Special Sessions at SSC 2017

Special Sessions at SSC2017

 

 

Special SIG Session on Education (SIG-Education)

Emile Chappin TU Delft

Conference submissions are welcome for this session on education. We see a need for bringing complexity thinking and social simulation to an educational setting broader than only universities. We aim to develop our next generations into one in which thinking in terms of complex systems is more central than it is now. We intend to do so by…

• Collecting and developing exemplary material for use inside and outside schools, specific to various age groups.
• Considering and developing trainings and training materials for teachers
• Thinking about toys embracing complexity for young children
• Finding appropriate arenas and participate in the debate around educational reforms and voice the need for complexity thinking
• Bringing together researchers that are interested in education of complexity in general and social simulation/agent-based modelling specifically
• Providing platforms to share experiences of complexity thinking in education
At the end of the day we aim to make use of the intuition for thinking in complex systems that children have, to nourish those capabilities and to develop a generation of people embracing complexity.

Special SIG Session on Testing policy options before and after implementation
(SIG-Policy Modelling)

Petra Ahrweiler EA European Academy of Technology and Innovation Assessment GmbH, Germany
Nigel Gilbert CRESS, University of Surrey, UK
Bruce Edmonds Centre for Policy Modelling, UK
Ruth Meyer Centre for Policy Modelling, UK

The Special Interest Group session is about policy modelling with a focus on complexity issues. Policy modelling means to identify areas that need intervention, to specify the desired state of the target system, to find the regulating mechanisms, to design policy and its implementation, and to control and evaluate the robustness of interventions. The methodological difficulty hereby is to bridge the gap between policy practice, often expressed in qualitative and narrative terms, and the scientific realm of formal models. Furthermore, policymaking in complex social systems is not a clear-cut cause-effect process but characterised by contingency and uncertainty. To take into account technological, social, economic, political, cultural, ecological and other relevant parameters, policy modelling has to be enhanced and supported by new ICT-oriented research initiatives. Reviewing the current state-of-the-art of policy context analysis such as forecasting, foresight, backcasting, impact assessment, scenarios, early warning systems, and technology roadmapping, the need for policy intelligence dealing with complexity becomes more and more obvious. This SIG provides a unique opportunity to gather together a range of well-established leading researchers working in the field and to provide a platform for interdisciplinary discussion.
Modelling of policy initiatives can take into account more parameters than previously possible and perform social simulations to forecast potential impacts of proposed policy measures. Changing parameters within ABMs is analogous to applying different policy options in the real world. These models could therefore be used to examine the likely real-world effects of different policy options before they are implemented. Thus, altering elements of the models that equate with policy interventions makes it possible to use ABM as a tool for evaluating the results of the policy interactions that typically occur between policy interventions, policy contexts and agents. The objective of this SIG session is to explore these issues.

Special SIG Session on Qualitative Data for informing behavioural rules (SIG-Qual2Rule)

Melania Borit, University of Tromsø – The Arctic University of Norway, Norway
Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University, UK

Many academics consider qualitative evidence (e.g. texts gained from transcribing someone talking or observations of people) and quantitative evidence to be incommensurable. However, agent-based simulations are a possible vehicle for bridging this gap. Narrative textual evidence often gives clues as to the in-context behavior of individuals and is thus a natural source for behaviors to inform the specification of corresponding agent behavior within simulations. They will not give a complete picture of this, but they will provide some of “menu” of behaviors that people use. During this session, we hope to further understanding of how to do this better. It is open to all approaches that seek to move from qualitative evidence towards a simulation in a systematic way. This includes, but is not limited to:
· Approaches based in Grounded Theory.
· Tools for facilitating such a process.
· Participatory processes that result in a simulation.
· Frameworks for aiding the analysis of text into rules.
· Elicitation techniques that would aid the capture of information in an appropriate structure.
· Models and ideas from psychology to aid in the above process.
· Insights and tools from Natural Language Processing that may help this process.
· Agent architectures that will facilitate the programming of agents from such analyses.
· Philosophical or Sociological critiques of this project, pointing out assumptions and dangers.
· Examples of where this approach has been tried.

Special SIG Session on Social Simulation and Serious Games (SIG-SSSG)

Melania Borit, University of Tromsø – The Arctic University of Norway, Norway
Harko Verhagen, Stockholm University, Sweden
This special session focuses on the interplay between social simulation and serious games. We wish to bring together researchers working on both fields to a crossroads at which synergies will be created between the two areas. In SSSG, we investigate how the fields of social simulation and serious games are linked. In particular, we focus on the following topics:
• Serious game design. Which level of abstraction is chosen for a serious game? Will it be close to a strict simulation or will it incorporate extensive metaphors? What are the factors based on which this choice is to be made? Which (serious) game mechanics are useful?
• Modelling the social situation. Which approach captures the situation with sufficient granularity? How should a choice be made to include specific theories and models that describe the situation? For example, using a data-driven methodology, how can the steps be made from data to theory to application (and game mechanics)? For agent-based modelling, how can artificially intelligent agents be made that act according to a specified model?
• Example implementations. Stories of success and failure: which elements in a serious game that includes social interaction turn out to be useful and which are counter-productive to the game’s goal? Which elements of social simulations can be used in the design of serious games?

Special Session on Modelling Social Science Aspects of Fisheries

Melania Borit, University of Tromsø – The Arctic University of Norway, Norway
Samaneh Heidari, Utrecht University, The Netherlands
Overall, world fisheries are unsustainable, with almost 90% of stocks being over exploited or fully exploited. Improved and innovative management solutions are required if this critical situation is to be remedied. Since management is about people, not fish, integrating social sciences aspects into the modelling of fisheries as a socio-ecological complex system might be such a new way of thinking about fisheries management. We are interested in solutions that look at fisheries as socio-ecological complex systems, with a focus on the social behavior components of the system. Submissions focusing on any aspects of fisheries management are welcome, including (but not restricted to): social norms and self-organization of fishers, applications of Ostrom´s work on managing social commons, compliance with rules under changing management regimes, culture and trust, simulations that combine complex representations of society and complex ecological models, simulations as public educational tools, participatory simulations of fisheries. This session is a SAF21initiative.

Special Session on Synthetic Population Databases and Agent-based Modelling: Informing Society and Policy-making

Diane Payne University College Dublin
Pablo Lucas University College Dublin
Georgiy Bobashev RTI International
Jay Rineer RTI International

Agent-based models simulate large-scale social systems and usually look to capture heterogeneous aspects of individuals. These models usually have additional data requirements compared to modelling approaches which use aggregated data. Agent based models assign behaviors and activities to “agents” (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks and other health challenges, urban growth and segregation, transportation modes and blockages, population dynamics and migration patterns, as well as many other uses. For agent based models that require quite detailed population characteristics, the recent progress in the development of synthetic population databases is very important. A synthetic population takes available statistical data about the population from existing data sets such as census data, social survey data as well as other kinds of data including large administrative data such as social welfare, education and labour market data, health data for example and creates a detailed model of the population. At the same time, the data is ‘synthesized’ so that it is impossible to identify actual persons, thus ensuring individual privacy. Moreover, by joining to the synthetic population the probabilities for key life events such as the probability of death, birth, union, and migration, we can construct population projection models and project these data into the future any number of years.
Research using synthetic population databases in agent based modelling involves a wide range of academic disciplines, including but not limited to sociology, geography, policy, law, computer science and history. It also needs support from a wide range of stakeholders, including policy makers, professional bodies, state agencies and civil groups. We invite submissions to this session which will help map and explore recent developments in the use of synthetic population databases and agent based modelling for informing society and policy in different sectors or in local, national or cross national applications. This session is part of the emerging SyNPOP project.

Special Session on Formalising and implementing human behaviour in agent-based models of natural resource use

Gunnar Dressler, Helmholtz-Centre for Environmental Research – UFZ. Leipzig.
Wander Jager, University College Groningen.
Felix John, Helmholtz-Centre for Environmental Research – UFZ, Leipzig.

Models of natural resource use address challenging problems like the sustainable management of fisheries or finding a trade-off between cash and subsistence crops in an agricultural system that is beneficial both for the land users as well as the ecosystem. Such models often put great emphasis on a detailed representation of the biophysical processes, whereas human decision-making is only poorly addressed. Agent-based models (ABMs) allow the explicit representation of individual motivations and decision processes in the context of utilizing resources in interaction with other users. Current formalizations of agent behaviour are mostly using a purely rational decision maker, or an ad hoc implementation of e.g. social influence in decision-making. In the social sciences, many behavioural theories have been developed demonstrating that people generally do not engage in purely rational decision-making. However, their use in social-ecological modelling has been very limited up to now. This lack of use of social theory is due to a few reasons (e.g., Schlüter et al, 2017). The more descriptive level of most behavioural theory forms the biggest hurdle. Having relations between theoretical constructs expressed in statistical (correlations) rather than formal terms makes a direct translation in models impossible. Second, behavioural theories generally start with drivers of behaviour and end with the performance of behaviour. It is often not being addressed how the behaviour of many individuals (macro-level) composes the social setting determining the behaviour of an individual (micro-level) in a next stage. Hence, the loop connecting micro and macro-level behaviour is not closed. Last but not least, the modeller is confronted with an abundance of different behavioural theories, often overlapping, with different explanatory aims and on different levels of aggregation. In the proposed session on agent-based models of natural resource use we invite researchers implementing human behaviour in addressing natural resource use and social-ecological models to submit their work for discussion. The session is organised to stimulate an exchange of the possibilities and difficulties in formalising and implementing behavioural theory in social-ecological models.

Special SIG session on Computational Organisation Theory

César García-Díaz (Universidad de los Andes, Colombia), Abhijit Sengupta (University of Essex)

Computational organisation theory involves understanding organisational processes by means of computer simulation. It considers organisations as complex adaptive systems where the processes of organising are dependent on factors such as structural interdependencies among organisational subunits and individual behaviour. Both intra-organisational issues (e.g., relationship between organisational structure and performance characteristics) and inter-organisational processes (e.g., firm strategy and competition dynamics) are of interest to this session.

Examples related to computational organisation theory include, but are not limited to, the following: Understanding the relationship between routines and generative explanations of organisational performance; firm behaviour and supply chain dynamics; organisational networks; computation, organisations and evolution; organisational design; markets; learning in organisations; computational models of strategy; coevolution of individual and structure; and team behaviour.

Both theoretical and methodological papers, as well as applications to real contexts are of interest. Diverse computational approaches are welcome (e.g. agent-based modelling, dynamic simulation, discrete-event modelling, etc.)

Special Session on Dynamic Structures: Connecting SNA and ABM

Filip Agneessens, University of Surrey, Jennifer Badham, Queen’s University Belfast, Corinna Elsenbroich, University of Surrey

Social sciences are generally interested in explaining the interactions (social relations) between people and their effects on society. Social network analysis (SNA) and agent-based modelling (ABM) provide two major approaches for studying such relations and processes. Social network analysis (SNA) allows us to understand (1) the mechanisms that form the basis for the emergence of network relations (e.g. homophily, reciprocity, clustering) and (2) the impact of these network relations for outcomes such as: well-being, career progression, innovation or even smoking behavior. Agent-based modelling (ABM) allows for the generation of social macro-phenomena from individual behaviour inputs (e.g. the emergence of opinion alignment) and an exploration of unfolding dynamics of the interactions between agents with each other and their social and physical environment. While there is a clear link between these approaches, their potential synergies remain underexplored. This special session aims to bring together research in their intersection.

Questions of interest include:

1)    How can ABM help understand the emergence of network structures and their implications for social phenomena?

2)    How can we combine SNA and ABM data collection?

3)    How can SNA data guide ABM?

4)    Case studies that integrate ABM and SNA methods to analyse a specific issue.

PEERE Session on Social Simulation of Peer Review

Flaminio Squazzoni (University of Brescia), Francisco Grimaldo (University of Valencia)

Supported by the COST Action PEERE , this session aims to attract contributions that apply social simulation to understanding peer review in science. A special attention will be given to works that model peer review dynamics, looks at scientist behaviour and its implications and test policy scenarios. Also contributions that aims to set methodological standards and propose ad-hoc platforms and models are welcome. The COST Action PEERE will support the participation of eligible speakers by covering travel and accomodation expenses. For any information, please contact info@peere.org