Meta-level argumentation framework for representing and reasoning about disagreement

The contribution of this thesis is to the field of Artificial Intelligence (AI), specifically to the sub-field called knowledge engineering. Knowledge engineering involves the computer representation and use of the knowledge and opinions of human experts.In real world controversies, disagreements can be treated as opportunities for exploring the beliefs and reasoning of experts via a process called argumentation. The central claim of this thesis is that a formal computer-based framework for argumentation is a useful solution to the problem of representing and reasoning with multiple conflicting viewpoints.The problem which this thesis addresses is how to represent arguments in domains in which there is controversy and disagreement between many relevant points of view. The reason that this is a problem is that most knowledge based systems are founded in logics, such as first order predicate logic, in which inconsistencies must be eliminated from a theory in order for meaningful inference to be possible from it.I argue that it is possible to devise an argumentation framework by describing one (FORA : Framework for Opposition and Reasoning about Arguments). FORA contains a language for representing the views of multiple experts who disagree or have differing opinions. FORA also contains a suite of software tools which can facilitate debate, exploration of multiple viewpoints, and construction and revision of knowledge bases which are challenged by opposing opinions or evidence.A fundamental part of this thesis is the claim that arguments are meta-level structures which describe the relationships between statements contained in knowledge bases. It is important to make a clear distinction between representations in knowledge bases (the object-level) and representations of the arguments implicit in knowledge bases (the meta-level). FORA has been developed to make this distinction clear and its main benefit is that the argument representations are independent of the object-level representation language. This is useful because it facilitates integration of arguments from multiple sources using different representation languages, and because it enables knowledge engineering decisions to be made about how to structure arguments and chains of reasoning, independently of object-level representation decisions.I argue that abstract argument representations are useful because they can facilitate a variety of knowledge engineering tasks. These include knowledge acquisition; automatic abstraction from existing formal knowledge bases; and construction, rerepresentation, evaluation and criticism of object-level knowledge bases. Examples of software tools contained within FORA are used to illustrate these uses of argumentation structures. The utility of a meta-level framework for argumentation, and FORA in particular, is demonstrated in terms of an important real world controversy concerning the health risks of a group of toxic compounds called aflatoxins

Unlocking Complexity: The Importance of Idealisation in Simulation Modelling

Idealisation is the process of finding simple representations of the real-world whilst conceptualising a model. There are three ways to limit complication in a model of a complex real-world: by focussing the scope of the modelling process onto a clearly defined issue; by idealising elements of the real-world during model conceptualisation; and by simplifying the implemented simulation program. Careful idealisation has the greatest potential for increasing model tractability whilst generating insights during the model design process. The Forest Land Oriented Resource Envisioning System (FLORES) project deals with social forest landscapes which are highly complex. Benefits of idealisation are demonstrated using six examples from this modelling work. These examples encompass issues dealing with land tenure, forest management, economic values, social diversity, communication and collaboration. Each example illustrates a different method to achieve an idealisation which yields insights relevant for policy players. A number of lessons about idealisation are also identified: (1) sometimes it is only possible to recognise what is key by omitting it; (2) an effective idealisation is not just achieved by leaving things out, or adding them back in; it can also be achieved by restructuring the representation; (3) it is important challenge the use of different units where consistency is possible; (4) it is easier to keep a simple model simple, than to make simple modifications to a large model. Similarly, it is easier to generate insights with a simple concept for a sub-model than with a simple modification to an existing model; and (5) even the most useful idealisations may have a limited shelf-life

Modelling Decision-Making in Rural Communities at the Forest Margin

The FLORES simulation model aims to capture the interactions between rural communities living at the forest margin and the resources that they depend upon, in order to provide decision-makers with a tool that they can use to explore the consequences of alternative policy options. A key component of the model is simulating how decision-making agents within the system (individuals, households and the whole village) go about making their decisions. The model presented here is based on an anthropological description of the rules and relationships that people use, rather than on the assumption that people behave in an economically optimal fashion. The approach addresses both short-term decision-making (primarily the allocation of labour to various activities on a weekly basis), and long-term strategic land-use planning, taking into account the variety of tenure and inheritance patterns that operate in real communities. The decision-making sub-model has been implemented in the Rantau Pandan (Sumatra) version of FLORES, using the Simile modelling environment

A Model to Help People to Realize Sustainable Forestry Futures

People usually know how they want their situation to change to secure a better future – but they do not always know how to change their situation. Initiatives intended to secure a better future do not always work as intended, and may have unintended side effects. Computer models can help advocates explore consequences of proposed initiatives, so they can make informed selections of alternatives, secure in the knowledge that consequences have been thoroughly investigated. By encouraging people to explore scenarios, models empower people to be more innovative and less dependent on technocrats. Models also enable planners to experiment with policy without risks to people or to the environment. Emerging software solves many technical limitations, but the real issue is not software, but rather the provision of a supportive framework within which people can express and experiment with ideas. FLORES, the Forest Land Oriented Resource Envisioning System, provides such a framework to stimulate interdisciplinary collaboration between researchers, practitioners and clients. Two recent workshops have demonstrated the feasibility of FLORES, one of which provides the subject matter for a forthcoming issue of Small-scale Forest Economics, Management and Policy. However, FLORES is not about software; it is about providing the means to explore the consequences of alternative scenarios. Ultimately, FLORES is not a physical package, but an association of users and the interactions they have amongst themselves, and with the people involved in policy-making. By promoting this emerging network and providing technical support we encourage more people, especially those from developing countries, to influence the development of FLORES and the issues that can be explored within it

FLORES: Helping People to Realize Sustainable Futures

People usually know how they want their situation to change to secure a better future, but they do not always know how to change their situation. Initiatives intended to secure a better future do not always work as intended, and may have unintended side effects. Computer models can help advocates explore consequences of proposed initiatives, so they can make informed selections of alternatives, secure in the knowledge that consequences have been thoroughly investigated. By encouraging people to explore scenarios, models empower people to be more innovative and less dependent on technocrats. New software solves technical limitations, but the real issue is not software, but rather the provision of a supportive framework within which people can express and experiment with ideas. FLORES, the Forest Land Oriented Resource Envisioning System, provides such a framework to stimulate interdisciplinary collaboration between researchers, practitioners and clients. A recent prototype demonstrated the feasibility of FLORES. However, FLORES is not about software; it is about providing the means to explore the consequences of alternative scenarios. Ultimately, FLORES is not a physical package, but a user group and the interactions they have amongst themselves, and with the people involved in policy-making. Fostering this emerging network through workshops and technical support will enhance FLORES by offering a better understanding of the concept, and by allowing more people, especially those from developing countries, to influence the development of FLORES and the issues that can be explored within it

Infectious Ideas: Modelling the Diffusion of Ideas Across Social Networks

Will the practice of collecting wild honey while wearing no clothes become a widespread practice in Zimbabwe? Or will beekeeping take over as the main way that people acquire honey? Both practices impact on forest resources; how can the foresters influence the uptake of these ideas? This paper describes an exploratory modelling study investigating how social network patterns affect the way ideas spread around communities. It concludes that increasing the density of social networks increases the spread of successful ideas whilst speeding the loss of ideas with no competitive advantage. Some different kinds of competitive advantage are explored in the context of forest management and rural extension

ZimFlores: A Model to Advise Co-Management of the Mafungautsi Forest in Zimbabwe

ZimFlores (version 4) is the outcome of a participatory modelling process and seeks to provide a shared factual basis for exploring land-use options for the communal lands surrounding the Mafungautsi forest. The ZimFlores experience underscores the importance of a sharing a common problem and a common location in which all participants have an interest. Participatory modelling has proved an effective way to consolidate a diverse body of knowledge and make it accessible. Results demonstrate the importance of model outputs that are diagnostic, and which offer insights into the issues under consideratio

Participatory Modelling to Enhance Social Learning, Collective Action and Mobilization among Users of the Mafungautsi Forest, Zimbabwe

Participatory modelling can be a useful process to encourage critical examination of livelihood options and foster sustainable natural resource use through enhanced social learning, collective action and mobilization. The broom-grass group in the Mafungautsi Forest Reserve serves as a case study of the process and outcomes of such participatory modelling. Innovative group facilitation methods enhanced participation in the modelling process. The modelling process complements broader efforts to achieve higher levels of adaptive collaborative management