditions (Cesaro et al., 2010). Historically, pastoralism has been the key strategy for managing the uncertainties of this harsh environment (Marega and Mering, 2018).

The specific study site, *Velingara-Ferlo* (Fig. 1), is located within the *sylvo-pastoral zone* and covers approximately 1,400 km², half of which is protected by *sylvo-pastoral reserves* and classified forest areas (Sy, 2011). The site is part of the *Dundi Ferlo* reforestation program, which aims to restore degraded lands through fenced reforestation plots managed by local communities (IRD / RESET Great Green Wall Moctar Sacande Panafrican Agency of Great Green Wall, FAO / Action Against Desertification (AAD), 2024). The area is dominated by lateritic soils and is covered mainly by shrubby and woody savannah (Ngom et al., 2009). The local economy revolves around two primary activities:

livestock farming and rain-fed agriculture, with a population of around 20,000 inhabitants in 2016 (*Velingara-Ferlo* municipality, 2018). Non-timber forest products, as well as wood harvesting and pruning, are key components of the local forestry practices (Cesaro et al., 2024).

Although the *Ferlo* region offers a specific socio-ecological context with its unique set of challenges, the methods and participatory approaches applied in this study, particularly the Companion Modeling (ComMod) and Social Learning Loops (SLL), could be adapted to other regions facing similar issues of land degradation and resource management. The co-construction of knowledge between stakeholders, the use of Agent-Based Models (ABM), and the integration of stigmergy provide a framework that could be valuable for promoting sustainable land management in various global contexts.

## 1.2. Project dynamics

The Companion Modeling (ComMod) process was scheduled to span 24 months, from December 2022 to December 2024, and was structured in three distinct stages.

1. The **first stage** focused on raising awareness among stakeholders about the ComMod approach and its potential contributions to degraded land restoration. This stage involved forest managers, local community leaders, and representatives from the local administration. Through a series of participatory workshops and individual interviews, a collective mandate was identified, leading to the co-construction of a conceptual model. This phase hihlighted the importance of building trust and mutual understanding between stakeholders and with the research team, while also clarifying the collective objectives related to managing agro-sylvo-pastoral resources.

2. The **second stage** of the project involves developing of an Agent-Based Model (ABM) with a smaller group of stakeholders representing diverse perspectives, such as natural resource users and managers. This phase was highly iterative, with several micro-loops of participatory workshops being conducted to co-construct and refine the model. The ABM was grounded in the conceptual model developed in the first stage, and its design reflects the values, interests, and knowledge of the stakeholders involved.

3. The **third and final stage** of the project, planned but not yet undertaken, will involve using the ABM to collectively explore a wide range of scenarios related to resource management and reforestation initiatives. The robustness of the model will be tested by generating multiple simulations based on scenario proposals put forward by the participants. This stage will seek to deepen stakeholders' understanding of potential outcomes and foster collective decision-making on sustainable land management practices.

This paper presents the results of the second stage of the ComMod approach applied in the Ferlo region of Senegal. We explain how we introduced stigmergy during this stage and discuss its impact. We also anticipate the potential impact that we expect to see during the third stage.

# 2. Materials and methods

## 2.1. Companion modeling framework

In this study, we used the *Companion Modeling (ComMod)* approach as the primary framework for involving local stakeholders in the co-construction of the model (Barreteau et al., 2003). The ComMod approach is specifically designed to address complex socio-ecological interactions by working collaboratively with stakeholders, including farmers, pastoralists, and local authorities. The process begins with a series of stakeholder engagement sessions aimed at defining the problem,
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Localization map. (a) shows the Great Green Wall path in Senegal and two sites of the Dundi Ferlo reforestation program, including our specific site Vélingara-Ferlo. (b) refers to the camps, reforestation plots and water infrastructure at both sites.</figcaption></figure>

identifying key resources, and establishing shared objectives for the modeling process (Étienne, 2009). These sessions enabled participants to contribute their knowledge and perspectives, which were vital for developing a conceptual model reflecting the realities and dynamics of the study area (see Fig. 2).

Throughout the participatory process, the modeler plays the role of a facilitator, fostering open dialogue and encouraging active listening to ensure that the model incorporates the insights and experiences of all stakeholders (Voinov and Bousquet, 2010). The aim is to create a shared representation of the system being studied, which not only improve the collective understanding of the socio-ecological interactions but also enhance the model's usefulness in supporting informed decision-making. Various tools and methods are employed

in ComMod processes, including both serious games and agent-based modeling (ABM) used in participatory workshops, which serve to stimulate debate and collaborative efforts (Szczepanska et al., 2022). These activities enable stakeholders to actively engage with the model, test different scenarios and explore potential solutions to the challenges they faced.

Use of the ComMod approach in this study aligned closely with the principles of commons management, where the recognition of shared resources and collaborative establishment of rules and norms are critical to achieving equitable and sustainable outcomes (Aubert and Botta, 2022). By integrating the knowledge and practices of local stakeholders, the model becomes a platform for collective reflection and action. This collaborative approach has been successfully applied
<figure><img src="image_5.png" /><figcaption>Fig. 2. One of our workshops in Vélingara-Ferlo as the first iteration of the model, exchanging with local stakeholders about the model contents and behaviors.</figcaption></figure>

in various contexts, including natural resource management, territorial
development, and ecosystem services, offering valuable insights into
the relevance of participatory modeling in fostering social learning and
adaptation (Bousquet, 2005; Étienne, 2014). The iterative nature of
the ComMod process, combined with the principles of social learning
loops (SLL), ensures that the model evolves alongside the stakeholders'
understanding, reinforcing the feedback loops necessary for improved
governance and resource management.

In this context, numerous modifications and improvements to the
model must be tracked and evaluated throughout the process. Integrat-
ing stigmergic principles ensures that all contributors can access and
understand the model's evolution and the rationale behind each step
of its co-construction.

## 2.2. Agent-based model (ABM) construction

In our study, we involved local stakeholders to collectively construct
an agent-based model that represented the dynamics of their socio-
ecological system, the impact of practices on land restoration and new
management methods that could emerge from the process. Designing an
ABM includes identifying a shared objective, conceptualizing a model,
developing scenarios and collectively analyzing their results.

Collectively building the model and interacting with simulations
enabled the participants (stakeholders and modelers) to propose, test
and observe alternative management methods, both collective and
individual, in a virtual world. It also encourages exchange between the
stakeholders and with us, which helped us discover valuable insights
into their environment and practices.

To implement our ABM, we used Cormas<fnref n="1" /> — an agent-based model-
ing platform which was initially built to support companion modeling
initiatives, especially those that deal with natural resources manage-
ment. Cormas provides tools for interacting with a simulation, so as to
collectively explore future scenarios. It also made possible to modify
the model on the fly, which is important for co-constructing a model
together with stakeholders (Bommel et al., 2016; Zaitsev et al., 2023).

## 2.3. Stigmergy and social learning loops

Some ComMod initiatives offer a comprehensive view of the re-
ciprocal learning that occurs between these diverse actors (Moreau
et al., 2019; Becu et al., 2017; Treuil et al., 2008). *Social Learn-
ing Loops* take three forms: **cognitive changes**, where participants

improve their knowledge and practices through the iterative explo-
ration of simulations; **normative changes**, where collective goals and
acceptable risks are redefined based on evolving assumptions; and
**relational changes**, which strengthen trust, mutual understanding,
and collaboration among stakeholders. In many cases, descriptions
of ComMod processes focus on specific aspects, without providing a
holistic overview of how knowledge is co-constructed and exchanged.

In our Companion Modeling (ComMod) process, we applied the
concept of *stigmergy* to track and stimulate *Social Learning Loops (SLL)*
and thus aimed to address this gap. Stigmergy, a term originally derived
from biology to describe the coordination of social insects through
indirect communication via environmental traces, has been increas-
ingly adapted for use in multi-agent systems and collaborative project
management (Hadeli et al., 2004; Bolici et al., 2015). In the con-
text of our companion modeling process, stigmergy was leveraged
as a means to document and enhance the iterative learning process
among stakeholders, modelers, and computer scientists throughout
model development (Drogoul et al., 2003).

Stigmergy can play a core role in the ComMod process. As collabora-
tive project, participants of the modeling process can use imprints from
previous iterations – whether digital, visual, or editorial – as feedback
and collective memory to refine their understanding, adjust their col-
lective strategies and observe how their inputs influenced the overall
process. This iterative approach, supported by interactive simulation
and exploration with frequent feedback loops, allows stakeholders to
engage more deeply with the model and contributes to continuous
improvement in decision-making (Le Page, 2017). By documenting
and analyzing these changes, we are able to assess the impact of the
model on participants’ (both stakeholders and modelers) learning and
collective action over time. The ability to track how social learning
evolve across multiple iterations provide valuable insights into the
dynamics of collaborative resource management and the role of ABMs
in fostering sustained engagement and adaptive governance.

## 2.4. Data collection and assessment

We thus used three ways of leaving imprints in our process. (i)
We produced video capsules of 5 min maximum at the beginning and
end of each workshop day, both by the local stakeholders and the
research team. These videos were considered as imprints of mutual
and collective understanding. They acted as catalysts for relational,
normative and cognitive learning between stakeholders and research
team. (ii) At the end of each iteration of the model, a GIT repository was
created in order to save and track changes made to the model over time

<footnote n="1">1 https://cormas.org</footnote>
as part of the co-design process. (iii) A report completed the traces for process monitoring. It included the topics discussed during the meeting, the reorientations taken in the model and the interactions between the model and the participants, as well as a detailed presentation of the model in the form of ODDs (Railback and Grimm, 2019).

Using the Banos and Sander analysis matrix (Banos and Sanders, 2013) in the report and as a reference, we wanted to observe how each characteristic of the model evolved over iterations. The matrix provided information about the evolution of model features at the intersection between two dimensions: “simple model (KISS) or descriptive model (KIDS)” and the “stylized” or “particular” nature of the model. It gave us an other way of assessing how the model and its components were taking shape.

## 3. Results: Social learning loops and stigmergic imprints

In this section, we present the initial design of our model and its gradual refinement through iterative interactions with participants. The first version was intentionally simplified compared to the conceptual model to encourage improvements from stakeholders during collaborative workshops. This preliminary version focused on replicating the spatial patterns emerging from the decisions of agents (households and herds) and the distribution of herbaceous forage around Sahelian camps.

As the workshops progressed, the model underwent several iterations shaped by social learning loops. These iterations led to three important transformations: relational, normative, and cognitive changes. Relational changes were evident as participants engaged more deeply with the model, fostering mutual understanding, building trust, and resolving social conflicts among different groups. Normative changes occurred when participants reevaluated their assumptions and objectives, leading to a redefinition of goals and new, innovative approaches to land and grazing management. This process prompted participants to adjust both their contributions to the model and their real-world practices. Cognitive changes were also key, as participants gained new insights and knowledge about the socio-ecological dynamics represented by the model, improving their understanding of how herd movements and resource management impacted their environment.

### 3.1. *Initial model design and progressive specification*

The first version of our model was intentionally simple to foster improvements arising from the groups of participants. This preliminary version attempted to reproduce the spatial patterns that can emerge from the decisions of agents (households and herds) and reproduction of the herbaceous resource around Sahelian camps. To represent reforestation plots, we also introduced prohibited space where agents and herds could not enter (gray cells in Fig. 3).

We describe below the gradual specification of our model starting from elements defined and reviewed with stakeholders and through several social learning loops that took place during our workshops and were registered as stigmergic imprints. We highlight three points to which we paid particular attention during the process of co-constructing the model, its collective observation and related discussions:

1. We looked at how the participants positioned themselves in relation to the model, how took on board its operations and what social conflicts or even conflict resolutions emerged.

2. We focused on how participants reevaluated their assumptions and collaborations and how those changes impacted the model's shape.

3. We study how a shared knowledge was being built among participants and how they reevaluated their practices through the simulations.

<figure><img src="image_6.png" /><figcaption>Fig. 3. Illustration of model implementation (a) and the real-world scenario (b). (a) Simulation space at initialization. Each patch represents a square kilometer, with color coding indicating a landscape unit, a reforestation plot, or a camp. Gray patches denote reforestation plots where agents are prohibited from accessing; black patches represent camps where households reside and around which herds move. Brown, green, light green, and yellow patches correspond to different landscape units, defining the maximum grass availability. The circles represent herds, with orange circles indicating sheep herds and brown circles representing cattle herds. (b) A real-world depiction of a reforested plot (gray in (a)) to the left of the fence and the surrounding bushland to the right (green in (a)). A stump of a fallen tree is visible in the non-reforested section.</figcaption></figure>

### 3.2. *Relational change: conflicts resolution, mutual understanding and trust*

Using our stigmergic imprints, we identified two kinds of relational changes that were needed and emerged across social learning loops:

* mutual understanding between local stakeholders and the research team through the model;

* mutual trust between the different groups of stakeholders involved in degraded land restoration.

In our following workshops, we observe how those two needs were reinforced through social learning. Our imprints acted as milestones to which all participants could refer during the process and check whether if these relational changes emerged at the end of the ComMod process.
The local stakeholders quickly shared their initial impressions and misunderstandings about the model. Initially, many over-interpreted its results, believing it included transhumance and agricultural elements, even though it was strictly limited to grazing management and herd movement. Additionally, the research team realized the model's shortcomings in capturing spatial variability and operating on a scale that would allow local stakeholders to imagine themselves in it and provide feedback. These confusions underscored the need for clear communication, precise explanations of the model's objectives and limitations, but also normative changes. After recognizing these issues – and with regular reminders about past workshops and efforts to highlight participants' respective expertise – the understanding of the model's components and functionality improved over time. This led to more focused discussions on adding new elements or adjusting rules, as well as a clearer grasp of the model's requirements and the necessary level of abstraction from reality.

> "We're farmers, but we've learned about breeding from the herders who live with us, and we accept that breeding is their area of expertise, but they also do the same thing. And that's how it is, everyone has their own area of expertise that they master better than the other".
> 
> [...] "Each of us has our own area of expertise that others may not know about. For example, the herder knows breeding better than the researcher. Therefore, it is in this sense that we must unite in the quest for better solutions and share the same reasoning".

— a local stakeholder during the third day of the second workshop.

In addition, we highlighted some of the most common conflicts in the pastoral world: on the one hand a conflict emerged between local agro-pastoralists and transhumant herders over pasture use. The locals worried that transhumant herders would exacerbate limited pastoral resources and threaten livestock, agriculture, and forest preservation through excessive pruning. This tension revealed deeper issues over access rights, emphasizing the need for open dialogue. Contrary to initial assumptions, discussions showed blurred distinctions between locals and transhumant herders, as many locals also practiced forms of transhumance, adjusting their herd movements according to seasons and climate, further complicating land-use dynamics. On the other hand, misunderstandings between agropastoralists dedicated to agriculture and those dedicated to pastoralism was revealed later, leading the model construction and debate around these issues. Over the iterations and after some focused discussions about adding new elements, shared knowledge between stakeholders lead them to spontaneously ask each others about respective choices and the reasonings behind them.

> "The space filled with more encampments is becoming more concentrated, and there are also transhumant herders who join the locals and create difficulties".

— a local stakeholder during the second day of the first workshop.

> "We need to unite, especially the farmers and herders. So that no one can separate us, so that we can live in better conditions and cohabit in our respective territories. All we have to do is try to live in harmony".

— a local stakeholder during the third day of the second workshop.

These discussions and exchanges revealed that tensions over resources and space arose on two social levels. Despite these distinctions, the stakeholders agreed that each new element introduced into the model must be reached by consensus, and that any hypotheses meeting each stakeholder's objectives should be collectively discussed and validated. Thanks to stigmergy, these two issues remained central guidelines during model development, were considered alongside the goal of preserving woody populations as the model evolved, and served to remind the collective of commitments made in previous iterations (see Fig. 4).

### 3.3. Normative change: re-framing assumptions, objectives and creating an innovative platform

To reduce misunderstanding and more accurately reflect socio-ecological dynamics, we had to adjust both our model's shape, the objectives and the collective assumptions. Researchers and participants agreed to hold additional workshops to refine the model. The aim was to ensure that the modifications reflected local knowledge and concerns, while incorporating human practices and ecological dynamics in a more accurate way in the model's objectives.

Two major adaptations to the workshops format were introduced, helping to make these refinements. (i) First, live simulation combined with activity diagrams was preferred to using static posters to explain behaviors, allowing real-time model adjustments so local stakeholders could see the consequences of their suggested adjustments or questions directly. (ii) Second, a model version on an intermediate scale, focused on a single settlement, was developed, for a more detailed examination of herd and household behavioral patterns. This simplified model, later complemented with a paper-based approach, was crucial in highlighting the behavior of the simulation model — representation of the agro-pastoralists and herders' real-life strategies. It enabled participants to better observe and understand the interactions between human practices and the surrounding environment in a restricted space, which led them to spontaneously discuss the diversity of practices and how they could be integrated into the larger-scale model using this medium. Both versions of the model were saved in our stigmergic imprints.

Beyond these methodological adaptations, discussions about pruning revealed the need for increased education and knowledge sharing in favor of sensible and selective pruning. Furthermore, pruning was closely linked to livestock feeding, showing how pruning practices influence the availability of tree leaves and fruits, which are essential for livestock, particularly during the lean period. These discussions clarified our perceptions and generated collective reflection on ways of improving cohabitation on grazing lands. Over subsequent iterations, broader hypotheses emerged around access rights to lands, stakeholder legitimacy and collaboration in the management of human practices, emphasizing collaboration, adaptive practices, and flexible governance to ensure the sustainability of shared resources in broader considerations than just focusing on replanting trees.

Assumptions that emerged from the workshop were saved in our imprints and served as guides for the ComMod process and model construction. For each stigmergic imprint we could assess if the following workshops had addressed earlier assumption and noted any new ones that arose, ensuring continuous adaptation and refinement. Stigmergic imprints act as a shared memory and track model adjustments, ensuring that each new workshop builds upon and refines earlier assumptions. By revisiting these imprints, participants remain aligned with shared objectives and maintain clarity on the next steps, facilitating more inclusive, adaptive, and sustainable outcomes.

### 3.4. Cognitive change: improving knowledge and practices

During the discussions, participants clearly identified key aspects in which the model needed to be enriched to better represent field realities, help them make more informed management decisions and thus answer assumptions. These discussions highlighted the main areas for making the model more functional and relevant by including specific aspects of their environment and practices. With the help of the Banos & Sanders framework (Banos and Sanders, 2013), we proposed to directly observe the direction in which components of the model were implemented and the form the model took or the way in which participants (whether stakeholders or the research team) wished it to be shaped (Fig. 5).

First, local stakeholders proposed enriching the environmental parameters to include not only the quantity of grass, but also its quality, its behavior relative to that of the herds and by specifying its growth,
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** The figure displays two simulations from the initial model developed for the first iteration. (4.a) shows results from a simulation with 16 households and 32 herds (half for each type of herd) after 700 steps. (4.b) those from a simulation with 68 households and 136 herds. Figures on the left show the spatial projections. The charts represent firstly the respective percentage of total corporal condition for cattle herds (in brown) and for sheep herds (in blue), then the average amount of grass in the simulation.</figcaption></figure>

the definition of whose the rate would evolve depending on the level of detail given to climatic variables (Fig. 5.a). After an initial proposal, a discussion revealed that there were two types of grass, and that herd feeding behavior was closely linked to the characteristics of each grass type. There was also a need to include trees, with their respective fruits, leaves and growth, according to ecological research on the subject. Due to the scale of the model, trees were grouped by population age and type, requiring advanced discussions on tree classification methods. This meant making our model more descriptive but not completely, with a certain form of genericity concerning tree and grass species and their respective behaviors.

Concerning households (Fig. 5.b), we moved from schematic, stylized and simple demographic characteristics to gradually corresponding to real demographic data. On the other hand, we moved from a clear distinction between local and transhumant households to a change in status. This made this aspect more descriptive, and more adapted to the change from local to transhumant. The behavior of ‘household’ agents, integrating the memory of the spaces experienced and traveled through to observe the results of the different ways in which knowledge

was shared between households, kept a form of genericity, we do not accurately describe each existing household’s known space. Deeper discussions revealed how decisions were distributed between households and shepherds, helped to share stakeholders’ knowledge about herding decisions and how diversity of choices could be abstract within a model.

After a series of discussions, it was agreed that each herd should be modelized as a single agent coupled with a shepherd who led it. The spatial and feeding behavior of herds was discussed with nuances that took into account the diversity of livestock types and their seasonal feeding habits (Fig. 5.c). Those habits were also distinct between cows and sheep, with cows being able to consume tall grasses but being restricted by the minimum amount of grass present, the behavior being reversed for sheep. It was collectively agreed that spatial behavior and especially pruning practices were assigned to herds, considering that these choices were made by shepherds. The herd size was related to the type of herder, making it more relatable to the census carried out for each pastoral unit.

Another discussion participating in the co-construction of the model was that the grazing strategies employed by herders took into account
<figure><img src="image_8.png" /><figcaption>Fig. 5. Diagrams inspired from the Banos & Sanders framework (Banos and Sanders, 2013). Each diagram corresponds to one aspect of the model. These aspects are linked to the evolution of their characteristics (circle) and behaviors (triangle) before the workshops (in orange) the stakeholders wishes after the first workshop (in blue) and those effectively integrated after the second workshop (in green) and the reconsiderations made throughout the workshops. (a) corresponds to the environmental aspect, (b) to households, (c) to herds, (d) to soil types. (e) corresponds to the average of all the aspects of the model and gives a general idea of the direction the model is taking.</figcaption></figure>

not only the quality of the pastures but also the accessibility of certain areas depending on the seasons and types of soil, which added an accessibility characteristic to the different types of soil (Fig. 5.d). We included the corresponding characteristics based on the available literature on the sensitivity of Sahelian soils to degradation in the Sahel (de Wispelaere, 1980). Their behavior still needs to be defined jointly with local stakeholders, experts, and the research team. Being more descriptive, adding these elements does not make it more specific, as we consider that these situations can occur throughout the entire Ferlo region.

Further discussions led to a need to integrate other key resources in order to address assumptions that emerged from the workshops, such as agricultural lands, farming practices and water infrastructures. Although these aspects have not yet been integrated into the model, discussions to integrate them led each of the stakeholder groups to share their respective knowledge with each other and with the research team.

All these improvement objectives and those actually achieved were saved in our stigmergic imprints to gradually monitor how the model was evolving and how the knowledge was shared between participants and integrated into the model. In this way, we can directly assess how the contents of the model and their characteristics and behaviors evolve throughout the iterations (Fig. 5.e).

## 4. Discussion

Our study focused on understanding social learning loops in the context of reforestation in Senegal taking a Companion Modeling (Com-Mod) approach. Past research on pastoralism has explored various dimensions such as the management of pastures, livestock, and the balance between pastoral and agricultural practices. However, there are few models that specifically address the interactions existing between pastoralism and forest management. We found inspiration from previous participatory modeling initiatives to verify our conceptual model and formalize the necessary knowledge (Bah et al., 2006; Toure et al., 2003). The stakeholders involved in our study represented diverse groups, including village chiefs, agro-pastoralists, local authorities, and water and forestry services, each with distinct representations of the

world based on their beliefs, values, and experiences. The model, supported by interactive simulation, served as a catalyst for social learning, confirming prior research on its effectiveness as a tool for collective and reciprocal learning, dialogue, and empowerment of local actors (Le Page, 2017).

### 4.1. The role of stigmergy in strengthening social learning loops

During our workshops, the scenarios explored provided intermediate objectives that functioned as research assumptions to be addressed by the model. The first step for local stakeholders was to learn to interpret the model correctly, understanding its capabilities and limitations. This mutual understanding between modelers and local stakeholders encouraged a normative change in how the modelers approached the problem, followed by cognitive changes through the co-production and formalization of new knowledge (Voinov and Bousquet, 2010). The process of co-producing knowledge revealed the complexity of herd management and highlighted how environmental conditions, such as tree populations, grass and their respective behavior and characteristics, could be integrated into the model. This led to a richer understanding of the interdependencies within the socio-ecological system, further enhancing the accuracy and relevance of the model.

Through dialogue, the participants reassessed their understanding of spatial dynamics. For example, the modelers had to revise their fixed and sedentary perception of space to accommodate the mobile nature of pastoralism, which sees space as fluid and shaped by needs and networks (Akrich, 1991; Retaillé, 2014). This shift allowed the model to integrate multiple spatial scales – micro, meso, and macro – each addressing different levels of agro-sylvo-pastoral practices. This multiscale approach, inspired by other studies, helped foster stronger collaboration across governance levels and better integration of local knowledge (Banos and Sanders, 2013). By navigating between these scales, the model allowed stakeholders to make a better contribution to the model's construction and to see how local actions influenced broader outcomes and vice versa, providing a framework for more adaptive and inclusive resource management strategies.

One critical relational learning process occurred during discussions between local stakeholders about forest degradation responsibilities
and herding strategies. A significant point of contention was raised between agro-pastoralists and transhumant herders regarding compliance with rules on wood harvesting. After productive dialogue, it became clear that all stakeholders, whether local or transhumant, share responsibilities depending on where they are situated. This mutual understanding helped resolve conflicts over forest resource management, emphasizing the importance of inclusive dialogue in resolving environmental challenges (Ducrot et al., 2015). Over iterations, the stakeholders agreed that discussion and consensus were key to collective management decisions. Such relational learning triggered further normative and cognitive learning loops, as the stakeholders revised their assumptions and integrated new shared knowledge into the model.

Stigmergy played a pivotal role in facilitating social learning loops by documenting the collective construction of the model, but also by acting as a collective memory and as a reminder of past agreements and assumptions that guided the model's coconstruction. The traces left by participants, such as contributions to code and discussions, allowed both internal and external observers to track how the model was evolving and how different forms of social learning were activated at various stages. Coupled with the model which acted as a catalyst for social learning loops, this innovation strengthened vertical and horizontal collaboration, making it easier to monitor and share knowledge among computer scientists, modelers, and local stakeholders (Medema et al., 2014). Furthermore, stigmergy can provide valuable information for future degraded land restoration initiatives, enabling them to track the construction process, identify participants, and understand the role of each group in the model's co-construction. By integrating both technical and social perspectives, stigmergy offers a promising tool for monitoring and communicating social learning processes and supporting decision-making in resource management initiatives (Lebel et al., 2010).

At the moment, stigmergic imprints are created and maintained through code, ODD, reports and video encapsulations that are stored in an online repository. However, this solution presents accessibility challenges: the materials are primarily in French, and navigating the repository demands a certain level of digital skills. Consequently, local stakeholders rely on researchers, who are the sole guarantors of updates, to remind them of the discussions and decisions made in previous workshops. After each workshop, the research team integrates new inputs into the repository and verifies accuracy, but the local stakeholders themselves cannot easily verify whether the records fully reflect their intentions. This language and literacy barrier undercuts their ability to be fully co-owners of the process, introducing a certain asymmetry in knowledge and power. Additionally, as everything is documented, there is a potential risk that participants might feel “under surveillance” if they cannot fully review or contest the captured information. To mitigate this, the team is exploring more accessible formats, such as specific reports, and considering more inclusive digital tools for the future.

## 4.2. Cognitive, normative and relational changes in a ComMod process through stigmergy

In the same way as Grimm et al. (2014) proposed in a process without participants with TRACE, or as Latour with “inscription devices” (Latour and Woolgar, 1986), stigmergy made it possible here to move the lines of the collective gathered around the model. Thus, integrating stigmergy into the ComMod process facilitated significant changes by reducing informational uncertainty, promoting knowledge co-construction and reinforcing the willingness to collaborate among diverse stakeholders and with modelers. The model acted as a bridge between scientific and vernacular knowledge. It also served as a catalyst for sharing perceptions and collectively envisioning strategies for a desired future. It enabled diverse stakeholders to exchange perspectives

and collectively decide how to integrate their insights into the simulation. To support these processes, we used the Banos model (Banos and Sanders, 2013), which monitored the evolution of the model's various aspects and helped the modelers to assess the accuracy of the model elements that local stakeholders wanted to see and those that were actually implemented following the workshops. Stigmergic imprints documented how different types of knowledge were gradually integrated into the model, guiding future iterations and fostering equitable learning partnerships among the groups involved.

Through stigmergy, we were able to track the model's redirections, knowledge integration within it across multiple iterations and to document the reflections of each stakeholder group on shared experiences. This continuous feedback process helped enhance collaboration and promoted a deeper understanding of the socio-ecological system. The co-production of knowledge, which was improved thanks the stigmergy principle, reinforced the transdisciplinary nature of the project, enabling the model to function as a platform for knowledge exchange and synthesis (Le Page, 2017).

Cognitive changes often depend on favorable conditions that emerge from normative or relational shifts. In our case, normative changes were triggered by the collective reconsideration of objectives, assumptions, and acceptable risks (Pahl-Wostl, 2009). Stigmergy provided all the participants, and others outside the co-construction process, with access to the model's code, its description, its evolution and the motives behind changes arising from social learning, thereby playing a clear role in revealing those orientations and helping to establish intermediate objectives that guided the co-construction of the model (Moreau et al., 2019; Star and Griesemer, 1989). As new insights emerged throughout the iterative process, the stakeholders adapted their goals and introduced innovative approaches to resource management. This adaptive process strengthened both horizontal and vertical collaboration, creating a more inclusive and transparent modeling environment (Lebel et al., 2010).

Using stigmergy as a conceptual tool for monitoring and evaluation encouraged critical self-reflection among both the stakeholders and the modelers, leading to several innovations in the thematic and methodological dimensions of the simulation. Observing and reconsidering the position of the model helped align the simulation with the stakeholders' evolving understanding of space and resource management practices (Latour, 2007). These iterative adjustments underscored the model's ability to mediate between different scales of governance and fostered stronger collaborations across multiple levels (Akrich, 1991). Currently, the team relies on qualitative evidence: documented changes in assumptions, references to past agreements, and improvements in collaborative atmosphere. In the future, more structured methods, such as ex-post surveys, could provide robust metrics on whether stigmergy-driven recollection of past iterations directly contributes to increased trust, conflict mitigation, or policy adaptation.

Finally, stigmergy contributed to relational changes in our ComMod process by fostering mutual understanding and trust between local stakeholders and with modelers (Ducrot et al., 2015). The reciprocal exchange of knowledge and perceptions that occurred during the workshops increased the capacities of local stakeholders and empowered them to engage more actively in the co-construction of the model (Lebel et al., 2010). These relational improvements were essential in identifying new objectives for the simulation and guiding the model's development. As the modelers reconsidered their vision of space and practices in relation to the environment, the model itself evolved to better reflect local realities and the needs of the broader governance structure (Coudel et al., 2017). Moreover, larger governance structures can also benefit from these open-access stigmergic imprints by consulting the repository for insights into local resource-management proposals, though their reliance on digital platforms and possibly unfamiliar languages remains a barrier. Future work may focus on bridging these gaps by adapting the recorded imprints into more policy-friendly formats or organizing intermediary forums where local insights and policy directions can intersect meaningfully.
## 5. Conclusion

This study, centered on the Companion Modeling (ComMod) process, incorporated the principle of stigmergy to facilitate both the modeling experience for participants and group mediation. By using stigmergic imprints, we enabled both internal and external observers to track the co-evolution of social learning and model improvement, as well as their contributions to achieving the objectives of companion modeling. Stigmergy provided a means to observe which stakeholder groups were engaged in dialogues, how the model evolved to address practical challenges, and how critical self-reflection emerged through the comparison of diverse perspectives.

The iterative approach in our fieldwork highlighted several key findings, such as the impact of grazing strategies on forest resources, the role of knowledge sharing, and the integration of ecological variables in improving model accuracy. These insights led to adjustments that enhanced the model's relevance to local conditions, with contributions from both scientific knowledge and collectively constructed perceptions. Stigmergy played a crucial role in fostering horizontal collaboration between stakeholders and modelers, facilitating knowledge co-production and improving the capacity of all the involved parties. Additionally, it strengthened vertical collaboration across governance levels, helping to balance local agro-sylvo-pastoral practices with transhumant herding. These improvements underscore a broader lesson: it is when all stakeholders (including researchers) work closely together, confronting current inconsistencies and developing context-specific responses, that modelling efforts can truly become agents of change.

Ultimately, a shared representation of the world emerged through this process, which reshaped perceptions and underscored the importance of mutual understanding. This experience led to (i) a strengthening of relational learning over the iterations by increasing trust and the willingness to collaborate among stakeholders, (ii) induced normative changes by reinforcing its role in mediating resource management conflicts as well as reframing assumptions and goals, (iii) a stimulation of cognitive changes by supporting the stakeholders' awareness of socio-ecological interdependencies. These findings confirm the potential of stigmergy to act as a collective memory, encouraging iterative self-reflection and collaborative decision-making that respects local agency (Latour, 2000).

Despite these successes, challenges remain. Accessibility constraints limit local stakeholders' ability to verify and re-use stigmergic imprints independently. Ongoing efforts aim to produce more inclusive formats and bridge these gaps through additional training and co-designing of user interfaces in the continuity of work such as Szangolies et al. (2024). Building on these efforts, future research can further examine the quantitative impacts of stigmergy-driven iterations on trust-building and conflict resolution.

By fostering collaboration and mutual understanding, this work has strengthened the ability of local stakeholders to navigate and manage complex socio-ecological systems in the face of environmental challenges. In doing so, we hope to contribute to advancing methodologies that not only improve model accuracy but also catalyze deeper transformations in governance and stakeholder empowerment through ComMod initiatives.

### CRediT authorship contribution statement

**François Vendel:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation, Conceptualization.
**Oleksandr Zaitsev:** Writing – review & editing, Validation, Software, Formal analysis, Conceptualization.
**Pierre Bommel:** Writing – original draft, Software, Methodology, Investigation, Conceptualization.
**Ibra Touré:** Resources, Investigation.
**Jean-Daniel Cesaro:** Resources, Project administration, Funding acquisition.
**Penda Diop:** Investigation, Data curation.
**Marieme Fall Ba:** Project administration.
**Tamsir Mbaye:** Project administration.
**Etienne Delay:** Writing – review & editing, Supervision, Software, Resources, Methodology, Investigation, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Francois vendel reports financial support, equipment, drugs, or supplies, and travel were provided by AVSF. Penda Diop reports financial support, equipment, drugs, or supplies, and travel were provided by AVSF. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This study benefited from the financial and logistical support of the NGOs WeForest and AVSF (Agronomists and Veterinarians Without Borders) and ISRA CNRF (Senegalese Agricultural Research Institute - National Forest Research Center) as part of the Dundi Ferlo project and financial support by CASSECS and SLAM-B Ferlo-Sine projects. We particularly thank the Municipality of Velingara-Ferlo and sub-prefecture in the district of the same name for allowing us to undertake our experiment and for supporting our work by facilitating local investigations. Special thanks go to the people who participated, from near and far, in this work, by contributing to the model's co-construction, or by logistically and organizationally supporting this work.

### Data availability

Data will be made available on request.

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