A-Z of Digital Research Methods
Catherine Dawson
- 414 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
A-Z of Digital Research Methods
Catherine Dawson
About This Book
This accessible, alphabetical guide provides concise insights into a variety of digital research methods, incorporating introductory knowledge with practical application and further research implications. A-Z of Digital Research Methods provides a pathway through the often-confusing digital research landscape, while also addressing theoretical, ethical and legal issues that may accompany each methodology.
Dawson outlines 60 chapters on a wide range of qualitative and quantitative digital research methods, including textual, numerical, geographical and audio-visual methods. This book includes reflection questions, useful resources and key texts to encourage readers to fully engage with the methods and build a competent understanding of the benefits, disadvantages and appropriate usages of each method.
A-Z of Digital Research Methods is the perfect introduction for any student or researcher interested in digital research methods for social and computer sciences.
Frequently asked questions
Information
CHAPTER 1
Agent-based modelling and simulation
Overview
- respond to the actions of others;
- respond to environmental stimuli;
- influence each other;
- learn from each other;
- learn from their experiences;
- adapt their behaviour as a result of other agentsâ behaviour;
- adapt their behaviour to suit their environment.
Questions for reflection
Epistemology, theoretical perspective and methodology
- Miller (2015: 175) proposes critical realism as a philosophical perspective to understand, orient and clarify the nature and purpose of agent-based modelling research. Does this perspective have resonance with your research and, if so, in what way? How might this perspective help you to evaluate, validate and assess models?
- Do you intend to use agent-based modelling as a standalone research method, or do you intend to adopt a mixed methods approach? Is it possible to integrate diverse forms of data (and interdisciplinary data) with agent-based modelling? Chattoe-Brown (2014) believes so, illustrating why and how from a sociological perspective, and Millington and Wainwright (2017) discuss mixed method approaches from a geographical perspective.
- How might ABMS be used to complement and improve traditional research practices? Eberlen et al. (2017) will help you to reflect on this question in relation to social psychology.
- Can phenomena emerging from agent-based models be explained entirely by individual behaviour? Silverman et al. (2018) provide a comprehensive discussion on this and other methodological considerations.
- Do models represent the real world, or are they a researcherâs interpretation of the real world?
- What are the strengths and weaknesses of ABMS? Conte and Paolucci (2014) will help you to address this question in relation to computational social science and Eberlen et al. (2017) discuss these issues in relation to social psychology.
Ethics, morals and legal issues
- Is it possible that modelling can be to the detriment of individuals? Can model outcomes lead to unethical or inappropriate action that can cause harm to individuals? Can individuals be singled out for action, based on models? What happens when predictions are based on past behaviour that may have changed? Can individuals correct model inputs?
- Have data been volunteered specifically for modelling purposes?
- Is it possible that individuals could be identifiable from models?
- Millington and Wainwright (2017: 83) ask a pertinent question that needs to be considered if you intend to use ABMS: âhow might new-found understandings by individuals about their agency be turned back to geographers to understand the role of agent-based simulation modelling itself as an agent of social change?â
Practicalities
- How will you go about building your model? Jackson et al. (2017: 391â93) provide a seven-step guide to creating your own model:
- Step 1: what are your worldâs dimensions?
- Step 2: how do agents meet?
- Step 3: how do agents behave?
- Step 4: what is the payoff?
- Step 5: how do agents change?
- Step 6: how long does your world last?
- Step 7: what do you want to learn from your world?
- Do you know which is the most appropriate agent-based modelling and simulation toolkit for your research? How do you intend to choose software and tools? A concise characterisation of 85 agent-based toolkits is provided by Abar et al. (2017).
- How accurate is your model? How important is accuracy (when action is to be taken, or decisions made, based on your model outcomes, for example?)
- How do you intend to verify and validate your model (ensuring the model works correctly and ensuring the right model has been built, for example)?
Useful resources
- Adaptive Modeler (www.altreva.com);
- AnyLogic (www.anylogic.com);
- Ascape (http://ascape.sourceforge.net);
- Behaviour Composer (http://m.modelling4all.org);
- Cougaar (www.cougaarsoftware.com);
- GAMA (https://gama-platform.github.io);
- JADE (http://jade.tilab.com);
- NetLogo (https://ccl.northwestern.edu/netlogo);
- OpenStarLogo (http://web.mit.edu/mitstep/openstarlogo/index.html);
- Repast Suite (https://repast.github.io);
- StarLogo TNG (https://education.mit.edu/portfolio_page/starlogo-tng);
- Swarm (www.swarm.org/wiki/Swarm_main_page).