Improving an Agent-Based Model by Using Interdisciplinary Approaches for Analyzing Structural Change in Agriculture Franziska Appel, Arlette Ostermeyer, Alfons Balmann, and Karin Larsen Theodor-Lieser-Straße 2, 06120 Halle (Saale), Germany
[email protected]
Abstract. Structural change in the German dairy sector seems to be lagged behind. Heterogeneous farm structures, a low efficiency and profitability are persistent although farms operate under similar market and policy conditions. This raises the questions whether these structures are path dependent and how they can eventually be overcome. To answer these questions we use the agentbased model AgriPoliS. The aim of our project is to improve assumptions in AgriPoliS by using it as an experimental laboratory. In a second part AgriPoliS will be used in stakeholder workshops to define scenarios for the dairy sector and communicate and discuss results to practitioners and decision makers.
1 Introduction and Motivation Farms in the different parts of Germany operate under relatively similar market conditions and policy environments. In spite of that, a huge regional heterogeneity in terms of farm sizes and specialization is noticeable. This project focuses on the German dairy sector. One reason is that structural change in this sector seems to be particularly lagged behind. The very most dairy farms either operate with inferior techniques or apply them in a less economical way. Also the regional heterogeneity of farm structures is particularly large with, e.g., many small farms in the southern parts of Germany (e.g. Bavaria) and a relatively low number of large dairy farms in the north-eastern parts of Germany (e.g. Saxony-Anhalt). A second reason is that the dairy sector is particularly affected by the ongoing liberalization of the European Union’s Common Agricultural Policy (EU CAP). Accordingly, dairy farmers, their representatives and politicians are highly concerned about the future of this sector. Lately the extension of the milk quota which should stepwise lead to a complete abolishment in 2015, causes a fall of the EU milk prices. Because of that, many dairy farmers are threatened in their existences and counteract the CAP reform via strong protests (Deutsche Welle 2009). An important aim of this research is to analyze structural change in agriculture. Relevant questions are; what are the determinants of structural heterogeneity, is structural change path dependent (cf. David 1985, Arthur 1989, Balmann 1995) and can such path dependences be overcome? In this regard the question arises how fast the structure of the dairy sector is able to adjust to the new EU CAP and how farms S.-K. Chai, J.J. Salerno, and P.L. Mabry (Eds.): SBP 2010, LNCS 6007, pp. 360–366, 2010. © Springer-Verlag Berlin Heidelberg 2010
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can cope with that situation. To address these issues, it is aimed firstly to evaluate the behavioral foundation of agents (farmers) in the agent-based model AgriPoliS (Agricultural Policy Simulator, cf. section 2.1). This will be done through incentivebased participatory experiments with a modified version of AgriPoliS in which persons can take over the role of managing one of the farms while others remain computerized. A second objective is to analyze structural change of two selected regions in Germany by using an adopted AgriPoliS version. The regions are heterogeneous in their agricultural structures and dairy farming plays a major role. Stakeholder platforms will be established in the study regions in order to discuss assumptions of AgriPoliS, to identify potential scenarios regarding policy instruments and general technological and economic trends, as well as to analyze and discuss simulation results.
2 Methods 2.1 Participatory Laboratory Experiments with AgriPoliS AgriPoliS is an agent-based model in which structural change is modeled as an endogenous process (Happe 2004; Happe et al. 2005). It can be viewed as an “experimental laboratory” for analyzing structural change in agriculture and builds on Balmann (1995; 1997). The main idea of the model is to consider the interactions between different agents (farmers) which, in turn, will affect their actions. In AgriPoliS farms have an endogenously evolving factor endowment and interactions are captured in the markets for products, labor, capital, land and quotas. As in Balmann (1997) and Berger (2001), the behavioral foundation in AgriPoliS is that farms are assumed to maximize profits or farm household income. This is implemented in a normative way by using mixed-integer programming where decision making is rational but myopic and nonstrategic and expectation formation is generally represented as adaptive (considering trends). The determinants of path dependence in AgriPoliS include sunk costs, frictions on land market and policy incentives to stay in business (Balmann 1995). Also the behavioral objective to maximize farm income may be a reason for path dependence in AgriPoliS as agents may ignore long-term trends. However, even a small individual divergence from rational behavior or selfishness can affect the behavior of all other agents and therefore influence market developments (Ockenfels 2009). In the current version of AgriPoliS, agents are assumed to maximize profits or incomes (homo economicus) but act myopically. As a result, path dependence can be affected by the agents' behavioral model. Until now, several alternatives exist to the assumption of myopic and non-strategic income maximization of AgriPoliS: e.g. a global optimization over all farms within AgriPoliS (Kellermann and Balmann 2009) and genetic algorithms (Kellermann and Balmann 2009). The question is, however, whether it is realistic to assume such high rationality. Whether the computer agents in AgriPoliS are “smart enough” with regard to strategic decisions can be discovered by using a fundamentally different approach: to directly include persons into AgriPoliS
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and let them replace the computerized agents. Such approaches can be understood as a kind of role playing game (e.g. Barreteau et al. 2001 and Bousquet et al. 2002). Therefore, human players have to be able to participate in AgriPoliS and compete against the computer agents. The players will have to decide on production, investment, renting land, buying quota, continuing or giving-up farming. For this purpose AgriPoliS has to be converted to a hybrid between role-playing game and management game. An approach of coupling agent-based simulations with role-playing games can be found in Barreteau and Bousquet (1999). In the proposed agent-based participatory simulations, both agents and players will be exposed to different economic, technological and policy scenarios. Observed differences between the behavior of players and that of computer agents provides a starting point for further analyses of behavior-based path dependence. It furthermore facilitates improvements of AgriPoliS, so that the agents display more realistic behavior. Pahl-Wostl and Ebenhöh (2004) elaborate on how to represent human behavior in agent based models and suggest specific attributes and heuristics. Guyot and Shinichi (2006) discuss several steps of building an agent-based participatory simulation. The first step is to build a “domain model” which is in our case AgriPoliS. In a second step this “domain model” has to be converted such that prospective players can understand it. Guyot and Shinichi (2006) refer to this converted model as “design model”. Kellermann (2002) developed PlayAgriPoliS which can be understood as such a “design model” of AgriPoliS. PlayAgriPoliS was developed to provide a tool which allows accessing AgriPoliS in an easy and intuitive way with regards to education and teaching purposes. In PlayAgriPoliS the player takes over the role of an agriculture minister who has to fulfill pre-election promises with a bounded budget. The player in PlayAgriPoliS does not make decisions on farm-level. In our case, however, we want real persons to manage a farm in AgriPoliS (i.d. to replace an agent). Therefore, a new design model has to be developed. In a last step, the design model is modified to the actual agent-based participatory simulation, which hereafter will be referred to as game. There are several important aspects that have to be considered when designing this game. First of all, the model must be simple enough to allow easy and quick assess to the game (Barreteau et al. 2001). Despite the necessary simplifications, it must at the same time be realistic for the participants in order to adopt realistic behavior (Guyot and Shinichi, 2006). Another challenge is to provide the players with all necessary information without assailing them with to much data. For this purpose a useful interface has to be designed. An interface similar to that of PlayAgriPoliS will be constructed in which the player is given a “sector report” as basis for his/her decisions. This report shows all relevant key data on the basis of a representative sample of farms. The interface furthermore allows the player to have a closer look at representative farms in detail. This provides the basis for the player to compare his/her farm with others. In addition, the player is provided with balance sheets of his/her own farm. The decision tools are another important part of the interface. The agents can rent land, invest in human capital, stables or machinery, produce crops or livestock, work off-farm or quit farming. As we are especially interested in strategic planning and decisions, such as enlarging the farm or investing in a new branch of production or
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exiting, the final production program itself will be defined by linear programming which will also deliver information for renting and investment decisions. When designing these kinds of games it has to be ensured that the players do not lose their motivation to participate. Bean (2001) emphasizes characteristics for this purpose. First of all, it must be fun for the player to participate. One thing that makes participation entertaining is competition. In our case the player is incited to perform better than the computer agents. More important is that the simulated situation is realistic enough, so that the players act as they would have acted in reality. Moreover, the players receive a payment according to their economic success in the game. The second important characteristic is accessibility. The game should not last longer than planned (Bean 2001). Guyot and Shinichi (2006) also mention that the presence of the organizer often is necessary to support the players in handling the interface. Finally, clarity is necessary. That means the player should know in the beginning of the game what the content and aim of the game are. It is also important to choose a terminology which is easily understandable. As Bean (2001) advises, several tests for usability are planned during the game development. After developing the game, laboratory experiments will be carried out in which a single player take over the role of a manager of a specific farm while all other agents remain computerized agents of AgriPoliS. This allows the comparison of each experiment with the outcome of a standard simulation in which the replaced agent remains computerized. As mentioned above, in order to create incentives for each player to maximize income, the players get an honorary according to their economic success in the game. Two types of players are considered: on the one hand master and PhD students and on the other hand farmers and agricultural experts. After each experiment, we will present the players the results of the simulations and ask for their strategy and reasons for some important decisions. The experiments will be analyzed in order to identify characteristic strategies and how they deviate from standard simulations as well as their impact on the farm's evolution and the whole sector. Moreover it is aimed to analyze whether different types of players operate in different manners. Finally, the outcomes will be analyzed regarding the question whether and how the standard agents should and could be adapted to get more realistic simulation results. The players’ observed behavior that differs from the original behavioral assumptions of the model is used to improve the decision routines in AgriPoliS (cf. Guyot and Shinichi 2006). 2.2 Participatory Analyses of the German Dairy Sector The next step is to analyze the opportunities to overcome path dependence of structural change and problems of CAP liberalization on a sector level. Therefore, AgriPoliS will be adapted to two regions in Germany: Allgäu (Southern Bavaria) and Stendal (Northern Saxony-Anhalt). While dairy farming in the Allgäu region is smallscaled, farms in the Stendal area are predominantly large-scaled. The different natural, historical and economic conditions in the two study regions result in differentiated structural problems. Whilst dairy farms in the Allgäu are inhibited in their growth due to high competition, farms in Stendal are confronted with other challenges such as the high share of external factors , e.g. credit capital (while equity
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capital is low), rented land and hired labor. Hence, the farms in the Stendal area are more susceptible to risk. To what extent dairy farmers are able to overcome structural deficits and to what extent subsidies and quotas solidify the structural problems of the dairy sector is analyzed with the improved model AgriPoliS. The different scenarios to be analyzed will be developed using participatory methods. In doing so, the knowledge, experiences, and different views of local actors will be utilized. A number of stakeholder workshops in the regions are therefore planned to identify conflicts relating to structural change in the dairy sector, to develop the scenarios of interest, and to discuss results and policies. Stakeholders can be found on three levels. There are directly affected persons – in our case the farmers. Also the views of a more objective level, e.g. representatives of the food chain and agricultural associations, consultants, and regional agricultural authorities are of relevance. On a third level an interested public, politicians, tax payers and other interest representatives will be involved in the scenario building. Thus, different views of local actors can be covered and a comprehensive picture of impacts on agricultural structural change in the regions can be gained. In the workshops stakeholders can build scenarios by deciding e.g. how much subsidies farmers get or if there are restrictions regarding the farm or herd size etc. Thinkable scenarios will be related to the dairy sector. Therefore, the milk quota regulation is particularly relevant. Furthermore, policy instruments such as the agricultural investment promotion program, compensation payments for less-favored areas, or the option of payments for land release are possible instruments which can be analyzed to determine their impact on structural change. The policy scenarios are accompanied by the assumption that direct payments of the CAP will be reduced after 2013 as funds from the first pillar will further increasingly be shifted to the second pillar. After developing scenarios, AgriPoliS will be used to run simulations. The results will be presented to the stakeholders in a second meeting in order to discuss them and to adjust the scenarios for a second set of simulations. The strategic aim is an ongoing networking with relevant stakeholders for the project phase. The establishment of stakeholder platforms allows a continuous exchange between research, practice and politics. The agent-based model AgriPoliS can thereby be improved and adopted to real conditions in the regions by the planned queries to the stakeholders. In return participants can gain insights in determinants of structural change.
3 Conclusion To clarify the objectives of our research they are summarized in the following: In a first step we want to improve AgriPoliS in a way to make it more realistic in terms of behavioral foundations. By using agent-based participatory simulation, we want to get hints about how real farmers act compared to computer agents. With the results of these simulations we want to answer the following questions: • What is the impact of the agents' behavioral rules and to which extend is the outcome of simulations with AgriPoliS dependent on them? We want to make sure for further use of the model that results are not ascribed to wrong behavioral assumptions.
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• Are agents modeled as too naive (i.e. long-term trends are ignored) and unable to overcome structural deficits? Have real persons, in contrast, a better ability to deal with a changing economic environment (cf. via strategic thoughts and actions, acting anticipatory, awaiting, risk awareness)? • How can AgriPoliS be used to analyze structural change in the German dairy sector? How “smart” should the agents be modeled when analyzing structural change? Ockenfels (2009) argue that even a small individual divergence from rational behavior or selfishness can affect the behavior of all other agents and therefore influence market developments. Since we want to analyze structural change and its constraints, we have to allow the agents to depart from the as hitherto uniform myopic and non-strategic behavior. In the next step we want to answer especially following questions by discussing with stakeholders about structural problems in the dairy sector: • How are dairy and other farms affected by policy instruments and measures? – On a sector level we want to analyze which policy instruments and measures concern (dairy) farmers to which degree. Regarding the ongoing liberalization of agricultural markets we furthermore want to analyze if dairy farmers and other stakeholders of the dairy sector follow specific mental models which create specific concerns against this liberalization? • To what extent will a changing policy environment (for example the abolishment of milk quotas) affect structural change within the dairy sector? – In a second step we will discuss possible effects of changing policy measures and implementing new ones. This policy analysis will be used to discover instruments which encourage path dependence of farm structures. • How can assumptions, scenarios and results be communicated between researchers and stakeholders? – Finally, concerning a long-term aim, we plan to communicate our results to stakeholders, especially to policy makers. The establishment of a network with regional stakeholders will give us the opportunity to orient our research to practical topics and needs. Through our participatory analyses we might find reasonable and feasible policy measures to solve structural deficits which can be used in policy advices. As mentioned in the introduction, dairy farmers are confronted with a decreasing milk price and simultaneously increasing factor prices especially for fodder. The political changes regarding an ongoing liberalization of agricultural markets and the related abolishment of the milk quota in 2015 provide challenges in view of the competitive position of the individual farm. The particular aim is to analyze how to overcome structural deficits in agriculture. By showing the results to farmers and discuss with them we intend to broaden their insights into structural change, impacts of policy instruments, and the role of mental models.
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