Johnson P (2012) Dynamic Land Use/cover Change Modelling: Geosimulation and Multiagent-Based Modelling (Springer Theses), JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION 15 (3) J A S S S
Johnson P (2013) Using the Telephone to Interview Professionals: Understanding the Use of Models in Environmental Policy, Sage Research Methods Cases
Social researchers have long been interested in studying organisations and institutions that play key roles in society. One part of this endeavour requires researchers to interview the professionals working in these organisations and institutions. However, researchers often find themselves ill-equipped by conventional methodological teaching and texts, which generally ignore the distinct issues involved in interviewing professional participants. This case study reflects on a PhD research project carried out during 2012 involving the use of the telephone to conduct semi-structured interviews with a set of professional participants working on environmental policy. Some key arguments from the literature and theory are introduced before the main focus of the case is presented, an honest reflection on the experience of using the telephone to conduct interviews in this context. It is concluded that semi-structured telephone interviews can be successful if the research project aims and objectives can be met by semi-structured interviewing and participants' characteristics mean they are able to use the telephone effectively.
Johnson P (2015) Agent-Based Models as ?Interested Amateurs?, Land 4 (2) pp. 281-299
Anzola D, Barbrook-Johnson P, Salgado M, Gilbert GN (2017) Sociology and Non-Equilibrium Social Science, In: Johnson J, Nowak A, Ormerod P, Rosewell B, Zhang Y-C (eds.), Non-Equilibrium Social Science and Policy: Introduction and Essays on New and Changing Paradigms in Socio-Economic Thinking 4 pp. 59-69
Springer International Publishing
Abstract This chapter addresses the relationship between sociology and Non-
Equilibrium Social Science (NESS). Sociology is a multiparadigmatic discipline
with significant disagreement regarding its goals and status as a scientific discipline.
Different theories and methods coexist temporally and geographically. However,
it has always aimed at identifying the main factors that explain the temporal
stability of norms, institutions and individuals? practices; and the dynamics of
institutional change and the conflicts brought about by power relations, economic
and cultural inequality and class struggle. Sociologists considered equilibrium
could not sufficiently explain the constitutive, maintaining and dissolving dynamics
of society as a whole. As a move from the formal apparatus for the study of
equilibrium, NESS does not imply a major shift from traditional sociological
theory. Complex features have long been articulated in sociological theorization,
and sociology embraces the complexity principles of NESS through its growing
attention to complex adaptive systems and non-equilibrium sciences, with human
societies seen as highly complex, path-dependent, far-from equilibrium, and selforganising
systems. In particular, Agent-BasedModelling provides a more coherent
inclusion of NESS and complexity principles into sociology. Agent-based sociology
uses data and statistics to gauge the ?generative sufficiency? of a given microspecification
by testing the agreement between ?real-world? and computer generated
macrostructures.When the model cannot generate the outcome to be explained, the
microspecification is not a viable candidate explanation. The separation between
the explanatory and pragmatic aspects of social science has led sociologists to be
highly critical about the implementation of social science in policy. However, ABM
allows systematic exploration of the consequences of modelling assumptions and
makes it possible to model much more complex phenomena than previously. ABM
has proved particularly useful in representing socio-technical and socio-ecological
systems, with the potential to be of use in policy. ABM offers formalized knowledge that can appear familiar to policymakers versed in the methods and language of
economics, with the prospect of sociology becoming more influential in policy.
George Johnson P, Balke T, Kotthoff L (2014) Integrating optimisation and agent-based modelling, Proceedings - 28th European Conference on Modelling and Simulation, ECMS 2014 pp. 775-781
A key strength of agent-based modelling is the ability to explore the upward-causation of micro-dynamics on the macro-level behaviour of a system. However, in policy contexts, it is also important to be able to represent downward-causation from the macro and meso-levels to the micro, and to represent decision-making at the macro level (i.e., by governments) in a sensible way. Though we cannot model political processes easily, we can try to optimise decisions given certain stated goals (e.g., minimum cost, or maximum impact). Optimisation offers one potential method to model macro-level decisions in this way. This paper presents the implementation of an integration of optimisation with agent-based modelling for the example of an auction scenario of government support for the installation of photovoltaic solar panels by households. Auction type scenarios of this kind, in which large groups of individuals or organisations make bids for subsidies or contracts from government, are common in many policy domains. Proceedings 28th European Conference on Modelling and Simulation © ECMS Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani (Editors).
Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals? protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.
Computational models are increasingly being used to assist in developing, implementing and evaluating
public policy. This paper reports on the experience of the authors in designing and using computational
models of public policy (?policy models?, for short). The paper considers the role of computational models in
policy making, and some of the challenges that need to be overcome if policy models are to make an effective
contribution. It suggests that policy models can have an important place in the policy process because
they could allow policy makers to experiment in a virtual world, and have many advantages compared with
randomised control trials and policy pilots. The paper then summarises some general lessons that can be extracted
from the authors? experience with policy modelling. These general lessons include the observation that
often the main benefit of designing and using a model is that it provides an understanding of the policy domain,
rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate
level of abstraction; that although appropriate data for calibration and validation may sometimes be in short
supply, modelling is often still valuable; that modelling collaboratively and involving a range of stakeholders
from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention
needs to be paid to effective communication between modellers and stakeholders; and that modelling for public
policy involves ethical issues that need careful consideration. The paper concludes that policy modelling
will continue to grow in importance as a component of public policy making processes, but if its potential is to
be fully realised, there will need to be a melding of the cultures of computational modelling and policy making.
Complexity science and its methodological applications have increased in popularity in social science during the last two decades. One key concept within complexity science is that of self-organization. Self-organization is used to refer to the emergence of stable patterns through autonomous and self-reinforcing dynamics at the micro-level. In spite of its potential relevance for the study of social dynamics, the articulation and use of the concept of self-organization has been kept within the boundaries of complexity science and links to and from mainstream social science are scarce. These links can be difficult to establish, even for researchers working in social complexity with a background in social science, because of the theoretical and conceptual diversity and fragmentation in traditional social science. This article is meant to serve as a first step in the process of overcoming this lack of cross-fertilization between complexity and mainstream social science. A systematic review of the concept of self-organization and a critical discussion of similar notions in mainstream social science is presented, in an effort to help practitioners within subareas of complexity science to identify literature from traditional social science that could potentially inform their research.
Lippe Melvin, Bithell Mike, Gotts Nick, Natalini Davide, Barbrook-Johnson Peter, Giupponi Carlo, Hallier Mareen, Hofstede Gert Jan, Le Page Christophe, Matthews Robin B., Schlüter Maja, Smith Peter, Teglio Andrea, Thellmann Kevin (2019) Using agent-based modelling to simulate social-ecological systems across scales, GeoInformatica
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.
Two leading camps for studying social complexity are case-based methods (CBM) and agent-based modelling (ABM). Despite the potential epistemological links between ?cases? and ?agents,? neither camp has leveraged their combined strengths. A bridge can be built, however, by drawing on Abbott?s insight that ?agents are cases doing things?, Byrne?s suggestion that ?cases are complex systems with agency?, and by viewing CBM and ABM within the broader trend towards computational modelling of cases. To demonstrate the utility of this bridge, we describe how CBM can utilise ABM to identify case-based trends; explore the interactions and collective behaviour of cases; and study different scenarios. We also describe how ABM can utilise CBM to identify agent types; construct agent behaviour rules; and link these to outcomes to calibrate and validate model results. To further demonstrate the bridge, we review a public health study that made initial steps in combining CBM and ABM.