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2017 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Simulating Social Complexity

A Handbook

herausgegeben von: Prof. Dr. Bruce Edmonds, Dr. Ruth Meyer

Verlag: Springer International Publishing

Buchreihe : Understanding Complex Systems

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This volume examines all aspects of using agent or individual-based simulation. This approach represents systems as individual elements having their own set of differing states and internal processes. The interactions between elements in the simulation represent interactions in the target systems. What makes this "social" is that it can represent an observed society.

Social systems include all those systems where the components have individual agency but also interact with each other. This includes human societies and groups, but also increasingly socio-technical systems where the internet-based devices form the substrate for interaction. These systems are central to our lives, but are among the most complex known. This poses particular problems for those who wish to understand them. The complexity often makes analytic approaches infeasible but, on the other hand, natural language approaches are also inadequate for relating intricate cause and effect. This is why individual an

d agent-based computational approaches hold out the possibility of new and deeper understanding of such systems.

This handbook marks the maturation of this new field. It brings together summaries of the best thinking and practices in this area from leading researchers in the field and constitutes a reference point for standards against which future methodological advances can be judged.

This second edition adds new chapters on different modelling purposes and applying software engineering methods to simulation development. Revised existing content will keep the book up-to-date with recent developments. This volume will help those new to the field avoid "reinventing the wheel" each time, and give them a solid and wide grounding in the essential issues. It will also help those already in the field by providing accessible overviews of current thought. The material is divided into four sections: Introduction, Methodology, Mechanisms, and Applications

. Each chapter starts with a very brief section called ‘Why read this chapter?’ followed by an abstract, which summarizes the content of the chapter. Each chapter also ends with a section on ‘Further Reading’.

Whilst sometimes covering technical aspects, this second edition of Simulating Social Complexity is designed to be accessible to a wide range of researchers, including both those from the social sciences as well as those with a more formal background. It will be of use as a standard reference text in the field and also be suitable for graduate level courses.

Inhaltsverzeichnis

Frontmatter
Erratum to: Verifying and Validating Simulations
Nuno David, Nuno Fachada, Agostinho C. Rosa

Introduction

Frontmatter
Chapter 1. Introduction
Abstract
This introduces the themes of the book inherent in its title: Simulating Social Complexity. In a deliberate homage to the work of Herbert Simon, it traces the roots of these themes back to his work. It then explains the structure of the handbook with its different parts: introductory, methodological on different kinds of mechanism and applications. It briefly introduces each chapter within this structure.
Bruce Edmonds, Ruth Meyer
Chapter 2. Historical Introduction
Abstract
This chapter gives an overview of early attempts at modelling social processes in computer simulations. It discusses the early attempts, its successes and its shortcomings and tries to identify some of them as forerunners of modern simulation approaches.
Klaus G. Troitzsch
Chapter 3. Types of Simulation
Abstract
This looks at various ways that computer simulations can differ not in terms of their detailed mechanisms but in terms of its broader purpose, structure, ontology (what is represented), and approach to implementation. It starts with some different roles of people that may be concerned with a simulation and goes on to look at some of the different contexts within which a simulation is set (thus implying its use or purpose). It then looks at the kinds of system that might be simulated. Shifting to the modelling process, it looks at the role of the individuals within the simulations, the interactions between individuals, and the environment that they are embedded within. It then discusses the factors to consider in choosing a kind of model and some of the approaches to implementing it.
Paul Davidsson, Harko Verhagen
Chapter 4. Different Modelling Purposes
Abstract
How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model is used for different purposes, which means that a model built for one purpose but now used for another may need to be rechecked, revalidated and maybe even rebuilt in a different way. Here we review some of the different purposes for building a simulation model of complex social phenomena, focussing on five in particular: theoretical exposition, prediction, explanation, description and illustration. The chapter looks at some of the implications in terms of the ways in which the intended purpose might fail. In particular, it looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated.
Bruce Edmonds

Methodology

Frontmatter
Chapter 5. Informal Approaches to Developing Simulation Models
Abstract
This chapter describes an approach commonly taken by most people in the social sciences when developing simulation models instead of following a formal approach of specification, design and implementation. What often seems to happen in practice is that modellers start off in a phase of exploratory modelling, where they don’t have a precise conception of the model they want but a series of ideas and/or evidence they want to capture. They then may develop the model in different directions, backtracking and changing their ideas as they go. This phase continues until they think they may have a model or results that are worth telling others about. This then is (or at least should be) followed by a consolidation phase where the model is more rigorously tested and checked so that reliable and clear results can be reported. In a sense what happens in this later phase is that the model is made so that it is as if a more formal and planned approach had been taken.
There is a danger of this approach: that the modeller will be tempted by apparently significant results to rush to publication before sufficient consolidation has occurred. There may be times when the exploratory phase may result in useful and influential personal knowledge, but such knowledge is not reliable enough to be up to the more exacting standards expected of publicly presented results. Thus, it is only in combination with a careful consolidation of models that this informal approach to building simulations should be undertaken.
Emma Norling, Bruce Edmonds, Ruth Meyer
Chapter 6. What Software Engineering Has to Offer to Agent-Based Social Simulation
Abstract
In simulation projects, it is generally beneficial to have a toolset that allows following a more formal approach to system analysis, model design and model implementation. Such formal methods are developed to support a systematic approach by making different steps explicit as well as providing a precise language to express the results of those steps, documenting not just the final model but also intermediate steps. This chapter consists of two parts: the first gives an overview of which tools developed in software engineering can be and have been adapted to agent-based social simulation; the second part demonstrates with the help of an informative example how some of these tools can be combined into an overall structured approach to model development.
Peer-Olaf Siebers, Franziska Klügl
Chapter 7. Checking Simulations: Detecting and Avoiding Errors and Artefacts
Abstract
The aim of this chapter is to simulations. The reader with a set of concepts and a range of suggested activities that will enhance his or her ability to understand agent-based simulations. To do this in a structured way, we review the main concepts of the methodology (e.g. we provide precise definitions for the terms “error” and “artefact”) and establish 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 assumptions are usually made and, consequently, where different types of errors and artefacts may appear. We then propose several activities that can be conducted to detect each type of error and artefact.
José M. Galán, Luis R. Izquierdo, Segismundo S. Izquierdo, José I. Santos, Ricardo del Olmo, Adolfo López-Paredes
Chapter 8. The Importance of Ontological Structure: Why Validation by ‘Fit-to-Data’ Is Insufficient
Abstract
This chapter will briefly describe some common methods by which people make quantitative estimates of how well they expect empirical models to make predictions. However, the chapter’s main argument is that fit-to-data, the traditional yardstick for establishing confidence in models, is not quite the solid ground on which to build such belief some people think it is, especially for the kind of system agent-based modelling is usually applied to. Further, the chapter will show that the amount of data required to establish confidence in an arbitrary model by fit-to-data is often infeasible, unless there is some appropriate ‘big data’ available. This arbitrariness can be reduced by constraining the choice of model. In agent-based models, these constraints are introduced by their descriptiveness rather than by removing variables from consideration or making assumptions for the sake of simplicity. By comparing with neural networks, we show that agent-based models have a richer ontological structure. For agent-based models, in particular, this richness means that the ontological structure has a greater significance and yet is all too commonly taken for granted or assumed to be ‘common sense’. The chapter therefore also discusses some approaches to validating ontologies.
Gary Polhill, Doug Salt
Chapter 9. Verifying and Validating Simulations
Abstract
Verification and validation are two important aspects of model building. Verification and validation compare models with observations and descriptions of the problem modelled, which may include other models that have been verified and validated to some level. However, the use of simulation for modelling social complexity is very diverse. Often, verification and validation do not refer to an explicit stage in the simulation development process, but to the modelling process itself, according to good practices and in a way that grants credibility to using the simulation for a specific purpose. One cannot consider verification and validation without considering the purpose of the simulation. This chapter deals with a comprehensive outline of methodological perspectives and practical uses of verification and validation. The problem of evaluating simulations is addressed in four main topics: (1) the meaning of the terms verification and validation in the context of simulating social complexity; (2) types of validation, as well as techniques for validating simulations; (3) model replication and comparison as cornerstones of verification and validation; and (4) the relationship of various validation types and techniques with different modelling strategies.
Nuno David, Nuno Fachada, Agostinho C. Rosa
Chapter 10. Understanding Simulation Results
Abstract
Simulation modelling is concerned with the abstract representation of entities within systems and their interrelationships; understanding and visualising these results is often a significant challenge for the researcher. Within this chapter we examine particular issues such as finding important patterns and interpreting what they mean in terms of causality. We also discuss some of the problems with using model results to enhance our understanding of the underlying social systems which they represent, and we will assert that this is in large degree a problem of isolating causal mechanisms within the model architecture. In particular, we highlight the issues of equifinality and identifiability—that the same behaviour may be induced within a simulation from a variety of different model representations or parameter sets—and present recommendations for dealing with this problem. The chapter ends with a discussion of avenues of future research.
Andrew Evans, Alison Heppenstall, Mark Birkin
Chapter 11. How Many Times Should One Run a Computational Simulation?
Abstract
This chapter is an attempt to answer the question “how many runs of a computational simulation should one do,” and it gives an answer by means of statistical analysis. After defining the nature of the problem and which types of simulation are mostly affected by it, the article introduces statistical power analysis as a way to determine the appropriate number of runs. Two examples are then produced using results from an agent-based model. The reader is then guided through the application of this statistical technique and exposed to its limits and potentials.
Raffaello Seri, Davide Secchi
Chapter 12. Participatory Approaches
Abstract
This chapter aims to describe the diversity of participatory approaches in relation to social simulations, with a focus on the interactions between the tools and participants. We consider potential interactions at all stages of the modelling process: conceptual design, implementation, use and simulation outcome analysis. After reviewing and classifying existing approaches and techniques, we describe two case studies with a focus on the integration of various techniques. The first case study deals with fire hazard prevention in Southern France, and the second one with groundwater management on the atoll of Kiribati. The chapter concludes with a discussion of the advantages and limitations of participatory approaches.
Olivier Barreteau, Pieter Bots, Katherine Daniell, Michel Etienne, Pascal Perez, Cécile Barnaud, Didier Bazile, Nicolas Becu, Jean-Christophe Castella, William’s Daré, Guy Trebuil
Chapter 13. Combining Mathematical and Simulation Approaches to Understand the Dynamics of Computer Models
Abstract
This chapter shows how computer simulation and mathematical analysis can be used together to understand the dynamics of computer models. For this purpose, we show that it is useful to see the computer model as a particular implementation of a formal model in a certain programming language. This formal model is the abstract entity which is defined by the input–output relation that the computer model executes and can be seen as a function that transforms probability distributions over the set of possible inputs into probability distributions over the set of possible outputs.
It is shown here that both computer simulation and mathematical analysis are extremely useful tools to analyse this formal model, and they are certainly complementary in the sense that they can provide fundamentally different insights on the same model. Even more importantly, this chapter shows that there are plenty of synergies to be exploited by using the two techniques together.
The mathematical analysis approach to analyse formal models consists in examining the rules that define the model directly. Its aim is to deduce the logical implications of these rules for any particular instance to which they can be applied. Our analysis of mathematical techniques to study formal models is focused on the theory of Markov Chains, which is particularly useful to characterise the dynamics of computer models.
In contrast with mathematical analysis, the computer simulation approach does not look at the rules that define the formal model directly but instead tries to infer general properties of these rules by examining the outputs they produce when applied to particular instances of the input space. Thus, conclusions obtained with this approach may not be general. On a more positive note, computer simulation enables us to explore formal models beyond mathematical tractability, and we can achieve any arbitrary level of accuracy in our computational approximations by running the model sufficiently many times.
Bearing in mind the relative strengths and limitations of both approaches, this chapter explains three different ways in which mathematical analysis and computer simulation can be usefully combined to produce a better understanding of the dynamics of computer models. In doing so, it becomes clear that mathematical analysis and computer simulation should not be regarded as alternative—or even opposed—approaches to the formal study of social systems but as complementary. Not only can they provide fundamentally different insights on the same model, but they can also produce hints for solutions for each other. In short, there are plenty of synergies to be exploited by using the two techniques together, so the full potential of each technique cannot be reached unless they are used in conjunction.
Luis R. Izquierdo, Segismundo S. Izquierdo, José M. Galán, José I. Santos
Chapter 14. Interpreting and Understanding Simulations: The Philosophy of Social Simulation
Abstract
Simulations are usually directed at some version of the question: What is the relationship between the individual actor and the collective community? Among social scientists, this question generally falls under the topic of emergence. Sociological theorists and philosophers of science have developed sophisticated approaches to emergence, including the critical question: to what extent can emergent phenomena be reduced to explanations in terms of their components? Modelers often proceed without considering these issues; the risk is that one might develop a simulation that does not accurately reflect the observed empirical facts or one that implicitly sides with one side of a theoretical debate that remains unresolved. In this chapter, I provide some tips for those developing simulations, by drawing on a strong recent tradition of analyzing scientific explanation that is found primarily in the philosophy of science but also to some extent in sociology.
R. Keith Sawyer
Chapter 15. Documenting Social Simulation Models: The ODD Protocol as a Standard
Abstract
The clear documentation of simulations is important for their communication, replication, and comprehension. It is thus helpful for such documentation to follow minimum standards. The ‘overview, design concepts, and details’ document protocol (ODD) is specifically designed to guide the description of individual- and agent-based simulation models (ABMs) in journal articles. Popular among ecologists, it is also increasingly used in the social simulation community. Here, we describe the protocol and give an annotated example of its use, with a view in facilitating its wider adoption and encouraging higher standards in simulation description.
Volker Grimm, Gary Polhill, Julia Touza

Mechanisms

Frontmatter
Chapter 16. Utility, Games and Narratives
Abstract
This chapter provides a general overview of theories and tools to model decision-making. In particular, utility maximization and its application to collective decision-making, i.e. Game Theory, are discussed in detail. The most important exemplary games are presented, including the Prisoner’s Dilemma, the Game of Chicken and the Minority Game, also known as the El Farol Bar Problem. After discussing the paradoxes and pitfalls of utility maximization, an alternative approach is introduced, which is based on seeking coherence between competing interpretations. An assessment of the pros and cons of competing approaches to modelling decision-making concludes the chapter.
Guido Fioretti
Chapter 17. Social Constraint
Abstract
This chapter examines how a specific type of social constraint operates in Artificial Societies. The investigation concentrates on bottom-up behaviour regulation. Freedom of individual action selection is constraint by some kind of obligations that become operative in the individual decision-making process. This is the concept of norms. The two-way dynamics of norms is investigated in two main sections of the chapter: the effect of norms on a social macro-scale and the operation of social constraints in the individual agent. While normative modelling is becoming useful for a number of practical purposes, this chapter specifically addresses the benefits of this expanding research field to understand the dynamics of human societies. For this reason, both sections begin with an elaboration of the problem situation, derived from the empirical sciences. This enables to specify questions to agent-based modelling. Both sections then proceed with an evaluation of the state of the art in agent-based modelling. In the first case, sociology is consulted. Agent-based modelling promises an integrated view on the conception of norms in role theoretic and individualistic theories of society. A sample of existing models is examined. In the second case, socialisation research is consulted. In the process of socialisation, the obligatory force of norms becomes internalised by the individuals. A simulation of the feedback loop back into the mind of agents is only in the beginning. Research is predominantly on the level of the development of architectures. For this reason, a sample of architectures is evaluated.
Martin Neumann
Chapter 18. Reputation for Complex Societies
Abstract
Reputation, the germ of gossip, is addressed in this chapter as a distributed instrument for social order. In literature, reputation is shown to promote (a) social control in cooperative contexts—like social groups and subgroups—and (b) partner selection in competitive ones, like (e-) markets and industrial districts. Current technology that affects, employs and extends reputation, applied to electronic markets or multi-agent systems, is discussed in light of its theoretical background. In order to compare reputation systems with their original analogue, a social cognitive model of reputation is presented. The application of the model to the theoretical study of norm-abiding behaviour and partner selection are discussed, as well as the refinement and improvement of current reputation technology. The chapter concludes with remarks and ideas for future research.
Francesca Giardini, Rosaria Conte, Mario Paolucci
Chapter 19. Social Networks and Spatial Distribution
Abstract
In most agent-based social simulation models, the issue of the organisation of the agents’ population matters. The topology, in which agents interact, be it spatially structured or a social network, can have important impacts on the obtained results in social simulation. Unfortunately, the necessary data about the target system is often lacking; therefore, you have to use models in order to reproduce realistic spatial distributions of the population and/or realistic social networks among the agents. In this chapter, we identify the main issues concerning this point and describe several models of social networks or of spatial distribution that can be integrated in agent-based simulation to go a step forwards from the use of a purely random model. In each case, we identify several output measures that allow quantifying their impacts.
Frédéric Amblard, Walter Quattrociocchi
Chapter 20. Learning
Abstract
Learning and evolution are adaptive or “backward-looking” models of social and biological systems. Learning changes the probability distribution of traits within an individual through direct and vicarious reinforcement, while evolution changes the probability distribution of traits within a population through reproduction and selection. Compared to forward-looking models of rational calculation that identify equilibrium outcomes, adaptive models pose fewer cognitive requirements and reveal both equilibrium and out-of-equilibrium dynamics. However, they are also less general than analytical models and require relatively stable environments. In this chapter, we review the conceptual and practical foundations of several approaches to models of learning that offer powerful tools for modeling social processes. These include the Bush-Mosteller stochastic learning model, the Roth-Erev matching model, feed-forward and attractor neural networks, and belief learning. Evolutionary approaches include replicator dynamics and genetic algorithms. A unifying theme is showing how complex patterns can arise from relatively simple adaptive rules.
Michael W. Macy, Steve Benard, Andreas Flache
Chapter 21. Evolutionary Mechanisms
Abstract
After an introduction, the abstract idea of evolution is analysed into four processes which are illustrated with respect to a simple evolutionary game. A brief history of evolutionary ideas in the social sciences is given, illustrating the different ways in which the idea of evolution has been used. The technique of Genetic Algorithms (GA) is then described and discussed including the representation of the problem and the composition of the initial population, the Fitness Function, the reproduction process, the Genetic Operators, issues of convergence and some generalisations of the approach including endogenising the evolutionary process. Genetic Programming (GP) and Classifier Systems (CS) are also briefly introduced as potential developments of GA. Four detailed examples of social science applications of evolutionary techniques are then presented: the use of GA in the Arifovic “cobweb” model, using CS in a model of price setting developed by Moss, the role of GP in understanding decision-making processes in a stock market model and relating evolutionary ideas to social science in a model of survival for “strict” churches. The chapter concludes with a discussion of the prospects and difficulties of using the idea of biological evolution in the social sciences.
Edmund Chattoe-Brown, Bruce Edmonds

Applications

Frontmatter
Chapter 22. Agent-Based Modelling and Simulation Applied to Environmental Management
Abstract
The purpose of this chapter is to summarize how agent-based modelling and simulation (ABMS) is being used in the area of environmental management. With the science of complex systems now being widely recognized as an appropriate one to tackle the main issues of ecological management, ABMS is emerging as one of the most promising approaches. To avoid any confusion and disbelief about the actual usefulness of ABMS, the objectives of the modelling process have to be unambiguously made explicit. It is still quite common to consider ABMS as mostly useful to deliver recommendations to a lone decision-maker, yet a variety of different purposes have progressively emerged, from gaining understanding through raising awareness, facilitating communication, promoting coordination or mitigating conflicts. Whatever the goal, the description of an agent-based model remains challenging. Some standard protocols have been recently proposed, but still a comprehensive description requires a lot of space, often too much for the maximum length of a paper authorized by a scientific journal. To account for the diversity and the swelling of ABMS in the field of ecological management, a review of recent publications based on a lightened descriptive framework is proposed. The objective of the descriptions is not to allow the replication of the models but rather to characterize the types of spatial representation, the properties of the agents and the features of the scenarios that have been explored and also to mention which simulation platforms were used to implement them (if any). This chapter concludes with a discussion of recurrent questions and stimulating challenges currently faced by ABMS for environmental management.
Christophe Le Page, Didier Bazile, Nicolas Becu, Pierre Bommel, François Bousquet, Michel Etienne, Raphael Mathevet, Véronique Souchère, Guy Trébuil, Jacques Weber
Chapter 23. Distributed Computer Systems
Abstract
Ideas derived from social simulation models can directly inform the design of distributed computer systems. This is particularly the case when systems are “open”, in the sense of having no centralised control, where traditional design approaches struggle. In this chapter, we indicate the key features of social simulation work that are valuable for distributed systems design. We also discuss the differences between social and biological models in this respect. We give examples of socially inspired systems from the currently active area of peer-to-peer systems, and finally we discuss open areas for future research in the field.
David Hales
Chapter 24. Simulating Complexity of Animal Social Behaviour
Abstract
Complex social phenomena occur not only among humans, but also throughout the animal kingdom, from bacteria and amoebae to non-human primates. At a lower complexity they concern phenomena such as the formation of groups and their coordination (during travelling, foraging, and nest choice) and at a higher complexity they deal with individuals that develop individual differences that affect the social structure of a group (such as its dominance hierarchy, dominance style, social relationships and task division). In this chapter, we survey models that give insight into the way in which such complex social phenomena may originate by self-organisation in groups of beetle larvae, in colonies of ants and bumblebees, in groups of fish, and groups of primates. We confine ourselves to simulations and models within the framework of complexity science. These models show that the interactions of an individual with others and with its environment lead to patterns at a group level that are emergent and are not coded in the individual (genetically or physiologically), such as the oblong shape of a fish school, variable shape in bird flocks, specific swarming pattern in ants, the centrality of dominants in primates, patterns of exchange and of ‘reconciliation’ and the task division among bumble bees. The hypotheses provided by these models appear to be more parsimonious than usual in the number of adaptive traits and the degree of cognitive sophistication involved. With regard to the usefulness of these simulations, we discuss for each model what kind of insight it provides, whether it is biologically relevant, and if so, whether it is specific to the species and environment and to what extent it delivers testable hypotheses.
Charlotte Hemelrijk
Chapter 25. Agent-Based Simulation as a Useful Tool for the Study of Markets
Abstract
This chapter describes a number of agent-based market models. They can be seen as belonging to different trends in that different types of markets are presented (goods markets, with or without stocks, or financial markets with diverse price mechanisms or even markets with or without money), but they also represent different aims that can be achieved with the simulation tool. For example, it is possible to develop precise interaction processes to include loyalty among actors; try to mimic as well as possible the behaviour of real humans, which have been recorded in experiments; or try to integrate psychological data to show a diffusion process. All these market models share a deep interest in what is fundamental in agent-based simulation, such as the role of interaction, interindividual influence and learning, which induces a change in the representation that agents have of their environment.
Juliette Rouchier
Chapter 26. Movement of People and Goods
Abstract
Due to the continuous growth of traffic and transportation and thus an increased urgency to analyze resource usage and system behavior, the use of computer simulation within this area has become more frequent and acceptable. This chapter presents an overview of modeling and simulation of traffic and transport systems and focuses in particular on the imitation of social behavior and individual decision-making in these systems. We distinguish between transport and traffic. Transport is an activity where goods or people are moved between points A and B, while traffic is referred to as the collection of several transports in a common network such as a road network. We investigate to what extent and how the social characteristics of the users of these different traffic and transport systems are reflected in the simulation models and software. Moreover, we highlight some trends and current issues within this field and provide further reading advice.
Linda Ramstedt, Johanna Törnquist Krasemann, Paul Davidsson
Chapter 27. Modeling Power and Authority: An Emergentist View from Afghanistan
Abstract
The aim of this chapter is to provide a critical overview of state-of-the-art models that deal with power and authority and to present an alternative research design. The chapter is motivated by the fact that research on power and authority is confined by a general lack of statistical data. However, the literal complexity of structures and mechanisms of power and authority requires a formalized and dynamic approach of analysis if more than a narrative understanding of the object of investigation is sought. It is demonstrated that evidence-driven and agent-based social simulation (EDABSS) can contend with the inclusion of qualitative data and the effects of social complexity at the same time. A model on Afghan power structures exemplifying this approach is introduced and discussed in detail from the data collection process and the creation of a higher order intuitive model to the derivation of the agent rules and the model’s computational implementation. EDABSS not only deals in a very direct way with social reality but also produces complex artificial representations of this reality. Explicit sociocultural and epistemological couching of an EDABSS model is therefore essential and treated as well.
Armando Geller, Scott Moss
Chapter 28. Human Societies: Understanding Observed Social Phenomena
Abstract
The chapter begins by briefly describing two contrasting simulations: the iconic system dynamics model publicised under the Limits to Growth book and a detailed model of first millennium Native American societies in the southwest of the United States. These are used to bring out the issues of abstraction, replicability, model comprehensibility, understanding vs. prediction and the extent to which simulations go beyond what is observed. All of these issues are rooted in some fundamental difficulties in the project of simulating observed societies that are then briefly discussed. Both issues and difficulties result in three “dimensions” in which simulation approaches differ. The core of the chapter is a look at 15 different possible simulation goals, both abstract and concrete, giving some examples of each and discussing them. The different inputs and results from such simulations are briefly discussed as to their importance for simulating human societies.
Bruce Edmonds, Pablo Lucas, Juliette Rouchier, Richard Taylor
Chapter 29. Some Pitfalls to Beware When Applying Models to Issues of Policy Relevance
Abstract
This chapter looks at some of the ways things can go wrong when mathematical or computational models are applied to inform policy on important issues. It looks at some of the pitfalls in the model construction and development phase, including choosing assumptions, the effect of ‘theoretical spectacles’, oversimplified models, not understanding model limitations, and not testing a model enough. It then goes on to discuss the pitfalls that can occur when a model is applied to inform policy, including entrenched policies based on models with little or no evidential support and how models can narrow the evidential base considered. It also looks at confusions concerning model purpose and kinds of question they may answer, when models are used out of context, asking unreasonable things of models, when the uncertainties are too great, when models give a false sense of security, and when the focus should be on values rather than facts. This discussion is then illustrated with two examples, one economic and one from fisheries. It concludes that most of these problems stem from the interface between the modelling and policy worlds. It ends with some simple recommendations to reduce these mistakes.
Lia ní Aodha, Bruce Edmonds
Backmatter
Metadaten
Titel
Simulating Social Complexity
herausgegeben von
Prof. Dr. Bruce Edmonds
Dr. Ruth Meyer
Copyright-Jahr
2017
Electronic ISBN
978-3-319-66948-9
Print ISBN
978-3-319-66947-2
DOI
https://doi.org/10.1007/978-3-319-66948-9

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