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Gossip, Sexual Recombination and the El Farol Bar: Modelling the Emergence of Heterogeneity

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 2 (3) 2

Abstract: An investigation into the conditions conducive to the emergence of heterogeneity amoung agents is presented. This is done by using a model of creative artificial agents to investigate some of the possibilities. The simulation is based on Brian Arthurs `El Farol Bar model but extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of strategies inside each agent. This evolutionary learning process is based on a Genetic Programming algorithm. This is chosen to make the agents as creative as possible and thus allow the outside edge of the simulation trajectory to be explored. A detailed case study from the simulations show how the agents have differentiated so that by the end of the run they had taken on qualitatively different roles. It provides some evidence that the introduction of a flexible learning process and an expressive internal representation has facilitated the emergence of this heterogeneity.

Model-To-Model Analysis

David Hales, Juliette Rouchier and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 6 (4) 5

Abstract: In recent years there has been an explosion of published literature utilising Multi-Agent-Based Simulation (MABS) to study social, biological and artificial systems. This kind of work is evidenced within JASSS but is increasingly becoming part of mainstream practice across many disciplines. However, despite this plethora of interesting models, they are rarely compared, built-on or transferred between researchers. It would seem there is a dearth of "model-to-model" analysis. Rather researchers tend to work in isolation, designing all their models from scratch and reporting their results without anyone else reproducing what they found. Although the opposite extreme, where all that seems to happen is the next twist on an existing model, is not to be wished for, there are considerable dangers if everybody only works on their own model. Part of the reason for this is that models tend to be very seductive – especially to the person who has built the model. What is needed is a third person to check the results. However it is not always clear how people who are not the modeller can interpret or utilise such results, because it is very difficult to replicate simulation models from what is reported in papers. It was for these reasons that we called on the MABS community to submit papers for a model-to-model (M2M) workshop. The aim of the workshop was to gather researchers in MABS who were interested in understanding and furthering the transferability of knowledge between models. We received fourteen submissions from which (after a process of peer review) eight were presented at the workshop. Of the six articles that comprise this special issue, five were presented at the workshop.

Replication, Replication and Replication: Some Hard Lessons from Model Alignmen

Bruce Edmonds and David Hales
Journal of Artificial Societies and Social Simulation 6 (4) 11

Abstract: A published simulation model (Riolo et al. 2001) was replicated in two independent implementations so that the results as well as the conceptual design align. This double replication allowed the original to be analysed and critiqued with confidence. In this case, the replication revealed some weaknesses in the original model, which otherwise might not have come to light. This shows that unreplicated simulation models and their results can not be trusted – as with other kinds of experiment, simulations need to be independently replicated.

When and Why Does Haggling Occur? Some Suggestions from a Qualitative but Computational Simulation of Negotiation

Bruce Edmonds and David Hales
Journal of Artificial Societies and Social Simulation 7 (2) 9

Abstract: We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly called "haggling". This approach also highlights the importance of what each agent thinks is possible in terms of actions causing changes and in what the other agents are able to do in any situation to the course and outcome of a negotiation. This simulation greatly extends other simulations of bargaining which usually only focus on the case of haggling over a limited number of numerical indexes. Three detailed examples are considered. The simulation framework is relatively well suited for participatory methods of elicitation since the "nodes and arrows" representation of beliefs is commonly used and thus accessible to stakeholders and domain experts.

The Use of Logic in Agent-Based Social Simulation

Frank Dignum, Bruce Edmonds and Liz Sonenberg
Journal of Artificial Societies and Social Simulation 7 (4) 8

Abstract: [No abstract for this editorial]

Towards Good Social Science

Scott Moss and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 8 (4) 13

Abstract: The paper investigates what is meant by "good science" and "bad science" and how these differ as between the natural (physical and biological) sciences on the one hand and social sciences on the other. We conclude on the basis of historical evidence that the natural science are much more heavily constrained by evidence and observation than by theory while the social sciences are constrained by prior theory and hardly at all by direct evidence. Current examples of the latter proposition are taken from recent issues of leading social science journals. We argue that agent based social simulations can be used as a tool to constrain the development of a new social science by direct (what economists dismiss as anecdotal) evidence and that to do so would make social science relevant to the understanding and influencing of social processes. We argue that such a development is both possible and desirable. We do not argue that it is likely.

The Emergence of Symbiotic Groups Resulting from Skill-Differentiation and Tags

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 9 (1) 10

Abstract: This paper presents a evolutionary simulation where the presence of 'tags' and an inbuilt specialisation in terms of skills result in the development of 'symbiotic' sharing within groups of individuals with similar tags. It is shown that the greater the number of possible sharing occasions there are the higher the population that is able to be sustained using the same level of resources. The 'life-cycle' of a particular cluster of tag-groups is illustrated showing: the establishment of sharing; a focusing-in of the cluster; the exploitation of the group by a particular skill-group and the waning of the group. This simulation differs from other tag-based models in that is does not rely on either the forced donation of resources to individuals with the same tag and where the tolerance mechanism plays a significant part. These 'symbiotic' groups could provide the structure necessary for the true emergence of artificial societies, supporting a division of labour similar to that found in human societies.

Open Access for Social Simulation

Gary Polhill and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 10 (3) 10

Abstract: We consider here issues of open access to social simulations, with a particular focus on software licences, though also briefly discussing documentation and archiving. Without any specific software licence, the default arrangements are stipulated by the Berne Convention (for those countries adopting it), and are unsuitable for software to be used as part of the scientific process (i.e. simulation software used to generate conclusions that are to be considered part of the scientific domain of discourse). Without stipulating any specific software licence, we suggest rights that should be provided by any candidate licence for social simulation software, and provide in an appendix an evaluation of some popularly used licences against these criteria.

Errors and Artefacts in Agent-Based Modelling

José Manuel Galán, Luis R. Izquierdo, Segismundo S. Izquierdo, José Ignacio Santos, Ricardo del Olmo, Adolfo López-Paredes and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 12 (1) 1

Abstract: The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.

Bootstrapping Knowledge About Social Phenomena Using Simulation Models

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 13 (1) 8

Abstract: There are considerable difficulties in the way of the development of useful and reliable simulation models of social phenomena, including that any simulation necessarily includes many assumptions that are not directly supported by evidence. Despite these difficulties, many still hope to develop quite general models of social phenomena. This paper argues that such hopes are ill-founded, in other words that there will be no short-cut to useful and reliable simulation models. However this paper argues that there is a way forward, that simulation modelling can be used to "boot-strap" useful knowledge about social phenomena. If each bit of simulation work can result in the rejection of some of the possible processes in observed social phenomena, even if this is about a very specific social context, then this can be used as part of a process of gradually refining our knowledge about such processes in the form of simulation models. Such a boot-strapping process will only be possible if simulation models are more carefully judged, that is a greater selective pressure is applied. In particular models which are just an analogy of social processes in computational form should be treated as "personal" rather than "scientific" knowledge. Such analogical models are useful for informing the intuition of its developers and users, but do not help the community of social simulators and social scientists to "boot-strap" reliable social knowledge. However, it is argued that both participatory modelling and evidence-based modelling can play a useful part in this process. Some kinds of simulation model are discussed with respect to their suitability for the boot-strapping of social knowledge. The knowledge that results is likely to be of a more context-specific, conditional and mundane nature than many social scientists hope for.

A Brief Survey of Some Relevant Philosophy of Science

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 14 (4) 7

Abstract: This briefly reviews some philosophy of science that might be relevant to simulating the social processes of science. It also includes a couple of examples from the sociology of science because these are inextricable from the philosophy.

Simulating the Social Processes of Science

Bruce Edmonds, Nigel Gilbert, Petra Ahrweiler and Andrea Scharnhorst
Journal of Artificial Societies and Social Simulation 14 (4) 14

Abstract: Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.

Measuring Simulation-Observation Fit: An Introduction to Ordinal Pattern Analysis

Warren Thorngate and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 16 (2) 4

Abstract: Most traditional strategies of assessing the fit between a simulation's set of predictions (outputs) and a set of relevant observations rely either on visual inspection or squared distances among averages. Here we introduce an alternative goodness-of-fit strategy, Ordinal Pattern Analysis (OPA) that will (we argue) be more appropriate for judging the goodness-of-fit of simulations in many situations. OPA is based on matches and mismatches among the ordinal properties of predictions and observations. It does not require predictions or observations to meet the requirements of interval or ratio measurement scales. In addition, OPA provides a means to assess prediction-observation fits case-by-case prior to aggregation, and to map domains of validity of competing simulations. We provide examples to illustrate how OPA can be employed to assess the ordinal fit and domains of validity of simulations of share prices, crime rates, and happiness ratings. We also provide a computer programme for assisting in the calculation of OPA indices.

A Context- and Scope-Sensitive Analysis of Narrative Data to Aid the Specification of Agent Behaviour

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 18 (1) 17

Abstract: A structure for analysing narrative data is suggested, one that distinguishes three parts in sequence: context (a heuristic to identify what knowledge is relevant given a kind of situation), scope (what is possible within that situation) and narrative elements (the detailed conditional and sequential structure of actions and events given the context and scope). This structure is first motivated and then illustrated with some simple examples taken from Sukaina Bhawani’s thesis (Bhawani 2004). It is suggested that such a structure might be helpful in preserving more of the natural meaning of such data, as well as being a good match to a context-dependent computational architecture, and thus facilitate the process of using narrative data to inform the specification of behavioural rules in an Agent-Based Simulation. This suggestion only solves part of the “Narrative Data to Agent Behaviour” puzzle – this structure needs to be combined and improved by other methods and appropriate computational architectures designed to suit it.

Different Modelling Purposes

Bruce Edmonds, Christophe Le Page, Mike Bithell, Edmund Chattoe-Brown, Volker Grimm, Ruth Meyer, Cristina Montañola-Sales, Paul Ormerod, Hilton Root and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 22 (3) 6

Abstract: How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model code is used for different purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be re-checked, re-validated and maybe even re-built in a different way. Here we review some of the different purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism

Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, Gary Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón
Journal of Artificial Societies and Social Simulation 23 (2) 7

Abstract: The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action

Flaminio Squazzoni, Gary Polhill, Bruce Edmonds, Petra Ahrweiler, Patrycja Antosz, Geeske Scholz, Émile Chappin, Melania Borit, Harko Verhagen, Francesca Giardini and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 23 (2) 10

Abstract: The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.