Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

Triggering change: Towards a conceptualisation of major change processes in farm decision-making

In this paper, we present a broad conceptualisation of major change in farm level trajectories. We argue that as a result of path dependency, major changes in farming practice primarily occur in response to 'trigger events', after which farm managers intensify their consideration of the options open to them, and may set a new course of action. In undertaking new actions, the farm system enters a period of instability, while new practices become established. Over time these new practices, if successfully achieving anticipated aims, lead to a further period of path dependency. Recognising and capitalising upon this pattern of events is important for the development of policies oriented towards incentivising major change in farming practices, and may explain why similar projects and/or policies influence some 'types' of farmers differently, and at different times. To illustrate our arguments, examples of this process are described in relation to empirical examples of major on-farm change processes, drawn from qualitative interviews with organic and conventional farmers in two English case study area

Triggering change: Towards a conceptualisation of major change processes in farm decision-making

In this paper, we present a broad conceptualisation of major change in farm level trajectories. We argue that as a result of path dependency, major changes in farming practice primarily occur in response to 'trigger events', after which farm managers intensify their consideration of the options open to them, and may set a new course of action. In undertaking new actions, the farm system enters a period of instability, while new practices become established. Over time these new practices, if successfully achieving anticipated aims, lead to a further period of path dependency. Recognising and capitalising upon this pattern of events is important for the development of policies oriented towards incentivising major change in farming practices, and may explain why similar projects and/or policies influence some 'types' of farmers differently, and at different times. To illustrate our arguments, examples of this process are described in relation to empirical examples of major on-farm change processes, drawn from qualitative interviews with organic and conventional farmers in two English case study area

Qualitative spatial representation in agent-based models

One of the advantages of agent-based models as simulations of social systems is the ease with which it is possible to spatially embed the agents and their interactions. Spatially explicit representations in agent-based models most typically take the form of raster-based representations in which the space is represented as a grid of squares. More recently, vectorbased representations have been used, usually importing data for the polygons from geographical information systems (GIS). However, for some models, what matters about the space for the purposes of simulation is less the quantitative spatial relationships among entities (e.g. area, distance or direction) than the qualitative relations these quantitative data are used to determine: neighbourhood, and accessibility (which is a general term covering movement and sensing from one region to another). This paper gives consideration to the use of qualitative spatial representations in agent-based modelling, using a model of everyday pro-environmental behaviour in the workplace as an example

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

Peer reviewedPreprin

Lessons learnt from the deployment of a semantic virtual research environment

The ourSpaces Virtual Research Environment makes use of Semantic Web technologies to create a platform to support multi-disciplinary research groups. This paper introduces the main semantic components of the system: a framework to capture the provenance of the research process, a collection of services to create and visualise metadata and a policy reasoning service. We also describe different approaches to authoring and accessing metadata within the VRE. Using evidence gathered from data provided by the users of the system we discuss the lessons learnt from deployment with three case study groups

Using agent-based modelling to simulate social-ecological systems across scales

Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this thought-piece to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc