Showing papers by "Bruce Edmonds published in 2015"

Using Qualitative Evidence to Inform the Specification of Agent-Based Models

Abstract: This is an introduction to the special section of JASSS on the above topic. It argues for the importance of qualitative evidence in social science, and particularly in the specification of agent-based models. It ends by suggesting some criteria for judging methods for using qualitative evidence for this purpose.

Policy Making and Modelling in a Complex World

Abstract: In this chapter, we discuss the consequences of complexity in the real world together with some meaningful ways of understanding and managing such situations. The implications of such complexity are that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions). We shortly discuss the problems arising from a too-narrow focus on quantification in managing complex systems. We criticise some of the approaches that ignore these difficulties and pretend to predict using simplistic models. However, lack of predictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from “complexity science” can help with such management. Managing a complex system requires a good understanding of the dynamics of the system in question—to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent-based simulation will be discussed as a tool that is suitable for this task, and its particular strengths and weaknesses for this are discussed.

Modelling Complexity for Policy: Opportunities and Challenges

Abstract: This chapter reviews the purpose and use of models from the field of complex systems and, in particular, the implications of trying to use models to understand or make decisions within complex situations, such as policy makers usually face. A discussion of the different dimensions one can formalise situations, the different purposes for models and the different kinds of relationship they can have with the policy making process, is followed by an examination of the compromises forced by the complexity of the target issues. Several modelling approaches from complexity science are briefly described, with notes as to their abilities and limitations. These approaches include system dynamics, network theory, information theory, cellular automata, and agent-based modelling. Some examples of policy models are presented and discussed in the context of the previous analysis. Finally we conclude by outlining some of the major pitfalls facing those wishing to use such models for policy evaluation.

A Context- and Scope-Sensitive Analysis of Narrative Data to Aid the Specification of Agent Behaviour

Abstract: A structure for analysing narrative data is suggested, one that distinguishes three parts in sequence: context (a heuristic to identify what knowledge is relevant given a kind of situation), scope (what is possible within that situation) and narrative elements (the detailed conditional and sequential structure of actions and events given the context and scope). This structure is first motivated and then illustrated with some simple examples taken from Sukaina Bharwani's thesis (Bharwani 2004). It is suggested that such a structure might be helpful in preserving more of the natural meaning of such data, as well as being a good match to a context-dependent computational architecture, and thus facilitate the process of using narrative data to inform the specification of behavioural rules in an Agent-Based Simulation. This suggestion only solves part of the 'Narrative Data to Agent Behaviour' puzzle — this structure needs to be combined and improved by other methods and appropriate computational architectures designed to suit it.

Simplification and analysis of a model of social interaction in voting

Abstract: A recently proposed model of social interaction in voting is investigated by simplifying it down into a version that is more analytically tractable and which allows a mathematical analysis to be performed. This analysis clarifies the interplay of the different elements present in the system --- social influence, heterogeneity and noise --- and leads to a better understanding of its properties. The origin of a regime of bistability is identified. The insight gained in this way gives further intuition into the behaviour of the original model.

Open Modelling for Simulators

Abstract: This chapter motivates and discusses the process of making a simulation model available for others to freely inspect and use. Firstly, it outlines the three reasons why this is necessary: democratic right, scientific scrutiny, and public value extraction. Then it describes the basic steps for doing this, including: making code comprehensible, documentation and licensing. It then describes some further things one might do when releasing a complex model to help ensure it is understood and re-used appropriately. It briefly looks as some tools and approaches to help in all this, and ends with a discussion about the change in underlying “modelling culture” that is needed.