Networks are an abstraction of complex social processes. Albeit themselves formal, the social processes on which they are based can be researched using both quantitative and qualitative methods. The problem in combining these approaches comes from the very different natures and levels on which they are based. Here we describe an approach which uses agent-based modelling (ABM) as a stepping stone towards the more abstract network models. These ABMs are more in the nature of complex and dynamic descriptions than general theories, and are ideally suited for integrating a variety of kinds of evidence into a coherent fashion - including quatitative evidence to inform the micro-level behaviours of agents, and quantitative evidence about the macro, aggregate levels. The assumptions behind these kinds of ABM are relatively transparent, and the ABMs used to generate networks in a precise manner. Thus this "staging" of the abstraction process allows a well-founded mixed-methods approach to social network research. A worked example of this on voting behaviour is presented.
The Evolution of e-Research: Machines, Methods and MusicDavid De Roure
David De Roure's Inaugural Lecture on 28th October at Oxford e-Research Centre, University of Oxford, UK
10 years ago we saw a few early adopters of e-Science technology; now we see acceleration of research through broader adoption and sharing of tools, techniques and artefacts, both for 'big science' and the 'long tail scientist'.
Will this incremental trend continue or are we seeing glimpses of a phase change ahead, where researchers harness these emerging digital capabilities to address research questions in ways that simply were not possible before?
This talk will describe three generations of e-Research, using the myExperiment social website as a lens to glimpse future research practice, and focusing on a web-scale computational musicology project as an illustration of 3rd generation thinking.
Also available from http://wiki.myexperiment.org/index.php/Presentations
This presentation is about how to best craft messages out of research. It highlights the importance of effective messages in the research informing policy process.
Manilla, Philippines
17-18 June, 2013
The Evolution of e-Research: Machines, Methods and MusicDavid De Roure
David De Roure's Inaugural Lecture on 28th October at Oxford e-Research Centre, University of Oxford, UK
10 years ago we saw a few early adopters of e-Science technology; now we see acceleration of research through broader adoption and sharing of tools, techniques and artefacts, both for 'big science' and the 'long tail scientist'.
Will this incremental trend continue or are we seeing glimpses of a phase change ahead, where researchers harness these emerging digital capabilities to address research questions in ways that simply were not possible before?
This talk will describe three generations of e-Research, using the myExperiment social website as a lens to glimpse future research practice, and focusing on a web-scale computational musicology project as an illustration of 3rd generation thinking.
Also available from http://wiki.myexperiment.org/index.php/Presentations
This presentation is about how to best craft messages out of research. It highlights the importance of effective messages in the research informing policy process.
Manilla, Philippines
17-18 June, 2013
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
What is e-research?
Enhancing research practice
e-Research Methods, Strategies, and Issues
Tips For Finding Useful Information
Some Search Tools for doing e-research
Research Design
Quantitative Research
Qualitative Research
Ethics & The e-Researcher
How The Net Complicates Ethics?
Privacy, Confidentiality, Autonomy, And The Respect For Persons
Tips For Ethical e-Research
Collaboration Tools
Why Consensus?
Net-based dissemination of E-research results
Dissemination through peer-reviewed articles
Advantages of a peer-reviewed article
Dissemination through email lists or Usenet groups
Dissemination through a virtual conference
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
Recommender systems are knowledge-based systems which support human decision-making. In an era of overwhelming choice, they help us decide which
products, services and information to consume. The focus of attention in recommender systems research and development has been on making recommendations to individual consumers. These places focus on the easier case, but ignore the fact that it is as common, if not more common, for us to consume items in groups such as couples, families and parties of friends. The choice of a date movie, a family holiday destination, or a restaurant for a celebration meal all require the balancing of the preferences of multiple consumers
Analysing a Complex Agent-Based Model Using Data-Mining TechniquesBruce Edmonds
A talk given at "Social Simulation 2014" at Barcelona in September.
A complex “Data Integration Model” of voter behaviour is described. However it is very complex and hard to analyse. For such a model “thin” samples of the outcomes using classic parameter sweeps are inadequate. In order to get a more holistic picture of its behaviour data- mining techniques are applied to the data generated by many runs of the model, each with randomised parameter values.
Paper is at: http://cfpm.org/aacabm/analysing a complex model-v3.4.pdf
A talk given to the "Social.Path" workshop at the University of Surrey, June 2014.
It is well established that many human abilities are context-dependent, including: language, preference judgement, memory, reasoning, learning and perception. This is usually taken as a negative – that there will be limits on our understanding and modelling of these abilities. However, what is not always appreciated is that context-dependency can be a powerful tool in social coordination and communication. This paper pulls together several theories about the cognition of context, and presents a computational model of context-dependency. It then sketches its role in social communication, coordination and embedding. It looks at some of the approaches to dealing with context in the computer science and social science literature and concludes that none of these squarely faces the problem of context dependency. This points towards a substantial gap in the research and hence a future programme.
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
What is e-research?
Enhancing research practice
e-Research Methods, Strategies, and Issues
Tips For Finding Useful Information
Some Search Tools for doing e-research
Research Design
Quantitative Research
Qualitative Research
Ethics & The e-Researcher
How The Net Complicates Ethics?
Privacy, Confidentiality, Autonomy, And The Respect For Persons
Tips For Ethical e-Research
Collaboration Tools
Why Consensus?
Net-based dissemination of E-research results
Dissemination through peer-reviewed articles
Advantages of a peer-reviewed article
Dissemination through email lists or Usenet groups
Dissemination through a virtual conference
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
Recommender systems are knowledge-based systems which support human decision-making. In an era of overwhelming choice, they help us decide which
products, services and information to consume. The focus of attention in recommender systems research and development has been on making recommendations to individual consumers. These places focus on the easier case, but ignore the fact that it is as common, if not more common, for us to consume items in groups such as couples, families and parties of friends. The choice of a date movie, a family holiday destination, or a restaurant for a celebration meal all require the balancing of the preferences of multiple consumers
Analysing a Complex Agent-Based Model Using Data-Mining TechniquesBruce Edmonds
A talk given at "Social Simulation 2014" at Barcelona in September.
A complex “Data Integration Model” of voter behaviour is described. However it is very complex and hard to analyse. For such a model “thin” samples of the outcomes using classic parameter sweeps are inadequate. In order to get a more holistic picture of its behaviour data- mining techniques are applied to the data generated by many runs of the model, each with randomised parameter values.
Paper is at: http://cfpm.org/aacabm/analysing a complex model-v3.4.pdf
A talk given to the "Social.Path" workshop at the University of Surrey, June 2014.
It is well established that many human abilities are context-dependent, including: language, preference judgement, memory, reasoning, learning and perception. This is usually taken as a negative – that there will be limits on our understanding and modelling of these abilities. However, what is not always appreciated is that context-dependency can be a powerful tool in social coordination and communication. This paper pulls together several theories about the cognition of context, and presents a computational model of context-dependency. It then sketches its role in social communication, coordination and embedding. It looks at some of the approaches to dealing with context in the computer science and social science literature and concludes that none of these squarely faces the problem of context dependency. This points towards a substantial gap in the research and hence a future programme.
How can we rely upon Social Network Measures? Agent-base modelling as the nex...Bruce Edmonds
All social network analysis of observed systems rely on assumptions, for example: how a link is defined is the right one, how the resulting network is analysed actually corresponds with our conclusions about it, etc. In other words the representation+analysis is a *model* of what we observe. Any model is fallible and thus needs independent validation, but this is rarely done in social network analysis due to the cost. Indeed, the only check is often that of face validity by the same person who collected the data and analysed it!
This lack of established validity is somewhat hidden by the divide within the field of social networks between the "formalists" who prove abstract properties of networks and those who apply its techniques to observed cases (who I will call "practioners"). The formalists might propose SN measures and prove their properties, but do not say anything about their applicability to any observed system. The practioners often proceed as if the measures will "work" on their networks - e.g. that a measure of centrality will tend to highlight the most influential actors.
However, agent-based models (ABM) might offer a potential solution to this. If a measure (or other SN technique) does not work with a plausible ABM of the phenomena (where we can actually check this), then we certainly can not rely on it for a similar model of observed phenomena. Some results and examples of this are given. Rather, it might be that SNA might be more reliable as a secondary analysis -- a model of a complex ABM of observed phenomena.
Winter is coming! – how to survive the coming critical storm and demonstrate ...Bruce Edmonds
A talk at the 2014 European Social Simulation Association summer school, at UAB in Barcelona 8th sept 2014
The talk covers some of the symptoms of hype in social simulation and argues that it needs to be more careful and rigourous. In particular that the (current) purpose of a simulation needs to be distinguished between theoretical, explanatory or predictive. Each having their own critieria.
Towards Institutional System Farming
A talk at the Lorentz Workshop on "Emerging Institutions: Design or Evolution?" September 2016, Leiden, NL (https://www.lorentzcenter.nl/lc/web/2016/836/info.php3?wsid=836&venue=Oort)
The slides from a class on the relationship of formal modelling and their use, with particular focus on different purposes for modelling and how they can go wrong.
Possibilistic prediction and risk analyses
A talk given at the EA annual Conference, Bonn, May 2015
Abstract:
It is in the nature of complex systems that predictions that give a probability are not possible.
Indeed I argue that giving "the most likely" or "rough" prediction is more harmful than useful.
Rather an approach which maps out some of the possible outcomes is outlined.
Agent-based modelling is ideal for producing these - including, crucially, possibilities that could not have been conceived just by thinking about it (due to the fact that events can combine in ways that are more complex than the human brain can cope with directly).
A characterisation of the real future possibilities and their nature allows some positive responses to events:
* putting in place 'early warning indicators' for the emergence of identified possibilities
* contingency planning for when they are indicated.
Such an approach would allow policy makers to better 'drive' their decision making, without abnegating responsibility to experts.
Policy Making using Modelling in a Complex worldBruce Edmonds
A talk given at the CECAN workshop, London July 2016
Abstract:
The consequences of complexity in the real world are discussed together with some meaningful ways of understanding and managing such situations. The implications of such complexity are that many social systems are fundamentally unpredictable by nature, especially when in the presence of structural change (transitions). This implies consequences for the way we model, but also for the way models are used in the policy process.
I discuss the problems arising from a too narrow focus on quantification in managing complex systems, in particular those of optimisation. I criticise some of the approaches that ignore these difficulties and pretend to approximately forecast using the impact of policy options using over-simple models. However, lack of predictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from "Complexity Science" can help with such management. Managing complex systems requires a good understanding of the dynamics of the system in question - to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent based simulation will be discussed as a tool that is suitable for this task, especially in conjunction with model-informed data visualisation.
Slides that describe a modelling framework to represent the process of making things. Presented at the Feb 2016 project meeting of the Digital DIY project..
Risk-aware policy evaluation using agent-based simulationBruce Edmonds
A talk about how modelling of complex issues of policy relevance. It covers some of the tensions and difficulties, as well as some of the unrealistic expectations of this kind of modelling. Rather it is suggested these kinds of model should be used as a kind of risk-analysis. Two examples of this are given.
Talk given in Reykjavik at University of Iceland, 30th Nov 2016.
Staged Models for Interdisciplinary ResearchBruce Edmonds
A talk give n at CosyDy, Leeds 12th May 2016.
The papers can be read at: http://arxiv.org/abs/1604.00903 (this work, soon in PLoSOne) and http://arxiv.org/abs/1508.04024 (the further simplification step, soon in EPJ-B)
The models are at: http://openabm.org/model/4368 and http://openabm.org/model/4686
An invited talk given at the Institute for Research into Superdiversity (IRIS), University of Brimingham, 31st Jan 2017
Abstract:
A simulation to illustrate how the complex patterns of cultural and genetic signals might combine to define what we mean by "groups" of people is presented. In this model both (a) how each individual might define their "in group" and (b) how each individual behaves to others in 'in' or 'out' groups can evolve over time. Thus groups are not something that is precisely defined but is something that emerges in the simulation. The point is to illustrate the power of simulation techniques to explore such processes in a non-prescriptive way that takes the micro-macro distinction seriously and represents them within complex simulations. In the particular simulation presented, groups defined by culture strongly emerge as dominant and ethnically defined groups only occur when they are also culturally defined.
Social complexity and coupled Socio-Ecological SystemsBruce Edmonds
Talk at the Stockholm workshop on "Analyzing the dynamics of social-ecological systems: Towards a typology of social-ecological interactions", SES-LINK project meeting - Stockholm, June 5-6, 2014.
A Model of Social and Cognitive CoherenceBruce Edmonds
An inbvited talk at the Workshop on Coherence -Based Approaches to Decision-Making, Cognition and Communication, Berlin July 2016
Human cognition can be usefully understood as a primarily social set of abilities - its survival benefit is from our ability to social organise and hence inhabit a variety of niches. From this point of view any ability makes more sense when put into a social context. This includes our innate ability to judge candidate beliefs in terms of their coherency with our existing beliefs and goals. However studying cognition in its social context implies high complexity, for this reason I describe an agent-based model of coherency based belief within a dynamic network of individuals. Here beliefs might be copied (or discarded) by an individual based upon the change in coherence it causes with its other beliefs, but also that an individual will change their social connections based upon the the coherence of their beliefs with those they socially interact with.
Culture trumps ethnicity! – Intra-generational cultural evolution and ethnoce...Bruce Edmonds
Ethnocentrism denotes behaviour and beliefs that are positive towards those who share the same ethnicity and negative towards others. Recent artificial society models have been interpreted as demonstrating how ethnocentrism can evolve under minimal assumptions. In these, evolution is modelled over generations of agents where new agents are born inheriting the ethnicity, behaviours and location of their parents. Behaviour does not change within generations but over many generations and agents only interact with their neighbours. We present a model that considers short-term cultural adaption, where agents may interact with any in a population and do not die or give birth but imitate and innovate their behaviours. While agents retain a fixed ethnicity they have the ability to form and join cultural groups and to change how they define their in-group based on both ethnic and cultural markers (or tags). We find that over a range of parameters cultural identity rather than ethnocentrism becomes the dominant way that agents identify their in-group producing high levels of positive interaction both within and between ethnicities. However, in some circumstances, cultural markers of group preference are supplemented by ethnic markers. In other words, whilst pure ethnocentrism (based only on ethnic identity only) is not sustained, groups that discriminate in terms of a combination of cultural and ethnic identities do occur. In these less common cases, high levels of ethnocentric behaviours evolve and persist – even though the ethnic markers are arbitrary and fixed – but they only emerge when combined with culture centric behaviour. Furthermore, cooperative ethnocentric groups do not emerge in the absence of cultural processes. The latter suggests the hypothesis that observed ethnocentrism in observed societies need not be the result of long-term historical processes based upon ethnic markers but could be more dependent upon short run cultural ones. We discuss these results as well as the danger of over interpretation of artificial society models.
Linking Heterogeneous Scholarly Data Sources in an Interoperable Setting: the...Platforma Otwartej Nauki
“Open Research Data: Implications for Science and Society”, Warsaw, Poland, May 28–29, 2015, conference organized by the Open Science Platform — an initiative of the Interdisciplinary Centre for Mathematical and Computational Modelling at the University of Warsaw. pon.edu.pl @OpenSciPlatform #ORD2015
Framework for opinion as a service on review data of customer using semantics...IJECEIAES
At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
Difference Between Qualitative and Quantitative Research.docxzekfeker
Literature search tools Zekarias Tilaye
Hints:
These tools help researchers to find and collect relevant scholarly literature, such as
academic journals, books, and conference proceedings. Some examples of literature
search tools include Google Scholar, PubMed, and Scopus.
Therefore, please provide us with clear information on this topic.
Using Data Integration Modelsfor Understanding Complex Social SystemsBruce Edmonds
Describing the use of complex, descriptive simulations to integrate the maximum amount of evidence in a staged manner. With an example from the SCID project (http://www.scid-project.org).
This is a North Central University PowerPoint presentation (EDR 8204-3). It is written in APA format, has been graded by an instructor(A), and includes references. Most education communities submit assignments to turnitin, so remember to paraphrase.
Tools and techniques in qualitative and quantitative researchDeepikakohli10
The presentation is about different Tools and techniques used for Research. It will help students, teachers, researchers and teacher educators to select appropriate tools and techniques for their research purpose.
Similar to Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis (20)
Staging Model Abstraction – an example about political participationBruce Edmonds
A presentation at the workshop on ABM and Theory (From Cases to General Principles), Hannover, July 2019
This reports on work where we started with a complex, but evidence driven model, and then modelled that model sto understand and abstract from it. As reported in the paper:
Lafuerza LF, Dyson L, Edmonds B, McKane AJ (2016) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261
Some supporting slides on modelling purposes and pitfalls when using ABM in policy contexts to accompany discussion on Modelling Pitfalls at the ESSA Summer School, Aberdeen, June 2019
A talk at the workshop on "Agent-Based Models in Philosophy: Prospects and Limitations", Rurh University, Bochum, Germany
Abstract:
ABMs (like other kinds of model) can be used in a purely abstract way, as a kind of thought experiment - a way of thinking about some aspect of the world that is too complicated to hold in our mind (in all its detail). In this way it both informs and complements discursive thought. However there is another set of uses for ABMs - empirical uses - where the mapping between the model and sets of observation-derived data are crucial. For these uses, one has to (a) use the mapping to get from some data to the model (b) use the model for some inference and (c) use the mapping again back to data. This includes both predictive and explanatory uses of ABMs. These are easily distinguishable from abstact uses becuase there is a fixed and well-defined relationship between the model and the data, this is not flexible on a case by case basis. In these cases the reliability comes from the composite (a)-(b)-(c) mapping, so that simplifying step (b) can be counterproductive if that means weakening steps (a) and (c) because it is the strength of the overall chain that is important. Taking the use of models in quantum mechanics as an example, one can see that sometimes the evolution of the formal models driven by empirical adequacy can be more important than the attendent abstract models used to get a feel for what is happening. Although using ABM's for empirical purposes is more challenging than for purely abstract purposes, they are being increasingly used for empirical explanation rather than thought experiments, and there is no reason to suppose that robust empirical adequacy is unachievable.
Mixing fat data, simulation and policy - what could possibly go wrong?Bruce Edmonds
A talk given at the CECAN workshop on "What Good Data could do for Evaluation" at the Alan Turing Institute, 25th Feb. 2019.
Abstract:
In complex situations (which includes most where humans are involved) it is infeasible to predict the impact of any particular policy (or even what is probable). Randomised Control Trials do not tell one: what kinds of situation a policy might work in, what are enablers and inhibitors of the effectiveness of a policy. Here I suggest that using 'fat' data and simulation might allow a possibilistic analysis of policy impact - namely an exploration of what could go surprisingly wrong (or indeed right). Whilst this does not allow the optimisation of policy, it does inform the effective monitoring of policy, and basic contingency planning. However, this requires a different approach to policy - from planning and optimisation to an adaptive approach, with richer continual monitoring and a readiness to tune or adapt policy as data comes in. Examples of this are given concerning domestic water consumption (in the main talk), and in supplementary slides: voter turnout and fishing.
Social Context
An invited talk at the 2018 Surrey Sociology Conference, Barnett Hill, Surrey, November 2018.
Although there is much evidence that context is crucial to much human cognition and social behaviour, it remains a difficult area to research. In much social science research it is either by-passed or ignored. In some qualitative research context is almost deified with any level of generalisation across contexts being left to the reader. At the other extreme, some qualitative research restricts itself to patterns that are generally detectable - that is the patterns that are left when one aggregates over many different contexts. Context is often used as a 'dustbin concept' to which otherwise unexplained variation is attributed.
This talk looks at some of the ways social context might be actively represented, understood and researched. Firstly the ideas of cognitive then social context are distinguished. Then some possible approaches to researching this are discussed, including: agent-based simulation, a context-sensitive analysis of narrative data and machine learning.
Using agent-based simulation for socio-ecological uncertainty analysisBruce Edmonds
A talk given in the MMU Big Data Centrem, 30th October 2018.
Both social and ecological systems can be highly complex, but the interaction between these two worlds - a socio-ecological system (SES) - can add even greater levels. However, the maintenance of SES are vital to our well being and the health of the planet. We do not know how such systems work in practice and we lack good data about them (especially the ecological side) so predicting the effect of any particular policy is infeasible. Here we present an approach which tries to understand some of the ways in which SES may go wrong, but constructing different complex simulation models and analysing the emergent outcomes. These, in silico, examples can allow for the institution of targeted data gathering instruments that give the earliest possible warning of deleterious outcomes, and thus allow for timely remedial responses. An example of this approach applied to fisheries is described.
How social simulation could help social science deal with contextBruce Edmonds
An invited plenary at Social Simluation 2018, Stockholm.
This points out how context-sensitivity is fundamental to much human social behaviour, but largely bypassed or ignored in social science. I more formal social science, it is usual to assume or fit universal models, even if this covers a lot of different contexts. In qualitative social science context is almost deified, and any generalisation across contexts is passed on to those that learn from it. Agent-based modelling allows for context-sensitive models to be developed and hence the role of context explored and better understood. The talk discussed a framework for analysing narrative text using the Context-Scope-Narrative-Elements (CSNE) framework. It also illustrates a cognitive model that allows for context-dependent knowledge to be implemented wthin an agent in a simulation. The talk ends with a plea to avoid uncecessary or premature summarisation (using averages etc.).
Agent-based modelling,laboratory experiments,and observation in the wildBruce Edmonds
An invited talk at the workshop on "Social complexity and laboratory experiments – testing assumptions and predictions of social simulation models with experiments" at Social Simulation 2018, Stockholm
Culture trumps ethnicity!– Intra-generational cultural evolution and ethnoce...Bruce Edmonds
Essential to understanding the impact of in-group bias on society is the micro-macro link and the complex dynamics involved. Agent-based modelling (ABM) is the only technique that can formally represent this and thus allow for the more rigorous exploration of possi-ble processes and their comparison with observed social phenomena. This talk discusses these issues, providing some examples of some relevant ABMs.
A talk given at the BIGSSS summer school on conflict, Bremen, Jul/Aug 2018.
An Introduction to Agent-Based ModellingBruce Edmonds
An introduction to the technique with two example models of in-group bias and voter turnout.
An invited talk at the BIGSSS Summer Schools in Computational Social Science, at the Jacobs Bremen University, July 2018.
Mixing ABM and policy...what could possibly go wrong?Bruce Edmonds
Invited talk at 19th International Workshop on Multi-Agent Based Simulation at Stockholm on 14th July 2018.
Mixing ABM and Policy ... what could possibly go wrong?
This talk looks at a number of ways in which using ABM in the context of influencing policy can go wrong: during model construction, with model application and other.
It is related to the book chapter:
Aodha, L. and Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity - a handbook, 2nd edition. Springer, 801-822.
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
Models are a tool, not a picture of reality. There are many different uses for models. The intended use of a model - its 'purpose' - affects how it is judged, checked and developed. Much confusion and bad practice in modelling can be attributed to not clearly identifying the intended 'purpose' for a model. Neo-classical Economics is used to illustrate some of these confusions. In some (but not all) uses the model stands in for a theory (at least key aspects of it), but this can happen in different ways and at different levels of abstraction. The talk looks at some of these different ways and advocates a staged, inductive methodology for theory development instead of one that jumps to high generality and simple models which confuse different uses.
A talk given at the Workshop on "From Cases To General Principles - Theory Development Through Agent-Based Modeling" see http://abm-theory.org
Socio-Ecological Simulation - a risk-assessment approachBruce Edmonds
An invited talk in Tromsoe, 5 June 2018.
Both social and ecological systems are complex, but when they combine (as when human societies farm/hunt) there is a double complexity. This complexity means it is infeasible to predict the outcome of their interaction and unwise to rely on any prediction. An alternative approach is to use complex simulations to try and discover some possible ways that such systems can go wrong. This can reveal risks that other approaches might miss, due to the fact that more of the complexity is included within the model. Once a risk is identified then measures to monitor its emergence can be implemented, allowing the earliest possible warning of this. An example of this approach applied to a fisheries ecosystem is described.
A talk at the workshop on "Thinking toys (or games) for commoning, Basel, 5/6 April, Switzerland.
This describes a simple model of anonymous donation of resources, with minimal group structuring.
Am open-access paper on this model is at: http://cfpm.org/discussionpapers/152
The model can be freely downloaded from:
http://openABM.org/model/4744
A talk at ESSA@Work, TUHH (Technical University of Hamburg), 24th Nov 2017.
Abstract: Simulation models can only be justified with respect to the models purpose or aim. The talk looks at six common purposes for modelling: prediction, explanation, analogy, theoretical exposition, description, and illustration. Each of these is briefly described, with an example and an brief analysis of the risks to achieving these, and hence how they should be demonstrated. The importance of being explicitly clear about the model purpose is repeatedly emphasised.
The Post-Truth Drift in Social SimulationBruce Edmonds
A talk at the Social Simulation Conference, Dublin, September 2017.
Abstract
The paper identifies a danger in the field of social simulation a danger of using weasel words to give a false impression to the world about the achievements of our field. Whether this is intentional or unintentional, the effect might be to damage the reputation of the field and impair its development. At the root of this is a need for brutal honesty and openness, something that can be personally difficult and that needs social support. The paper considers some of the subtle ways that this kind of post-truth drift might occur, including: confusion/conflation of modelling purpose, wishing to justify pragmatic limitations in our work, falling back to unvalidated theory, confusing using a model for a way of looking at the world for something more reliable, and seeking protection from critique in vagueness. It calls on social simulation researchers to firmly reject such a drift.
Drilling down below opinions: how co-evolving beliefs and social structure mi...Bruce Edmonds
A talk at ODCD2017, Jocob's University, Bremen, July 2017. (http://odcd2017.user.jacobs-university.de/)
The talk looks at an alternative to "linear" models which deal with a euclidean space of opinions (usually a 1D space). This is a model of belief change, where both social influence and internal consistency of beliefs co-evolve with social structure. Thus this goes beyond most opinion dynamics models in a number of ways: (a) it deals with beliefs that may underlie measured opinions (b) the internal coherency among sets of beliefs is important as well as social influence (c) the social structure co-evolves with belief change and (d) the social structures are complex and continually dynamic. The internal consistency of beliefs is based on Thagard's theory of explanatory coherence, which has some empirical support. The model seems to display some of the tensions and processes that are observed in politics, for example: the tension between moderating views so as to connect with the public vs. reinforcing the in-group coherency. It displays a dynamic that can reflect a number of different courses including those that result turning points in opinions.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis
1. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 1
Using Agent-based Simulation to Integrate
Micro/Qualitative Evidence, Macro-
Quantitative Data and Network Analysis
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
Slides available at: http://slideshare.net/BruceEdmonds
2. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 2
The SCID Project
The Social Complexity of Immigration and Diversity is a 5-year project with
the Institute for Social Change and the Department of Theoretical Physics at
University of Manchester. It is funded under the “Complexity Science for the
Real World” initiative of the EPSRC and will last until August 2015. Staff
involved are: Nick Crossley, Louise Dyson, Bruce Edmonds, Ed
Fieldhouse, Alan McKane, Ruth Meyer, Luis Fernandez Lafuerza, Laurence
Lessard-Phillips, Yaojun Li, Nick Shryane, Gennaro Di Tosto, and Huw
Vasey.
The project is applying the techniques and tools of complexity science to
real world issues: (1) why people bother to vote and how social influence
within/across communities affects this (2) how the impoverished networks of
immigrants may limit effective job search and (3) inter-community trust.
Project Website:
http://scid-project.org/
3. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 3
Example problems in mixed-methods
(including some SNA) research
• It is often quite ad hoc, and hence hard to repeat
• It can be difficult to tell if qualitative and quantitative
elements are consistent with each other
• Models in mixed-methods research can have
elements whose meaning is not completely clear
• If models from mixed-methods research do not work it
can be difficult to tell what part of it might be wrong
• Validation can be very weak – it can sometimes not
be clear if the model was, in fact, successful/useful
• It is not always clear when it is helpful to use one
method/tool on the results from another method/tool
4. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 4
Some Guiding Principles
Unlike some areas of qualitative and quantitative
science, mixed methods has not been formalised.
So here are some principles I use to guide my practice:
• In science one should not ignore evidence without a
very, very, very good reason.
– including available qualitative and quantitative evidence
• As far as possible, in any model the reference of its
elements should be as clear as possible
– what parts of a model mean should not be fudged/vague
• The more drastic/heroic the abstraction, the more the
resulting model needs validating
• Modelling choices/steps should be as transparent and
replicable as possible – including reasons for choices
5. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 5
Staging Abstraction
Data-Integration Simulation Model
Micro-Evidence Macro-Data
Abstract Simulation
Model 1
Abstract Simulation
Model 2
SNA Model Analytic Model
IncreasingAbstraction
6. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 6
Data Integration Models
• Are a particular style of agent-based simulation
• You may be aware of some simple, abstract
simulation models that purport to be a theory…
• …this is at the opposite end of the spectrum.
• Intended more as a computational description of a
particular case than a (generalistic) theory
• Aims to represent as much of the relevant evidence
as possible in one coherent and dynamic simulation
• Provides a precise target for abstraction (which are
then checkable against it)
• Thus it separates representation and abstraction
7. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 7
Agent-Based Simulation
• Is a kind of computer simulation…
• …where individual social actors and their interactions are
separately represented (agents)
• The heterogeneity of actors is represented, different:
characteristics, behaviours and contexts
• What happens is not centrally determined, but rather
emerges from the interactions of the agents
• Both “top-down” constraint and “bottom-up” emergence
can occur simultaneously in models
Representations of OutcomesSpecification (incl. rules)
8. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 8
Aims and Objectives of DIM
• To develop a simulation that integrates as much
as possible of the relevant available evidence,
both qualitative and statistical
(a Data-Integration Model – a DIM)
• Regardless of how complex this makes it
• A description of a specified kind of situation (not a
general theory) that represents the evidence in a
single, consistent and dynamic simulation
• This simulation is then a fixed and formal target
for later analysis and abstraction
9. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 9
Using Qualitative Behaviour to Inform
the Agent Specification
• Narrative data (from semi-structured interviews,
observations etc.) can be used to inform the
behavioural rules of agents within these simulations
• This can be done in an informal or semi-formal
manner (e.g. using techniques extended from GT)
• This can provide a broader “menu” of possible
behaviours and strategies that are used and thus
import some of the “messiness” of social reality
instead of overly neat formulations (e.g. economic)
• Meso-level outcomes can be fed back using
participatory techniques to aid validation
• Macro-level measures can also be extracted and
compared to known quantitative data
10. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 10
The “54” Causal Stories
• Reviewing the literature we extracted different “causal
stories” impacting on whether people vote
• Examples:
– Out of a feeling of civic duty
– Due to sheer habit, “its what I have always done”
– Interest in politics due to discussions within household,
partner and friends
– Due to participation in higher education
– Evaluation of past efficacy of voting
– Member of household taking them with them to vote
• Some of these confirmed via a small qualitative
survey
• These provided the skeleton for the “menu” of
behaviours that were programed into the agents
11. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 11
Overall Structure of Model
Underlying data about
population composition
Demographics of people in
households
Social network formation and
maintenance (homophily)
Influence via social networks
• Political discussions
Voting Behaviour
Input
Output
12. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 12
Discuss-politics-with person-23 blue expert=false
neighbour-network year=10 month=3
Lots-family-discussions year=10 month=2
Etc.
Memory
Level-of-Political-Interest
Age
Ethnicity
Class
Activities
AHousehold
An Agent’s Memory of Events
Etc.
Changing personal
networks over which
social influence occurs
Composed of households of
individuals initialised from
detailed survey data
Each agent has a rich variety of
individual (heterogeneous)
characteristics
Including a (fallible) memory of
events and influences
13. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 13
Example Output: why do people vote (if
they do)
Intervention: voter
mobilisation
Effect: on civic
duty norms Effect: on habit-
based behaviour
14. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 14
Example Output: Simulated Social Network
at 1950
Established
immigrants: Irish,
WWII Polish etc.
Majority: longstanding
ethnicities
Newer
immigrants
15. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 15
Example Output: Simulated Social Network
at 2010
16. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 16
Example Output: Psuedo-Narrative
Output
Following a single, randomly chosen agent…
4: (person 578)(aged 5) started at (school 1)
17: (person 578)(aged 18) stops going to (school 1)
21: (person 578)(aged 22) moved from (patch 11 3)
to (patch 12 2) due to moving to an empty home
21: (person 578)(aged 22) partners with (person
326) at (patch 12 2)
24: (person 578)(aged 25) started at (workplace 8)
24: (person 578)(aged 25) voted for the blue party
29: (person 578)(aged 30) voted for the blue party
17. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 17
Retaining Maximally Clear Reference
Data-Integration Simulation Model
Micro-Evidence Macro-Data
Abstract Simulation
Model 1
Abstract Simulation
Model 2
SNA Model Analytic Model
18. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 18
Context-Dependency
• In the simulation (as in our social life) decisions,
adaption, communication, learning all take place
within a local context
• Both “upwards” (emergent) and “downwards” (social
control) forces operate within local contexts allowing
social embeddedness
• Abstraction to aggregates (e.g. averages) only takes
place post-hoc (just as in social statistics)
• The DIM allowed the formal representation of context-
dependent behaviour, albeit within a more specific
“descriptive” simulation, that can be itself hard to
understand
• Thus opening the way to the study of context itself!
19. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 19
Fixing “Weaknesses” of SN Models
In much social network research:
• The definition of links is often unclear and/or
inconsistent
• The machinery of social network models do not
explain changing networks
• Validation of social network models is often weak
• Network measures are often used as if it is known
that they give reliable indicators (e.g. centrality)
• How to apply narrative data is not clear
However, all of these are at least partially fixable as
an abstraction of a well-founded simulation model
20. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 20
Conclusions
• Complex agent-based models are good vehicles for
integrating different kinds of data
• In particular qualitative data can very usefully inform
the “menu” of micro-level behaviours, importing some
of the “mess” of social reality
• Data Integration Models can provide consistent
pictures including dynamics, albeit complicated
• Staging abstraction into more gentle steps can help
retain meaning reference in the modelling
• Network models are useful, but with other very
abstract models, higher up the abstraction “chain”
with the qual/quat integration occuring “lower down”
• Sometimes macro-level phenomena needs to be
explained from micro-level detail and embedding
21. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 21
The End!
Bruce Edmonds:
http://bruce.edmonds.name
Centre for Policy Modelling:
http://cfpm.org
The SCID Project:
http://www.scid-project.org
Slides available at: http://slideshare.net/BruceEdmonds