SSW2020 Programme (Central European Time-CET)
|9:30-10AM||ESSA Welcome – (ESSA President, Gary Polhill, The James Hutton Institute, Aberdeen, United Kingdom)
SSW2020 Welcome – (SSW2020 Chair, Flaminio Squazzoni, University of Milan, Italy)
|10-11AM||ESSA@work – Dynamic Feedback for Work in Progress I – (Chair: Julia Eberlen, Université Libre de Bruxelles, Belgium)
Topics. Background: ESSA@work is a concept born out of the desire to give and receive feedback on work in progress (agent-based) models. In a typical ESSA@work session, participants present a model they are working on to gather feedback and suggestions to improve, adapt and/or extend their model. The model can be at any stage of development, from a budding idea to a completed one, but is usually one that’s not fully explored. Some participants have specific modelling questions they need help with, while some of them seek inspiration and ideas to become unstuck in their model development process. The feedback from ESSA@work sessions helps participants achieve these objectives. Feedback to participants comes from three different sources: the co-presenters (i.e., fellow ESSA@work participants), two expert modellers, and the audience. In the feedback process, emphasis is placed on constructive exploration of possible solutions to the problems raised by participants. The feedback process also allows the session experts to reflect and offer insights about the models presented. In doing so, participants are not asked to “defend” their modelling choices. Rather, they are provided with an opportunity to listen to two experts discuss their work with complete involvement. Participants find this to be an enriching experience whereby sometimes they are surprised to see their model/work in new light. ESSA@work is a work-in-progress session in its truest sense – it provides a platform to present and gather feedback on work that is under development within an enriching, kind and positive atmosphere. Proposal/Session format: In this 1h session, we will first introduce the ESSA@work concept, to benefit those unfamiliar with its format, and also discuss the possibilities to organise local
|11-12AM||ESSA@work – Dynamic Feedback for Work in Progress II – (Chair: Julia Eberlen, Université Libre de Bruxelles, Belgium)
|2-5PM||Tutorial: ODD2ABM – Creation of NetLogo Agent-Based Models from a Formalised ODD – Themis Dimitra Xanthopoulou & Andreas Prinz (University of Adger, Norway)
Topics. In this tutorial, we will guide you through the tool “ODD2ABM”. The tool serves as a means for automatic verification of models, concept clarification, and communication regarding model contents. ODD2ABM automatically transforms ODD descriptions into NetLogo code. To achieve the automatic transformation we have developed a new language in MPS,an open-source software from JetBrains, and we have formalized the ODD descriptions. The user interface is in the MPS environment. The user writes in the user interface the specifications of the model, in the same way, she would write an ODD protocol of an Agent-Based Model. With a click, the ODD description is transformed by the tool into the NetLogo code. Then the NetLogo code can be run in the NetLogo platform and used as a regular simulation model. One description in the tool will always produce the same simulation model and therefore, ODD2ABM serves as a means of verification. The formality we have introduced in the ODD descriptions enhances the transparency behind the modelling decisions and the concepts we use. As such, it opens the ground for communication around the model and debate around the use of concepts.
|Tutorial: Julia: Modeling agent‐based simulations and network interactions in the Julia programming language – Przemyslaw Szufel & Bogumil Kaminski (SGH Warsaw School of Economics, Poland)
Topics. The goal of this workshop is to help social scientists to leverage the power of Julia language to more efficiently build and run large scale agent‐based simulation models with a special focus on modeling social interactions on networks. We will start with an introductory information about the Julia language. In the main part of the workshop we will present how to build ABM simulations in Julia using networks and spatial data. We will als discuss how to run simulation experiments on a massively parallelized infrastructure. The workshop will be hands‐on for those who are interested to follow the examples on their computers – Jupyter notebooks will be provided for experimenting with source codes. We will distribute to the registered participants the information about how they should configure their computers before the online event.
Please, visit this page for instructions before the beginning of the tutorial: https://szufel.pl/ssw2020/. The page includes: installation instructions, materials and model examples that will be used during the tutorial.
|5-6PM||Invited talk: Maja Schlüter (Stockholm Resilience Centre, Sweden) “Theorizing about social-ecological phenomena through collaborative modelling” – (chair: Melania Borit, UiT The Artic University of Norway).Abstract. Social-ecological phenomena such as regime shifts or sustainable resource management emerge from multiple interactions between people and nature. While there is an increasing number of empirical descriptions of such phenomena in particular places and contexts, social-ecological systems (SES) research lacks explanations and theories about how and under which conditions they may come about. Disentangling complex causation and dealing with the context-dependence of social-ecological processes is difficult. We argue that combining synthesis of empirical knowledge with agent-based modelling may provide a way forward for developing explanations of complex social-ecological phenomena. To this end, possible explanations of a phenomenon of interest are developed through empirical synthesis. They are then formalized in an ABM to test whether the model can generate the phenomenon and, if so, disentangle the underlying mechanisms and investigate the conditions under which they hold. The development, formalization and testing of the explanation is an iterative and collaborative process involving empirical researchers and modellers to facilitate a process of critical reflection and integration or contrasting of different perspectives. I will present the methodology and illustrate it with several examples of our work on natural resource governance which were instrumental for developing it. I will conclude with reflecting on its potential to contribute to the development of middle range theories of SES.|
|8-9AM||Tutorial: Data Distribution for Japanese Synthesized Population and Real-Scale Social Simulations I – Chair: Tadahiko Murata (Kansai University, Japan)
Topics. In this tutorial, we would like to explain the needs of synthesized population for whole population in a country. When we try to develop a social simulation tool for a real community, we need detail compositions of the real population in that community. In these years, we have synthesized whole Japanese populations that includes compositions of each household. We have synthesized the Japanese population based on the national census in 2000, 2005, 2010 and 2015. That is, we have released four synthesized populations according to the national census. Since we have developed the synthesized method employing a simulated annealing method, the synthesized population depends on some random numbers. Therefore, we have prepared 100 sets of synthesized populations for each census. Researchers can employ all the 100 sets when they employ their simulation tool with the synthesized population. In this tutorial, we would like to explain how to synthesize the populations using the national census, and the rules for distributing the synthesized populations for researchers. We also explain how we can utilize the population in simulating in some area of real-scale social simulations. We would like to call for researchers who are interested in synthesizing the nation-wide populations for real-scale social simulations for their countries.
|9-10AM||Tutorial: A Software Architecture for Multi-theory Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models I – Tuong Vu, Charlotte Buckley & Robin Purshouse (University of Sheffield, United Kingdom)
Topics. This tutorial introduces the recently published MBSSM (Mechanism-Based Social Systems Modelling) software architecture. The MBSSM architecture is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams.
|Tutorial: Data Distribution for Japanese Synthesized Population and Real-Scale Social Simulations II – Chair: Tadahiko Murata (Kansai University, Japan)|
|10-11AM||Tutorial: A Software Architecture for Multi-theory Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models II – Tuong Vu, Charlotte Buckley & Robin Purshouse (University of Sheffield, United Kingdom)||Workshop: Integrating Qualitative and Quantitative Evidence using Social Simulation I (Chairs: Melania Borit, UiT The Artic University of Norway & Bruce Edmonds, Manchester Metropolitan University, United Kingdom)
Topics. Agent-based simulation can be related to qualitative as well as quantitative data. For example, qualitative input might be used to inform the micro-level specification of agent behaviour in simulations that are then run and compared to aggregate quantitative data. However using qualitative data can seem daunting, partly because there are no established methods for doing this. The goal of this SSW2020 session is to discuss methods for integrating qualitative and quantitative data in agent-based models, with reference to worked examples.
Programme Part I
Note: Besides registering for the Social Simulation Week 2020, please register for your participation in this workshop by September 10th using this link: https://forms.gle/1MUTSYGA4BxYD3DX7.
|11-12AM||Workshop: Changes in food consumption habits and production I (chairs: Juliette Rouchier and Pedro López Merino, Université Paris Dauphine, France & Sylvie Huet, Irstea − Lisc, France).
The workshop will feature four short presentations followed by an open discussion. Each of the presentation brings in a different approach (spatial optimisation, adaptive behaviour, innovation diffusion as well as an experimental protocol) and finds itself at different places of two analytical postures:
Relation between data and theory. Two of them are more data-grounded and two more theory-gronded.
11:15. Welcome organic coffee and healthy, local, and responsibly sourced snacks (bring your own!)
GeoPAT: a spatially explicit tool for prospecting reallocation of cultural patches – Nicolas Dumoulin (Laboratoire d’Ingénierie pour les Systèmes Complexes, INRAE, France)
You are what you eat! Using Experiments to Study (Online) Social Influence Effects on Food Choices – Adrian Lueders (Laboratoire de Psychologie Sociale et Cognitive / CNRS) Université Clermont Auvergne, France)
Modeling Extended Agro-Food Supply Chain: Pathways to Sustainability through Consumer Behavioral Change – Firouzeh Taghikhah (Center on Persuasive Systems for Wise Adaptive Living, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia)
An agent-based model of (food) consumption: Accounting for the Intention-BehaviourGap on three dimensions of characteristics with limited knowledge
|Workshop: Integrating Qualitative and Quantitative Evidence using Social Simulation II (Chairs: Melania Borit, UiT The Artic University of Norway & Bruce Edmonds, Manchester Metropolitan University, United Kingdom)
Programme Part II
Note: Besides registering for the Social Simulation Week 2020, please register for your participation in this workshop by September 10th using this link: https://forms.gle/1MUTSYGA4BxYD3DX7.
|12-1PM||Workshop: Changes in food consumption habits and production II (chairs: Juliette Rouchier and Pedro López Merino, Université Paris Dauphine, France & Sylvie Huet, Irstea − Lisc, France).
12:20. Discussion chaired by Juliette Rouchier and Sylvie Huet
If you’d like to participate, please register to the SSW 2020 first and then fill in the following form (to be closed at 12pm the day before the Workshop):
|Workshop: Integrating Qualitative and Quantitative Evidence using Social Simulation III (Chairs: Melania Borit, UiT The Artic University of Norway & Bruce Edmonds, Manchester Metropolitan University, United Kingdom)
Note: Besides registering for the Social Simulation Week 2020, please register for your participation in this workshop by September 10th using this link: https://forms.gle/1MUTSYGA4BxYD3DX7.
|2-5PM||Workshop: Agent-based models of social networks: Integrating agents’ decision-making to network dynamics I – Chairs: Filip Agneessens (University of Trento, Italy), Federico Bianchi (University of Milan, Italy), Andreas Flache (University of Groningen, Netherlands) & Károly Takács (Linköping University, Sweden)
Topics. Bridging micro- and macro-scales is pivotal to both social network research and agent-based modelling (ABMs). Exchanges between SNA and ABM have recently increased. ABMs can explain how network dynamics and macro-level outcomes can be linked through micro-level mechanisms. The network component of an ABM can be empirically calibrated, which allows ABM modellers to move beyond the use of abstract networks in early ABMs. Recent developments in statistical models of repeated network observations have brought up discussions on the use of simulations in social network research. Moreover, ABM can be used as tools to increase the reliability of statistical analysis of network data. This session invites papers that rely on ABMs relating macro-level outcomes with social network dynamics in e.g., opinion polarization, social inequality, social conflicts, or economic collaboration. Particularly — but not exclusively — welcome are contributions bridging theoretical ABM, empirical data and statistical models of network generating processes (e.g., ERGM, SAOM).
Agent-based models of cultural and opinion dynamics have explored how, in social networks, a simple process of interpersonal influence between actors leads to the emergence of cultural consensus or political polarization. Despite their explanatory power on the micro-macro linkage, previous models reflect only the topological features of social networks (e.g., a network structure is random or small-world), missing its relational features that are characterized by various social entities, such as norms and institutions. In this study, we suggest two formal parameters, which are applicable to most previous models, reflecting two relational features of social networks: the strength of norm-controlled ties (i.e., ties in dense connections survive longer than in sparse connections due to the norms enforced) and induced homophily (i.e., institutions, such as job, school, and neighborhood, yields network segregation by gender, race, and class). We apply both parameters to a previous model (Flache and Macy 2011a), maintaining all other simulation conditions the same. Unlike the previous model that reveals more polarization by more long-range ties in a small-world network, our updated model produces more complex and unexpected outcomes—random emergence of extreme polarization or consensus. However, when the parameters are applied separately, the outcomes show no difference from the previous one.
|Workshop: Simulations in Economics – Chairs: Juan Gabriel Brida & Emiliano Alvarez (Universidad de la República, Uruguay).
Topics. The event proposes to present the most recent work on simulations in Economics of some LatinAmerican research groups working in the area of Complex Systems, Agent-based Models, Nonlinear Dynamics and related topics. The collection of papers to be presented include some theoretical models that require the use of simulations, as well as empirical exercises applying simulation models to data. The individuals and research groups proposing the virtual workshop is an active group in LatinAmerica that works on the area of Complex Systems and particularly in Agent Based Models, with emphasis on issues related to emerging markets and the links between the economy, production systems and underlying socio-ecological systems.
Martha Alatriste-Contreras (Universidad Nacional Autónoma de México, México): “The impact of sectoral shocks in the North America Production Network“. [co-authors: Alatriste-Contreras, M. & Puchet, M]
Abstract: In the context of the new trade agreement between the countries of North America (TMEC acronym in Spanish), it is important to evaluate the posible impact of sectoral shocks in the North America Production Network. We use input-output data for Canada, Mexico, the USA, and the region, a network diffusion model, and computer simulations to evaluate the effect of specific sectoral shocks in the countries’ and region’s economy. The diffusion model we use assumes that a sectoral shock changes the input-output connections between sectors, thus changes the way sectors produce. The results of the simulations shed light on the posible impacts of the new trade agreement and provide useful information to design an industrial policy focused on the development of the production network. In particular, we focus on recommendations for the Mexican economy.
Marcelo Álvez (Universidad de la República & Central Bank of Uruguay, Uruguay): “Progressive income tax and its emerging growth effects: a complex systems approach” [Co-authors: Alvarez, E., Álvez, M. & Brida, J.G.]
Abstract: The State as a distorting agent of the markets or the State as a corrector in the face of market failures, these are two characterizations that allow sizing the diversity of positions regarding the role of this particular agent of the economy. In this work, an agent-based stock-flow consistent model (AB-SFC) is applied to analyze the differences in the economies when establishing different types of taxes on personal income, proportional and progressive. Different combinations of threshold and rate are tested. There are no significant differences in economic performance in the presence of one tax scheme or the other. In the scenarios with higher rates, a slower recovery of the economy is noticed after a period of stagnation, but the difference is not significant at 90% significance. This design, which only distinguishes two sections of income, is not able to reduce the inequality generated throughout the income distribution. The tax design seems to offset the inequality in the lower section of income distribution through tax exemption for low-income households, but not the one generated in the section of higher income. An additional policy is necessary to offset the differences generated in the range of higher-income individuals. In this exercise, there is no evidence of a deterioration of economic growth in the presence of a progressive income tax, instead of a proportional one.
Emiliano Alvarez (Universidad de la República, Uruguay): “Agent Based Models and Simulation in Social Sciences: A bibliometric review” [co-authors: Alvarez, E. & Brida, J.G.]
Abstract: Since the first agent-based models (ABM), the scientific community has been interested in making not only the results of computational models understandable but also the modeling description, to facilitate their replication. The form that has been adopted to a greater extent has been the ODD (Overview, Design concepts, and Details) protocol, which provides a generic structure for its documentation. This protocol provides a way to clearly explain the procedures and interactions of the complex systems to be analyzed, with applications that have spread across
Nicolás Garrido (Universidad Diego Portales, Chile): “Crowding and price dynamics in tourism destination choice” [co-authors: Alvarez, E., Brida, J.G. & Garrido, N.]
Abstract: This paper analyzes how the preferences for crowding in destinations by tourists interact along the time with the pricing strategies of the resorts to determines the number of tourist visiting a tourist attraction. Destinations are experience goods and the stakeholders of the destinations use multiple signals to reduce the uncertainty of the consumers before their choice. Taking into consideration companies that seek to increase their profits and customers with budget restrictions and with both individual and social preferences, this work analyzes price dynamics and conditions of market competition, under an Agent-Based Modelling (ABM) setting. An emerging result of this process is the formation of companies with greater market power and high customer differentiation, although less than in the case without budget restrictions. At the same time, adjustment speed of the companies have non-linear effects on prices, the benefits of the resorts and individuals’ utility. Moreover, price dynamics is highly sensitive to the set of information that agents in the market have.
Daniel Heymann (Universidad de Buenos Aires, Argentina): “Behavioral heuristics and market patterns in a Bertrand–Edgeworth game” [co-authors: Heymann, D., Kawamura, E., Perazzo, R. & Zimmermann, M.G.]
Abstract: This paper studies Bertrand price-setting behavior when firms face capacity constraints (Bertrand–Edgeworth game). This game is known to lack equilibria in pure strategies, while the mixed-strategy equilibria are hard to characterize. We explore families of heuristic rules for individual price-setting behavior and the resulting market patterns, through simulations of agent-based models and laboratory experiments. Overall, the individual pricing strategies observed experimentally can be represented approximately by a sales-based simple rule. In the experiments, average market prices tend to converge from above and approach a state resembling a steady state, with slow aggregate price variations and low price dispersion around an average near the competitive level. However, that configuration can be disturbed occasionally by excursions triggered by discrete price raises of some agents. Salient features of experimental results can be described by simulations where agents use sales-based heuristics with parameters calibrated from the experiments. The results obtained here suggest the existence of useful complementarities between analytical, experimental and agent-based simulation approaches.
|5-6PM||JASSS-The Journal of Artificial Societies and Social Simulation: The editor meets authors and readers (Flaminio Squazzoni, University of Milan, Italy)
Topics. This event aims to provide some ‘behind-the-scene’ information on JASSS and allows interested authors, referees and readers to discuss the journal development with the editor.
|6-7PM||Invited talk: Anima Anandkumar (Caltech Computing + Mathematical Science Department, Pasadena, CA, United States) “How to create generalizable AI?” (chair: Flaminio Squazzoni, University of Milan, Italy)
Abstract. Current deep-learning benchmarks focus on generalization on the same distribution as the training data. However, real-world applications require generalization to new unseen scenarios, domains and tasks. I will present key ingredients that I believe are critical towards achieving this. (1) Augmenting with simulations in domains where it is expensive to collect large-scale datasets. (2) Causal discovery and inference that capture underlying relationships and invariances. (3) Semi-supervised disentanglement learning for controllable generation. Further, I will show how domain knowledge and structure can help enable learning in challenging settings such as robot learning.
|10-1PM||Workshop: Simulation in the times of COVID-19 – Chairs: Harko Verhagen (Stockholm University, Sweden) & Alexis Drogoul (Institut de Recherche pour le Développement- IRD, France)
Topics. Following the editorial “Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action” in JASSS 23(2), there have been ample reactions on the RofASSS site (see https://rofasss.org/tag/jasss-covid19-thread/) indicating a large interest within the social simulation community on the modelling of global pandemics and COVID-19 in particular. Ranging from the value of modelling in general or for policy makers and making, criteria for transparent modelling, development of simulation models and tools, experiences and ideas for fast model adaptation and development in a crisis situation, KISS versus KIDS, etc. We assume this broad and varied interest is a solid base for a Social Simulation Week workshop to extend the current asynchronous discussion with synchronous discussion and possible the start of integrative cooperation as well as deep epistemological debate. We aim to invite some guests from outside the social simulation community who have worked with individual-based epidemiological modelling..
Christopher Watts (independent): An agent-based modeller looks at an epidemiologist’s model
Nick Gotts (Geography, University of Leeds ): Agent-Based Modelling of Covid-19 Transmission in Hospital Settings: Plans in the SAFER Project
Jason Thompson (Melbourne School of Design): Bringing computational social science into mainstream policy making for COVID
Juliette Rouchier (LAMSADE, Paris): Information and modelling during crisis
|2-4PM||Workshop: Agent-based models of social networks: Integrating agents’ decision-making to network dynamics II – Chairs: Filip Agneessens (University of Trento, Italy), Federico Bianchi (University of Milan, Italy), Andreas Flache (University of Groningen, Netherlands) & Károly Takács (Linköping University, Sweden)
|Workshop: Agent-based modelling can be used for prediction in complex social systems – Chairs: Corinna Elsenbroich (University of Surrey, United Kingdom) & Gary Polhill (The James Hutton Institute, Aberdeen, United Kingdom).
Topics. The Covid-19 Pandemic has exposed stresses and strains in societies, political systems and sciences. Some governments contend that they entirely “follow the science” (e.g. UK) whilst scientists within and between disciplines show how (seemingly) contradictory recommendations can be. This highlighted interface of policy and science is an opportunity for all kinds of modelling, including agent-based modelling – but not without potential hurdles, bear traps and pitfalls. A ‘call to action’ in JASSS has led to a succession of interesting responses in RofASSS, highlighting the complexity of the endeavour of prediction in complex social systems and using ABM as a suitable method. This session proposes to hold a formal debate on the motion “Agent-based modelling can be used for prediction in complex societal systems”. We will invite a proposer and an opponent for the motion, each of which will speak for 5-10 minutes. We will then seek contributions which, if accepted, will be scheduled in the debate for 3-5 minute talks. At the end of the debate, the proposer and opponent are given a ‘right of reply’, and we will hold a vote on which side won the debate.
|4-5PM||Invited talk: Giulia Andrighetto (Institute of Cognitive Sciences and Technologies, National Research Council, Italy) “Understanding human cooperation through natural and artificial data” (chair: Iris Lorscheid (University of Applied Science Europe, Hamburg, Germany).
From climate change and ecosystem and habitat destruction to the spread of infectious diseases such as COVID-19, many contemporary societal challenges are exacerbated by collective action problems. In these situations, groups would benefit from a shared outcome but the incentives available to individuals drive them to free ride. While laws, treaties and other formal institutions could in principle address these global issues and create cooperation, they are often unavailable, unenforceable, or insufficient and informal institutions, such as social norms become essential. Under the right conditions, poor and destructive norms may disappear and new norms may spontaneously emerge, which motivate people to act against their self-interest and cooperate for the good of the collective. Despite their importance, evidence on the causal effect of social norms in promoting cooperation in humans is still limited. In this talk, I will present work on the formation and change of social norms and their effect in promoting human cooperation. I will discuss results from recent laboratory and simulation experiments showing that social norms are causal drivers of behavior and can explain cooperation-related regularities
|5-7PM||Tutorial: Best practices of making your computational models available for your future self (and others) – Marco Janssen, Allen Lee & Michael Barton (Arizona State University, United States)
Topics. CoMSES Net, the Network for Computational Modeling in Social and Ecological Sciences, is an open community aiming to improve the way we develop, share, use, and re-use agent based and other computational models for the study of social and ecological systems. More than 80% of publications with computational models do not share their code which limit the reuse of models and slows down the academic progress. Journals and sponsors are increasingly require better standards in sharing data and code. In this tutorial we will present best practices on the workflow of modeling using Github, demonstrate containerization of models, and discuss best ways to archive your models. We will also discuss recent developments on the Open Modeling Foundation, an organization of modeling organizations, to develop standards for accessibility, documentation, interoperability and reproducibility.
For info, contact the chair here.
|10-1PM||Workshop: Othering, Polarisation and Social Identity – Chairs: Bruce Edmonds (Metropolitan Manchester University, United Kingdom), Geeske Scholz (University of Osnabrueck, Germany) & Julia Eberlen (Uinversity Libre de Brussels, Belgium)
Topics. ‘Othering’ and polarisation have immediate and potentially severe consequences for politics across Europe – in terms of Populist denigration of sub-groups but also when politics is so divided that each side will not listen to the other (e.g. Brexit in the UK, or on measures to fight Corona, e.g. in Germany). Unfortunately, relevant theory, knowledge and perspectives on these phenomena are splintered across many disciplines, ones that normally do not talk to each other. The Social Identity approach (SIA) refers to the combination of Social Identity Theory (Tajfel & Turner, 1979) and Self-Categorization Theory (Turner et al., 1987; Reicher et al. 2010). SIA proposes that people derive a significant part of their concept of self from the social groups they belong to (Tajfel, 1978; Tajfel & Turner, 1979; Tajfel & Turner 1986; Turner, Hogg, Oakes, Reicher & Wetherell, 1987). SIA proposes that social identification, and the perception of people as fellow group members (or outsiders), is a fundamental basis for collective behaviour. SIA investigates how and when individuals come to feel, think and act as members of a group rather than as individuals. Agent-based modelling (ABM) is a means for bringing the different knowledge and perspectives on these issues together – bringing cognitive and social aspects within a single, coherent framework and sparking interdisciplinary debate. The SIA connects the cognitive to the social outcomes and is amenable to formalisation within ABMs, and is thus one possible means for making simulations. This workshop would present and discuss research on uses of ABM (either existing or prospect) to represent and explore this cluster of phenomena.
|Workshop: Challenges of modelling complex health behaviour – Chair: Alice MacLachlan (University of Glasgow, United Kingdom)
Topics. The coronavirus pandemic has brought agent-based models (ABM) to the attention of public health researchers and policymakers with key government decisions made based on agent-based infectious disease models. However, measures to reduce transmission of the virus have had wide reaching impacts on other aspects of physical and mental health and the economy, highlighting the complex nature of public health issues facing decision makers. In this webinar hosted by the Population Health Agent-based Simulation nEtwork (PHASE), we will discuss potential applications of ABM to address wider public health challenges and highlight key considerations when developing models of public health. Drawing on examples of ABM for adult social care and contact tracing, we will examine issues such as model specification and obtaining suitable data for model calibration and sensitivity analysis. We will also discuss the role of cross-disciplinary partnerships involving health practitioners and decision makers in developing effective and useful models of public health and the ways in which PHASE aims to support these collaborations.
10-10.10 Welcome – Dr Alice MacLachlan, University of Glasgow (chair)
|2-4PM||Workshop: Agent-based models of social networks: Integrating agents’ decision-making to network dynamics III – Chairs: Filip Agneessens (University of Trento, Italy), Federico Bianchi (University of Milan, Italy), Andreas Flache (University of Groningen, Netherlands) & Károly Takács (Linköping University, Sweden)
|Workshop: Games and Agent-Based Modelling- Investigating Synergies – Chairs: Timo Szczepanska and Melania Borit (UiT The Artic University of Norway) & Harko Verhagen (Stockholm University, Sweden)
Topics. Games can aid the development and refinement of agent-based models (ABM) by providing interactive and engaging environments in which social dynamics, perceptions, and behaviours of the players can unfold and be studied. This could then replace or complement lab experiments as well as empirical observations. There are, however, also caveats for the transfer of observations form gameplay to agent-based models since much like agent-based models are an abstraction of reality, so are games. Fine-tuning games to serve as reliable input for ABM development may itself give insights for the fine-tuning of ABM in general and vice versa. Examples of the use of games for ABM development do exist, but the potential is yet to be fully realized. During the workshop, we present a review of established practices and new developments in the field and provide detailed insights from two hands-on methods to integrating games and ABM. All workshop participants are encouraged to engage with the speakers and have the opportunity to articulate their remarks in an interactive session.
Liu Yang (Southeast University, China) will talk about integrating agent-based modelling and serious gaming for planning transport infrastructure and public spaces – 20 min presentation + 10 min discussion.
Break: 5 min.
Timo Szczepanska (UiT – The Arctic University of Norway, Norway) will bring up recent findings of a systematic literature review on established practices and new developments in the field for discussion – 15 min presentation + 15 min discussion.
Break: 5 min
Harko Verhagen (University of Stockholm, Sweden) and Melania Borit (UiT – The Arctic University of Norway, Norway) will initiate an interactive session to explore why we want to mix games and ABM, why we don’t do it, and how we can advance investigating the potential synergies of games and ABM.
Break: 5 min:
Christophe LePage (The French Agricultural Research Centre for International Development- CIRAD-, France) will grant insights to his experiences with applying games in companion modelling – 20 min presentation + 10 min discussion.
Reading material relevant to the session will be circulated in advance. Thus, besides registering for the Social Simulation Week 2020, please register for your participation in this workshop by September 10th using this link: https://forms.gle/hkHMwEWNAvq7M9Yw5.
|4-5PM||Invited talk: Petra Ahrweiler (Johannes Gutenberg University Mainz, Germany) “What Social Simulation can tell Artificial Intelligence – and vice versa” (chair: Patrycja Antosz, University of Groningen, Netherlands).
Abstract. In many countries, public administrations increasingly use Artificial Intelligence (AI) algorithms to decide on public service provisions among their citizens. Citizen profiles are assessed for their worthiness to receive public services, scoring using value criteria to distinguish between legal /fraudulent recipients, deserving/non-deserving, or needy/non-needy. Although types and degrees of AI implementation vary between countries, delegating decisions about the distribution of scarce resources based on value judgements to machines leads everywhere to important questions of ethics, justice, quality, responsibility, accountability, and transparency of welfare decisions. However, perceptions, attitudes and acceptance of AI use in service provision vary between countries due to different norms and values in-use, different technology status, economic models, civil society sentiments, and legislative, executive and judicial characteristics. Are existing cultural value patterns driving the use of AI, or is AI driving cultural change? What are the impacts of AI for future societies and their value systems? Which policies, behavioural changes and institutional developments are necessary and appropriate to prevent or support certain scenarios? The appraisal of potential social futures is a huge research challenge. This talk presents an approach that uses cultural comparison dimensions to build context-specific ABM simulations for policy advice and testing policy interventions. Simulations work with data at the country level using intelligent agents to model future societies and experiment with AI-in-use for projecting societal techno-futures.
|5-7PM||Workshop: Model discovery: Concepts, methods, tools and applications – Chairs: Robin Purshouse (University of Sheffield, United Kingdom), Ivan Garibay (University of Central Florida, United States) & Joshua M. Epstein (New York University, United States)
Topics. The generative, or mechanism-based, approach to modeling of social systems uses agent-based models (ABMs) to ‘grow’ the phenomenon under investigation. The modeler designs and implements the ABM, and chooses its parameters and initial conditions (i.e., inputs). Then the model is run to generate an emergent output – if this output can, in some sense, reproduce the phenomenon then it becomes a candidate explanatory model; otherwise it is rejected. Whilst the ABM community is now focusing heavily on methodology for the consideration of model inputs (e.g, calibration techniques), surprisingly little attention is given to the consideration of model structure – i.e., the nature of the entities and equations in the ABM. Whilst initiatives such as the Overview, Design concepts and Details (ODD) Protocol encourage modelers to articulate ABM structure in a thorough manner, these initiatives do not stimulate scientific consideration of the plurality at the heart of model structure selection decisions. Where does a particular structure come from? Does it arise from the art of the modeler, or does it arise from a scientific process? How does ABM speak to theory, and vice versa? How do we choose between alternative mathematical and computational realizations of a specified mechanism, and when do we know that a mechanism can be rejected? What is special about the structure that has been identified, compared to the universe of other structures that could have been chosen? Do multiple, meaningful candidate structures exist and, if so, do these share any similarities? To embark on the journey to answering these questions, the ABM community now needs to bring the issue of model plurality, and methods for model discovery, to the forefront. This workshop will introduce the philosophy and ideas that underpin the concept of model discovery, most recently articulated by Epstein as ‘inverse generative social science’, but which have also arisen under the designations of ‘model crunching’ and ‘structural calibration’; related ideas have also arisen in the areas of pattern oriented modeling, model alignment, and model replication and breaking. The workshop will then set out recent developments in computational intelligence and machine learning methods that have been harnessed for computer-aided model discovery, including how to conduct an efficient search over the wide range of ABM entities and equations that might explain a target phenomenon, and how to use the results to perform abductive inference of key causal mechanisms. The workshop will then introduce recently developed tools that enable these methods to be used by the ABM community – including object-oriented software architectures and synthetic agent populations, NetLogo and Repast implementations, and integrated software platforms for model discovery and inference. Next, the workshop will present learnings from a diverse set of recent applications of model discovery, including civilization growth and decline, alcohol use in US society, and message cascading on social media platforms. Finally, the workshop will set the stage for a discussion on the potential of model discovery as a new grand challenge for the ABM community.
<href=”https://www.sheffield.ac.uk/cascade/events/modeldiscovery”>The detailed programme is here.
|09:30-11AM||ESSA General Assembly|
|11-12PM||ESSA Distinguished Dissertation Award|
|2-6PM||Workshop: The Boundaries of Agent-Based Modelling – Chairs: Raffaello Seri (Università degli Studi dell’Insubria, Italy) & Davide Secchi (University of Southern Denmark, Denmark)
Topics. The workshop is on “The Boundaries of ABM”. The topic is not so much about the applications of ABMs in other disciplines, but rather about how other disciplines can help us get the best out of ABMs. It is not about what ABMs can do for other disciplines, but what other disciplines can do for ABMs. It is expected to be an “unworkshop”, in the spirit of “unconferences” (https://en.wikipedia.org/wiki/Unconference). The invited participants will be organized around themes, and they will arrange their contributions inside a given time frame. This gives the possibility to organize (short) tutorials, presentations, comments, panels, Q&A, etc. The limits on the time frame imply that no theme will be completely dealt within the unworkshop, but the emphasis will be on creating informal connections and starting discussions that can be continued after the SSW is over. The interventions will be directed towards a general audience.
Discussion themes (with lead contributors):
|6-7PM||Rosaria Conte Oustanding Award Lecture: Joshua M. Epstein (New York University, United States) “Agent-Based Modeling: Backward and Forward” (chair: Gary Polhill, The James Hutton Institute, Aberdeen, United Kingdom)|