A Farming Scenario in Athienou, Cyprus

“EXPERT” AND “NON-EXPERT” DECISION MAKING IN A PARTICIPATORY GAME SIMULATION: A FARMING SCENARIO IN ATHIENOU, CYPRUS

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree of Master of Arts in the Graduate School of The Ohio State University

David Park Massey, B.A.

Geography Graduate Program

The Ohio State University

2012

Thesis Committee:

Dr. Karl (Ola) Ahlqvist (Advisor)

Dr. Daniel Sui

Dr. Mark Moritz

DAVID PARK MASSEY

2012

Abstract

The Greek-Cypriot village of Athienou, located in the UN Buffer Zone in Cyprus, lies at the front lines of a politically complex issue that divides the island of Cyprus.

Developing an understanding of how Greek-Cypriot farmers’ agricultural decisions affects land use/cover change (LUCC) allows researchers to formulate models and assessment plans for future scenarios. Drawing from the Companion Modeling

(ComMod) approach, this research uses ethnographic fieldwork to develop knowledge about Greek-Cypriot farming practices and the drivers of agricultural LUCC in Athienou through grounded theory. A conceptual model of the Athienou agricultural system is then built as a Role Playing Game (RPG). The RPG simulates the farming strategies and agricultural LUCC in Athienou in a scenario where the Turkish Occupied land to the north of the village becomes available for farming again. Two sets of participants,

Greek-Cypriot farmers (“experts”) and undergraduate students (“non-experts”), then play the RPG. An examination of the outcomes from decision-making strategies of the

“experts” and “non-experts” during the RPG scenario suggests a potential way to crowd- source information.

Keywords: Agent Based Modeling, Agriculture, Companion Modeling, Complex Systems, Crowdsourcing, Cyprus, Ecosystems, Ethnography, Grounded Theory, LUCC, Role Playing Games

Acknowledgements

This thesis would not have been possible without the encouragement, guidance, and support of many people who believed in me. For this, I am thankful.

I am deeply grateful for my advisor, Dr. Ola Ahlqvist. His expertise, generosity, patience, and time over the past two years allowed me to push forward through challenging times, while his direction opened me to new areas of research I would have never encountered on my own. My heartfelt thanks for all you have done. Thanks also to Dr. Dan Sui and Dr. Mark Moritz for willing to be on my committee.

Many thanks go to the people of Athienou and the Athienou Archaeological Project. Eleni Kalapoda’s selfless generosity helped immeasurably in this case study. My thanks also go to many of the staff and field students from the 2011 season that provided useful suggestions for my thesis. I am indebted to Dr. Michael Toumazou, Dr. Nick Kardulias, and Dr. Derek Counts for their generosity and support. In addition, my sincere thanks go to Dr. Nick Kardulias for his continuous guidance, and support throughout my academic and professional career.

To my friends in the Physics department whose offices I’ve probably spent too much time in (Nick Harmon, Mike Fellinger, John Draskovic, Geoff Smith, Rob Guidry, Sheldon Bailey, Jim Davis, Greg Viera, Rob Guidry, Kevin Driver, and Jeff Stevens), thank you.

To my friends and colleagues in the Geography department, thank you for all of your support and encouragement.

Finally, thanks Mom for all of your support and love. iii

Vitae

2004...... B.A. Archaeology, College of Wooster

2004-2006 ...... GIS Quality Control 1

TechniGraphics, Wooster, OH

2007-2009 ...... Geospatial Assistant/Assistant Archaeologist

John Milner Associates, West Chester, PA

2009-present ...... Department of Geography

The Ohio State University

Publications

Massey, D. P., and P. N. Kardulias. 2012. “Viewing the Digital Landscape: The Use of GIS in the Malloura Valley Survey.” Crossroads and Boundaries: Archaeology of the Past and Present in the Malloura Valley, Cyprus. Eds. M. K. Toumazou, P. N. Kardulias, D. B. Counts, 281-290. Boston: American Schools of Oriental Research.

Fields of Study

Major Field: Geography

Table of Contents Abstract ...... ii Acknowledgements ...... iii Vitae ...... iv Table of Contents ...... v List of Figures ...... viii List of Tables ...... x Chapter 1: Examining Land Use/Cover Change (LUCC) ...... 1 1.1 Structure of the Case Study...... 1 Chapter 2: Foundation for the Study ...... 3 2.1 The Millennium Ecosystem Assessment (MA) ...... 3 2.2 Complexity Theory and Complex Adaptive Systems (CAS) ...... 7 2.3 Modeling Complexity with Cellular Automata (CA), Agent Based Models (ABM), Multi-Agent Systems (MAS) ...... 9 2.4 Using MAS to model Land Use/Cover Change (LUCC) (MAS/LUCC) ...... 12 2.5 Multi-Agent Systems (MAS) as Role Playing Games (RPG) ...... 13 2.6 Companion Modeling (ComMod) ...... 14 2.6.1 The ComMod Methodology ...... 15 2.6.2 ComMod Research ...... 18 2.7 The Case Study ...... 21 2.7.1 Web 2.0 and Volunteered Geographic Information ...... 22 2.7.2 Grounded Theory ...... 23 Chapter 3: Research Methodology ...... 25 3.1 The Conceptual Framework for the Case Study ...... 25 3.2 The Problem Definition ...... 26 3.2.1 The Recent History of the Republic of Cyprus ...... 26 3.2.2 Study Location: Geography of Athienou...... 31 3.2.3 Agricultural History around Athienou ...... 32 3.2.4 Participants and Sampling Technique ...... 33

3.3 Model Conceptualization ...... 35 3.3.1 Ethnographic fieldwork in Athienou with the “Experts” ...... 35 3.3.2 Understanding Cereal, Dairy, and Chicken farming in Athienou ...... 36 3.3.3 Grounded Theory Analysis ...... 38 3.3.4 Discrepancies about the Occupied Land? ...... 40 3.3.5 Remote Sensing ...... 44 3.3.6 The Athienou Agricultural Game Simulation Model (AAG-SiM) ODD Protocol ...... 46 3.3.7 The AAG-SiM RPG Scenario ...... 51 3.4 The Game Workshop ...... 56 3.4.1 Observations from the Game session with the “Expert” Greek-Cypriot Farmers ...... 56 3.4.2 Observations from the Game sessions with the “Non-Expert” Undergraduate Students ...... 60 3.4.3 Moderator Observations ...... 69 Chapter 4: Preliminary Analysis ...... 71 4.1 Examining the Strategies ...... 71 4.1.1 The “Expert” Strategies ...... 72 4.1.2 The “Non-Expert” Strategies ...... 73 4.2 Examining the “Experts” and “Non-Experts” Outcomes ...... 75 4.3 Emergent Properties of AAG-SiM ...... 76 Chapter 5: Conclusion and Discussion ...... 78 5.1 Limitations ...... 78 5.1.1 Companion Modeling (ComMod) ...... 78 5.1.2 Positionality ...... 79 5.1.3 Validity ...... 79 5.2 Revisions to the AAG-SiM RPG ...... 80 5.3 Application of the Case Study ...... 81 5.4 Future Assessment ...... 81 References ...... 83 Appendix ...... 95 Appendix A: IRB Approval from Ohio State University ...... 96 Appendix B: Recruitment flyers for Greek-Cypriot farmers to participate in research experiment...... 97

Appendix C: Recruitment flyer for undergraduate students to participate in research (Translated from Greek flyer) ...... 98 Appendix D: Rules of the AAG-SiM RPG given to the farmers and the undergraduate students 99 Appendix E: Results of the “Expert” Game Workshop ...... 100 Appendix E: Results of the “Expert” Game Workshop (Continued) ...... 101 Appendix F: Results of the “Non-Expert” Group 1 Game Workshop 30 September 2011 ...... 102 Appendix F: Results of the “Non-Expert” Group 1 Game Workshop 30 September 2011 (Continued) ...... 103 Appendix G: Results of the “Non-Expert” Group 2 Game Workshop 7 October 2011 ...... 104 Appendix G: Results of the “Non-Expert” Group 2 Game Workshop 7 October 2011 (Continued) ...... 105 Appendix H: Cereal Farmer (“Expert”) ...... 106 Appendix I: Chicken Farmer (“Expert”) ...... 107 Appendix J: Dairy Farmer (“Expert”) ...... 108 Appendix K: Cereal Farmer (“Non-Expert” Group 1) ...... 109 Appendix L: Chicken Farmer (“Non-Expert” Group 1) ...... 110 Appendix M: Dairy Farmer (“Non-Expert” Group 1) ...... 111 Appendix N: Cereal Farmer (“Non-Expert” Group 2) ...... 112 Appendix O: Chicken Farmer (“Non-Expert” Group 2) ...... 113 Appendix P: Dairy Farmer (“Non-Expert” Group 2) ...... 114

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List of Figures Figure 1. The Millennium Ecosystem Assessment (MA) methodology for developing scenarios ...... 5 Figure 2. The Companion Modeling (ComMod) methodology...... 15 Figure 3. Conceptual Framework for the case study...... 25 Figure 4. The location of Cyprus in the eastern Mediterranean along with ancient place names...... 27 Figure 5. Relief map of Cyprus showing major cities, Athienou, and the Green Line. .... 29 Figure 6. Athienou in relation to the Green Line, between the Malloura Valley and the Mesaoria Plain...... 31 Figure 7. Threshing sledges in Athienou, 1895...... 33 Figure 8. The Athienou Municipality extracted from the CADiP georeferenced image with OpenStreetMap data...... 42 Figure 9. ΤΟΠΙΚΟ ΣΧΕΔΙΟ ΑΘΗΕΝΟΥ [The Local Plan of Athienou] ...... 43 Figure 10. Georeferenced local plan of Athienou with the location of the Green Line. .. 44 Figure 11. Landsat 1 imagery from January 1973...... 45 Figure 12. Landsat 7 imagery from July 2011...... 45 Figure 13. AAG-SiM entities...... 48 Figure 14. UML diagram of the AAG-SiM game play...... 48 Figure 15. The AAG-SiM RPG scenario game board with game pieces...... 53 Figure 16. AAG-SiM RPG scenario entities...... 54 Figure 17. A UML Diagram of the AAG-SiM RPG game play...... 54 Figure 18. Initial Starting positions for the "Expert" Greek-Cypriot Farmers...... 57 Figure 19. September (L) and October (R) (Expert)...... 59 Figure 20. November (L) and December (R) (Expert)...... 60 Figure 21. Initial starting positions for "Non-Expert" Group 1...... 62 Figure 22. September (L) and October (R) "Non-Expert" Group 1...... 62 Figure 23. November (L) and December (R) "Non-Expert" Group 1...... 63

viii

Figure 24. January (L) and February (R) "Non-Export" Group 1...... 64 Figure 25. March (L) and April (R) "Non-Expert" Group 1...... 65 Figure 26. Initial starting positions for "Non-Expert" Group 2 ...... 66 Figure 27. September (L) and October (R) "Non-Expert" Group 2...... 67 Figure 28. November (L) and December (R) "Non-Expert" Group 2...... 67 Figure 29. January (L) and February (R) "Non-Expert" Group 2...... 68 Figure 30. March "Non-Expert" Group 2...... 69 Figure 31. Outcomes from the "Expert" (L), "Non-Expert" Group 1 (C), and "Non- Expert" Group 2 (R)...... 76

List of Tables

Table 1. The prices of game pieces in AAG-SiM...... 49 Table 2. The monthly return on 'Agriculture' in AAG-SiM...... 49 Table 3. Rational and Irrational choices in AAG-SiM. Rational choices are bolded...... 50 Table 4. Components of the AAG-SiM...... 51 Table 5. Prices of the game pieces in the AAG-SiM RPG scenario...... 55 Table 6. Return on each game piece in the AAG-SiM RPG scenario...... 55 Table 7. Features of the AAG-SiM RPG scenario...... 55 Table 8. Excel Table used during the Game Workshop...... 69 Table 9. Placement of all 'Agriculture' by all "Expert" Greek-Cypriot Farmers...... 72 Table 10. Adjusted table of rational and irrational "Expert" decisions...... 73 Table 11. Placement of 'Agriculture' by all "Non-Experts" Group 1...... 73 Table 12. Adjusted table of rational and irrational "Non-Expert" Group 1 decisions...... 74 Table 13. Placement of 'Agriculture' by all "Non-Experts" Group 2...... 74 Table 14. Adjusted table of rational and irrational "Non-Expert" Group 2 decisions...... 75 Table 15. Rational and irrational decisions among the "Experts" and "Non-Experts." .... 75

Chapter 1: Examining Land Use/Cover Change (LUCC)

Archaeological research demonstrates that humans have altered Earth’s physical environment for thousands of years (Redman 1999). However within the past 300 years, the scale and magnitude at which humans have transformed Earth’s ecosystems or biomes, has accelerated and intensified. Through sustained interaction with the physical environment, humans have transformed these ecosystems into a diverse landscape of anthropogenic biomes in the form of dense settlements, villages, croplands, rangelands, forested areas, and wildlands. Cumulatively, these land use and land cover (LUCC) transformations have fundamentally altered Earths’ terrestrial ecosystem landscape (Ellis et al. 2010; Ellis and Ramankutty 2008; Turner 1990).

Examining the causes of LUCC on a local and regional level is a critical goal for understanding ecosystem change. This case study examines agricultural LUCC in the Athienou agricultural system in central Cyprus. Athienou is a small farming town, dominated by the dairy industry in the UN Buffer Zone in Cyprus. Through fieldwork, modeling, and simulation, this research examines agricultural change in Athienou, adevelops knowledge about Greek-Cypriot farming practices in Athienou through fieldwork, and develops this knowledge into a RPG. Two sets of participants, Greek- Cypriot farmers (“experts”) and undergraduate students (“non-experts”) then play the RPG that simulates a scenario where the Turkish occupied land to the north of Athienou becomes available for farming again.

1.1 Structure of the Case Study

The introductory chapter provides some context for this case study. Chapter 2 examines and reviews the existing literature about ecosystem modeling, modeling complex systems, and the Companion Modeling methodology used in this case study. Chapter 3 presents the conceptual framework for the research. It reviews the Companion Modeling 1

(ComMod) process, from the Problem Definition, to the Model Conceptualization to the Game Workshop with the “expert” Greek-Cypriot farmers and the “non-expert” undergraduate students. Chapter 4 discusses the analysis of the outcomes from the RPG Game Workshop. Chapter 5 provides a summary the research, a discussion of the limitations, and ideas discussion for future investigations.

Chapter 2: Foundation for the Study

This chapter uses the lens of ecosystem management to trace through idea of complex systems and ways to model them. It also lays out the methodological framework and briefly introduces the case study at the end of the chapter.

2.1 The Millennium Ecosystem Assessment (MA)

The Millennium Ecosystem Assessment (MA) (2005a; 2005b; 2005c; 2005d; 2005e; 2005f), sponsored by the United Nations in 2000 and supported by numerous governments, nongovernmental organizations (NGOs), businesses, and scientists, was the first attempt to evaluate ecosystem change on a global scale. The goal of the MA was to bring scientists together to determine “how changes in ecosystem services have affected human well-being, how ecosystem changes may affect people in future decades, and what types of responses can be adopted at local, national, or global scales to improve ecosystem management and thereby contribute to human well-being and poverty alleviation” (2005a p. x). Over four volumes, the MA synthesized existing scientific literature, datasets, and models to examine the current status and of Earth’s ecosystems (2005b), the future of Earth’s ecosystems by developing scenarios (2005c), the policies used for ecosystem management (2005d), and the lessons learned from integrating the diverse and multi-scalar viewpoints of the ecosystem to assist stakeholders at all levels (2005e). The study found that in the past fifty years, humans had degraded and transformed nearly all of Earth’s ecosystems at a faster rate and wider scale than ever before in history. At the same time, economic development had substantially improved the quality of life in which humans lived. However, given humans’ increasing pressures and demands, the MA found that Earth’s ecosystems will continue to degrade and transform in an increasingly unsustainable way. The MA determined that reversing the effects of the ecosystem degradation while maintaining improved human well-being would require significant policy changes by decision-makers, and technological

innovations (Millennium Ecosystem Assessment 2005f).

The MA identified two critical factors that directly relate to this case study. First, the report identified and classified the drivers of ecosystem change. Second, the report recognized the important role that decision-makers have in influencing the ecosystem drivers (Millennium Ecosystem Assessment 2005c; 2005f). The MA defined a driver as “any natural or human-induced factor that directly or indirectly cases a change in an ecosystem” (Millennium Ecosystem Assessment 2005a p.87). Direct drivers of change include physical, biological, and chemical forces such as land used/cover change (LUCC), invasive species, and pollution that directly influence ecosystem processes. Indirect drivers include demographic, economic, sociopolitical, scientific and technological, cultural and religious forces, and function by affecting one or more of the direct drivers. It is the decision-makers’ interaction with the multiple drivers across different temporal and spatial scales that cause changes in ecosystems (Millennium Ecosystem Assessment 2005a; Nelson et al. 2006). The MA categorized decision-makers on three general levels: individuals and small groups; public and private organizations at municipal, provincial, and national levels; and public and private organizations at the international level. Although not all decision-makers fit within a single category, generally the scale at which decision makers operate influences the degree to which they affect, or are affected, by drivers. For example, individuals and small groups can directly influence their choice of land use and technological equipment, but they have little control over indirect drivers such as economic markets, property rights, or the local climate. Public and private organizations that operate at a larger spatial scale have more control over many indirect drivers, such as economic policy, technology development, and political or diplomatic issues (Leeman 2009; Millennium Ecosystem Assessment 2005a; 2005f).

One of the primary assumptions of the MA is the idea that complex linkages exist between ecosystem services and human well-being, and that access to these ecosystem services directly affects human well-being (Powledge 2006). The MA defines ecosystem services as benefits provided by the ecosystem. These include things like food and water,

flood and disease control, spiritual/cultural/recreational benefits, and nutrient recycling. Human well-being includes factors such as material needs, health, security, social relations, and freedom and choice (Millennium Ecosystem Assessment 2005a). Given this assumption, the MA recognized the need to help decision-makers and stakeholders plan and anticipate for future changes in drivers, ecosystems, ecosystem services, and human well-being. The MA developed four narrative scenarios based on qualitative and quantitative data from previous modeling and ecosystem simulations that addressed the effects of ecosystem development and human well-being (Millennium Ecosystem Assessment 2005c).

Figure 1. The Millennium Ecosystem Assessment (MA) methodology for developing scenarios (MA 2005c, p.6)

As shown in Figure 1, developing these scenarios was a multistep and iterative process. Keys to this scenario development methodology were 1) a literature review to identify focal questions, uncertainties, and assumptions in current environmental issues, and 2) open-ended interviews with all levels of decision makers from local communities to governments, NGOs, and businesses. This fieldwork helped develop the narratives and provided a qualitative and quantitative basis to begin to fill in details for each scenario. Specifically, information from the interviews were matched to models that represented the indirect drivers of ecosystem change while the quantitative data consisted of existing models of direct drivers of ecosystem change (Millennium Ecosystem Assessment 2005c). The four scenarios developed through the MA explore the future of Earth’s ecosystems in the context of sustainable development and globalization.

Global Orchestration Scenario In the Global Orchestration scenario, the world unites its markets and economic policies for the social and economic benefits of human well-being. Ecosystem services are equally available to all people, and multi-national organizations coordinate to tackle global environmental problems. This approach is reactive because it leaves many people vulnerable to unexpected changes and the multi-national organizations (Millennium Ecosystem Assessment 2005c; Powledge 2006).

Order from Strength Scenario The Order from Strength scenario depicts a reactionary world where nations look only after their own interests as protection against dynamic economic markets and the influx of goods and people. In this highly regionalized and fragmented society, nations are more concerned with protecting their own economic markets through government restrictions rather than the ecosystem and humanitarian issues. Technological solutions provide the answer to environmental problems, and high degree of inequality exists between nations (Millennium Ecosystem Assessment 2005c; Powledge 2006).

Adapting Mosaic Scenario In the Adapting Mosaic scenario, large multi-national organizations are unable get nations to cooperate on ecosystem and humanitarian issues. This results in an increased focus on local ecosystem management strategies and the strengthening of local institutions. Although this focus creates a better understanding of local ecosystems and human well-being, it also creates a multitude of approaches that cannot be scaled to address global environmental problems and humanitarian issues (Millennium Ecosystem Assessment 2005c; Powledge 2006).

TechnoGarden Scenario The TechnoGarden scenario describes a world linked through technology and highly managed natural and engineered ecosystems on a global scale. It has a similar effect as the Green Revolution had on the developing world during the 1960s (Evenson and Gollin 2003). An emphasis is placed on ecosystem policy reform and technology to solve environmental problems and expanding property rights to cover ecosystem services. The

stakeholders and decision-makers’ interest in maintaining their ecosystem services fosters more investment in green technologies. Although this scenario provides global benefits, the scenario heavily relies on human driven innovation (Millennium Ecosystem Assessment 2005c; Powledge 2006).

The purpose of these scenarios was not to predict future events, but rather to explore the existing research and knowledge about the ecosystem to help develop planning strategies for decision makers in an informative and educational way. Through each scenario, the MA allows researchers to explore a narrative of connections and contingencies through time (Agar 2004). Although each scenario is plausible, it is unlikely any of them will actually occur (Millennium Ecosystem Assessment 2005c). Predicting or projecting future ecological conditions is difficult, if not impossible, because human-environmental interactions are heterogeneous, non-linear, dynamic and complex processes involving multiple drivers of ecosystem change on many scales (Ellis et al. 2010; Lambin and Geist 2006; Rindfuss et al. 2004). One way to understand these interactions is to consider them as aggregate components in a complex adaptive system.

2.2 Complexity Theory and Complex Adaptive Systems (CAS)

Complexity research challenges the long-standing Newtonian paradigm of assuming that a system exists in equilibrium, is easily reducible to its component parts, and exhibits predictable behavior (Janssen 2002). It also draws heavily from the biologist Ludwig von Bertalanffy’s (1968) work on general systems theory. Bertalanffy aimed to develop a science of systems whose general principles and models could explain and unify philosophy, science and technology. General systems theory recognizes that systems are made of multiple heterogeneous components that dynamically interact and receive feedback from their environment. The very nature of these “open” systems means researchers cannot investigate them in a “closed” system, isolated from their environment. This holistic approach to systems greatly benefits from the development of computers to help build complex representations of these systems. For this reason, researchers often view complexity research as the evolution of general systems theory (Phelan 1999; Manson and O’Sullivan 2006; Richardson 2005). Mason and O’Sullivan

(2006) identifies three types of complexity research: algorithmic complexity, deterministic complexity, and aggregate complexity. Briefly, algorithmic complexity borrows from mathematical complexity theory and information theory to refer to the difficulty faced in describing system characteristics. Deterministic complexity uses chaos theory and catastrophe theory to describe how the interaction of a few key variables can create a system susceptible to sudden disruptions. The most important type of complexity related to this study is aggregate complexity. Aggregate complexity examines how “individual elements working in concert create complex systems which have internal structure relative to a surrounding environment, and which may also exhibit learning and emergence” (Manson and O’Sullivan 2006, p. 678).

Complex systems or complex adaptive systems (CAS) are self-organizing hierarchical systems composed of heterogeneous agents whose dynamic non-linear interactions with other agents and their environment can adapt and transform the internal structure of a system, creating emergent and complex phenomena. In turn, these emergent phenomena filter back down to the agents and the process repeats on multiple scales (Crawford et al. 2005). As the Millennium Ecosystem Assessment discusses, there are direct and indirect drivers of ecosystem change. Under a Newtonian paradigm however, a model of ecosystem change would not include the non-physical indirect drivers because they are not reducible to matter. Aggregate complexity ontology places few restrictions on its assumptions of the world, instead focusing on entities and the relationships between them. Its holistic perspective emphasizes emergence from a system of constituent parts. This flexibility allows researchers to take a range a wide range of approaches (from realist to constructivist) to complexity. Epistemologically, aggregate complexity research relies heavily on computer models, such as Cellular Automata (CA), Agent Based Models (ABM) and Multi-Agent Systems (MAS) to examine these dynamic and non- linear relationships between agents and their environment. Unlike methodologies that reduce systems into independent and dependent variables, aggregate complexity epistemology emphasizes a multiplicity of computer models approaches with few restrictions on how to represent entities or how they interact (Abbot 2006; Axelrod and Cohen 1999; Batty 2005; Bousquet and Le Page 2004; Epstein 1999; Holland 1992,

1998; Janssen 2002; Kohler and Gumerman 2000; Manson and O’Sullivan 2006; McMillan 2004; Miller and Page 2007; Waldrop 1992).

2.3 Modeling Complexity with Cellular Automata (CA), Agent Based Models (ABM), Multi-Agent Systems (MAS)

Researchers have used CA, ABM, and MAS models extensively to model and simulate complexity and the emergent dynamics of CAS in the social sciences, specifically for ecosystem management and human-environmental interactions (An 2012; Bousquet and Le Page 2004; Gilbert and Troitzsch 2000; Hare and Deadman 2004; Hoekstra et al. 2010; Kohler and van der Leeuw 2007; Matthews et al. 2007; Parker et al. 2003). Researchers often refer to ABMs and MAS collectively as agent-based simulations (ABS) (Hare and Deadman 2004).

Cellular Automata The foundations of cellular automata (CA) stem from John von Neumann’s investigations of self-reproduction in biological systems in the late 1940s. Specifically, von Neumann’s interest drew from research in cybernetics, the study of how a system’s components and processes communicate, adapt, and self-regulate to become efficient and effective. Through his robotic Universal Constructor, he was able to demonstrate a self-replicating machine that could endlessly reproduce a sequence of information. More importantly, he noted that through introducing “mutations” into information sequences, the pattern could evolve and become more complex (McMullin 2000; Wolfram 2002). CA became more widely popular with the development of John Conway’s computerized Game of Life in 1970. Using an infinite grid of two-dimensional cells, Conway developed a game whereby a user would select a set of cells whose behavior would follow a simple rules of “births” “deaths” and “survivals”. Beginning with a user generated pattern of cells marked with counters:

1. Every counter with two or three neighboring counters survives for the next generation. 2. Each counter with four or more neighbors dies (is removed) from overpopulation. Every counter with one neighbor or none dies from isolation. 3. Each empty cell adjacent to exactly three neighbors--no more, no

fewer--is a birth cell. A counter is placed on it at the next move. (Gardner 1970)

Importantly, the Game of Life demonstrated how simple rules of behavioral interaction could lead to the emergence of complex patterns. Its applicability at the time was widely seen throughout many disciplines including geography (Tobler 1979). In general, a CA is composed of a network of cells in an initial predefined state. A cell changes its state based on a fixed mathematical function that governs its own behavior and its interactions with neighboring cells (Brown 2006; Janssen and Ostrom 2006).

Agent Based Models Agent Based Models (ABM) also has its developmental roots in von Neumann’s work; however, ABMs are structurally different from CA in many ways. ABMs are dynamic computational models composed of autonomous heterogeneous decision-making entities called agents. These agents, governed by a set of flexible programmable rules that regulates their decisions, often exhibit non-linear behavior as they adapt and interact with each other and to their environment (Bonabeau 2002; Gilbert 2007; Miller and Page 2007). Craig Reynolds' Boids program (1987) is an early example of simple ABM. In the program, which simulates a flock of birds in an environment with obstacles, each “boid” follows three rules:

1. Collision Avoidance: avoid collisions with nearby flockmates; 2. Velocity Matching: attempt to match velocity with nearby flockmates; 3. Flock Centering: attempt to stay close to nearby flockmates; (Reynolds 1987, p. 6)

Despite the simplicity of these rules, the “boid” agents iteratively develop complex behavior as they interact and swarm with other “boids” in coordinated maneuvering patterns. Importantly, this ABM simulation emulates a real-world system and provides insight into the dynamics of the complex behavioral patterns of birds (Bonabeau 2002). Also, consistent with general systems theory, examining an individual “boid” cannot predict or explain the interdependencies or hierarchical nature of the agents because of the holistic nature of the system. Similarly, removing the “boid” agent from its environment would disrupt the system itself because complex macro-level phenomena

emerge from the interaction of these lower level heterogeneous agents across a temporal scale within the system (Agar 2004; Cilliers 1998; Crawford et al. 2005; Miller and Page 2007). This bottom-up approach to social sciences through simulation has been called “a third way of doing science” because:

Like deduction, [a simulation] starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be analyzed inductively. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid intuition. (Axelrod 1997, p. 24)

The ability of ABMs to demonstrate how complex macro level properties can emerge from lower level entities makes these simulations a popular tool for identifying and understanding complexity in social, biological, and physical phenomena (Axelrod 1997; Berry et al. 2002). A notable example is Epstein and Axwell’s social simulation platform called Sugarscape which they explain in their book Growing Artificial Societies (1996). Sugarscape examines how social structures and group behaviors can arise from the micro level interaction of agents. In Sugarscape, agents interact with each other and their environment in their search for sugar. Epstein and Axwell simulate a series of social phenomena over three Sugarscape models called ‘Life and Death’, ‘Sex, Culture, and Conflict’, and ‘Sugar and Spice’ that deals with trade. During each simulation, the model assigns each agent random values for physical attributes such as speed, metabolism, and vision, and then the agents are placed in a landscape consisting of two mounds of sugar. The movement of agents is very simple: “Look around as far as your vision permits, find the spot with the most sugar, go there and eat the sugar” (Epstein and Axwell 1996, p. 6). In general, agents burns sugar according its metabolic rate when they move, and sugar re- grows at varying speeds. Agents die when they burn up all their sugar. As in the Boids program, despite the simple rules, a wide range of complex, adaptive, and organizational behavior emerges from each artificial society.

Epstein and Axwell described Sugarscape as neither inductive nor deductive science, but instead as a “generative” science, because their aim was to specify the lower level rules that could generate the emergent complex behavior on a macro level (Epstein and Axwell 1996; Epstein 1999). However, in examining and defining these lower level entities, ABMs sometimes are criticized as being reductionist and in direct conflict with the idea of CAS’s holistic emergence (Manson and O’Sullivan 2006). In addition, although the Boids and the Sugarscape model both draw from the observations of real biological and social systems, many ABMs do not go beyond a proof of concept (Janssen and Ostrom 2006).

Multi-Agent Systems Although sometimes used as a synonym for ABMs in many simulations, Multi-Agent Systems (MAS) developed separately through research in Distributed Artificial Intelligence (DAI), a branch of Artificial Intelligence (AI) (Castle and Crooks 2006). Similar to an ABM, a multi-agent environment is one where multiple agents interact with each other; however, the primary difference is the emphasis MAS places on sensing, learning, and feedback. Individual agents in MAS are not omniscient -- They do not know what other agents know about their environment. Each agent experiences and learns from their environment differently and individually adjusts its behavior according to its own knowledge (Panait and Luke 2005). A criticism from MAS researchers is that agents in ABM simulations, although regarded as autonomous, adaptable, decision- making entities, actually do not exhibit any of the properties assigned to agents in a MAS or DAI. MAS agents uses logic and machine learning to adapt and learn, whereas ABM agents cannot really learn or modify their knowledge behaviors because they are mathematically based (Conte et al. 1998; Drogoul et al. 2003). In short, the MAS field examines societies of artificial agents, while the ABM field examines artificial societies of autonomous agents (Conte et al. 1998).

2.4 Using MAS to model Land Use/Cover Change (LUCC) (MAS/LUCC)

Researchers have used MAS extensively to model ecosystem management and human- environmental dynamics. MAS are particularly important in the modeling of land

use/cover change (LUCC), the complex socio-ecological system that describes human transformations of the Earth’s surface (Ellis et al. 2010). Like the Millennium Ecosystem Assessment, identifying and understanding the multiple drivers of LUCC is a global and multi-disciplinary investigation. Parker et al. (2003) notes that multi-agent system models of land use/cover change (MAS/LUCC) that combine cellular landscape models with the decision-making processes of agents in an ABM are robust because of their ability to model adaptation, emergence, and complexity. MAS/LUCC models also allow agents to negotiate in an environment that represents their decisions spatially in a virtual laboratory (Matthews et al. 2007). MAS/LUCC models have been implemented in diverse fields such as natural resource management (Bousquet and Le Page 2004), agricultural economics (Farolfi et al. 2008), archaeology (Kohler and van der Leeuw 2007), and urban development (Batty 2005; Torrens 2002)

MAS/LUCCs have strong ties to participatory modeling because these simulations can resemble the actual interactions and decision-making processes of individuals in stakeholder positions (Matthews et al. 2007). Participatory modeling generates insight into complex socio-environmental systems by using stakeholder input and feedback. Participatory modeling also improves stakeholders’ knowledge and understandings of a system by demonstrating potential solutions to problems (Becu et al. 2008; Walker et al. 2002; Souchere et al. 2010; Voinov and Bousquet 2010). Parker et al. (2003) identifies three types of participatory models based on the level of stakeholder participation. In the first type of participatory model, stakeholders and modelers work together throughout all stages of model development (Bousquet et al. 2002). In the second type, stakeholders are not necessarily involved in the modeling, but they act as agents to play a game directly related to the model (Barreteau et al. 2001). The third type of model is designed to be used as a scenario-analysis tool for policy makers to test proposals (Ligtenberg et al. 2002).

2.5 Multi-Agent Systems (MAS) as Role Playing Games (RPG)

Most agent-based simulations (ABS) are limited in their convenience and utility by stakeholders because of the highly technical nature of the model itself and because of its

development in laboratory settings (Becu et al. 2008). However, as researchers increasingly look to combine their models with empirically based data, participatory modeling techniques allow researchers to develop role playing games (RPGs) for stakeholders to play and directly evaluate the validity of a model (Barreteau 2003; Bousquet and Trébuil 2005; Janssen and Ostrom 2006; Ligtenberg et al. 2010; Manson 2003; Pak and Castillo 2010). RPGs replace the computational agents with stakeholders, who through participatory modeling, improve the model through their feedback while enhancing their understanding of their environment (Barreteau et al. 2001; Barreteau et al. 2007; Gurung et al. 2006; Le Page et al. 2012). RPGs have the additional benefit of being a framework for facilitation discussions and building trust among diverse stakeholders, especially when researchers have little in common with the individuals in their study (Castella et al. 2005a; Castella and Verburg 2007; Washington-Ottombre et al. 2010).

The Common-pool Resources and Multi-Agent Systems (CORMAS) group has developed this MAS/RPG methodology extensively. CORMAS research primarily focuses on natural resource management through a methodology called Companion Modeling (ComMod). The overall goals of ComMod are to are to interpret and understand complex human-environmental interactions, and to facilitate collective decision-making processes in complex situations (Barreteau 2003; Barreteau et al. 2007; Becu et al. 2008; Bousquet and Le Page 2004; Castella et al. 2005a, 2005b; D'Aquino et al. 2002; Guyot and Honiden 2006).

2.6 Companion Modeling (ComMod)

The Companion Modeling (ComMod) methodology emphasizes diverse stakeholder participation throughout a cyclical progression of fieldwork, modeling, and simulation for the development of a shared representation of a complex system. ComMod draws from a constructivist approach, which states that knowledge is contextual and results from experience and perceptions (Röling 1996). The ComMod approach accepts all stakeholders’ points of view as legitimate. Methodologically, ComMod is different from traditional scientific research because it requires researchers to begin experiments with no

a priori hypotheses (Barreteau 2003; Boissau and Castella 2003; Bousquet and Trébuil 2005).

Constructing a model with multiple stakeholder perspectives exposes researchers and stakeholders to multiple viewpoints, supporting a better understanding of a complex system. In turn, it creates a framework to support communication and collective decision-making among stakeholders (Barreteau 2003; Bousquet and Trébuil 2005). The underlying objective for ComMod is to demonstrate to stakeholders the diversity of perspectives within their socio-environmental system, and their consequences in terms of actions (Voinov and Bousquet 2010). The strength of ComMod methodology is its iterative methodology and the incorporation of multiple stakeholders. However, ComMod can be costly and time-consuming for the researchers and the stakeholders. The constructivist nature of ComMod also leads to claims that it is relativistic (Matthews et al. 2007).

Figure 2. The Companion Modeling (ComMod) methodology.

2.6.1 The ComMod Methodology The ComMod methodology (Figure 2) consists of fieldwork, modeling, and simulations stages. Throughout this iterative process, researchers incorporate stakeholder participation and feedback into each phase of fieldwork, modeling, and simulation. Each

stage within the ComMod is often revisited times during the research process, and the entire ComMod methodology is usually repeated several times over a multi-year period as new understandings of the system are developed and incorporated into the model.

Fieldwork The ComMod process begins by identifying the project goals and defining the problem through fieldwork and a literature review. During this stage, researchers identify and invite stakeholders to participate in the research. The fieldwork data collection always involves human subjects research in the form of ethnography, which includes interviews and surveys. Geospatial information and other contextual data may also be gathered during this fieldwork (Campo et al. 2005). The ComMod ethnographic process requires researchers to understand and incorporate the stakeholders’ point of view (POV1) into the formulation of a model, as opposed to the researchers’ point of view, POV2 (Agar 2006). Similarly, from a cultural materialist perspective, Marvin Harris (1987; 1999) defines the emic approach as an attempt by the researcher to learn the local rules for actions and customs of a native population. The etic approach is an empirical perspective that does not incorporate native thinking into interpretation. From this perspective, the anthropologist is always an “outsider” studying a culture. Depending on the questions, a researcher can gather emic or etic knowledge from the fieldwork. ComMod is different from other methodologies precisely because it incorporates multiple POV1, or emic perspectives, into its models. Through this process, new perspectives and concepts can emerge from the fieldwork. During this stage, researchers also begin to gather information to begin to develop an understanding of the system and choose the tools for collection and modeling. Evaluating the data from the ComMod fieldwork allows the researchers to learn about the system and to formulate hypotheses to test in a model (Boissau and Castella 2003; Bousquet et al. 2002; Bousquet and Trébuil 2005; Voinov and Bousquet 2010).

Modelling The modeling phase begins by processing the data from the ComMod fieldwork and converting it into a conceptual model of the system. Typically, this translation of

knowledge examines the representations and decision-making processes of stakeholders and graphically maps the interactions through a Unified Modeling Language (UML) diagram. The goal of the modeling phase is to present a model of the system to the stakeholders to elicit their feedback. ComMod allows this conceptual model to be a MAS or an RPG (Bousquet and Trébuil 2005; Voinov and Bousquet 2010).

Multiple ABS platforms exist to develop computational simulation models. Most of these platforms are based on the Java programming language due to its object-oriented language and extensive library of modules. Ascape, Mason, Repast, and Swarm are all Java-based. Although robust, a drawback to all ABS platforms is that they require an advanced level of computer programming. A notable exception is NetLogo, which is aimed at entry-level programmers (Tisue and Wilensky 2004; Wilensky 1999). The CORMAS group uses a platform based on an object-oriented language called Smalltalk (Le Page et al. 2012).

Simulation The simulation stage consists of a game workshop where the stakeholders play an RPG model of the system, and researchers develop scenarios to challenge existing understandings of the system. Researchers typically simulate these scenarios through an ABS because of the difficulty of managing participants over long and repeated sessions of RPGs and handling the voluminous data generated from the RPG. After the data from the RPG game session is played and the scenarios are run, the results are then presented to the stakeholders and decision makers for more feedback (Bousquet and Trébuil 2005; Voinov and Bousquet 2010). Through feedback from the stakeholders, the ComMod process cycles back to the fieldwork and the model continuously evolves. This iterative process creates a family of models, each representing a successive interaction between the research and the field. Together, this family of models forms the basis for a Knowledge-Based System (KBS), or an intelligent reasoning and decision-making toolset specific to the system (Barreteau 2003).

2.6.2 ComMod Research Since the CORMAS group first began combining RPGs with MAS in 1996, researchers have conducted several ComMod experiments (Le Page et al. 2012). This section briefly reviews the SHADOC, SYLVOPAST, and SAMBA ComMod projects.

SHADOC The first ComMod experiment was Barreteau’s SHADOC model, which examined natural resource management in the form of irrigation schemes in northern Senegal (Barreteau et al. 2001). The SHADOC model was designed to investigate the extent to which the irrigation output was a result of the coordination of farmers within the system. Through SHADOC, the farmers from Senegal were able to understand the irrigation scheme from multiple stakeholder perspectives and see how their individual decisions impacted the larger system (Barreteau et al. 2001; Gilbert 2007).

The SHADOC game workshop lasted three day and each game consisted of ten to fifteen players. Over a half day, the game was divided into three stages: the presentation of the game and roles, the game workshop, and a discussion. The SHADOC game board is drawn on a chalkboard, and equal sized boxes representing land are assigned to each player. During the game, players draw three cards that describe their behavior for the game: the farmer type; the social status of the farmer; and the farmers’ ability to repay loans. The game uses the environmental seasons as turns in the model. During each season, the players must decide how they will obtain credit and decide on their irrigation plan. After each turn, the seasonal yields are calculated and their strategies are discussed. When the turn ends, players are then allowed to draw new cards and change roles. After the game workshop ended, the RPG was then converted into a MAS (Barreteau et al. 2001; Bousquet et al 2002).

SYLVOPAST Silvopasture is an agro-forestry strategy that integrates timber production with foraging and animal grazing in the same environment. This strategy is designed to produce timber in the long term, and animals and crops in the short term. Etienne (2003) designed

SYLVOPAST to examine the development of silvopastoral environments with livestock farmers and foresters in southern France in the context of forest fire prevention. A key difference in SYLVOPAST was that the RPG was played with groups other than the stakeholders. Agricultural students and “amateurs” with no knowledge of farming also played the RPG. Although primary goal of the SYLVOPAST was to develop mutually cooperative strategies for livestock farmers and foresters, in playing with the agricultural students the goal was to teach students about the management strategies used by both groups. The amateur group simply tested the clarity of the rules (Bousquet et al 2002; Etienne 2003).

Originally designed with cardboard and pins, the computer based SYLVOPAST RPG is composed of a 10 x 10 grid of cells, each populated by different combination of trees, shrubs, grass, and rock. Vegetation growth and regeneration is set at a specified rate, and unmanaged shrub growth is the cause of forest fires. The game roles consist of a shepherd and a forester, each of whom have their own interests in the RPG and in real life. The SYLVOPAST RPG incorporates the seasonal environmental conditions of southern France into the game. The game begins in September and proceeds incrementally in one-month turns. Shepherds earn money based on their animal gazing. Each month the shepherd must pay the forester a grazing duty and decide where to move the animals to graze. The distance a shepherd can move is dependent on the size of the flock. The RPG purposely does not specify the price of the duty. This allows the shepherd and forester to negotiate the price throughout the game. Foresters can choose one of three strategies each month: reducing fire hazards, increasing the number of forest cells, or increasing vegetation diversity. At the end of each season, the forester can receive an incentive from the government if they have addressed all three strategies. The shepherds do not operate between June and August, but during this time they must pay workers for their help the previous season, decide whether to increase the size of their flock, and negotiate with the foresters to address different strategies during the season. Fires are triggered deterministically during the game and serve to examine the interactions and negotiations between the shepherds and foresters (Bousquet et al. 2002; Etienne 2003).

SAMBA – GIS The SAMBA – GIS is a spatially explicit model examines how direct and indirect drivers of ecosystem change affect LUCC at different scales in the culturally diverse Bac Kan province in Vietnam (Boissau and Castella 2003; Boissau et al. 2004; Castella et al. 2005a, 2005b; Castella and Verburg 2007). Bac Kan has a complex agricultural history due to multiple economic, social, and technological changes that Vietnam experienced in the early 1990s. The SAMBA – GIS model combines a narrative conceptual model, an ABM, an RPG, and a GIS model to examine the dynamic interactions between the farmers’ individual decision strategies, the institutions that define resource access and usage, and changes in the biophysical and socio-economic environment.

The narrative conceptual model of Bac Kan was developed through extensive fieldwork to investigating the land use in the Bac Kan province. This conceptual model was then developed into an ABM that examined the agricultural land use dynamics. Boissau et al. (2004) and Castella et al. (2005a; 2005b) built an RPG based on the ABM and recruited farmers to participate in a weeklong workshop called SAMBA – Week. During this workshop, the researchers used the RPG to examine how farmers made decisions about the production of rice from their paddies to feed their families. The RPG itself consisted of 1600 wooden cubes that were color-coded based on different land uses, representing an abstract version of the landscape. To play, each player drew cards that specified their labor force and number of mouths to feed. During each round, the players then had to decide how to allocate their land, labor, and capital. At the end of each round, moderators distributed "paddy cards" to players containing the yield from each crop. The researchers were able to simulate six years of agricultural decisions. During the next three days, the researchers interviewed the farmers about their decisions during the RPG game to understand processes involved in farming. Simultaneously the researchers then developed an ABM of the system that incorporated the decision-making strategies of the farmers. At the end of the week, the researchers presented the ABM to the farmers for them to assess the accuracy, and to talk about potential scenarios (Boissau and Castella 2003; Boissau et al. 2004; Boissau 2005; Castella et al. 2005a, 2005b; Castella and Verburg 2007).

The SAMBA – Week workshop was repeated in several areas of the Bac Kan province, and multiple ABMs were developed from each workshop. The researchers found two main strategies that emerged from the workshop with the farmers. Farmers with an abundant labor force focused on rice production until they were self-sufficient, and sent additional labor to cultivate cash crops. Surpluses were then sent to the “bank” or to other farmers. Farmers with a limited labor force were forced to adapt a “borrowing strategy” because they were not able to be self-sufficient (Boissau et al. 2004). Although the RPG and ABM were abstract representations of the landscape, the process helped develop algorithms about land use and other socio-economic decisions. Based on these algorithms, Boissau et al. (2004) and Castella et al. (2005a; 2005b) developed a ABM which incorporated land use GIS data from 1990 of the entire province to examine LUCC at a province wide scale (Boissau and Castella 2003; Boissau et al. 2004; Boissau 2005; Castella et al. 2005a, 2005b; Castella and Verburg 2007; Castella 2009).

2.7 The Case Study

Following the call from the Millennium Ecosystem Assessment to develop more regional and place-specific scenarios, this case study examines how Greek-Cypriot farmer’s decision-making strategies manifest through agricultural LUCC in Athienou, Cyprus. Athienou is located in the UN Buffer Zone in Cyprus, a “free” zone that separates the southern Greek Cyprus from the northern Turkish occupied zone, and has a complex agricultural history due to its geographic location. This study develops a conceptual model of the Athienou agricultural system as a RPG, and then develops a scenario in which the Turkish occupied land surrounding Athienou becomes available for farming again. Two sets of participants play the RPG scenario: “Expert” Greek-Cypriot farmers from Athienou and “Non-Expert” undergraduate students from Ohio State. This case study uses the term “expert” and “non-expert” to refer to specific types of knowledge about a system. The Greek-Cypriot farmers are the “experts” because they have local knowledge and a native perspective of farming in Athienou. The undergraduate students are the “non-experts” because they bring their own outsider non-native observations and understanding of farming in Athienou. Etienne’s SYLVOPAST model also uses a set of

“amateur” participants with similar qualities as the “non-experts” in this case study; however, the main difference is that the “amateur” participants only test the rules of the SYLVOPAST model. ComMod research primarily focuses on developing solutions to conflicts regarding common pool resources between different stakeholders. The RPG in these investigations serves as a tool for testing the researchers’ understanding of the system, and for fostering discussion among the various stakeholders. However, the scope of this case study is not to develop a solution to a common pool resource problem. Through comparing the outcomes from the “expert” and “non-expert” game session, this study uses the ComMod methodology as a way to build games to crowd source information.

2.7.1 Web 2.0 and Volunteered Geographic Information

This idea of comparing two sets of participants with differing knowledge draws from literature about Web 2.0 and the growth of user-generated content through volunteered geographic information (VGI) and public participation GIS (PPGIS) (Goodchild 2007; Siebert 2006). Unlike in the past when the internet was used as a one-directional mode of transmitting information, users are now producing rich data sets of geospatial data in the form of blogs, wikis, and other social media. Web 2.0 shifts the users’ perceptions of the internet as not just a medium to receive information but also as platform to share content. This shift, coupled with the greater availability of geospatial tools like GIS and GPS, has allowed the average individual to become a citizen scientist in the production of geospatial-temporal information (Goodchild 2007; Sui and DeLyser 2012). In this ‘Neogeography’ movement, crowds of citizen scientists are producing massive amounts of knowledge and distributing it through Web 2.0 (Brabham 2008; Hudson-Smith et al. 2009). Remarkably, the information that emerges from these crowds is accurate enough to develop maps to assist first responders in natural disasters (Zook et al. 2010) and even to assist in AIDS research (Khatib et al. 2011). Although this case study consists of sample sizes closer to an individual level rather than a crowd, aggregating and synthesizing multiple instances of this data should provide interesting emergent patterns and new ideas.

2.7.2 Grounded Theory The preliminary understanding of the factors or drivers that affect agricultural LUCC in Athienou occurs during the ComMod fieldwork through the interviews with the Greek- Cypriot farmers. Grounded theory provides a framework to analyze these interviews and contextualize agriculture in Athienou.

Sociologists Barney Glaser and Anslem Strauss, in The Discovery of Grounded Theory (1967), challenged the existing positivist paradigm that both social and natural sciences should undercover universal truths about reality through testing existing hypotheses and theories. Positivists believed that through this process, the social sciences could uncover universal explanations for social behaviors. Conversely, Glaser and Strauss believed scientific “reality” was an ongoing interpretation of meanings that developed from research observations. Specifically, their book proposed a methodology of data collection that allowed explanatory and descriptive theories to inductively emerge from data, rather than be tested through hypotheses (Chamaz 2006; Suddaby 2006).

Grounded theory is a bottom up approach to theory building, relying on data to form the foundation to develop explanatory and descriptive theories. Its methodology provides a systematic and flexible framework to synthesize the qualitative data through “constant comparison” and “theoretical sampling” (Charmaz 2006; Suddaby 2006). Glaser and Strauss identify the key components of grounded theory as:

(a) Simultaneous involvement in data collection and analysis; (b) Constructing analytic codes and categories form data, not from preconceived logically deduced hypothesis; (c) Using the constant comparative method, which involves making comparisons during each stage of the analysis; (d) Advancing theory development during each step of data collection and analysis; (e) Memo-writing to elaborate categories, specify their properties, define relationships between categories, and identify gaps; (f) Sampling aimed toward theory construction, no for

population representativeness; (g) Conducting the literature review after developing an independent analysis. (Charmaz 2006, p. 6, 7)

Today, Glaser and Strauss have very different methodological approaches to grounded theory. The key difference centers on the coding of the data. Glaser’s approach is interpretive and inductive, which allows categories and codes to emerge from the data itself. Conversely, Anslem Strauss teamed up with Juliet Corbin to write the Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (1990) where they developed a conditional matrix to help researchers develop validation criteria. Glaser argued this approach forced the data into predetermined codes, limiting potential interpretations of the data (Heath and Cowley 2004). This study follows Glaser’s approach to grounded theory.

Procedurally, grounded theory begins by coding the data. In reviewing a transcript during the initial coding phase, a researcher can examine on a line-by-line or incident-by- incident scale. Through contextualizing the data and determining what the data suggests, researchers then begin to code “segments of data with a label that simultaneously categorizes and summarizes, and accounts for each piece of data” (Charmaz 2006, p. 43). These labels are constructed from the data and should emphasize the action occurring within the data. During focused coding, the researcher examines the codes from the initial coding phase and distills the information into codes that explain the larger themes or concepts from the data. Through theoretical coding, the researcher relates the codes developed through the earlier coding stages to formulate hypotheses about the data (Charmaz 2006; Glaser 1998).

Chapter 3: Research Methodology

This chapter presents the ComMod conceptual framework used in this case study. It discusses the first three phases of the ComMod process: The ‘Problem Definition’, the ‘Model Conceptualization’, and the ‘Game Workshop’. The ‘Preliminary Analysis’ is discussed in the next chapter.

3.1 The Conceptual Framework for the Case Study

The conceptual framework for this case study follows an adapted version of the ComMod methodology and consists of four phases: The ‘Problem Definition’, the ‘Model Conceptualization’, the ‘Game Workshop’, and the ‘Preliminary Analysis’ (Figure 3).

Figure 3. Conceptual Framework for the case study.

These phases directly support the main objectives for this case study:

 To develop knowledge about drivers of agricultural LUCC affecting Greek- Cypriot decision making strategies in Athienou;  To examine the decision-making strategies of the “expert” and “non-expert” participants in a RPG scenario.

This case study also addresses the use of game-board style simulations as tools for simulating data, and their wider potential for crowdsourcing at different scales.

The ComMod methodology often requires the researcher to revisit different phases during the research, as discussed in Section 2.6.1. Although I revisited phases in the conceptual framework throughout the research process, I present the research for each phase at the same time. The ‘Problem Definition’ phase consisted of the fieldwork component of the ComMod process. During this phase, I conducted a background literature review and recruited the “expert” and “non-expert” participants. The ‘Model Conceptualization’ phase involved interviewing the “expert” Greek-Cypriot farmers from Athienou and translating the data gathered from the ComMod fieldwork into a conceptual model of the Athienou agricultural system. A scenario was then developed and played in the ‘Game Workshop’. The ‘Game Workshop’ phase consisted of three game sessions: one with the “expert” Greek-Cypriot farmers, and two with the “non-expert” undergraduate students. The outcomes from ‘Game Workshop’ are presented and discussed in the ‘Preliminary Analysis’.

3.2 The Problem Definition

This study begins with a background literature review to provide the socio-political context to the case study location of Cyprus and Athienou. I also describe the recruitment of participants for the study.

3.2.1 The Recent History of the Republic of Cyprus The island of Cyprus (Figure 4), the mythical birthplace of Aphrodite, is located in the eastern Mediterranean at the crossroads between Africa, the Middle East, and Asia Minor. With an area of 9251 km2, Cyprus is the third largest island in the Mediterranean after Sicily and Sardinia. Archaeological and philological evidence has documented the numerous civilizations and empires that have occupied Cyprus throughout history: Hittites, Egyptians, Greeks, Phoenicians, Assyrians, Persians, Macedonians, Romans, Byzantines, Arabs, Franks, Genoese, Venetians, Turks, and most recently the British (Hunt and Coldstream 1982; Thirgood 1987).

Figure 4. The location of Cyprus in the eastern Mediterranean along with ancient place names.

A common theme throughout the modern history of Cyprus is the tension between the two major ethnic populations on Cyprus: the Greek Cypriot majority and the Turkish Cypriot minority. Cyprus received its independence from Great Britain in 1960; however, this transition was not a smooth process. During the late 1950’s each community had formulated different self-determination movements. The Greek-Cypriots movement, enosis, aimed to join the island of Cyprus with Greece. The Turkish-Cypriot movement, taksim, was a reaction against enosis, and aimed to protect Turkish interests by dividing the island into Greek and Turkish territories. The Zurich and London Agreement of 1959 between Greece, Turkey, Great Britain, and Greek and Turkish Cypriot community leaders laid the foundation for a new constitution. Notably, it called for a Greek Cypriot president and a Turkish Cypriot vice-president and power sharing agreements between both communities and outlawed enosis and taksim. In addition, two treaties were signed by Great Britain, Greece, and Turkey to ensure the territorial integrity of Cyprus (Treaty of Guarantee) and to provide Greek and Turkish soldiers to

defend the island (Treaty of Alliance). On 16 August 1960, Cyprus gained its independence from Great Britain (Solsten 1991; Morelli 2012).

Archbishop Michail Makarios III and Dr. Fazıl Küçük were the first elected leaders of the Republic of Cyprus, but the constitution only lasted until 1963 when the Turkish Cypriot representatives decided to boycott the Greek-Cypriot government permanently. Sectarian violence that year between the Ethniki Organosis Kyprion Agoniston (EOKA), or the National Organization for Cypriots Fighters, and Türk Mukavemet Teşkilatı (TMT), or the Turkish Resistance Organization required the deployment of UN peacekeeping missions. These clashes continued throughout the late 1960s, further dividing Greek Cypriot and Turkish Cypriot communities. Although Archbishop Makarios III was elected again in 1968, determined to resolve the issues between the Greek and Turkish Cypriots, he now faced external pressure from a military junta that took control of the Greek government that favored enosis. On 15 July 1974, a coup supported by the Greek military junta replaced Archbishop Makarios III with Nikos Sampson, a hard-line supporter of enosis. Makarios III was forced to flee Cyprus and on 19 July 1974, he accused Greece of invading Cyprus in front of the UN Security Council. The next day, Turkey deployed military forces to Cyprus to protect the Turkish Cypriots, citing the Treaty of Guarantee as a basis for its action. This Turkish response caused the collapse of the military junta in Greece, leaving the Greeks unable to support the Greek-Cypriots on Cyprus. Great Britain was pressured not to intervene by Henry Kissinger, US Secretary of State, because he feared a war between two NATO countries would lead to a Soviet intervention (Hitchens 2001: Morelli 2012).

Through two separate military operations, Turkey took control of 37 percent of Cyprus, dividing the island into its current form (Figure 5). Although the Greek-Cypriots view this Turkish military operation as an invasion, Turkey officially refers to this action as the “Cyprus Peace Operation”. In years following the invasion, the UN passed multiple resolutions recognizing the sovereignty of Cyprus, and condemning its illegal occupation by Turkey. Despite this, in 1983 the Turkish-controlled Cyprus declared its independence as the Turkish Republic of Northern Cyprus (TRNC). Only Turkey

recognizes the TRNC as a nation, and it does not recognize the Republic of Cyprus. A UN patrolled buffer zone and a line called the Green Line mark the cease-fire line and the defacto division between the Republic of Cyprus and the TRNC (Solsten 1991; Morelli 2012).

Figure 5. Relief map of Cyprus showing major cities, Athienou, and the Green Line.

Several high-level negotiations have attempted to resolve the ‘Cyprus Problem’ since the Turkish invasion. Former UN Secretary General Kofi Annan’s proposal to create a United Republic of Cyprus composed of a Turkish and Greek state coincided with Cyprus’s entrance into the European Union (EU) in 2004. However, both Greek and Turkish Cypriot leaders opposed the Annan plan and a referendum vote rejected the proposal. Only the internationally recognized Republic of Cyprus entered the EU and many Greek-Cypriots criticized the plans for not incorporating their opinions (Hannay 2005; ICG 2006). In 2008, a chance political alignment created another opportunity for negotiations. That year the Greek-Cypriots elected communist Demetris Christofias as the president; his TRNC counterpart, Mehmet Ali Talat, was a socialist. In early

discussions, Christofias and Talat were able to agree on the goal of establishing a single state and common citizenship and continuing discussions were able to create working groups to address reunification issues such as governance and power sharing, security and guarantees, territory, property, economics, and the EU. However the progress eventually stalled and Talat was voted out of office in 2010 (ICG 2008; ICG 2010; Morelli 2012). Recent developments between Cyprus and the TRNC are not promising as Christofias’ new counterpart, Dr. Derviş Eroğlu, favors stronger ties with Turkey. Furthermore, beginning on 1 July 2012, Cyprus begins its six-month rotating EU presidency. Turkey, since it does not recognize the Republic of Cyprus, has already declared it will not acknowledge the EU during the Cypriot EU presidency, and Christofias has already decided not to run for re-election in 2013 (ICG 2012).

Property and land ownership of displaced refugees is a central issue in the negotiations between the Republic of Cyprus and the TRNC. The recent landmark case of Apostolides v Orams (2010) highlights the frustration felt by Greek-Cypriots. The 1974 invasion forced Meletios Apostolides and his family to abandon their property currently in the Turkish occupied region of Cyprus. In 2003, Apostolides discovered his family’s property had been sold to the Orams, a British couple, who had built a vacation home on his land. Although the Orams claimed they legally purchased the property from the TRNC, Apostolides filed a case that alleged the Orams had illegally purchased his land because he still held the original deed. Apostolides demanded the Orams demolish their vacation home and return his land. In addition, Apostolides claimed the Orams owed him damages and monthly rental fees with interest until the final court ruling. The district court in Cyprus ruled in favor of Apostolides, however since the Republic of Cyprus could not enforce the judgment in the TRNC, he appealed the decision to be registered and enforced against the Orams’ assets in the United Kingdom. In 2006, the High Court of Justice of England and Wales ruled in favor of the Orams, but Apostolides appealed the decision to the European Court of Justice (ECJ). The ECJ sided with Apostolides, ruling that although the courts in Cyprus do not have legal control over the TRNC, any cases regarding property rights in the occupied territory in Cypriot courts are enforceable under its law. This case was significant because the EU ruling from Apostolides v Orams

acknowledged the legitimacy of property and land ownership claims by displaced Cypriots (ICG 2010). Land and property rights among the Greek Cypriots in Athienou are an especially contentious issue due to its geographic location.

3.2.2 Study Location: Geography of Athienou The village of Athienou (population 6000) is located in the Larnaka District in central Cyprus, approximately halfway between the cities of Nicosia and Larnaka on the cusp of the Mesaoria plain and the Malloura Valley (Figure 6). Today Athienou is one of four villages on Cyprus located in the UN Buffer Zone that separates the Republic of Cyprus from the TRNC.

Figure 6. Athienou in relation to the Green Line, between the Malloura Valley and the Mesaoria Plain.

The Mesaoria, to the north, is the traditional agricultural breadbasket of the island, stretching between the Troodos and Kyrenia mountain ranges to the eastern shore of Cyprus. Conversely, the Malloura Valley to the south of Athienou contains rolling limestone/chalk hills filled with alluvium, colluvium, marl, and interspersed chert beds

(Butzer and Harris 2007). Unlike the Mesaoria plain, the Malloura Valley contains very little groundwater and it is among the driest places in Cyprus. Athienou receives on average only 15 inches of rain per year with the majority falling from October to March (Christodoulou 1959). During this rainy season, several ephemeral rivers flow from the hills in the Malloura Valley down into Yalias River. The landscape around Athienou becomes almost unrecognizable during these winter months, completely covered in green fields.

3.2.3 Agricultural History around Athienou Archaeological and ethnographic evidence demonstrates that humans have used the landscape surrounding Athienou for agricultural production for thousands of years (Toumazou et al. 2012). During an archaeological survey, the Athienou Archaeological Project discovered evidence of tool making from the chert outcroppings in the hills in the Malloura Valley. In addition, preliminary archaeological investigations around Athienou have demonstrated the importance of farming in the region throughout history (Spigelman 2012) and of various culture changes adaptations through GIS (Massey and Kardulias 2012). Before modernized farm machinery, a threshing sledge or dhoukani (Figure 7) was an important agricultural tool. Threshing sledges consisted of a platform of wooden boards with chert, flint, or basalt hammered into chiseled slots. To operate, a draft animal would pull a weighted down threshing sledge over harvested grains separating the chaff from the stalk. Until the 1950s, threshing sledges were very common throughout Cyprus (Yerkes 2012; Yerkes and Kardulias 1994).

Prior to 1974, many residents of Athienou were self-sufficient farmers who owned plots of land to the north of town in the Mesaoria plain. The aftermath of the Turkish invasion drastically changed the landscape around Athienou because of its proximity to the Green Line, which cut off all access to the Mesaoria plain. Residents could no longer access their land and were forced to adapt to their farming strategies. The Malloura Valley, which primarily had been used for growing olive, carob, and almond trees, was razed to make room for new farms. Since 1974, much of the Malloura Valley’s rocky soil and limestone outcropping have been plowed down with mechanized farm equipment to farm

barley and other cereals for cattle feed (Kardulias 2008). Today, the dairy industry in Athienou has the second most head of dairy cattle on all of Cyprus and the town is known throughout Cyprus for its dairy products and egg production.

Figure 7. Threshing sledges in Athienou, 1895. © Marfin Popular Bank Cultural Foundation.

3.2.4 Participants and Sampling Technique Prior to the recruitment of participants for this case study, the researcher received approval from The Ohio State University’s Behavior and Social Sciences Institutional Review Board (IRB) to conduct research on Human Subjects. Throughout the research, specific guidelines for recruiting and informing participants about the research were followed to assure the confidentiality. The recruitment material for the participants is located in the in the appendices at the end of this manuscript.

This case study consisted of fifteen participants separated into “expert” or “non-expert” stakeholder categories. This case study defines an “expert” stakeholder as a Greek- Cypriot farmer from Athienou, Cyprus, who is familiar with agriculture in the region and a “non-expert” participant as an undergraduate student from the US unfamiliar with farming in Athienou, Cyprus. Developing an understanding of how the “experts” view agriculture in Athienou is a critical component of formulating the conceptual model. I began the fieldwork with the “expert” stakeholders in Athienou, Cyprus, before moving to the “non-expert” stakeholders.

The recruitment of “expert” stakeholders occurred in July of 2011 through flyers posted on the Athienou municipal building and in the local farmers’ coffee shop, called The Rea. The flyer, translated into Greek, briefly described the research and asked volunteers to contact the researcher if they were interested in participating in an interview and in playing a RPG. After expressing interest in participating, the researcher gave each “expert” participant a script that further described the research project along with a privacy statement in Greek. A translator was present during this interaction to answer questions. If the farmer agreed to participate, the researcher then obtained informed consent. For their participation, the “expert” Greek-Cypriot farmers received €10 for their interview and €15 for playing the game. Through this process, I recruited seven farmers from Athienou through self-selection and snowball sampling. I also recruited two additional participants through snowball sampling to play the RPG, but they were not part of the initial interview group. In total, nine “expert” farmers were involved in this study.

The “non-expert” stakeholders were recruited in September of 2011 through flyers distributed to five sections of the introductory Geography 200 class. The researcher recruited six students through self-selection and snowball sampling. The flyer asked the “non-expert” undergraduate students to email the researcher if they were interested in participating in the research. After contacting the researcher, but prior to participating in the study, a script that further described the research project along with a privacy statement was provided in person to the undergraduate student. If the undergraduate

student agreed to participate, informed consent was then obtained. Through email, arrangements were made for two groups of “non-experts” to play the RPG. The “non- expert” undergraduate students received $15 for their participation in the game.

3.3 Model Conceptualization

Accurate model development relies on the interactions and feedback from stakeholders during the fieldwork portion of ComMod. ComMod researchers usually build a conceptual model of a system through CORMAS as an ABM in conjunction with an RPG. The conceptual model can also be built directly as MAS in the form of an RPG as mentioned previously (Bousquet and Trébuil 2005). One change to the ComMod guideline was that the conceptual model was not played before the development and playing of the scenario. This case study builds the conceptual model of the Athienou agricultural system and develops it into an RPG scenario. Interviews and literature reviews helped shape the development of the model and the RPG.

3.3.1 Ethnographic fieldwork in Athienou with the “Experts” The Greek-Cypriot farmers who chose to participate in the case study were asked to complete in two tasks: (1) sit down for an interview about their farming techniques, and (2) agree to play the RPG game developed based on their interviews. After each interview, a time was arranged with each farmer to play the RPG at a later date. However due to scheduling and timing, two additional farmers had to be recruited to play the game. During all of the interviews, a native Greek-Cypriot translator was present. The translator was almost unnecessary because seven of the nine farmers spoke fluent English in their interviews, and I quickly discovered that learning English was a requirement when Cyprus was a British Colony. The interviews themselves took place during July of 2011 and varied between 15 minutes and 2 hours in length. In five cases, the interview took place at the “Rea” Kafenio, the Farmer’s coffee house, and in the other two cases, the researcher was invited to the famer’s farm. The two farmers who showed up only to play the game were not interviewed during this initial phase.

These meetings consisted of semi-structured interviews, which attempted to gain an insiders’ (POV1) understanding the “rules” or institutions (Ostrom 1990) and culture of 35

how Greek-Cypriot farmers operated their farms in the Athienou region. Field notes and a camera helped document and collect these interviews. The prompt questions for these semi-structured interviews were:  What types of farming works in Athienou?  Tell me about your farm, and how do you make farming decisions?  What are the associated costs with your farm?  What do you know about of other kinds of farms in Athienou?  How has farming in Athienou changed since the war?

This fieldwork served two purposes. First, the qualitative data gathered from these interviews, along with a literature review of farming in Cyprus, assisted in formulating the general rules for the conceptual model and the RPG of the Athienou agricultural system. In addition, the data gathered from the fieldwork helped to develop the primary themes from the interviews about Greek-Cypriot farming culture through grounded theory coding. This process addresses the first objective of this case study. As discussed further in the next chapter, grounded theory provided context during the RPG game session and guided the researcher through understanding, and interpreting the outcomes from the RPG.

3.3.2 Understanding Cereal, Dairy, and Chicken farming in Athienou The preliminary research demonstrated that Athienou had three major types of farmers: Dairy farmers, Chicken egg farmers, and Cereal or Grain farmers. Through the interviews with the seven Greek-Cypriot farmers, specific values were determined about the costs related to the major types of farming in Athienou. Data from the Cypriot Department of Agriculture and the European Union supplemented these interviews. The seven farmers interviewed during the Fieldwork phase. For privacy, the Greek-Cypriot farmers are referred to as “Farmer 1 - Farmer 7”. All of the farmers were male and middle aged.

Cereal Farmers All seven of the Greek Cypriot farmers interviewed participated in growing barley, wheat, oats, or hay to sell and for their own use. Cereal farming in Athienou occurs primarily during the rainy season, which lasts from October until March. The farmers all

said they begin plowing their fields in late September or October and begin planting their seed from October to November. A good farmer, “Farmer 6” said, always finishes by December 1st. None of the cereal farmers interviewed said they planted outside the rainy season in Cyprus. This meant they only “cut” their crops one time per year, in April and May. In 2009, the seed prices per ranged between €600 – €700 per ton of barley, wheat, and oats and approximately 20 kilos of seed and 40 kilos of manure are needed for each decare of cereal. Each decare can produce around two 350 kilo round barrels. These barrels are then sold to a government board for €300 a ton.

Dairy Farmers All seven of the Greek Cypriot farmers interviewed agreed that Athienou was dominated by Dairy farming. “Farmer 2” and “Farmer 5” were dairy farmers themselves and the specific details about dairy farming came directly from their interviews. The most common dairy cows in Athienou are the Holstein-Friesian variety from the Netherlands, which cost around €2000 each. The hot summers in Cyprus affect the milk production of the cows. The Dutch cows only produce around 7500 liters of milk per year, and it is sold at €0.53 per liter. These cows consume around 2.5 metric tons of hay per year, sold in 350 kilo round barrels. These round barrels cost around €50 each, or €275 per ton. In addition, many farmers grown their own corn to feed their cows. Every decare of corn requires 750 tons of water and 40 kilos of manure, which at €0.17 a ton for water and €13 per kilo of manure, makes it a very expensive process. Much of the milk produced from dairy farming in Athienou goes into making various milk products such as yogurts, and Halloumi and Anari, both traditional Cypriot cheeses.

Chicken Farmers The researcher was not able to interview a chicken farmer for this study; however, the interview questions did ask the farmers about the different types of farming in Athienou. All of the Greek Cypriot farmers agreed that egg and poultry production is a major industry in Athienou, although none was too familiar with the specific costs associated with it. According to the 2003 Census of Agriculture in Cyprus, Athienou had over 435,000 chickens (250,000 raised for consumption, 171,000 for egg laying, and over

13,000 roosters). In general, addition, a hen can produce around 250 eggs per year, or 25 kilograms of eggs. The average price of 100 kilos of eggs was around €184 in 2008, while 100 kilos of poultry meat was around €151. Most of the chickens in Athienou come from Israel. Although no specifics about the type of cages used in Athienou were available, extreme temperatures during the summer affects egg production and the health of the hen.

Additional Observations When discussing farming in Athienou before 1974, all of the farmers said the invasion changed everything. Commercial farming before 1974 was almost non-existent. Farmers from Athienou because of its productivity favored the land to the north of Athienou on the Mesaoria plain. According to Solsten (1991), small family farms and subsistence farming dominated Cyprus throughout the 1960’s. In the interviews, the farmers recalled the land to the north of Athienou on the Mesaoria plain consisted of small scale and subsistence farms and the land to the south in the Malloura Valley was full of almond, carob, and olive trees. Although the soil in the south is very poor compared to the north, these tree require little to no water after their first year. The war forced the farmers to change their farming strategies. Many farmers relocated south towards the Malloura Valley, tearing down almond, carob, and olive groves to farm in the less fertile soil, significantly altering the landscape. Today, the farmers reported there are government incentives to begin replanting the trees that were destroyed in the Malloura Valley.

3.3.3 Grounded Theory Analysis Two categories of farmers emerged from the interviews: Small scale farmers and large scale farmers. “Farmer 1”, “Farmer 3”, and “Farmer 4”, “Farmer 6” could be classified as small scale farmers, and “Farmer 2”, “Farmer 5”, and “Farmer 7” were large scale. The most obvious difference between the small scale farmers and the large scale farmers was their involvement in commercial farming.

A theme throughout all of the interviews was the importance of water, although the perceptions of water differed between the large scale and small scale farmers. The small

scale farmers’ response to the first question of the type of farming that worked the best in Athienou was best summarized by “Farmer 4’s” response: “Crops that require no water”. Although all of the farmers took advantage of the rainy season during the winter to plant cereals, the small scale farmers depended on the rainfall much more than the large scale commercial farmers. In contrast, large scale farmers thought dairy farming was the best type of farming in Athienou. Water to the large scale farmers, although expensive, was something that could be purchased year round from a reservoir or from a desalinization plant. A surprising amount of things can be grown without water, even during the summer, in Athienou. “Farmer 1” said that carob, almond, and olive trees only require water during their first year, and in subsequent years, they do not need to be watered. “Farmer 1” also claimed the watermelons he grew during the summer were sweeter because they were not watered.

Another common theme throughout the interviews was how environment in Athienou and in Cyprus was changing. “Farmer 1” and “Farmer 7” remembered how it used to be possible to dig wells in Athienou for water, but now the water table was very deep. “Farmer 1” also remembered a stream that ran through Athienou all year around. All of the small scale farmers noted that the soil conditions were getting poorer. When asked if he rotated his crops, “Farmer 4” said he stopped because he grew the same crop every year. All of the farmers noted that they used fertilizer. “Farmer 4” and “Farmer 7” also noted that there was also a growing dependence on imports for crops that are too expensive to grow in Cyprus, such as animal feed. On a more local scale, however, the theme of interdependence between the large scale farmers and the small scale farmers also emerged from the interviews. Notably when asked how much of Athienou is involved in dairy farming, “Farmer 5” replied, “100%, if you consider that everyone is part of the system.” All of the small scale farmers said they sold their cereals to the animal farmers in Athienou.

A theme common to the large scale commercial farmers was how their farms were affected by outside influences. “Farmer 5” and “Farmer 7” both said their production output drops during the summer, but at the same time, the demand increases. “Farmer 7”

said at the end of the summer, farmers use the saying “one minute the glass is empty, the next minute it is full” to reflect how the mass exodus of tourists affects their supply. Also, “Farmer 2” and “Farmer 5” both mentioned that European Union regulations affected their production and were concerned how laws would affect them in the future.

3.3.4 Discrepancies about the Occupied Land? According to the interviews with the farmers, Athienou lost around 100,000 decares, or approximately 25,000 acres (39.06 mi2, 101.2 km2) due to the 1974 invasion. In Cyprus, land is classified into a unit called a decare, equal to 1000 m2 (Thirgood 1987). To place this unit into perspective, one decare is approximately the size of four doubles tennis courts and there are approximately four decares in one acre. An acre is approximately the 3/4 the size of an American Football field. This 100,000 decares number formed the basis for the RPG during the model conceptualization process because it was not possible to acquire a shapefile of the Athienou municipality to determine the exact area of Athienou or the Turkish occupied land during the model development. However, after the RPG had been developed and played, new information suggested the porportions needed to be investigated further. An exhibit in the Kallinikeio Municipal Museum of Athienou about the 1974 invasion was acquired which states that the Larnaka district encompasses approximately 100,000 acres (484,686 decares, 156.2 mi2, 404.7 km2) but nearly 65,000 acres (263,046 decares, 101.6 mi2, 263 km2) are inaccessible because of the Turkish occupation. Based on the interviews, the museum exhibit and Figure 5, it is unlikely that if the area Larnaka was actually 100,000 acres, that the town of Athienou made up 25 percent of Larnaka. I also made two approximate calculations using free available data. One calculation was made based by downloading the Larnaka District polygon and UN Green Line shapefiles from OpenStreetMap and then georeferencing an image from the Cyprus Archaeological Digitization Programme (CADiP) which contained the shape of all the municipalities in Cyprus (Figure 8).

This GIS approximation calculated the Athienou Municipality to have an area of 16,582 acres (67,105 decares, 25.91 mi2, 67.1 km2). Using the Green Line as the dividing line, Athienou is split into 11,528 acres (46,652 decares, 18.01 mi2, 46.65 km2) of Greek

Cypriot land, and 5054 acres (20,453 decares, 7.90 mi2, 20.45 km2) of Turkish occupied land. Another calculation was made from a map of Athienou called “ΤΟΠΙΚΟ ΣΧΕΔΙΟ ΑΘΗΕΝΟΥ” or “LOCAL PLAN OF ATHIENOU” (Figure 9) found in a document called “REVISION AND AMENDMENT OF LOCAL PROJECTS NICOSIA, LIMASSOL, LARNACA, AND PAPHOS: EXPERT VIEWS FORM LOCAL AUTHORITIES TO THE COMMON COUNCIL (2008 – 2010)”. This document came from the Athienou Municipality website [http://www.athienou.org.cy/]. When this map was georeferenced to a 2011 satellite image, I discovered that the OpenStreetMap UN Green Line did not match the division shown on the “LOCAL PLAN OF ATHIENOU”, shown in teal (Figure 10). Through a GIS calculation, the Athienou Municipality was determined to have an area of 15,527 acres (62,836 decares, 24.26 mi2, 62.84 km2), split into 7808 acres (31,598 decares, 12.2 mi2, 31.6 km2) in Greek Cyprus and 7719 acres (31,238 decares, 12.06 mi2, 31.24 km2) in the Turkish occupied area. There are approximately 931 acres (3768 decares, 1.46 mi2, 3.77 km2, of build-up area in Athienou, leaving 6877 acres (27,830 decares, 10.75 mi2, 27.83 km2) of surrounding land. The difference in the accounts from the interviews with the Greek-Cypriots and the figures from the GIS calculation is perplexing, but follows the ComMod methodology of accepting all viewpoints as valid. It also potentially suggests examining the perceptions of the occupied land could provide more insight into the thinking of Greek-Cypriot farmers in this area.

Figure 8. The Athienou Municipality extracted from the CADiP georeferenced image with OpenStreetMap data.

Figure Figure

ΤΟΠΙΚΟ ΣΧΕΔΙΟ ΑΘΗΕΝΟΥ [The Local Plan of Athienou] (2010).Plan ΤΟΠΙΚΟΑΘΗΕΝΟΥLocal of ΣΧΕΔΙΟ Athienou] [The

Figure 10. Georeferenced local plan of Athienou with the location of the Green Line. The occupied areas appear in teal.

3.3.5 Remote Sensing Regardless of the discrepancy in the amount of land lost during the war, Athienou went through a dramatic agricultural LUCC shift after the 1974 Turkish invasion. One way to track and visualize LUCC is through remote sensing. Launched in 1972, the Landsat program has provided the longest continuous record of the Earth’s surface through satellite imagery. Early Landsat satellites were equipped with a multispectral scanner (MSS) which captured four-bands of radiation reflected from Earth’s surface. The newest Landsat Thematic Mapper (TM) satellite collects seven bands. Remote sensing uses these bands to detect the spectral signatures of agricultural vegetation from the Landsat imagery. ERDAS/Imagine software was used for this quick visualization. Landsat imagery of Cyprus from January 1973 and July 2011 was obtained from the United States Department of Agriculture (USDA) Geospatial Data Gateway server. The remotely sensed images (Figure 11, 12) visually document how agriculture has shifted

between 1973 and 2011, and show a spatially fragmented heterogeneous landscape of many land use types.

Figure 11. Landsat 1 imagery from January 1973. Agriculture appears in red.

Figure 12. Landsat 7 imagery from July 2011. Agriculture appears in red.

3.3.6 The Athienou Agricultural Game Simulation Model (AAG-SiM) ODD Protocol The Athienou Agricultural Game – Simulation Model (AAG-SiM) serves as conceptual model of the Athienou agricultural system as well as the basis for the RPG simulation tool. This distinction is important in describing the AAG-SiM, and the AAG-SiM RPG scenario. Although this case study did not use NetLogo or the CORMAS modeling platform to develop an ABM of the Athienou agricultural landscape, the Overview, Design concepts, and Details (ODD) protocol suggested by Grimm et al. (2006; 2010) to explain ABMs is still followed. Accordingly, this case study uses the ODD protocol to describe AAG-SiM. This ODD protocol consists of:  A statement about the purpose of the model;  A description of the state variables and the scale of the model;  A description of the processes overview and how the model proceeds temporally;  The design concepts of the model;  A discussion of how the model is initialized;  A discussion of additional inputs while model is operating;

The AAG-SiM RPG scenario is described separately because scenario descriptions do not fit within the ODD protocol (Grimm et al. 2006). A criticism of many ABMs is that many of their descriptions are vague and irreproducible. ODD protocols provide a complete description of the model by explaining the overview of the model, the design concepts, and the specific parameters and details of how the model works and address. Here, the ODD protocol allows others to understand and replicate the AAG-SiM conceptual modeling process and the creation of the AAG-SiM RPG (Treibig and Klugl 2009; Janssen et al. 2008; Richiardi et al. 2006).

The Purpose of the AAG-SiM As discussed in the Millennium Ecosystem Assessment, multiple direct and indirect drivers of ecosystem change affect agricultural LUCC in Athienou. Local decision makers like the Greek-Cypriot farmers have limited control over biological, environmental, economic, social, cultural, political and technological forces. As shown through the interviews with the seven farmers, some of these forces include water, the changing environment, and outside influences such as tourism and the European Union. The decisions made by the Greek-Cypriot farmers are complex because these drivers

influence their decisions, leading to interdependencies among farmers on a local level, and dependencies on a national level. The purpose of the AAG-SiM is to model how these decisions manifest through agricultural LUCC. An important difference between AAG-SiM and an ABM or a MAS is that the stakeholders are people and not programmable agents. This distinction means that although indirect and direct forces are not explicitly “programmed” into AAG-SiM, the stakeholders consider these influences while making decisions because they are familiar with these direct and indirect drivers in real life. Using people allows richer social interaction to develop throughout the game. In addition, games can be used as platforms for receiving feedback about a model, and for building trust among stakeholders and the researcher (Castella et al. 2005a).

State Variables and Scale The AAG-SiM game board is a 15 x 15 square composed of 225 wooden tiles. The game board environment draws from previous ComMod RPG game boards built to represent landscapes in an abstract but familiar and recognizable form (Barreteau et al. 2001; Boissau et al. 2004; Boissau 2005; Castella et al. 2005a; Etienne 2003). Although the size of the game board was arbitrarily chosen, the square design is meant to mirror the layout of an ABM modeling environment such as NetLogo or CORMAS while modularity allows AAG-SiM to be customizable and transportable. The scale of AAG- SiM is intended to only investigate agricultural LUCC in within Athienou. (ABM/LUCC) Each wooden tile represents 1000 decares, or approximately 250 acres making the entire game board 225,000 decares or 56,250 acres. These numbers are based on interviews with the farmers as described in Section 3.3.3. There are three low-level entities in the AAG-SiM: players, agriculture, and land (Figure 13). ‘Players’ are characterized by four state variables: farmer type, land held, agriculture type held, and funds. ‘Agriculture’ is characterized by type: cereal, chicken, dairy, or trees. ‘Land’ in the conceptual model is not characterized. There are three ‘farmer types’: cereal, dairy, and chicken. Only three ‘players’ play AAG-SiM at one time. These types were based on the ComMod fieldwork that determined the most common types of farming in Athienou. The ‘land held’ is a numerical count of tiles held by a ‘player’. The ‘agricultural type held’ is a numerical

count of the each type of ‘agriculture’ owned by the ‘player’. ‘Funds’ are a numerical count of the points each ‘player’ has as a result of owning agriculture.

Figure 13. AAG-SiM entities.

Process Overview and Scheduling The AAG-SiM proceeds temporally in discrete time steps. Each month the farmers must decide whether they will purchase land and agriculture, along with the location of the land and the type of agriculture, or pass on their turn. The overall flow of AAG-SiM is shown as a UML diagram in Figure 14. A month passes after all three players have taken their turn.

Figure 14. UML diagram of the AAG-SiM game play.

General economic rules bound by the farmers’ decisions determine the growth rate and spatial expansion of their farm. These rules are shown in Table 1, and Table 2. Instead of money, carob seeds and beans were used to represent a point system. The carob seeds, from a tree that grows natively in Cyprus, were worth ten points while beans were worth one point.

UNIT COST Tile of Land 15 Dairy Farm 25 Chicken Farm 15 Cereal Farm 5 Tree Farm 5 Table 1. The prices of game pieces in AAG-SiM.

COST OF NET UNIT RETURN PRODUCTION RETURN Dairy Farm 8 4 4 Chicken Farm 6 3 3 Cereal Farm 10* 1* 9* Tree Farm 2 0 2 Table 2. The monthly return on 'Agriculture' in AAG-SiM. Cereal farms only provide returns in April and May.

The AAG-SiM Design Concepts I examined and discussed several arrangements to make the game more realistic with colleagues, including overlaying an aerial photograph on top of the wooden tiles to make the game board more accurate, coloring the tiles based on productivity, varying the production point values by month to account for economic and environmental conditions, and allowing players sell land and borrow money. During the development of AAG-SiM however, it quickly became apparent that since only one moderator would oversee AAG- SiM, keeping track of each player’s decisions and incorporating all of these rules would be very difficult for this case study. I kept the rules for AAG-SiM very simple for this reason.

Through incorporating points into the AAG-SiM, rational and irrational behavior can be examined (Table 3). In a simple economic model, rational behavior would attempt to maximize profits at a faster rate, whereas irrational behavior would have the opposite effect.

Total Yearly Net after Turns until

Cost Return 1 Year Profitable Cereal Occupied 25 26 1 6 to 8 South 20 18 -2 18 to 21

Chicken Occupied 35 36 1 12 South 30 36 6 10

Dairy Occupied 45 48 3 11 South 40 48 8 10

Trees Occupied 25 24 -1 13 South 20 24 4 10 Table 3. Rational and Irrational choices in AAG-SiM. Rational choices are bolded.

Initialization Before the AAG-SiM begins, a moderator randomly assigns each player the role of a cereal, chicken, or dairy farmer. Each player gets colored tacks to represent their role. Different colored pins represent the ‘agriculture’. Throughout the game, the tacks and pins are stuck into the wooden tiles of the game board to represent the ‘land held’ and the ‘agriculture type held’ for each player. Each player starts out with fifteen tiles of land (tacks) with their assigned agriculture (pins) and 150 points. Players are allows to choose any fifteen tiles on the game board to place their farms. The components of AAG-SiM are shown in Table 4.

The AAG-SiM begins in the month of September to coincide when cereal farmers in Athienou begin preparing for the rainy season. The cereal farmer always makes the first decision at the beginning of each month. The chicken farmer goes second and then the dairy farmer goes last. This scheduling was an arbitrary decision. During the game, players are free to purchase any open tiles. In addition, players are free to purchase any type of agriculture. Only one agriculture pin can be placed on a land tile and a player must own a tile without any existing agriculture on it order to purchase agriculture. Players are not obligated to place agriculture on tiles they have purchased. The values shown in Table 1 and Table 2 are simplified and generalized values based on the agricultural prices and their monthly returns determined through the literature review and the interviews with the Greek-Cypriot farmers. Interviews with the farmers and price data from the Department of Agriculture in Cyprus helped developed the returns for each game piece.

Players (1) Greek-Cypriot Farmers (1) Cereal Farmer Farmer (2) Chicken Farmer Type (3) Dairy Farmer (1) Tiled game board (2) Tacks to represent land ownership Game Set (3) Pins to represent cereal, chicken, dairy, and trees (4) Carob seeds and large beans for points Turns 1 Turn = 1 Month Table 4. Components of the AAG-SiM.

Input All tiles on the game board have equal productivity throughout the game. The AAG-SiM environmental conditions are optimal and remain the same but they reflect the conditions in Athienou Cyprus of hot and dry summers and cooler, wetter winters.

3.3.7 The AAG-SiM RPG Scenario Given the provocative land ownership issues caused by the Turkish occupation, which directly affected farmers in Athienou, along with the drastic shifts in agricultural LUCC in Athienou after 1974, I developed a short hypothetical scenario.

AAG-SiM RPG Scenario How would the Greek-Cypriot farmers from Athienou develop the Turkish Occupied land to the north of Athienou if it were to become available for farming again?

Based on the background literature review and on interviews with the Greek-Cypriot farmers, I hypothesized that the Greek-Cypriot farmers would behave rationally to develop the occupied land because of their familiarity of the landscape and their local knowledge of the more fertile soil. Building off the AAG-SiM, the AAG-SiM RPG scenario is designed to simulate this scenario in the Athienou agricultural landscape by having Greek-Cypriot farmers play the RPG scenario. As mentioned previously, this case study uses two sets of participants play the AAG-SiM RPG scenario: The “expert” Greek-Cypriot farmers and the “non-expert” undergraduate students. This design addresses the second objective of the case study can be addressed:

 To examine the decision-making strategies of the “expert” and “non-expert” participants in a RPG scenario.

Finally, in comparing the outcomes and strategies, the AAG-SiM RPG scenario also became a tool to examine the use of board games for crowdsourcing. As mentioned in the ODD protocol, using an RPG for simulation fosters discussion among the stakeholders and builds trust. In addition, immediate feedback can be obtained from the participants about the model.

The AAG-SiM and the AAG-SiM RPG scenario game boards are the same dimensions. However, the AAG-SiM RPG scenario board (Figure 15) is divided into a northern Turkish occupied section that contains 100 tiles (100,000 decares) and southern Greek- Cypriot section that contains 125 tiles (125,000 decares).

Figure 15. The AAG-SiM RPG scenario game board with game pieces.

This division reflects the territorial division in Athienou caused by the 1974 invasion as reported by the farmers in the interviews. The choice of a 125 tile southern “Greek- Cypriot” section was based on a general impression that the occupied area was a smaller area. It was not possible to obtain a shapefile to determine the actual proportions during the development of the AAG-SiM RPG scenario.

As in the AAG-SiM, there are three low-level entities in the AAG-SiM RPG scenario: players, agriculture, and land and only three players play at one time. The classification of whether a player is an “expert” or a “non-expert” does not change during the game. Although the ‘players’ and ‘agriculture’ are characterized in the same way, the primary difference is that ‘land’ is further separated into north and south (Figure 16). This separation is the primary difference between the AAG-SiM and the AAG-SiM RPG scenario. A UML diagram (Figure 17) and new rules (Table 5, Table 6), and game components (Table 7) are provided to reflect this difference.

Figure 16. AAG-SiM RPG scenario entities.

Figure 17. A UML Diagram of the AAG-SiM RPG game play.

UNIT PRICE Tile of Land in 15 South Tile of Land in 20 North Dairy Farm 25 Chicken Farm 15 Cereal Farm 5 Tree Farm 5 Table 5. Prices of the game pieces in the AAG-SiM RPG scenario.

COST OF NET UNIT RETURN PRODUCTION RETURN Dairy Farm 8 4 4 Chicken Farm 6 3 3 101 1 91 Cereal Farm 142 1 132 Tree Farm 2 0 2 Table 6. Return on each game piece in the AAG-SiM RPG scenario. 1Return and Net Return for Cereal Farms in the South. 2Return and Net Return for Cereal Farms in the North.

(1) Greek-Cypriot Farmers Players (2) Undergraduate Students (1) Cereal Farmer Farmer (2) Chicken Farmer Type (3) Dairy Farmer (1) Tiled game board (2) Tacks to represent land ownership Game Set (3) Pins to represent cereal, chicken, dairy, and trees (4) Carob seeds and large beans for points Turns 1 Turn = 1 Month Table 7. Features of the AAG-SiM RPG scenario.

The AAG-SiM RPG scenario is initialized in the same way as the AAG-SiM, and follows the same procedure. The difference is that each player starts by choosing fifteen tiles in the southern Greek-Cypriot section of the game board to place their farms. Before the RPG scenario begins, a moderator randomly assigns each player the role of a cereal,

chicken, or dairy farmer. Each player gets colored tacks to represent their role. Different colored pins represent the ‘Agriculture’. Throughout the game, the tacks and pins are stuck into the wooden tiles of the game board to represent the ‘Land Held’ and the ‘Agriculture Type Held’ for each player. Each player starts out with fifteen tiles of land (tacks) with their assigned agriculture (pins) and 150 points. Players are allows to choose any fifteen tiles on the game board to place their farms.

3.4 The Game Workshop

I supervised three sessions of the RPG scenario for the Game Workshop in two different locations. The “expert” Greek-Cypriot farmers played one session in Athienou, Cyprus, while the “non-expert” undergraduate students played two sessions in Columbus, Ohio. Each AAG-SiM RPG scenario game session simulated 13 months, or 13 turns and 36 decisions, from September until the September.

The results of every Game Workshop were digitized and a figure for each month was created for to help visualize the progression of the game. In these figures, yellow represents a cereal farm, red represents a chicken farm, and blue represents a dairy farm. The numbers in each box represent each farmer. In every game session, the cereal farmer [1] began each month, the chicken farmer [2] went next, followed by the dairy farmer [3]. A complete chart of figures for each game session is located in the Appendix

3.4.1 Observations from the Game session with the “Expert” Greek-Cypriot Farmers

The AAG-SiM game session with the Greek-Cypriot farmers took place in the Rea coffee shop in Athienou on 23 July 2011. Despite coordinated efforts to have three farmers to participate in the Game Workshop, only one of the seven farmers I interviewed was able to play the game. I refer to him as “Farmer 7.” He is the same “Farmer 7” from the interviews. “Farmer 7” brought along his son, and his son invited one his friends to play the game instead. The farmers’ son, referred to as “Costas”, and his friend, referred to as “Nikos”, both grew up in Athienou. They were both attending a university in England but were home for the summer. In this game, “Farmer 7” [1] was the cereal farmer, “Nikos” [2] was the chicken farmer, and “Costas” [3] was the dairy farmer. 56

Observations from the “Expert” Group I began by providing each player with an explanation of the RPG, and a list of the rules to the game shown in Table 5 and Table 6. I emphasized that the RPG was a simulation, rather than a game, and that there was no way to “win” the RPG. Each rule was explained before the game started. As the initial tiles were being placed (Figure 18), I asked “Farmer 7” who was playing the role of a cereal farmer why he had placed all his yellow tiles in one area. He said:

“The reason I put only half... it’s no way that a farmer would take all of this area...every farmer has his own area. Say he’s gonna move 100sq km, he won’t go from here to Paphos [located on the opposite side of the island]. For you to do your job, you’d have to go every day…costs a lot more money to go, so the cost will go up. You don’t want to do that, so this is what do you try to do. There are famers over this side also [points to other side of the board], but they also stay in their area, we stay in our area, that’s the way it goes.”

Figure 18. Initial Starting positions for the "Expert" Greek-Cypriot Farmers.

During the initial set up, “Nikos” was mostly concerned with figuring out the rules. He asked several general questions about how much land costs. While explaining how cereals only return points for two months of the year, “Costas” believed the chicken farmer was at more of a disadvantage then the cereal farmer, although he did not elaborate. “Farmer 7” picked up and said:

“If you want to be realistic, and rational, you can’t put the same numbers of cereals with the same number of cows and chickens. It would be better

if you had less cows, and the same cereal, and less chickens. That’s the rate in Cyprus. Chicken farming doesn’t use any land because they buy the barley and wheat… The cow farming needs more land because they grow their own crops”

“Farmer 7” explained that most farming in Athienou is “mixed”, particularly with dairy farming, meaning that many dairy farmers also grow their own feed for their animals. He suggested that perhaps on dairy farms, a cereal pin could be added. In terms of the initial starting conditions for the farmers, “Costas” explained that it would be better for chicken and dairy farmers to start with fewer tiles to match the number of farmers in Cyprus.

Through the first and second turns, all of the players had general questions about the rules to the game itself. Questions having to do with the realism of the game began to appear during September and October. Questions from “Nikos” [2] such as “Is it possible, if I don’t want to buy, just to sell?” and “Can I send land to another player?” During his first turn, “Farmer 7” said his strategy was to spend all of money on land. Notably, after “Farmer 7” purchased land in the occupied area, “Costas” brought up that he had an issue with purchasing land tiles in the occupied region. “How can I buy something that doesn’t belong to me?” he asked. “Some of this land belongs to us already through land titles.” “Farmer 7” was confused as well, and he said that “[he could] buy from a Turkish Cypriot, but not from the Turks.” I reiterated that the RPG was just a scenario, and that in the scenario the land would be available for farming again. I also said to assume that the owners were selling the land. “Costas” agreed on this point he said: “Please make a note, that we are not buying our land again. We are not buying our land twice.”

Figure 19. September (L) and October (R) (Expert).

After receiving the returns from the first month, “Farmer 7 said he “couldn’t do anything with [his returns]” and began purchasing trees. Costas” brought up the issue of cost with the occupied land. “The [occupied] land should be cheaper because there is no infrastructure” he said. “There is no water supply, the land is better, yes, but there is no infrastructure. If you want to see how we will act, you cannot have the land be more expensive.” “Farmer 7” agreed, and said it would take three to four years to build it up. I did not want to change the rules of the game however, so I said to assume there was infrastructure in the area.”

“Farmer 7” did not have enough points during November to do anything so his turn was skipped, although he did ask if he was allowed to borrow money. “Costas” again brought up the issue of the cost of land in the occupied area: “Nobody buys on this side [Occupied] because it’s more expensive, it should be cheaper, I’m telling you. How can you take advantage of it being open?”

During December, as “Nikos” and “Costas” started to ask about some of the general rules to the game, “Farmer 7” wanted to me to let them figure it out themselves, which would work towards his advantage. He also said: “Each player [should not be able to] see what the other player is doing. It’s like war. You play your own game. You have your own income. You try to be in a better spot than the other player.”

Figure 20. November (L) and December (R) (Expert).

“Costas” mentioned his goal was to make “as much money as possible.”

After December, the players were very quiet, and there was not much strategizing discussion. After “Farmer 7” received the returns from his cereal crops, both “Costas” and “Nikos” speculated what he would buy. At this point “Farmer 7” purchased all of the remaining tree pins. This was unexpected, because I did not anticipate all of the tree farms to be purchased. In addition, this large purchase began a competition to buy up the last of the cheap land in the south. Due to the number of pins and tacks on the board, identifying open tiles became tricky. Throughout the game, there were periodic discussions in Greek, which I could not understand. The translator was not present because all of the “experts” spoke English. A full progression of the “expert” game board, including the January to September turns not presented here, can be found in Appendix E.

3.4.2 Observations from the Game sessions with the “Non-Expert” Undergraduate Students

The AAG-SiM game sessions with “non-expert” undergraduate students took place in Derby Hall on Ohio State’s campus on 30 September 2011, and on 7 October 2011. Two groups of three students participated in the “non-expert” Game Workshops. To protect their identities, I refer to all of the students as “Student A – Student G”. I began

each game session with the students with a brief introduction where I provided some geographic and political context to the research by talking about Cyprus and Athienou before I went over the rules for the RPG. As with the Greek-Cypriot farmers, I emphasized there was no way to “win” the RPG, rather it was simply a simulation. I also asked each student if they had any farming experience, and what they knew about Cyprus.

“Non-Expert” Student Group 1 (Student A, Student B, Student C) The first student group played in Derby Hall on 30 September 2011. The meeting time was coordinated through email but due to a cancellation, the group comprised of two undergraduate students and a graduate student. In the initial introductions, “Student A” mentioned growing up on a farm and had an extreme familiarity with farming. “Student A” also had been interested in participating in research for the farming aspect. Neither “Student B” nor “Student C” had prior experience with farming, and all participants were unfamiliar with Cyprus. In this game, “Student A” [1] was the cereal farmer, “Student B” [2] was the chicken farmer, and “Student C” [3] was the dairy farmer.

Observations from “Non-Expert” Student Group 1 As with the “expert” group, before playing the RPG, I explained the RPG and provided the list of the rules shown in Table 5 and Table 6. The strategies discussed during the game are reviewed in the next chapter. The initial tile placement is shown in Figure 21. During the initial introduction, the students were very curious about and about life in Cyprus and in Athienou.

I was surprised as to how conglomerated each player had placed their tiles. Questions came from all students about general questions related to Cyprus and Athienou. “Student A” shifted these questions to the specific farm-related rules such as “Is it possible to sell land?”, and “If we buy the land, do we have to farm it that month?”

During September, there were several questions about clarifying the rules, particularly around the planting season of cereals and its returns based on location. After the players

received their returns for the month, “Student A” who received no returns for the month remarked, “It’s just like real farming. You’ve got to have a strategy.”

Figure 21. Initial starting positions for "Non-Expert" Group 1.

Soon after “Student A” asked about borrowing points. As October began “Student B” brought up a comment about how the AAG-SiM game board was tricky to use because of the difficulty of pushing tack and pins into the wood. Cardboard was suggested instead.

Figure 22. September (L) and October (R) "Non-Expert" Group 1.

As November began, “Student A” asked: “Since we’re only playing one year is it worth it to buy more land?”

Although I had set aside a set time to play AAG-SiM and I mentioned that the game would probably take two hours to simulate one year, I said that it was not my intention for the participants to see it as a game that ended at the end of one year. I told them to imagine the game continued on for several years. As the returns were handed out from, “Student C” said: “I bought cereal the first round, but then I got smart and bought cows because you get so much money back.”

A discussion about strategy and the specific returns of each agriculture unit began as “Student A” said he only had enough to buy one tree: Student A: “I could buy trees, and since they produce two, it would take me two months to pay it off.” Student B: “Can you put more than one item on a tile?” Moderator: “No you can only have one pin on each tile.” Student B: “So it’s really not that great a deal to buy trees?” Student A: “Well you need to make some money, and it only takes two months to pay it off.” Student B: “I’d rather put a cow on my land than put a tree.” Student A: “But, I can’t afford a cow because I put all my money into cereals.”

Figure 23. November (L) and December (R) "Non-Expert" Group 1.

“Student A” justified buying a tree as a long-term investment so he could use the land for cereals the next year. Although this rule is not in the game, “Student A” suggested that an option should be that you could rotate between dairy and chicken, and cereals and

trees. “Student B” also noted that “if we all buy dairy, then the price of dairy would probably go down in real life.” “Student A” suggested adding in a loan system where:

“You could have the other players loan someone money, with a set of terms, and if they don’t pay it back, they lose that tile.”

“Student A” also was curious to know the type of farm equipment used in Cyprus. The discussion of cereals came up again and so I asked “Student A” if the planting season was familiar: “We plant our wheat in November and take it off in July, because it’s winter wheat….we double crop, so once the wheat is done we grow beans.”

During January and February, Student A” could not play because of funds, but had developed a long term strategy. “Student A” asked whether the original inputs on the land had to be repaid once the year started again. One question from “Student C” provide some insight into the students thinking: “Is there any actual advantage to having land in the occupied area other than the soil?”

None of the students thought there was any other advantage.

Figure 24. January (L) and February (R) "Non-Export" Group 1.

Figure 25. March (L) and April (R) "Non-Expert" Group 1.

As “Student A” developed a long-term strategy of after receiving the returns on the cereal from April and May. Land was purchased in the north and points were saved until planting season in September began. In addition, “Student A” said:

“I’m gonna keep buying land in the north because you get better returns on the cereal crops.”

After the returns from April and May, there were no major discussions. A full progression of the “non-expert” group 1 game board, including the May to September turns not presented here, can be found in Appendix F.

“Non-Expert” Student Group 2 (Student D, Student E, Student F) The second student group played in Derby Hall on 7 October 2011. The meeting time was coordinated through email and the group consisted of two undergraduate students and a graduate student. In the initial introductions, none of the students said they were familiar with farming or with Cyprus, although “Student F” was a self-described game enthusiast. In this game, “Student D” [1] was the cereal farmer, “Student E” [2] was the chicken farmer, and “Student F” [3] was the dairy farmer.

Observations from “Non-Expert” Student Group 2 As with the other two groups, I explained the RPG and provided the list of the rules shown in Table 5 and Table 6. There were no contextual questions about Cyprus asked during the explanation of the rules. “Student F” began the questions about the layout of the board:

“So other than our starting positions, there is no real difference between one side and the other? And no bonus points for clumping our land together?”

Several general questions were asked relating to the rules of cost of production and returns before the game began. The initial starting positions are shown in Figure 26.

Figure 26. Initial starting positions for "Non-Expert" Group 2

As September began, specific questions relating to the rules of cereal planting and cost of production for all agriculture, and returns were discussed. The players seem to learn these rules very quickly and the first month went by without much comment.

As the players received their returns, “Student D” asked about borrowing money. “Student F” started buying more land in the south, the reasoning was that the property was cheaper than the occupied area. The first four turns went by quickly and there was very little strategizing discussion until “Student F” said: “If the goal of this game is to make money, I stop buying property because it’s not going to pay for itself by August.”

Figure 27. September (L) and October (R) "Non-Expert" Group 2.

I asked “Student F” to elaborate on the reasoning behind this strategy. It came down to a similar issue the first student group had with the artificial limit on playing only one year. It also had to do with the mindset of thinking the AAG-SiM RPG was a game where there was a winner. I told the players to make their decisions based on the assumption that RPG continued for several years. This discussion also raised similar questions about selling land and loaning money among all three players.

Figure 28. November (L) and December (R) "Non-Expert" Group 2.

During January, a discussion about best strategy to pursue for the game developed and resulted in statements from each player:

“Player D”: “Buy as much dairy as you can afford because cereal and trees are not a good return….As soon as I get the returns on the cereals, I’m diversifying my crops.” “Player E”: “The only incentive is to buy land quicker than your competitors in this game.” “Player F”: “Buy as much dairy as you can afford at the beginning of the game, because anything makes more than cereal.”

“Player F” also noted that a lot more interaction could occur if there was an incentive to collaborate with other players. At the end of February, “Player D” noted that supply and demand would definitely affect the cost and prices.

Figure 29. January (L) and February (R) "Non-Expert" Group 2.

As March began, “Player F” noted that another strategy could be to start buying the cheap land in the south because at some point it would run out. Importantly, “Player F’s” observation hit on a key point discussed in the Preliminary Analysis.

There was not much discussion after March. A full progression of the “non-expert” group 2 game board, including April to September turns not presented here, can be found in Appendix G.

Figure 30. March "Non-Expert" Group 2.

3.4.3 Moderator Observations I had initially believed the game would not take more than an hour to simulate a few years. However after the first game workshop with the “experts” took over three hours and simulated only 13 months, I realized that the RPG took a long time to play. Each game session with the “non-experts” lasted over two hours as well. The most challenging part of moderating the Game Workshops with the “experts” and the “non-experts” was keeping track of all of the decisions. An excel table with formulas for each transaction was used to record the players moves (Table 8).

Player 1 Player 2 Player 3 Land South 15 15 15 North Total 15 15 15 Cereal [Beans] 0 0 0 Chicken [Beans] 0 0 0 Dairy [Beans] 0 0 0 Trees [Beans] 0 0 0 Table 8. Excel Table used during the Game Workshop.

I was in charge of recording all of the purchases and distributing and exchanging points for tacks and pins. I did not expect the process of counting, and distributing carob seeds and beans to be difficult, but it proved to be very tedious. In addition, it was challenging to ensure each player was playing by the rules. Although the point system was meant to limit each player in the decisions, during the “expert” game session and during one of the “non-expert” game sessions, two players managed to take on debts likely through a moderator oversight. I only noticed these errors after reviewing the photographs from each game session. A more descriptive Excel table was created for each farmer. These tables are also located in the appendix.

Chapter 4: Preliminary Analysis

As outlined in Section 3.1, the second objective for this case study was to examine the decision-making strategies of the “expert” and “non-expert” participants in an RPG scenario. My hypothesis for the RPG game was that the “expert” Greek-Cypriot farmers would develop the occupied land rationally with cereals to take advantage of the more fertile soil. This chapter begins by examining the strategies and outcomes of the RPG with the “experts” to assess my hypothesis. I examine the strategies and outcomes of both “non-expert” groups, and then compare the outcomes of the “expert” and “non- expert” groups. Finally, I discuss the emergent properties of AAG-SiM.

4.1 Examining the Strategies

In examining the ODD protocol for the AAG-SiM RPG, on the surface it appears the outcomes for the RPG could be uninteresting. The AAG-SiM RPG appears to follow the Hardin’s (1968) ‘Tragedy of the Commons’ as players race to purchase land tiles and maximize their points through the purchasing of agriculture that provides the best returns, acting only in their own self interests. This outlook ignores the decision-making processes taking place within the RPG itself because the “expert” and “non-expert” participants use very different sets of knowledge when playing the RPG. The focus is less about the inevitable physical progression of the game board and more about understanding the decision-making behaviors among the “experts” and “non-experts” during the Game Workshop. In comparing the two groups, the hypothesis touches on the deeper question: How does “non-expert” outsider knowledge compare with “expert” local knowledge when placed in the same situation? The RPG component of ComMod allows researchers to examine the strategies that emerge from stakeholder interaction with each other and the model. Furthermore, the emergent properties of the system itself manifest as the outcomes from the game sessions.

4.1.1 The “Expert” Strategies In order to assess how rationally the farmers behaved to develop the occupied land, I first created an excel table with a count of each agricultural type and its location on the game board at the end of the “experts’” game session. This table also shows the agricultural make-up of the occupied and south areas by percentages (Table 9).

% of % Agriculture Occupied Occupied Total Cereal 33 0.47 0.17 Chicken 19 0.27 0.10 Dairy 15 0.21 0.08 Trees 3 0.04 0.02 Total 70

% Agriculture South % of South Total Cereal 20 0.16 0.10 Chicken 33 0.27 0.17 Dairy 32 0.26 0.17 Trees 38 0.31 0.20 Total 123 Table 9. Placement of all 'Agriculture' by all "Expert" Greek-Cypriot Farmers.

Using Table 3 as a guide, I separated out the rational and irrational behavior into a table. Since the initial starting conditions added fifteen tiles to rational behavior for the dairy and chicken, and fifteen irrational tiles to the cereal, I removed these numbers from each count. This left the number of total rational decisions on the game board at 106, and the number of irrational decisions at 42, for 148 total decisions. Overall, the “experts” behaved rationally 73.3% of the time and irrationally 27.3% of the time in their decision- making (Table 10).

In focusing in on the occupied area, the total count of rational decisions is 33 while the total count of irrational decisions is 37. This means that out of 70 decisions for the placement of agriculture, 47.1% of the time the decision was rational, while 53.9% of the time the decisions were irrational. This means that my hypothesis that farmers would

behave rationally to develop the occupied land is false. The farmers behaved slightly more irrationally, albeit by a 6% margin or only by about four decisions.

Agriculture Occupied Cereal 33 Chicken 19 Dairy 15 Trees 3 Total 70

Agriculture South Cereal 5 Chicken 18 Dairy 17 Trees 38 Total 78 Table 10. Adjusted table of rational and irrational "Expert" decisions. Rational decisions are bolded and irrational decisions are italicized.

4.1.2 The “Non-Expert” Strategies In examining the “non-expert” strategies, I followed the same procedure as the “expert” group. Non-Expert Group 1

% Agriculture North % of North Total Cereal 29 0.74 0.20 Chicken 0 0.00 0.00 Dairy 8 0.21 0.06 Trees 2 0.05 0.01 Total 39

% Agriculture South % of South Total Cereal 20 0.19 0.10 Chicken 22 0.21 0.11 Dairy 60 0.57 0.31 Trees 3 0.03 0.02 Total 105 Table 11. Placement of 'Agriculture' by all "Non-Experts" Group 1.

The first “non-expert” group behaved incredibly rationally throughout the RPG and the tile placement was very homogeneous (see Figure 21). In total, the “non-expert” Group

1 made 99 total decisions without the initial 45 tiles. They behaved rationally 84.8% (84) of the time and irrationally 15.2% (15) of the time (Table 12).

Agriculture Occupied Cereal 29 Chicken 0 Dairy 8 Trees 2 Total 39

Agriculture South Cereal 5 Chicken 7 Dairy 45 Trees 3 Total 60 Table 12. Adjusted table of rational and irrational "Non-Expert" Group 1 decisions. Rational decisions are bolded and irrational decisions are italicized

Within the occupied area, “non-expert” Group 1 behaved rationally 74.4% of the time (29) and irrationally 25.6% (10) of the time.

Non-Expert Group 2 In total, “non-expert” Group 2 made 121 total decisions, without the initial 45 tiles (Table 13). The group behaved rationally 68.6% (83) of the time and irrationally 31.4% (38) of the time. % of % Agriculture Occupied Occupied Total Cereal 10 0.24 0.06 Chicken 6 0.14 0.04 Dairy 14 0.33 0.08 Trees 12 0.29 0.07 Total 42

% Agriculture South % of South Total Cereal 21 0.17 0.11 Chicken 25 0.20 0.13 Dairy 69 0.56 0.36 Trees 9 0.07 0.05 Total 124 Table 13. Placement of 'Agriculture' by all "Non-Experts" Group 2.

Agriculture Occupied Cereal 10 Chicken 6 Dairy 14 Trees 12 Total 42

Agriculture South Cereal 6 Chicken 10 Dairy 54 Trees 9 Total 79 Table 14. Adjusted table of rational and irrational "Non-Expert" Group 2 decisions. Rational decisions are bolded and irrational decisions are italicized.

Within the occupied area, the “non-expert” Group 2 behaved rationally 23.8% of the time (10) and irrationally 76.2% (32) of the time (Table 14).

4.2 Examining the “Experts” and “Non-Experts” Outcomes

In examining the outcomes from the “expert” and “non-expert” groups, it is clear that each group used different strategies when it came to developing the occupied land despite receiving the exact same rules to the RPG (Table 15).

% % Rational Irrational Rational Irrational "Experts" 33 37 47.1% 52.9% "Non-Expert" Group 1 29 10 74.4% 25.6% "Non-Expert" Group 2 10 32 23.8% 76.2% Table 15. Rational and irrational decisions among the "Experts" and "Non-Experts."

However, this should not be surprising because embedded in each of the “expert” and “non-expert” decisions is local knowledge adapted to a specific system (Geertz 1973). Although it is not possible to say anything about whether the “experts” are slightly more irrational then rational in general, the more interesting phenomena occurs when you begin to compare the outcomes of the two “non-expert” groups with the “expert” group. At first glance, the two “non-expert” groups used nearly opposite strategies to develop

the occupied area. Interestingly, when the rationality and irrationally percentages between “non-expert” groups strategy are averaged (74.4% and 23.8%, 25.6% and 76.2%), the resulting figures, 49.1% and 50.9%, are remarkably similar to the actual “expert” percentages. Although not conclusive, this outcome provides an interesting case for examining AAG-SiM at a much larger scale to determine if this pattern would continue if more “experts” and more “non-experts” could participate.

4.3 Emergent Properties of AAG-SiM

The diverse spatial outcomes from the game sessions in this case study are immediately distinguishable (Figure 31). Based on the findings from this study, the variation between the groups means the rules alone do not determine the spatial outcome of the RPG. As with the analysis of the strategies in the previous section, this suggests there are additional factors or benefits that influence the spatial decisions of the “expert” and “non- expert” groups that are not captured through AAG-SiM.

Figure 31. Outcomes from the "Expert" (L), "Non-Expert" Group 1 (C), and "Non-Expert" Group 2 (R).

Economists define these benefits as psychic costs and benefits. In the game sessions, the players made decisions with an objective in mind. However, the choice about the type and placement of agriculture depends on a variety of cultural, emotional, and non- monetary factors. Individuals consider these psychic costs and benefits when making decisions (Sjaastad 1962). Seen in this way, each player considers the following when making a decision:

(Actual Benefit + Psychic Benefit) – (Actual Costs + Psychic Costs) = Net Benefit

Although making larger generalizations from this case study it is not possible, two observations can be made based on the preliminary findings. While both groups used the same rules during the RPG,

 The “experts” developed the occupied land faster than the “non-expert” groups;  The “expert” game session developed a more spatially heterogeneous game board than the “non-experts” game sessions.

These emergent properties from the AAG-SiM RPG scenario can be preliminarily discussed in terms of psychic costs and benefits. Perhaps the “expert” Greek-Cypriot farmers found developing the occupied land more beneficial than “non-expert” undergraduate groups because they have a stronger connection to the land and have a strong desire to regain the occupied land. In other words, since the “non-experts” lack the local knowledge of the perceived benefits of expansion into the occupied area, they behave differently than the “experts”. Similarly, the spatial outcome of the game boards may reflect the difference in local knowledge about the costs and benefits of farming in Athienou. A larger number of participants must be included in order to examine these system properties in more detail.

Chapter 5: Conclusion and Discussion

This case study examined agricultural LUCC in Athienou, Cyprus using an adapted ComMod methodology, which is an iterative process of fieldwork, modeling and simulation. Through an iteration of the research process, ethnographic fieldwork helped develop an initial understanding of the drivers affecting the local agricultural LUCC and an RPG called the Athienou Agricultural Game – Simulation Model (AAG-SiM) was designed to model the Athienou agricultural system. A scenario was developed and played through the AAG-SiM RPG with a group of “expert” Greek Cypriot Farmers and two groups of “non-expert” undergraduate students. The scenario examined the decision- making strategies of the Greek-Cypriot farmers from Athienou to see how they would develop the Turkish Occupied land to the north of Athienou if it were to become available for farming again. The AAG-SiM RPG tested a hypothesis that examined rational and irrational decision-making behavior in the “expert” Greek-Cypriots’ development of the occupied land. Although the hypothesis, which believed the Greek- Cypriot farmers would behave rationally, was falsified, the outcomes from the “non- expert” undergraduate student group suggested an exciting suggestion about a new way to crowd source information. The case study also lays the groundwork for continued investigations in Athienou Cyprus.

5.1 Limitations

There were several limitations to this study. They are addressed in three categories.

5.1.1 Companion Modeling (ComMod) The ComMod process is designed to be an iterative multi-year process. Given the limited time for this project, there were many issues that were not addressed in the fieldwork, the model, and the simulation. I also had never used the ComMod approach or developed a

board game. Within the fieldwork, I was unable to arrange to talk with a Chicken farmer. This knowledge would have been useful during the analysis of the interviews with the farmers and in the creation of the rules for the game. During the modeling phase, I was unable to develop a model or an RPG that incorporated any of the emergent themes from the interviews. If this had been included, there would have been more of a connection between the fieldwork and the game itself. During the RPG simulation, I was unable to make sure people were following the rules and there were cases of cheating. I only discovered this after the game was finished.

5.1.2 Positionality Positionality must be taken into account when conducting interviews, as it is not easy to tell who has an agenda or a bias, or who might be telling the full story (Rose 1997). Positionality is potentially a very serious limitation in this case study because of the constructivist approach of ComMod. I had been to Athienou previously four times for an archaeological project so I was familiar with the town. The town of Athienou is very generous to the archaeologists who have been coming since 1990. One of the directors of the project has some family connections to this small town. However, this was the first time I interacted with farmers. I found all of the farmers open in their answers during my interviews, but I was still an outsider. Using a translator helped me with some of the interviews, but it is not the same as communicating directly. Also, since this research was well outside my previous research scope especially considering I am unfamiliar with farming.

The most curious data that was constantly reported to me was the area of the occupied land in Athienou (Section 3.3.4). Several of the farmers claimed Athienou lost 100,000 decares, or 25,000 acres. When I measured the area in GIS using a map from the local government, the proportions were nowhere near 100,000 decares. Follow up research is necessary to determine if I am missing information.

5.1.3 Validity One of the strengths of the ComMod approach is its consistent feedback from the stakeholders involved in the study about the modeling process. However in this case

study, I was only able to get feedback from the “expert” Greek-Cypriot farmers during the playing of the RPG. In other ComMod studies, there are several presentations of a model before proceeding to developing a RPG scenario. However, this was due to a lack of time in the field, and can be fixed.

Although the only stakeholder roles in this RPG are dairy, chicken, and cereal farmers, many other stakeholders influence agricultural LUCC in Athienou. In addition, agricultural LUCC would likely not be the only type of LUCC involved if this scenario were to occur. Although in other ComMod studies these stakeholders would have been included, those studies also have more time to establish a relationship with the community and recruit participants. AAG-SiM best serves as a test model for launching future investigations.

5.2 Revisions to the AAG-SiM RPG

The primary drawback to the AAG-SiM RPG was that it did not directly incorporate any of the themes that emerged from the interviews with the farmers to find further connections. This was because it was difficult to develop a game with enough interaction, but also simple enough for a moderator to keep track of all the movements. I would have liked AAG-SiM to be more like SYLVOPAST and SAMBA, however the SYLVOPAST model was completely on a computer and SAMBA had years of research time and a team of researchers working on the project.

A new model would continue the ComMod procedure and return to the fieldwork with the Greek-Cypriot farmers to examine the themes of water, the changing environment, and outside influences in more detail. Through the fieldwork in this case study, I discovered that dairy farming plays a major role on the agricultural landscape of Athienou. The new fieldwork would focus on exploring the interactions and interdependences between the dairy industry and other farmers in Athienou. The goal would be to revise the AAG-SiM RPG or develop a model that examines the extent to which the dairy industry has on the surrounding landscape.

The new or revised RPG would incorporate many of the suggestions from the Game Workshop with the Greek-Cypriot farmers and the students. Incorporating realistic socio-economic functions such as the selling of land, the loaning of money, price dynamics, and costly punishment would be essential. In addition developing better rules local rules for the game, encouraging interaction between different players through incentives would create a more realistic game. Finally, environmental conditions, along with parameters that highlight the importance of water should added to the next RPG.

5.3 Application of the Case Study

This case study was an examination of methodological tools for investigating the use of games to model environments and complex systems. In my adapted ComMod methodology approach, I demonstrate the viability of building models using stakeholder (POV1) input. There are tradeoffs to relying entirely on a stakeholder-constructed perspective, such as how in this study, the dimensions of the occupied land were reported differently. Overall, this study showed it is possible to build a board game to represent an environment and use it as a conceptual model and an RPG.

The long-term application of this study lies in understanding how the drivers of agriculture change in Athienou affect the interdependencies between farmers at a local and regional level. This information potentially has significant implications for future policy assessments for agriculture in Athienou and throughout the region. Building models that capture agricultural decisions on this local level also helps address the Millennium Ecosystem Assessments’ call for the development of more regional and place- specific scenarios to address the much larger issue of ecosystem change.

5.4 Future Assessment

This case study was set up to examine how two diverse sets of participants would use their own knowledge with the backdrop of a game environment. Game environments have great potential for social simulation, learning, and tackling real world problems (McGonigal 2011). This study uses the ComMod methodology of developing RPGs to examine if games can be used as crowdsourcing tools. More recently, Ahlqvist et al.

(2012) has begun to examine the use of massively multiplayer online gaming (MMOG) tied to a real-world location through an online “Geo-Game” which simulates the Green Revolution. The potential of using games in this way, coupled with the preliminary findings from this case study sets up a framework for the next iteration of research.

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Appendix

Appendix A: IRB Approval from Ohio State University

Appendix B: Recruitment flyers for Greek-Cypriot farmers to participate in research experiment.

Appendix C: Recruitment flyer for undergraduate students to participate in research (Translated from Greek flyer)

Appendix D: Rules of the AAG-SiM RPG given to the farmers and the undergraduate students

Appendix E: Results of the “Expert” Game Workshop

100

Appendix E: Results of the “Expert” Game Workshop (Continued)

101

Appendix F: Results of the “Non-Expert” Group 1 Game Workshop 30 September 2011

102

Appendix F: Results of the “Non-Expert” Group 1 Game Workshop 30 September 2011 (Continued)

103

Appendix G: Results of the “Non-Expert” Group 2 Game Workshop 7 October 2011

104

Appendix G: Results of the “Non-Expert” Group 2 Game Workshop 7 October 2011 (Continued)

105

Appendix H: Cereal Farmer (“Expert”) Farmer Cereal H: Appendix

Appendix I: Chicken Farmer (“Expert”) Farmer Chicken I: Appendix

Appendix J: Dairy Farmer (“Expert”) Farmer Dairy J: Appendix

Appendix K: Cereal Farmer (“Non Farmer Cereal K: Appendix

Expert”

Group 1) Group

Appendix L: C L: Appendix

hicken

Farmer (“Non Farmer

Expert”

Group 1 Group

)

Appendix M: Dairy Farmer (“Non Farmer Dairy M: Appendix

Expert” Group 1) Group Expert”

Appendix N: Cereal Farmer Farmer Cereal N: Appendix

(“Non

Expert” Group 2) Group Expert”

Appendix O: Chicken Farmer (“Non Chicken Farmer O: Appendix

Expert” Group 2) Group Expert”

Appendix Appendix

: Dairy Farmer (“Non Farmer Dairy :

Expert” Group 2) Group Expert”