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Findings

Understanding Stakeholder Perception & Behaviour

Individuals perceptions and behaviour can show us how they react to public policies and compliance. Thus, this information can be leveraged in the strategic governance of the resource system.

Boats
Fishers with their boats at Lake Victoria

We analyse stakeholders perceptions via the use of mental models and identify the differences in perceptions among fishers. We also examine the risk-taking behaviour among fishers.

Mental Models and the M-Tool

Differences in Perceptions Among Fishers

Risk-Taking Behaviour Among Fishers

 

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Mental Models and the M-Tool

Mental models are internal representations of an external system. They consist of causal beliefs about the how a system functions (Bostrom, 2017) and can be used to provide insights on a system based on stakeholders perceptions and behaviour (Forrester, 1992). Therefore, they are identified as a leverage point within psychology research for addressing sustainability challenges (Goldberg et al., 2020). Diagram drawing methods are commonly used to illicit mental models. The current software developed for this purpose has been successful in producing insights (e.g. Gray et al., 2014; Wood et al., 2017). However, the resulting mental models may be difficult to compare because respondents enter their own system variables. Moreover, these software have not been designed to asses mental models of individuals with low literacy.

One of the key success of MultiTip has been the development of a universal tool for mental model elicitation. The Mental Model Mapping Tool or M-Tool allows participants to express their views and perceptions about causal relationships in their environment with minimal literacy and numeracy requirements (van den Broek et al., 2021a). This is due to the use of video instructions, pictograms and audio descriptions. In this tool, researchers provide participants with a set of system variables to construct their mental model which eases mental model comparability. Once the researcher has constructed the model, they can share the created study with participants via a link on the web-based app or setting it up on a tablet via the application. A CSV file of the data can be downloaded and read by analysis software such as R. The tool also captures the time that participants take to create their mental model which can be informative of the mapping process (LaMere et al., 2020). The web-based application has additional benefits such as allowing researchers to link the data to an external survey and a bar chart drawing task that displays participants views on the relation between two variables, e.g. fluctuation of a resource over time.

Stakeholders Fig1
(Click to enlarge)
Screenshots of M-Tool: (A) practice session on how to create a mental model (B) presentation of the pictograms and (C) the mental model mapping screen

 

The M-Tool software can be downloaded for free in the App store and Google Play Store and a web-based application can be found on www.m-tool.org. The tool was developed by Heidelberg University, TAFIRI and Lambdaforge as part of the MultiTip project. It has already been downloaded over 600 times.

 

 

Mtool Participant
A participant using the M-tool app.

We also tested the M-tool’s application in two field studies with fishers in Tanzania (van den Broek et al., 2021b). We first compared the M-tool to an alternative elicitation technique that is also appropriate for measuring mental models among participants with low literacy: semi-structure, face-to-face interviews (Findlater et al, 2018). Despite similar model composition across the two methods, we found that M-tool mental models were significantly more complex than interview mental models in the total number of drivers that participants used. Second, we related the M-tool models to participants level of education. The higher the participants level of education, the more complex the mental model the participant creates (Levy et al., 2018; Varela et al., 2020). Our study confirmed this and thus, the M-tool is validated in this context. These findings suggest that the M-Tool can successfully capture mental models among diverse participants.

Researchers: Dr. Karlijn van den Broek, Dr. Sina Klein, Dr. Helen Fisher

Partners: Dr. Jan van den Broek, Joseph Luomba (TAFIRI), Lambdaforge

Publications:

van den Broek, K. L., Klein, S. A., Luomba, J., & Fischer, H. (2021a). Introducing M‐Tool: A standardised and inclusive mental model mapping tool. System Dynamics Review, 37(4), 353–362. https://doi.org/10.1002/sdr.1698

van den Broek, K. L., Luomba, J., van den Broek, J., & Fischer, H. (2021b). Evaluating the Application of the Mental Model Mapping Tool (M-Tool). Frontiers in Psychology, 12, 761882. https://doi.org/10.3389/fpsyg.2021.761882

 

 

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Differences in Perceptions Among Fishers

Stakeholders’ interactions with environmental resources are strongly influenced by their mental models of the resource. Mental model elicitation can help identify disparities in perceptions between stakeholders. Individual differences in such mental models are particularly important to identify as diverse mental models may be associated with different behaviour or policy preferences and affect collaborative conservation efforts (Rouwette, 2016). During the preparatory phase of MultiTip, we explored the mental models of Nile perch fish stock among stakeholders at Lake Victoria (van den Broek, 2018). The stakeholders consisted of 76 participants from 33 different institutions (including governmental, NGO’s, business organisations, research institutions and community groups) in Uganda, Kenya and Tanzania. This analysis revealed large differences in the perceptions among stakeholders about the state of the Nile perch stocks and drivers of changes to the stock. These differences in perception are important as they may hinder management of the SES. In our following studies, we looked at what may influence these structural differences.

First, we looked at the structural differences in perceptions based on the type of knowledge acquisition, i.e. if the participant received knowledge of the system formally or informally (Klein at al., 2021). We conducted a survey on 225 participants across Tanzania, Uganda and Kenya and found that most participants agreed that the stock has declined. However, participants with informally acquired knowledge focused on examples of fewer drivers related to tangible human activities (e.g., the use of illegal fishing gear), whilst participants with formally acquired knowledge used more abstract and a larger variety of drivers related to the presence of humans (e.g., overpopulation).

Stakeholders Fig2
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The mean mental model of Tanzanian fishers of the drivers of the Nile perch stock. The image shows how fishers’ mental models focus on their influence on the stock, such as fishing in breeding grounds, the use of destructive gear and overfishing.

Next, in our present work, we explore stakeholders’ mental models of a socio-ecological system and assess content and complexity differences across experience levels, migration status, and regions (van den Broek et al., under review). We mapped the mental model of drivers of the Nile perch fish stock fluctuation at Lake Victoria for 185 Tanzanian fishers. The findings show that (1) fishers’ mental models were complex and diverse, (2) mental models tended to focus on the use of destructive fishing activities, (3) mental model complexity, but not content, varied across regions, and (4) fishing experience and migration status were not consistently related to mental model complexity or content. In particular, we found that fishers converge in their perception that fishers’ activities, in particular non-compliance with regulations, contributes to stock decline in a major way. These results have important implications for environmental resource management at Lake Victoria.

Researchers: Dr. Karlijn van den Broek, Dr. Helen Fisher, Dr. Sina Klein

Partners: Dr. Jan van den Broek, Joseph Luomba (TAFIRI), Horace O. Onyango (KMFRI), Bwambale Mbiling (NaFIRRI), Joyce Akumu (NaFIRRI)

Publications:

Klein, S. A., van den Broek, K. L., Luomba, J., Onyango, H. O., Mbilingi, B., & Akumu, J. (2021). How knowledge acquisition shapes system understanding in small-scale fisheries. Current Research in Ecological and Social Psychology, 2, 100018.

van den Broek, K. L. (2018). Illuminating divergence in perceptions in natural resource management: A case for the investigation of the heterogeneity in mental models. Journal of Dynamic Decision Making, 4, 1–5. https://doi.org/10.11588/jddm.2018.1.51316

van den Broek, K. L., Luomba, J., van den Broek, J., & Klein, S. (Under review). Fishers’ mental models reveal their perceived role in conserving fish stock and regional differences.

 

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Risk-taking Behaviour Among Fishers

Risk is both an essential part of fishing decisions and also determines anthropogenic pressure on the resource system. Fishers at Lake Victoria face many risky decisions in their daily lives. For example, they have to decide on whether to engage only in fishing or also in other activities, how much to invest in equipment and whether to comply with regulations. The socio-ecological system of Lake Victoria adds important dimensions of risk and uncertainty because fishers income and livelihoods would be threatened if aggregate fish levels were to exceed a tipping point. Therefore, understanding fishers risk-taking behaviour is important when considering policies that include a risk dimension, for example, selective fishing gear regulation. To this end, MultiTip investigated the potential factors influencing fishers risk behaviour with a lab-in-the-field experiment in Tanzanian at Lake Victoria (Dannenberg et al. 2022). We found asymmetric positive feedback effects in risk-taking among fishers.

In the main task, fishers had to decide how much money to invest in a risky lottery where the investment triples if a person is lucky and is lost if a person is unlucky. We then analysed how fishers decisions change when they experience good / bad luck just before the decision is made and when they are informed about others investments. The results show that fishers have a slight tendency to take more risk in a lottery if they have been unlucky shortly before making the decision. Furthermore, there were positive feedback effects among fishers: when fishers learn that their peers are engaging in high-risk behaviour, they increase their own risk-taking. However, the feedback effect was asymmetric: learning that their peers engage in low-risk behaviour does not decrease risk-taking.

Stakeholders Fig3
(Click to enlarge)
The cumulative distribution of fishers’ investment decisions by experience of good or back luck and social information. Note: The cumulative distribution in this graph shows the fraction of participants within the same treatment condition who invest the respective amount or less. Participants with high and low stakes in the betting task are pooled.

Researchers: Prof. Dr. Astrid Dannenberg, Jun.Prof Dr. Florian Diekert, Phillipp Händel (Ph.D. Candidate)

Partner: Joseph Luomba (TAFIRI)

Publication: Dannenberg, A., Diekert, F. & Händel, P. (2022) The Effects of Social Information and Luck on Risk Behavior of Small-Scale Fishers at Lake Victoria, Journal of Economic Psychology,90,102493. https://doi.org/10.1016/j.joep.2022.102493

 

 

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References:

Bostrom, A. (2017). Mental Models and Risk Perceptions Related to Climate Change. Oxford Research Encyclopedia of Climate Science, 1–31. https://doi.org/10.1093/acrefore/9780190228620.013.303

Diekert, F., T. Goeschl, C. König-Kersting (2021): Social Risk Effects: The 'Experience of Social Risk' Factor. AWI Discussion Paper 701, Heidelberg University

Findlater, K. M., Donner, S. D., Satterfield, T., and Kandlikar, M. (2018). Integration anxiety: The cognitive isolation of climate change. Glob. Environ. Chang. 50, 178–189. doi: 10.1016/j.gloenvcha.2018.02.010

Forrester, J. W. (1992). Policies, decisions and information sources for modeling. European Journal of Operational Research, 59(1), 42–63.

Goldberg, M. H., Gustafson, A., & van der Linden, S. (2020). Leveraging social science to generate lasting engagement with climate change solutions. One Earth, 3(3), 314–324. https://doi.org/10.1016/j.oneear.2020.08.011

Gray, S. R. J., Gagnon, A. S., Gray, S. A., O'Dwyer, B., O'Mahony, C., Muir, D., Devoy, R. J. N., Falaleeva, M., & Gault, J. (2014). Are coastal managers detecting the problem? Assessing stakeholder perception of climate vulnerability using Fuzzy Cognitive Mapping. Ocean and Coastal Management, 94, 74–89. https://doi.org/10.1016/j.ocecoaman.2013

LaMere, K., Mäntyniemi, S., Vanhatalo, J., & Haapasaari, P. (2020). Making the most of mental models: Advancing the methodology for mental model elicitation and documentation with expert stakeholders. Environmental Modelling and Software, 124(104589). https://doi.org/10.1016/j.envsoft.2019.104589

Levy, M. A., Lubell, M. N., and Mcroberts, N. (2018). The structure of mental models of sustainable agriculture. Nat. Sustain. 1, 413–420. doi: 10.1038/ s41893-018-0116-y

Rouwette, E. A. J. A. (2016). The impact of group model building on behaviour. In Behavioral Operational Research: Theory, Methodology and Practice, Kunc M, Malpass J, White L (eds). Palgrave MacMillan, London; 213–241.

van den Broek, K. L. (2018). Illuminating divergence in perceptions in natural resource management: A case for the investigation of the heterogeneity in mental models. Journal of Dynamic Decision Making, 4, 1–5. https://doi.org/10.11588/jddm.2018.1.51316

Varela, B., Sesto, V., and García-Rodeja, I. (2020). An investigation of secondary students’ mental models of climate change and the greenhouse effect. Res. Sci. Educ. 50, 599–624. doi: 10.1007/s11165-018-9703-1

Wood, M. D., Thorne, S., Kovacs, D., Butte, G., & Linkov, I. (2017). Mental Modeling Approach. Springer New York. https://doi.org/10.1007/978-1-4939-6616-5

 

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Updated on: 12.08.2022
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