Browsing by Subject "Agentenbasierte Modellierung"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Publication Agent-based simulation modeling for analysis and support of rural producer organizations in agriculture(2014) Latynskiy, Evgeny; Berger, ThomasDevelopment of smallholder agriculture is widely recognized as an important pathway to poverty reduction in rural areas, particularly in Sub-Saharan Africa. Many researchers propose collective action of smallholder farmers by means of rural producer organizations (RPO) as a promising opportunity to improve commercialization and market access of small farms, which in turn will result in improvement of rural livelihoods. However, little is known about the determinants of RPO success. Currently, there is a broad demand for detailed analyses of RPO performance and for ex-ante assessments of the developmental interventions and policies of RPO support. This thesis focuses on the provision of high-resolution quantitative data for the design of such interventions using a case study of coffee producers in the lake-shore Uganda and their RPO. This work demonstrates the effective ways to increase farmers’ welfare through the network of RPO and analyzes the associated risks and opportunities. This work applies agent-based computer simulation to analyze the RPO. Designed virtual simulation experiments assess the broad portfolio of development interventions and economic scenarios that are challenging to investigate by means of real-world empirical research. The agent-based nature of the model allows for a holistic integration of several modeling concepts in the developed model application. This leads to the inclusion into the model of a number of important aspects of the bio-economic system of coffee production in Uganda. The first aspect is the heterogeneity among farming households, reflected by differences in natural conditions, resource endowments, production and market constraints, time and consumption preferences. The second aspect is the inseparability of decisions that are taken on the farm (i.e. investment, production, consumption and marketing) from one another. The third aspect is human-environment interaction cycles and the dynamics of the bio-economic system, including interactions across levels of hierarchy (here: individual farmers and RPO). The constructed model is parameterized, calibrated and validated using the empirical data from project and country-level surveys. The set-up of the model and the results of simulation experiments are further complemented by (i) a detailed review of relevant literature, (ii) community-based participatory research with members of RPO and (iii) interviews with key informants. Results of simulation experiments indicate that RPO activities can cause significant increases in members’ sales revenues and consequently can improve their household incomes. The posvitive impacts of RPO can be amplified through external assistance. Recommended RPO-level interventions include (i) on-the-spot payments for RPO members’ transactions and (ii) support for group certification. Both are expected to have high cost efficiency and a low risk of failure. In addition, results of this thesis suggest that improvement of agricultural productivity through the provision of quality planting material and the promotion of good agricultural practices is likely to be highly beneficial for the rural households. In order to stream the related development policies to smallholder farmers it is recommended to use RPO networks. Findings of the participatory research in Ugandan RPO indicate that the establishment of transparent rules of reception of RPO services and allocation of earned benefits, together with frequent and formal reporting of RPO administration might increase members’ cooperation within an RPO. This thesis also shows the vulnerability of coffee producing households and their RPO to the risks imposed by the volatility of agricultural prices. The role of development policy is, therefore, to provide price risk insurance for smallholder farmers and to facilitate the formation of accurate price expectations. However, viable and sustainable models of smallholder risk insurance are yet to be found.Publication Investigating climate change perception and expectation formation for the advancement of agent-based models applied to agricultural adaptation assessment(2019) Eisele, Marius; Berger, ThomasTo inform more realistic representations of farmer decision making in agent-based simulation models applied to agricultural adaptation assessment at the regional scale, the present thesis investigates three areas of central importance for judgments about farm-level reactions to climate change: (i) perception of changes in local weather conditions and expectations about their effects; (ii) reception of signals from the biophysical environment and their interpretation in terms of socially constructed understandings of climate change, farm-level risks, and perceived adaptation capacity; and (iii) the nature of expectation mechanisms involved in the formation of judgments about climatic changes. For this purpose, three types of empirical approaches were used: questionnaire-based surveys conducted with farmers from two study areas in Southwest Germany, the Central Swabian Jura and the Kraichgau; a questionnaire-based comparative study of farmer school students and pre-first-semester undergraduate university students enrolled in study programs related to agriculture without experience in farming and no study experience; and economic lab experiments conducted with farming practitioners (experienced farmers and farmer school students) and university students from agriculture-related study programs with several semesters of study experience. Based on these empirical findings, the following recommendations for the agent-based modeling software MPMAS are derived: (i) agent-specific levels of climate change awareness should be accounted for to reflect the effects of personal experiences with climate change outcomes, social norms and individual-specific learning patterns and coping behavior; (ii) the effects of incomplete information assessment and risk aversion should be reflected in the imputed selection mechanism for climate change response, i.e. for the choice of adaptation measures; and (iii) experimental results should be used to inform modeled expectation mechanisms of agents, currently implemented for judgments about future prices and yields.Publication United we stand, divided we fall : essays on knowledge and its diffusion in innovation networks(2019) Bogner, Kristina; Pyka, AndreasKnowledge is a key resource, allowing firms to innovate and keep pace with national and international competitors. Therefore, the management of this resource within firms and innovation networks is of utmost importance. As the collection and generation of (new) knowledge gives such competitive advantage, there is a strong interest of firms and policy makers on how to foster the creation and diffusion of new knowledge. Within four studies, this doctoral thesis aims at extending the literature on knowledge diffusion performance by focussing on the effect of different network structures on diffusion performance as well as on knowledge types besides mere techno-economic knowledge. Study 1 analyses the effect of different structural disparities on knowledge diffusion by using an agent-based simulation model. It focuses on how different network structures influence knowledge diffusion performance. This study especially emphasizes the effect of an asymmetric degree distribution on knowledge diffusion performance. Study 1 complements previous research on knowledge diffusion by showing that (i) besides or even instead of the average path length and the average clustering coefficient, the (symmetry of) degree distribution influences knowledge diffusion. In addition, (ii) especially small, inadequately embedded agents seem to be a bottleneck for knowledge diffusion in this setting, and iii) the identified rather negative network structures on the macro level seem to result from the myopic linking strategies of the actors at the micro level, indicating a trade-off between ‘optimal’ structures at the network and at the actor level. Study 2 uses an agent-based simulation model to analyse the effect of different network properties on knowledge diffusion performance. In contrast to study 1, this study analyses this relationship in a setting in which knowledge is diffusing freely throughout an empirical formal R&D network as well as through four benchmark networks. In addition, the concept of cognitive distance and differences in learning between agents in the network are taken into account. Study 2 complements study 1 and further previous research on knowledge diffusion by showing that (i) the (asymmetry of) degree distribution and the distribution of links between actors in the network indeed influence knowledge diffusion performance to a large extend. In addition, (ii) the extent to which a skewed degree distribution dominates other network characteristics varies depending on the respective cognitive distance between agents. Study 3 analyses how so called dedicated knowledge can contribute to the transformation towards a sustainable, knowledge-based Bioeconomy. In this study, the concept of dedicated knowledge, i.e. besides mere-techno economic knowledge also systems knowledge, normative knowledge and transformative knowledge, is first introduced. Moreover, the characteristics of dedicated knowledge which are influencing knowledge diffusion performance are analysed and evaluated according to their importance and potential role for knowledge diffusion. In addition, it is analysed if and how current Bioeconomy innovation policies actually account for dedicated knowledge. This study complements previous research by taking a strong focus on different types of knowledge besides techno-economic knowledge (often overemphasized in policy approaches). It shows, that i) different types of knowledge necessarily need to be taken into account when creating policies for knowledge creation and diffusion, and ii) that especially systems knowledge so far has been insufficiently considered by current Bioeconomy policy approaches. Study 4 analyses the effect of different structural disparities on knowledge diffusion by deducing from theoretical considerations on network structures and diffusion performance. The study tries to answer whether the artificially generated network structures seem favourable for the diffusion of both mere techno-economic knowledge as well as dedicated knowledge. Study 4 especially complements previous research on knowledge diffusion by (i) analysing an empirical network over a long period of time, and (ii) by indicating a potential trade-off between structures favourable for the diffusion of mere techno-economic knowledge and those for the diffusion of other types of dedicated knowledge. Summing up, it is impossible to make general statements that allow for valid policy recommendations on network structures ‘optimal’ for knowledge diffusion. Without knowing the exact structures and context, politicians will hardly be able to influence network structures. Especially if we call for knowledge enabling transformations as the transformation towards a sustainable knowledge-based Bioeconomy, creating structures for the creation and diffusion of this knowledge is quite challenging and needs for the inclusion and close cooperation of many different actors on multiple levels.