ODD Updated

An update to Volker Grimm and colleagues\' Overview, Design concepts and Details (ODD) protocol for documenting individual and agent based models (I/ABM) has recently been published in Ecological Modelling. This renames the \'State variables and scales\' element to \'Entities, state variables and scales\', and the \'Input\' element to \'Input data\', introduces two new Design concepts (\'Basic principles\' and \'Learning\'), and renames another (\'Fitness\' is now generalised to \'Objectives\'). The Design concepts element can now also be shortened such that it is not required to include any design concept that is irrelevant to the model, and expanded to include new design concepts more appropriate to the model being described. Other clarifications of intentions in the original protocol have been made.ODD, Individual Based Models, Agent Based Models, Replication, Documentation

Is Your Model Susceptible to Floating-Point Errors?

This paper provides a framework that highlights the features of computer models that make them especially vulnerable to floating-point errors, and suggests ways in which the impact of such errors can be mitigated. We focus on small floating-point errors because these are most likely to occur, whilst still potentially having a major influence on the outcome of the model. The significance of small floating-point errors in computer models can often be reduced by applying a range of different techniques to different parts of the code. Which technique is most appropriate depends on the specifics of the particular numerical situation under investigation. We illustrate the framework by applying it to six example agent-based models in the literature.Floating Point Arithmetic, Floating Point Errors, Agent Based Modelling, Computer Modelling, Replication

Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change

This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.Agent-Based Modelling, Land Use/Cover Change, Qualitative Research, Interdisciplinary Research

Combining diverse data sources for CEDSS, an agent-based model of domestic energy demand

CEDSS (Community Energy Demand Social Simulator) is an empirical agent-based model designed and built as part of a multi-method social science project investigating the determinants of domestic energy demand. Ideally, empirical modellers, within and beyond social simulation, would prefer to work from an integrated dataset, gatheredfor the purposes of developing the model. In practice, many have to work with less than ideal data, often including processed data from multiple sources external to the project. Moreover, what data will be required may not be clear at the start of the project. This paper describes the approach to dealing with these factors taken in developing CEDSS, and presents the completed model together with an outline of the calibration and validation procedure used. The discussion section draws together the most distinctive features of empirical data collection, processing and use for and in CEDSS, and argues that the approach taken is sufficiently robust to underpin the model’s purpose – to generate scenarios of domestic energy demand to 2049

The CEDSS model of direct domestic energy demand

This paper describes the design, implementation and testing of the CEDSS model of direct domestic energy demand, and the first results of its use to produce estimates of future demand under a range of scenarios. CEDSS simulates direct domestic energy demand at within communities of approximately 200 households. The scenarios explored differ in the economic conditions assumed, and policy measures adopted at national level

A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change

Agent-based models, perhaps more than other models, feature large numbers of parameters and potentially generate vast quantities of results data. This paper shows through the FEARLUS-G project (an ESRC e-Social Science Initiative Pilot Demonstrator Project) how deploying an agent-based model on the Semantic Grid facilitates international collaboration on investigations using such a model, and contributes to establishing rigorous working practices with agent-based models as part of good science in social simulation. The experimental workflow is described explicitly using an ontology, and a Semantic Grid service with a web interface implements the workflow. Users are able to compare their parameter settings and results, and relate their work with the model to wider scientific debate.Agent-Based Social Simulation, Experiments, Ontologies, Replication, Semantic Grid

When and How to Imitate Your Neighbours: Lessons from and for FEARLUS

This paper summarises some previously published work on imitation, experimentation (or innovation) and aspiration thresholds using the FEARLUS modelling system and reports new work with FEARLUS extending these studies. Results are discussed in the context of existing literature on imitation and innovation in related contexts. A form of imitation in which land uses are selected on the criterion of their recent performance within the neighbourhood of the land parcel concerned (called here 'Best-mean Imitation'), outperforms comparably simple forms of imitation in a wide range of FEARLUS Environments. However, the choice of criterion is shown to interact with both the way the criterion is applied, and the land manager's aspiration threshold: the level of return with which they are satisfied. The implications of work with FEARLUS for the broader bodies of research discussed, and vice versa, are considered.Imitation, Innovation, Aspiration, Land-Use, Spatio-Temporal Heterogeneity

Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER)

Exascale computing can potentially revolutionise the way in which we design and build agent-based models (ABM) through, for example, enabling scaling up, as well as robust calibration and validation. At present, there is no exascale computing operating with ABM (that we are aware of), but pockets of work using High Performance Computing (HPC). While exascale computing is expected to become more widely available towards the latter half of this decade, the ABM community is largely unaware of the requirements for exascale computing for agent-based modelling to support policy evaluation. This project will engage with the ABM community to understand what computing resources are currently used, what we need (both in terms of hardware and software) and to set out a roadmap by which to make it happen