Planificación de desarrollo de la Propuesta

Games and social simulation tools are said to allow social learning and facilitate cognitive learning through the experience (e.g. Barreteau et al., 2003). In this thesis, four gaming and simulation tools were developed to explore the concept of resilience in social-ecological systems in contested agricultural landscapes and to facilitate social and cognitive learning of participants on these concepts (Chapter 3, 4, 5 and 6). Participants interacted and learnt through gaming and simulation tools in groups (Chapter 3 and 6), in couples (Chapter 4 and 5) or on an individual basis (Chapter 4). The tools focused on different types of learning. In chapter 3 the main focus was on social learning, in chapter 4 on cognitive learning and in chapter 5 both learning methods were combined. In all three chapters, participants experienced the concepts by managing simulated systems (Kolb, 1984) (Chapter 3, 4 and 5). In particular in chapter 5, role-play, negotiation and knowledge acquisition allowed forward-looking or anticipatory learning on complex systems behavior. Learning and in particular anticipatory learning can prepare stakeholders for dealing with complex systems behavior e.g. uncertainty, sudden shocks and changes, and to the non-linear behavior of systems (Tschakert and Dietrich, 2010).

Assessing the effects of (social) learning remains difficult and objective measures on the process and effects of (social) learning through participatory methods, gaming and simulation are still scare (e.g. Gosen and Washbush, 2004; Scholz et al., 2013). In the majority of the studies analyzed by Gosen and Washburn (2004) little attention was given to assessing learning effects. The additional investment of time and resources required to assess the effects of gaming and simulation tools on learning seems to be substantial (e.g.

Kok et al., 2007; Van Paassen et al., 2007) and is often outside the scope of projects. The most commonly used assessment on learning process was self-reported learning, a highly subjective measure (Gosen and Washbush, 2004). In addition to self-reported learning,

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group discussion, written evaluations and interviews ex-post were implemented in this thesis. An objective assessment on the acquiring of knowledge through the interaction with a computer simulation model was developed (Chapter 3). The analysis of this detailed evaluation on learning confirmed self-reported accounts of learning in previous workshops with the model. Participants gained in-depth knowledge and understanding on the concepts introduced and explored and shared ideas on land-use decision-making and reflected on their own and each other’s decisions.

Since the first development of games as tools to facilitate learning in business education (Duke, 1974), games have been developed and used in a variety of settings for distinct goals.

In the last years, an ongoing trend towards more active and experiential learning is seen in higher education (Lean et al., 2006). Participatory methods that allow social learning in problem solving processes or explorations of future have been successfully used especially in the western world through e.g. cognitive mapping and scenario building (Kok, 2009; van Vliet et al., 2010). In the context of natural resource management and/or agricultural landscapes problem settings, the companion modelling approach (COMMOD) has been widely applied to address a variety of issues e.g. water management (Dray et al., 2005;

Gurung et al., 2006; Ferrand et al., 2009; Barreteau et al., 2012), soil erosion (Souchère et al., 2010), and collective awareness (Mathevet et al., 2007).

The majority of current methods does not particularly focus on anticipatory learning, but are part of a specific problem solving project. As a consequence the tools developed in these projects are highly site-specific and require major adjustments to be used in other situations. This one-problem-one-game approach is potentially more costly than the development of more generic tools that create a general understanding of processes of change and prepare for unknown change. The tools developed in this thesis aimed to be more generically applicable while yet still remaining useful in the context of resilience in contested agricultural landscapes (Chapter 3, 4, and 5).

Future research should focus on developing relevant anticipatory learning tools for smallholders and other local stakeholders in contested agricultural landscapes. These tools should be as simple as possible to facilitate the participation of stakeholders that are illiterate or functional illiterate while at the same time complex enough to create realistic system’s behavior to allow understanding and experience with processes of change.

Social-ecological systems behave like complex adaptive systems when a long time perspective is taken into account. Humans, as the managers of social-ecological systems, then become an integral part of the system and including human decision-making in model explorations will allow a more comprehensive understanding of system’s behavior (Walker et al., 2004). Innovative computer-supported modelling tools in which human decision-making was simulated based on e.g. probabilistic, microeconomics, and statistical

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159 empirical rules, have been developed to facilitate the exploration of the behavior of complex social-ecological systems (An., 2012).

A simulation tool for the exploration of land-use dynamics in complex social-ecological systems in which decision-making is based on social-psychology, currently under development, was presented in chapter 6. This model aimed to increase understanding on how farmers respond to change and which instruments and mechanisms e.g. collaboration benefits and/or subsidies, might be best suited to maintain system functions under economic and/or institutional change scenarios. Broad social-psychological theory was selected for the decision-making processes in the model to be able to generate plausible output under unknown change. Preliminary simulation results show responses to change that were qualitatively similar to those identified in chapter 2. In addition, the results suggest that decision-making processes change when systems are under pressure and the subsistence needs (i.e. provision of income and food) become more important. Positive economic changes had a minor effect on land-use decisions. In contrast, negative changes resulted in larger adjustment in landscape composition. Beneficial collaboration was only reached when the system was affected by a large negative change.

This type of holistic modeling of social-ecological systems allows analyzing processes of change at different levels and from distinct angles. Figure 7.1 illustrates a stylized example of the dynamics of a social-ecological system in response to an external driving variable such as price fluctuations or policy changes. The illustration was based on driver-response data collected from the case study area and presented in chapter 2. Four aspects of an agriculture-based social-ecological system are shown in response to changes in the driving variable. This tool shows the diversity of potentially slow and immediate response mechanisms at the various scales and levels. An important addition could be the inclusion of the simulation of satisfaction of local resource managers or farmers. Similar social facets were previously modelled by e.g. Bregt and Ligtenberg (2013).There are often sentiments of dissatisfaction that remain unnoticed. Through human interactions these sentiments can spread fast and result in surprising social uprising (Gladwell, 2000). Agricultural related uprisings resulting from price vitality were e.g. Zapatista movement in Chiapas, Mexico triggered by the ratification of NAFTA in 1994; farmer protests against lower prices in response to European trade and agricultural policies were intermittent since the late 1980s;

worldwide food riots during 2007-2008 and 2011-2012 as a consequence of reduced and failed harvest in various part of the world. The latter has been predicted by computer simulations.

Future research should focus on the further development of similar tools by explicitly simulating the impacts of social processes on ecological processes and vice versa. In

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addition, Social mechanisms that could improve system resilience such as beneficial collaboration in view of economic and institutional change should be explored further.

Figure 7.1: An example of simulated output from the LUSES model on land-use dynamics in social-ecological systems based on social-psychological theory. An overview is presented of a permanent change in a driving variable (a), and subsequent effects on (b) relative land use changes, (c) the state of a landscape function, (d) individual satisfaction of farmers, and (e) collective behavior.

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3 Resilience thinking in contested agricultural