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.
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.
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)
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
Gigamap example by Manuela Aguirre: https://www.slideshare.net/ManuelaAguirre/policy-support-full-presentation
In this presentation you will learn about design tools and techniques to solve wicked problems, using Systems Thinking.
Systems Thinking looks at the whole of a system rather than focusing on its individual parts, to better understand complex phenomena. Systems Thinking contrasts with analytic thinking: you solve problems by going deeper, by looking at the greater whole of a system and the relations between its elements, rather than solving individual problems in a linear way via simple cause and effect explanations.
You can apply Systems Thinking principles in different situations: to understand how large organisations function and design for the enterprise (e.g. when you are trying to revamp a large intranet), but also to solve social problems and issues (e.g. unemployment with disadvantaged youth or mobility in larger cities). So basically whenever there is complexity and conflict (of interest) in your project, Systems Thinking will be helpful.
After an introduction to Systems Thinking and its core concepts, we will first explain and practice a few techniques that you as a designer can apply to better understand complex systems, for example creating a System Map and drawing Connection Circles. In the second part of the workshop, we will introduce techniques that help you shape solutions, for example using Paradoxical Thinking for ideation and writing ‘What-if’ Scenarios.
Presented at EuroIA 2015 with Koen Peters.
A talk at the ESSA Silico Summer School in Wageningen, June 2017. It looks at some of the different purposes for a simulation model, and how complicated one should make one's model
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
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.
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)
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
Gigamap example by Manuela Aguirre: https://www.slideshare.net/ManuelaAguirre/policy-support-full-presentation
In this presentation you will learn about design tools and techniques to solve wicked problems, using Systems Thinking.
Systems Thinking looks at the whole of a system rather than focusing on its individual parts, to better understand complex phenomena. Systems Thinking contrasts with analytic thinking: you solve problems by going deeper, by looking at the greater whole of a system and the relations between its elements, rather than solving individual problems in a linear way via simple cause and effect explanations.
You can apply Systems Thinking principles in different situations: to understand how large organisations function and design for the enterprise (e.g. when you are trying to revamp a large intranet), but also to solve social problems and issues (e.g. unemployment with disadvantaged youth or mobility in larger cities). So basically whenever there is complexity and conflict (of interest) in your project, Systems Thinking will be helpful.
After an introduction to Systems Thinking and its core concepts, we will first explain and practice a few techniques that you as a designer can apply to better understand complex systems, for example creating a System Map and drawing Connection Circles. In the second part of the workshop, we will introduce techniques that help you shape solutions, for example using Paradoxical Thinking for ideation and writing ‘What-if’ Scenarios.
Presented at EuroIA 2015 with Koen Peters.
A talk at the ESSA Silico Summer School in Wageningen, June 2017. It looks at some of the different purposes for a simulation model, and how complicated one should make one's model
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
The talk I gave at the 2015 IxDA Education Summit about using systems thinking and emergence as a lens to integrate systems thinking/emergence, distributed cognition, Christopher Alexander's pattern languages, scenarios, and lean processes.
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
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.
This presentation provides an introduction to system dynamics.
Peter S. Hovmand, PhD, MSW
Founding Director, Social System Design Lab
Brown School of Social Work
Washington University in St. Louis
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
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
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
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
Introduction to the "Business Dynamics (Sterman)" - Chapter 1
---
Lecture notes for the "MB-411 Introdução a Dinâmica de Sistemas" of the Instituto Tecnológico de Aeronáutica - ITA.
Part of the Soft systems methodology, Rich Pictures provide a mechanism for learning about complex or ill-defined problems by drawing detailed ("rich") representations of them.
This short introductory webinar explains the basic graphic elements that are normally used in RPs and highlights their value as an action learning process.
A system is a network of interdependent components that work together to try to accomplish the aim of the system. A system must have an aim. Without an aim, there is no system. The aim of the system must be clear to everyone in the system.
But what does it all mean really and how does it apply to our businesses? What does it take to have a systems thinking or holistic view and approach?
In this presentation, we'll take a look at systems thinking, how we can get into this mindset and how it is used in the real world. With some interactive exercises, historical and present examples we hope this session will leave you with an understanding of systems thinking and its many benefits.
Extending the Gillespie's Stochastic Simulation Algorithm for Integrating Dis...Danilo Pianini
Whereas Multi-Agent Based Simulation (MABS) is emerging as a reference approach for complex system simulation, the event-driven approach of Discrete-Event Simulation (DES) is the most used approach in the simulation mainstream.
In this talk, we elaborate on two intuitions: 1) event-based systems and multi-agent systems are amenable of a coherent interpretation within a unique conceptual framework; 2) integrating MABS and DES can lead to a more expressive and powerful simulation framework.
Accordingly, we propose a computational model integrating DES and MABS, based on an extension of the Gillespie's stochastic simulation algorithm.
Then, we discuss a case of a simulation platform (Alchemist) specifically targeted at such a kind of complex models, and show an example of urban crowd steering simulation.
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 talk I gave at the 2015 IxDA Education Summit about using systems thinking and emergence as a lens to integrate systems thinking/emergence, distributed cognition, Christopher Alexander's pattern languages, scenarios, and lean processes.
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
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.
This presentation provides an introduction to system dynamics.
Peter S. Hovmand, PhD, MSW
Founding Director, Social System Design Lab
Brown School of Social Work
Washington University in St. Louis
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
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
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
it provide you information about public policy, its elements , policy cycle and its importance it also provide you information about problem solving process..These 8 lectures provide you the complete knowledge about public policy analysis.
Introduction to the "Business Dynamics (Sterman)" - Chapter 1
---
Lecture notes for the "MB-411 Introdução a Dinâmica de Sistemas" of the Instituto Tecnológico de Aeronáutica - ITA.
Part of the Soft systems methodology, Rich Pictures provide a mechanism for learning about complex or ill-defined problems by drawing detailed ("rich") representations of them.
This short introductory webinar explains the basic graphic elements that are normally used in RPs and highlights their value as an action learning process.
A system is a network of interdependent components that work together to try to accomplish the aim of the system. A system must have an aim. Without an aim, there is no system. The aim of the system must be clear to everyone in the system.
But what does it all mean really and how does it apply to our businesses? What does it take to have a systems thinking or holistic view and approach?
In this presentation, we'll take a look at systems thinking, how we can get into this mindset and how it is used in the real world. With some interactive exercises, historical and present examples we hope this session will leave you with an understanding of systems thinking and its many benefits.
Extending the Gillespie's Stochastic Simulation Algorithm for Integrating Dis...Danilo Pianini
Whereas Multi-Agent Based Simulation (MABS) is emerging as a reference approach for complex system simulation, the event-driven approach of Discrete-Event Simulation (DES) is the most used approach in the simulation mainstream.
In this talk, we elaborate on two intuitions: 1) event-based systems and multi-agent systems are amenable of a coherent interpretation within a unique conceptual framework; 2) integrating MABS and DES can lead to a more expressive and powerful simulation framework.
Accordingly, we propose a computational model integrating DES and MABS, based on an extension of the Gillespie's stochastic simulation algorithm.
Then, we discuss a case of a simulation platform (Alchemist) specifically targeted at such a kind of complex models, and show an example of urban crowd steering simulation.
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.
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
Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-q...Bruce Edmonds
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.
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.
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.
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.
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..
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
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.
"A 30min Introduction to Agent-Based Modelling" for GORSBruce Edmonds
Introduction to Agent-Based Modelling by Bruce Edmonds
Centre for Policy Modelling, Manchester Metropolitan University
For a workshop at the "Government Operational Research Service"
Many aspects of modern society are highly complex, in the sense that they are not easy to understand without taking into account the detailed interactions between the social actors that comprise it. For such cases mathematical and system dynamics models are insufficient to be useful for understanding what is going on or forecasting what might occur. Statistical models are useful when the detail of the interactions can be treated as noise, and such models can make useful projections into the future, but it is not clear when the assumptions behind such projections will fail (and hence the projections being wrong - qualitatively as well as the error rate).
Agent-based modelling (ABM) is a relatively new technique that has the potential to play a part in this. In this technique social actors (people, departments, firms, households etc.) are individually represented as separate entities within the simulation and the interactions between the actors are represented as separate interactions within the simulation. The entities in the simulation that correspond to the social actors are called "agents". Sets of "rules" determine the micro-level behaviour of each agent. Each agent may have different characteristics and, indeed, different rules. One then "runs" the simulation where (effectively in parallel) all the agents obey their rules and a complex web of interactions between them result. This "mess" of detail can then be abstracted/graphed/measured in various ways (very similar to the techniques we use to understand what is happening in society itself) to give us macro-level statistics and visualisations which can be compared with existing aggregate statistics.
Two examples of such ABM are presented: (1) a very simple one that illustrates how the detailed interactions of individuals can affect the global outcomes, and (2) a more complex descriptive model that illustrates some of the social complexity that such models can represent.
Unsurprisingly the increased expressive power of ABM comes with downsides, including: (a) such models require a lot of data in order to be able to validate them and (b) the models are so complex that it can be difficult to understand the model itself. As a result of these difficulties, ABMs should not be considered to give probabilistic forecasts, but rather possibilistic - that is, they can produce some of the possibilities that are inherent in the system, but not (reliably) the probabilities of each (nor indeed will they be able to produce all of the real possibilities). In the context of policy making this is particularly relevant to risk-analyses of policies, where one wants to know some of the possible ways a policy might go wrong. This will allow one to design and implement "early warning" systems ...
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.
This SMART Seminar was presented on June 28, 2012.
Abstract: Socio-technical systems comprise both individuals and groups of people (the social side), and information and processes (the technological side. Examples of socio-technical systems include logistics, customs, and management at an airport, time and task management of an office worker, and optimal usage of an enterprise computer network.
We study one instance of a process within such a complex system: the progress of containers through customs. This process is more often an exercise in negotiation rather than a structured queuing system. Once regulatory processes involves negotiation, corruption becomes a factor. Studies by the OECD and other organizations reveal that customs corruption is not easily combated by policy changes.
We suggest that simulation of potential reform policies in the maritime customs context can provide insights for decision makers. In this talk we describe work in progress towards a simulation calibrated on processes at the Port of Beirut, and argue for the applicability of agent-based modelling in the domain. This is joint work with P. Attie, R. Outa, and F. J. Srour.
Bio: Neil Yorke-Smith is an Assistant Professor of Business Information and Decision Systems at the Suliman S. Olayan School of Business, American University of Beirut, and a Research Scientist at SRI International, USA. His research focuses on technologies that assist human decision making, with interests including intelligent agents, simulation and serious games, preference modelling, constraint-based reasoning, machine learning and data mining, and their real world applications.
Publications and further information are available at: http://www.aub.edu.lb/~nysmith
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.
http://home.ubalt.edu/ntsbarsh/business-stat/opre/partIX.htm
Tools for Decision Analysis: Analysis of Risky Decisions
If you will begin with certainties, you shall end in doubts, but if you will content to begin with doubts, you shall end in almost certainties. -- Francis Bacon
Making decisions is certainly the most important task of a manager and it is often a very difficult one. This site offers a decision making procedure for solving complex problems step by step.It presents the decision-analysis process for both public and private decision-making, using different decision criteria, different types of information, and information of varying quality. It describes the elements in the analysis of decision alternatives and choices, as well as the goals and objectives that guide decision-making. The key issues related to a decision-maker's preferences regarding alternatives, criteria for choice, and choice modes, together with the risk assessment tools are also presented.
Professor Hossein Arsham
MENU
1. Introduction & Summary
2. Probabilistic Modeling: From Data to a Decisive Knowledge
3. Decision Analysis: Making Justifiable, Defensible Decisions
4. Elements of Decision Analysis Models
5. Decision Making Under Pure Uncertainty: Materials are presented in the context of Financial Portfolio Selections.
6. Limitations of Decision Making under Pure Uncertainty
7. Coping with Uncertainties
8. Decision Making Under Risk: Presentation is in the context of Financial Portfolio Selections under risk.
9. Making a Better Decision by Buying Reliable Information: Applications are drawn from Marketing a New Product.
10. Decision Tree and Influence Diagram
11. Why Managers Seek the Advice From Consulting Firms
12. Revising Your Expectation and its Risk
13. Determination of the Decision-Maker's Utility
14. Utility Function Representations with Applications
15. A Classification of Decision Maker's Relative Attitudes Toward Risk and Its Impact
16. The Discovery and Management of Losses
17. Risk: The Four Letters Word
18. Decision's Factors-Prioritization & Stability Analysis
19. Optimal Decision Making Process
20. JavaScript E-labs Learning Objects
21. A Critical Panoramic View of Classical Decision Analysis
22. Exercise Your Knowledge to Enhance What You Have Learned (PDF)
23. Appendex: A Collection of Keywords and Phrases
Companion Sites:
· Business Statistics
· Success Science
· Leadership Decision Making
· Linear Programming (LP) and Goal-Seeking Strategy
· Linear Optimization Software to Download
· Artificial-variable Free LP
Solution
Algorithms
· Integer Optimization and the Network Models
· Tools for LP Modeling Validation
· The Classical Simplex Method
· Zero-Sum Games with Applications
· Computer-assisted Learning Concepts and Techniques
· Linear Algebra and LP Connections
· From Linear to Nonlinear Optimization with Business Applications
· Construction of the Sensitivity Region for LP Models
· Zero Sagas in Four Dimensions
· Systems Simulation
· B.
Chapters 4,5 and 6Into policymaking and modeling in a comple.docxtiffanyd4
Chapters 4,5 and 6
Into policymaking and modeling in a complex world
From Building a model to adaptive robust decision- making using systems modelling
Features and added value of simulation Models using different modelling approaches supporting policymaking: A comparative analysis.
Chapter Goals and Objectives Overall – students will learn and understand
consequences of complexity in the real-world, and meaningful ways to understand and manage such situations
the implications of complexity and that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions)
natural tendency to criticize the approaches that ignore difficulties and pretend to predict using simplistic models
that managing a complex system 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.
4. Policymaking and modeling in a complex world
the word “complexity” can be used to indicate a variety of kinds of difficulties
identification of complexity and uncertainty in policy-making
in very simple physical systems, interactions may give rise to complex behavior, expressed in different types of behavior, ranging from very stable to chaotic
reasons why complex adaptive systems have a strong capacity to self-organize
two of the ways systems are oversimplified: quantification and compartmentalization
models are assessed by their ability to predict/mirror observed aspects of the environments
5. From building a model to adaptive robust decision-making using systems modeling
System Dynamics Modeling and Simulation of Old
✓ methods for modeling and simulating dynamically complex systems
✓ evolutions in modeling and simulation with recent explosive growth in computational power, data, social media, to support decision-making
Recent Innovations and Expected Evolutions
✓ Why often seemingly more revolutionary—innovations have been introduced and demonstrated, but that they have not been massively adopted yet
Current and Expected Evolutions
✓ Three current evolutions expected to further reinforce - “experiential art” to “computational science.”
Future State of Practice of Systems Modeling and Simulation
✓ modeling and simulation with sparse data to modeling and simulation with (near real-time) big data;
✓ simulating and analyzing a few simulation runs to simulating and simultaneously analyzing well-selected ensembles of runs;
✓ using models for intuitive policy testing to using models as instruments for designing adaptive robust robust policies;
✓ developing educational flight simulators to fully integrated decision support.
Features and added value of simulation models using different modelling approaches to policy-making: A Comparative analysis
Foundations of Simulation Modelling
✓ model simplification definitions—smaller, less detailed, le.
Chapters 4,5 and 6Into policymaking and modeling in a comple.docxmccormicknadine86
Chapters 4,5 and 6
Into policymaking and modeling in a complex world
From Building a model to adaptive robust decision- making using systems modelling
Features and added value of simulation Models using different modelling approaches supporting policymaking: A comparative analysis.
Chapter Goals and Objectives Overall – students will learn and understand
consequences of complexity in the real-world, and meaningful ways to understand and manage such situations
the implications of complexity and that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions)
natural tendency to criticize the approaches that ignore difficulties and pretend to predict using simplistic models
that managing a complex system 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.
4. Policymaking and modeling in a complex world
the word “complexity” can be used to indicate a variety of kinds of difficulties
identification of complexity and uncertainty in policy-making
in very simple physical systems, interactions may give rise to complex behavior, expressed in different types of behavior, ranging from very stable to chaotic
reasons why complex adaptive systems have a strong capacity to self-organize
two of the ways systems are oversimplified: quantification and compartmentalization
models are assessed by their ability to predict/mirror observed aspects of the environments
5. From building a model to adaptive robust decision-making using systems modeling
System Dynamics Modeling and Simulation of Old
✓ methods for modeling and simulating dynamically complex systems
✓ evolutions in modeling and simulation with recent explosive growth in computational power, data, social media, to support decision-making
Recent Innovations and Expected Evolutions
✓ Why often seemingly more revolutionary—innovations have been introduced and demonstrated, but that they have not been massively adopted yet
Current and Expected Evolutions
✓ Three current evolutions expected to further reinforce - “experiential art” to “computational science.”
Future State of Practice of Systems Modeling and Simulation
✓ modeling and simulation with sparse data to modeling and simulation with (near real-time) big data;
✓ simulating and analyzing a few simulation runs to simulating and simultaneously analyzing well-selected ensembles of runs;
✓ using models for intuitive policy testing to using models as instruments for designing adaptive robust robust policies;
✓ developing educational flight simulators to fully integrated decision support.
Features and added value of simulation models using different modelling approaches to policy-making: A Comparative analysis
Foundations of Simulation Modelling
✓ model simplification definitions—smaller, less detailed, le ...
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docxfelicidaddinwoodie
1
PPA 670
Public Policy Analysis
Basic Policy Terms and Concepts
Essential Definitions
POLICY
Lasswell & Kaplan: Policy is a projected program
of goals, values and practices.
Thomas Dye: Whatever government chooses to do
or not do.
Charles Jones: Functional analytic category -- a
course of action rather than specific decisions.
Essential Definitions
PUBLIC POLICY
Definition: "Purposive action by actors
acting in public institutions to produce
direction in government.
Key terms:
Purposive action
Acting in public institutions
Direction in government
2
Approaches to Policy Analysis
The Eight-fold path:
1. Define the problem
2. Assemble some evidence
3. Construct the alternatives
4. Select the criteria
5. Project the outcomes
6. Confront the trade-offs
7. Decide
8. Tell your story
The Process Model I.D.
Recognize
Structure
Agendize
Prob. State.
Alt. Det.
Alt. TestDecision
Implement
Monitor
Evaluate
Verification
Feedback
Iteration
Public Policy Environment
Key Institutions:
Chief Executive
Bureaucracy
Legislature
Courts
Interest Groups & Lobbyists
Media
3
Public Policy Environment
Key Individuals:
President, governors, city managers,
mayors
Senior policy-makers (eg.
Department heads)
Key legislators (eg. Speaker)
Key lobbyists (eg. Nader)
Media stars
Public Policy Environment
Key feature: Policy is a product of public
institutions, must be legitimized.
Policy without legitimization is just rhetoric.
Ethical Issues:
Roles of ideology and
objectivity of key individuals.
Recognizing and serving the “Public
Good”
4
Why PUBLIC Policy?
Political reasons
Moral or Ethical reasons
Economic and Market Failures
PROBLEM RECOGNITION
Key to the start of the analytical
process. If the wrong problem is
identified, the quality of analysis
is moot.
Public policy problems arise as a
result of change or pressure for
change.
I.D.
Recognize
Structure
Agendize
Prob. State.
Alt. Det.
Alt. TestDecision
Implement
Monitor
Evaluate
Problem
Recognition
Phase
5
Recognizing Public Policy
Problems
Public vs. private problems
Policy vs. management problems
Solvable vs. Unsolvable problems
If we fail to properly recognize a public
policy issue, we cannot hope to solve it:
G.I.G.O.
Problem Recognition Criteria
Asking proper questions is critical to
establishing the correct criteria:
Four key questions:
Where did the problem come from?
How do you know about the problem?
What are the dimensions of the problem?
Who is involved and why?
Where did the problem come from?
What is the history?
Have we seen this problem before?
What do we know now?
What do we need to know to solve the problem?
6
How do you know about the problem?
How did this problem come to public
awareness?
What are your sources of information?
Facts
Opinions
Primary and secondary data
Who do you trust? ...
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.
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'aMahmoud Bahgat
#Mahmoud_Bahgat
#Marketing_Club
Join us by WhatsApp to me 00966568654916
*اشترك في صفحة ال Marketing Club* عالفيسبوك
https://www.facebook.com/MarketingTipsPAGE/
*اشترك في جروب ال Marketing Club* عالفيسبوك
https://www.facebook.com/groups/837318003074869/
*Marketing Club Middle East*
25 Meetings in 6 Cities in 1 year & 2 months
Since October 2015
*We have 6 groups whatsapp*
*for almost 600 marketers*
From all middle east
*since 5 years*
& now 10 more groups
For Marketing Club Lovers as future Marketers
أهم حاجة الشروط
*Only marketers*
From all Industries
No students
*No sales*
*No hotels Reps*
*No restaurants Reps*
*No Travel Agents*
*No Advertising Agencies*
*Many have asked to Attend the Club*
((We Wish All can Attend,But Cant..))
*Criteria of Marketing Club Members*
•••••••••••••••••••••••••••••••••••••
For Better Harmony & Mind set.
*Must be only Marketer*
*Also Previous Marketing experience*
●Business Managers
●Country Manager,GM
●Directors, CEO
Are most welcomed to add Value to us.
■■■■■■■■■■■■■■■■
《 *Unmatched Criteria*》
Not Med Rep,
Not Key Account,
Not Product Specialist,
Not Sales Supervisor,
Not Sales Manager,
●●●●●●●●●●●●●●●●●●
But till you become a marketer
you can join other What'sApp group
*Marketing Lover Future Club Group*
■■■■■■■■■■■■■■■■
《 *Unmatched Criteria*》
For Conflict of Intrest
*Also Can't attend*
If Working in
*Marketing Services Provider*
=not *Hotel* Marketers
=not *Restaurant* Marketers
=not *Advertising* Marketer
=not *Event Manager*
=not *Market Researcher*.
■■■■■■■■■■■■■■■■
■■■■■■■■■■■■■■■■
*this Club for Only Marketers*
Very Soon we will have
*Business Leaders Club*
For Sales Managers & Directors
Will be Not for Markters
●●●●●●●●●●●●●●●●●●●●
■ *Only Marketers* ■
*& EPS Marketing Diploma*
●●●●●●●●●●●●●●●●●●●●
Confirm coming by Pvt WhatsApp
*To know the new Location*
*#Mahmoud_Bahgat*
00966568654916
*#Marketing_Club*
http://goo.gl/forms/RfskGzDslP
*اشترك بصفحة جمعية الصيادلة المصريين* عالفيسبوك
https://lnkd.in/fucnv_5
■ *Bahgat Facbook Page*
https://lnkd.in/fVAdubA
■ *Bahgat Linkedin*
https://lnkd.in/fvDQXuG
■ *Bahgat Twitter*
https://lnkd.in/fmNC72T
■ *Bahgat YouTube Channel*
https://www.Youtube.com /mahmoud bahgat
■ *Bahgat Instagram*
https://lnkd.in/fmWPXrY
■ *Bahgat SnapChat*
https://lnkd.in/f6GR-mR
*#Mahmoud_Bahgat*
*#Legendary_ADLAND*
www.TheLegendary.info
OR is defined as a scientific approach to optimal decision making through modelling of
deterministic and probabilistic systems that originate from real life.
Scientific approach: LPP, PERT/CPM, Queueing model, NLP, DP,MILP, Game
theory, heuristic programming.
Deterministic system: - a system which gives the same result for a particular set of
input, no matter how many times we recalculate it
Computational Modelling of Public PolicyReflections on Prac.docxmccormicknadine86
Computational Modelling of Public Policy:
Reflections on Practice
Nigel Gilbert1, Petra Ahrweiler2, Pete Barbrook-Johnson1, Kavin
Preethi Narasimhan1, Helen Wilkinson3
1Department of Sociology, University of Surrey Guildford, GU2 7XH United Kingdom
2Institute of Sociology, Johannes Gutenberg University Mainz, Jakob-Welder-Weg 20, 55128 Mainz, Germany
3Risk
Solution
s, Dallam Court, Dallam Lane, Warrington, Cheshire, WA2 7LT, United Kingdom
Correspondence should be addressed to [email protected]
Journal of Artificial Societies and Social Simulation 21(1) 14, 2018
Doi: 10.18564/jasss.3669 Url: http://jasss.soc.surrey.ac.uk/21/1/14.html
Received: 11-01-2018 Accepted: 11-01-2018 Published: 31-01-2018
Abstract: Computational models are increasingly being used to assist in developing, implementing and evalu-
ating public policy. This paper reports on the experience of the authors in designing and using computational
models of public policy (‘policy models’, for short). The paper considers the role of computational models in
policy making, and some of the challenges that need to be overcome if policy models are to make an e�ec-
tive contribution. It suggests that policy models can have an important place in the policy process because
they could allow policy makers to experiment in a virtual world, and have many advantages compared with
randomised control trials and policy pilots. The paper then summarises some general lessons that can be ex-
tracted from the authors’ experiencewith policymodelling. These general lessons include the observation that
o�en themain benefit of designing andusing amodel is that it provides anunderstanding of the policy domain,
rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate
level of abstraction; that although appropriate data for calibration and validation may sometimes be in short
supply, modelling is o�en still valuable; that modelling collaboratively and involving a range of stakeholders
from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention
needs to be paid to e�ective communication betweenmodellers and stakeholders; and thatmodelling for pub-
lic policy involves ethical issues that need careful consideration. The paper concludes that policy modelling
will continue to grow in importance as a component of public policy making processes, but if its potential is to
be fully realised, there will need to be amelding of the cultures of computationalmodelling and policymaking.
Keywords: Policy Modelling, Policy Evaluation, Policy Appraisal, Modelling Guidelines, Collaboration, Ethics
Introduction
1.1 Computationalmodels have been used to assist in developing, implementing and evaluating public policies for
at least three decades, but their potential remains to be fully exploited (Johnston & Desouza 2015; Anzola et al.
2017; Barbrook-Johnson et al. 2017). In this paper, using a selection of examples of ...
DAT 520 Final Project Guidelines and Rubric Overview .docxsimonithomas47935
DAT 520 Final Project Guidelines and Rubric
Overview
You must complete a decision analysis research project as your final project for this course. Your research project will focus on a real-world topic of your choice,
as approved by your instructor. You will pick a topic from the list provided or with approval from your instructor, and create a data analysis plan and decision
tree model based on a real-world scenario. This assessment will provide you with the opportunity to employ highly valued decision support skills and concepts
for data within a real-world context. You can use the Final Project Notes document, found in the Assignment Guidelines and Rubrics section of the course.
The project is divided into three milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final
submissions. These milestones will be submitted in Modules Two, Five, and Seven. The final submission will occur in Module Nine.
This project will address the following course outcomes:
Appraise data in context according to industry-standard methods and techniques for its utility in supporting decision making
Determine suitable data manipulation and modeling methods for decision support
Articulate data frameworks for organizational decision support by applying data manipulation, modeling, and management concepts
Evaluate the ethical issues surrounding organizational use of decision-oriented data based on industry standards and one’s personal ethical criteria
Create and assess the agility of solutions through application of data-mining procedures for decision support in various industries
Prompt
Your decision analysis model and report should answer the following prompt: How does your model and evaluation resolve uncertainty in making a decision? In
order to produce your analytic report, you will need to choose and investigate a data set using the decision analysis techniques you learned in class. Then you
will formulate a research question, write an analytic plan, and implement it. Your report should not solely consist of descriptions of what you did. It should also
contain detailed explorations into the meaning behind your model and the implications of its results. You will also be testing your model’s fitness and evaluating
its strengths and weaknesses.
The project in a nutshell:
1. Choose a data set (get ideas from the source list in the spreadsheet Final Project Topics and Sources.xls)
2. Formulate your decision analysis research question
3. Write an analytic plan
4. Perform the top-down or bottom-up modeling
5. Perform model diagnostics
6. Evaluate
These activities are broken up into milestones so that the work is spread throughout the term and you can get early assistance with any obstacles.
A decision analysis report is similar to any other analytic report. These reports introduce a problem, state a line of inquiry, explain a model th.
Modelling: What’s next for Financial Services in Europe?GRATeam
This paper outlines a practical roadmap to realising cost savings, delivering a material reduction in the volume and complexity of models by outlining five key principles of model optimisation: develop a comprehensive review of models, harmonise methodologies, re-design model validation/monitoring process, re-think its modelling team’s organisation & governance and build new expertise and recruit talent.
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.
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.
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.
Modelling Innovation – some options from probabilistic to radicalBruce Edmonds
A talk on the various kinds of innovation based on Margret Boden's types of creativity . Given at the European Academy, Ahrweiler, Germany 31st May 2017.
Co-developing beliefs and social influence networksBruce Edmonds
Argues that many social phenomena needs ABM models with both cognitive and social change co-developing
Presented at the AISB workshop in Bath, April 2017 on "The power of Immergence...". See last slide for details of where to get the paper and the model
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Risk-aware policy evaluation using agent-based simulation
1. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 1
Risk-aware policy evaluation using
agent-based simulation
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
2. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 2
Simple systems…
… may be complicated but behave in predictable
ways, allowing them to be represented by models...
• where one can use them to numerically forecast
• where uncertainty can be analytically estimated
• where one can get rough estimates cheaply, and
better estimates with increasing investment
• which one can sensibly plan and execute
systematically
• where there is a basically one right way of doing it
• so that one can fully understand the model
3. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 3
However…
Even with only two bits of wood the result can be complex
See video at: http://www.youtube.com/watch?v=czLIj-4suOk
4. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 4
The Main Point of the Talk…
…is that complex systems need to be dealt with in a
different way to that of simple systems...
...not only using different techniques but also how
models about complex systems are used in policy
development process needs to change including
moving away from prediction.
• Simulation modelling will be increasingly important
as we try to develop better policies and deal with
complex and fast moving situations
• But it can not be ‘business as usual’ – just doing
better modelling with the same modeller–policy
actor relationship will not work well
5. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 5
Structure of the (rest of the) Talk
1. A bit about modelling context, purposes
and tensions
2. Some of the underlying assumptions
and habits that need to change
3. An eample model – A model of
Domestic Water Demand
4. An example model – Stefano Picascia’s
Modelling of the Housing Rental Market
5. Some suggestions as to ways forward
6. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 6
Tensions and difficulties for the
modeller
Part 1
7. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 7
The Complexity facing Modellers
• Many of the situations or issues we need to
understand are mixtures of: technical, social,
behavioural and ecological factors
• They are not only complicated, but also
unexpected outcomes can ‘emerge’ from the
interaction of the actors and internal processes
• We do not have good general models for how
people behave (regardless of what economists
claim)
• How to approach using models to understand
complex phenomena is not fully developed
8. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 8
Different modelling purposes
Models can be used for a wide variety of different
purposes, and these impact upon the kind of
techniques needed and its difficulties, e.g.
• Forecasting – predicting unknown (e.g. future)
situations and outcomes
• Explanation – understanding how known
outcomes might have come about
• Theoretical Exploration – understanding a
complex model by exploring some of its properties
and behaviours
• Analogy – using a model as a way of thinking
about something else
9. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 9
Model Scope
• The scope of a model is the conditions under which it
is useful for its planned purpose
• Whilst this is implicit and stable for many simple
systems, this is not the case for many complex ones
• Thus trying to make scope explicit is important, and
these relate to model assumptions
• A process not included in the model (and hence
outside its scope) can overwhelm the results…
• ..but in complex systems internal processes of change
can also emerge, and some of these can be usefully
modelled (but only in more complex ways)
10. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 10
Possible modelling trade-offs
• Some desiderata for
models: validity,
formality, simplicity and
generality
• these are difficult to
obtain simultaneously
(for complex systems)
• there is some sort of
complicated trade-off
between them (for each
modelling exercise)
simplicity
generality
validity
formality
Analogy
Solvable
Mathematical
Model
Data
What
Policy
Actors
Want
11. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 11
A picture of modelling
whatisobservedor
measured
themodel
themodellers
themodelusers
12. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 12
Assumptions and expectations from
Policy Actors
Part 2
13. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 13
Expectations of Scientists
• What works well with simple systems does not
necessarily work well with complex ones
• Many of the expectations of complexity scientists
by policy makers and the public come from:
– What economists have claimed to be able to do
– Or how physical scientists have been able to do
• As I hope will be clear, complex simulation
modelling can usefully inform policy making
• But these expectations can get in the way
• So we will look next at some of these expectations
14. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 14
The Cost-Benefit Approach
• Basically weighing the benefits – the costs
• As if an economist had written a manual for policy
actors in how to think (i.e. as their theory states)
This assumes that one can:
1. list the main alternative options
2. forecast the results of these
3. put meaningful numerical values on these
4. decide on the best one, adopt that option
• Allows policy optimisation…
• ...if it were possible
15. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 15
Quantification
• Makes life much easier for policy actors – choose
the one with the biggest (or smallest) number!
• Especially when asked to justify an approach
• But can be more misleading than helpful because
it gives a false impression of accuracy
• And implicitly leads to a focus on the measurable
and that things will ‘average out’ etc.
• Was a limitation of purely mathematical
approaches, but computer simulation does not
have to be focused on these aspects
• 1D quantification is often an inadequate
representation of what we need to understand
16. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 16
Planning and Managing Modelling
• In a simple case one can apply an approach
where one carefully plans, manages and
evaluates models
• As if this was like building a bridge!
• But in complex cases complications about what
needs to be included or not requires a more
iterative approach…
• ...where models are repeatedly built for a purpose
and the lessons learnt as you go along...
• Becuase the difficulties can not be predicted in
complex cases!
17. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 17
No gradual approximation, but
scope-limited usefulness
It is often assumed that as time and effort increase
the accuracy of the results improve, but this is not
the case with complex systems and models
Rather in order for the outcomes to be within scope
enough iterative development has to occur
Before this the results are worse than nothing
Time and cost
Error
18. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 18
Compartmentalism
• That some problems can be separated into
smaller sub-problems which can be modelled
more simply
• Not true in many complex cases, where the scope
of modelling is dependent on having enough of
the key processes represented
• Sometimes several different modelling
approaches with different (but overlapping)
assumptions can be more helpful
• Just fiddling, incrementally expanding an existing
(and failing) model will probably not help here
19. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 19
An Example: A model of Domestic Water
Demand
Part 3
20. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 20
Context of model
• As part of a broader model which sought to
understand the impact of climate change on the
domestic demand for water in the UK
• For the UK government and water companies
• Looked at the impact of some present and
extrapolated weather patterns under four different
future economic/cultural scenarios
• Included sophisticated statistical models for
prediction of demand
• Plus our agent-based model as a contrasting
approach
21. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 21
Monthly Water Consumption
REL_CHNG
.88
.75
.63
.50
.38
.25
.13
0.00
-.13
-.25
-.38
-.50
20
10
0
Std. Dev = .17
Mean = .01
N = 81.00
22. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 22
Relative Change in Monthly
Consumption in a small village
Date
FEB
2001
SEP
2000
APR
2000
N
O
V
1999
JU
N
1999
JAN
1999
AU
G
1998
M
AR
1998
O
C
T
1997
M
AY
1997
D
EC
1996
JU
L
1996
FEB
1996
SEP
1995
APR
1995
N
O
V
1994
JU
N
1994
REL_CHNG
1.0
.8
.6
.4
.2
-.0
-.2
-.4
-.6
23. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 23
Purpose of the Model
• Not long-term prediction
• But to begin to understand the relationship of
socially-influenced consumer behaviour to
patterns of water demand
• By producing a representational agent model
amenable to fine-grained criticism
• And hence to suggest possible interactions and
outcomes
24. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 24
Model Structure - Overall Structure
• Activity
• Frequency
• Volume
Households
Policy
Agent
• Temperature
• Rainfall
• Sunshine
Ground
Aggregate Demand
25. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 25
Model Structure - Microcomponents
• Each household has a variable number of micro-
components (power showers etc.): bath
other_garden_watering shower hand_dishwashing
washing_machine sprinkler clothes_hand_washing
hand_dishwashing toilets sprinkler power_shower
• Actions are expressed by the frequency and
volume of use of each microcomponent
• Actions-Volume-Frequency distribution in model
calibrated by data from the Three Valleys
26. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 26
Model Structure - Household
Distribution
• Households distributed randomly on a grid
• Each household can copy from a set of
neighbours (those within a certain distance )
• Households have different mixtures of
motivations: self, social, global
• They decide which is the neighbour most similar
to themselves – this is the one they are most likely
to copy – but all neighbours have some influence
• Depending on their evaluation of actions they
might adopt that neighbour’s actions
• Or do the action they are used to (habit)
• Or that suggested by the policy agent
27. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 27
An Example Social Structure (main
influence only)
- Global Biased
- Locally Biased
- Self Biased
28. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 28
Household Behaviour -
Endorsements
• Action Endorsements: recentAction neighbourhoodSourced
selfSourced globallySourced newAppliance
bestEndorsedNeighbourSourced
• 3 Weights moderate effective strengths of
neighbourhoodSourced selfSourced globallySourced
endorsements and hence the bias of households
• Can be summarised as 3 types of households
influenced in different ways: global-;
neighbourhood-; and self-sourced depending on
the dominant weight (though this is a
simplification, all three weights and factors can
play a part)
29. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 29
History of a particular action
from one agent’s point of view
Month 1: action 1330 used, endorsed as self sourced
Month 2: action 1330 endorsed as recent (from personal use) and
neighbour sourced (used by agent 27) and self sourced
(remembered)
Month 3: action 1330 endorsed as recent (from personal use) and
neighbour sourced (agent 27 in month 2).
Month 4: action 1330 endorsed as neighbour sourced twice, used by
agents 26 and 27 in month 3, also recent
Month 5: action 1330 endorsed as neighbour sourced (agent 26 in month
4), also recent
Month 6: action 1330 endorsed as neighbour sourced (agent 26 in month
5)
Month 7: replaced by action 8472 (appeared in month 5 as neighbour
sourced, now endorsed 4 times, including by the most alike
neighbour – agent 50)
30. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 30
Policy Agent - Behaviour
• After the first month of dry conditions, suggests
AFV actions to all households (reducing water
usage)
• These actions are then included in the list of those
considered by the households
• If the household’s weights predispose it, it may
decide to adopt these actions
• Some other neighbours might imitate these
actions etc.
• Others, more self-sourced may not be influenced
31. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 31
Number of consecutive dry months in
historical scenario
0
1
2
3
4
5
6
7
8
9
J-73
J-74
J-75
J-76
J-77
J-78
J-79
J-80
J-81
J-82
J-83
J-84
J-85
J-86
J-87
J-88
J-89
J-90
J-91
J-92
J-93
J-94
J-95
J-96
J-97
Simulation Date
Numberofconsequativedrymonths
32. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 32
Simulated Monthly Water
Consumption
REL_CHNG
.075
.063
.050
.037
.025
.012
-.000
-.013
-.025
-.038
-.050
120
100
80
60
40
20
0
Std. Dev = .01
Mean = -.000
N = 325.00
33. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 33
Monthly Water Consumption (again)
REL_CHNG
.88
.75
.63
.50
.38
.25
.13
0.00
-.13
-.25
-.38
-.50
20
10
0
Std. Dev = .17
Mean = .01
N = 81.00
34. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 34
Simulated Change in Monthly
Consumption
Date
SEP
1997
APR
1996
N
O
V
1994
JU
N
1993
JAN
1992
AU
G
1990
M
AR
1989
O
C
T
1987
M
AY
1986
D
EC
1984
JU
L
1983
FEB
1982
SEP
1980
APR
1979
N
O
V
1977
JU
N
1976
JAN
1975
AU
G
1973
M
AR
1972
O
C
T
1970
REL_CHNG
.10
.08
.06
.04
.02
0.00
-.02
-.04
-.06
35. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 35
Relative Change in Monthly
Consumption (again)
Date
FEB
2001
SEP
2000
APR
2000
N
O
V
1999
JU
N
1999
JAN
1999
AU
G
1998
M
AR
1998
O
C
T
1997
M
AY
1997
D
EC
1996
JU
L
1996
FEB
1996
SEP
1995
APR
1995
N
O
V
1994
JU
N
1994
REL_CHNG
1.0
.8
.6
.4
.2
-.0
-.2
-.4
-.6
39. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 39
What did the model tell us?
• That it is possible that social processes within
communities:
– can cause a high and unpredictable variety in patterns
of demand
– can ‘lock-in’ behavioural patterns and partially ‘insulate’
them from outside influence (droughts only occasionally
had a permanent affect on patterns of consumption)
• Thus identifying and taking measures at high-
usage areas at an early stage might be sensible
• Also that the availability of new products could
dominate effects from changing consumptions
habits
40. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 40
An Example: A Model of the Rental
Housing Market
Part 4
41. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 41
The model
• By Stefano Picascia, an PhD student of mine, now
at Sienna University, Italy
• Is an agent-based simulation that represents both
tenants and developers co-adapting
• Is geographically based with tenants making
decisions as where to move to based on location
as well as quality of housing and price
• Developers put in captial to build/rennovate
housing for tenants
• Rents are determined by the quality and prices of
surrounding housing
42. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 42
The Manchester Case
Waves of price
changes can
spread
Can have different
outcomes each
time it is run
Has also been
applied to London
and Beirut
43. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 43
Average prices in a run
44. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 44
Different Sectors of the City in a run
45. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 45
What it does and does not tell us
In the model (which is the private rental sector only):
• That change is fundamentally internally driven as well
as due to outside events
• Price oscillations are endemic to the system
• That some regions of cities will be stuck as low quality
housing for long periods of time depending on the
state of neighbouring areas
• The very high price regions stay that way
• That under certain conditions sudden ‘gentrification’
may occur to some degree raising standards but
maybe also displacing existing functional communities
• For poorer districts decline is gradual and continual
between any such periods
46. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 46
Concluding discussion and some ways
forward
Part 5
47. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 47
From Probabilistic to Possibilistic
• When outcomes can not be sensibly forecast…
• And especially numerically forecast…
• …where even probability zones or 90% bounds
are misleading
• Then moving to an approach that models and
understand (more of) underlying processes...
• ...in terms of the different kinds of outcome might
be much more informative
• Each outcome tagged with its own assumptions
and scopes (if they differ)
48. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 48
From Forecasting to Risk Analysis
• However much one might like forecasting, often it is simply
not possible…
• ...let alone in a way such that the outcomes from different
options can be compared!
• Predicting outcomes can be more misleading than helpful
• Rather it may be more approapriate to use models for risk
analysis – finding all the ways a policy might go wrong (or
right!)
• Techniques are available to help discover and understand
how endogenous processes might result in different future
possibilities
• Which can then inform the design of ‘early warning’
monitors giving the most immediate feedback to policy
makers
49. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 49
Informing the adaptive ‘driving’ of
policy
• Complex models are no good for policy makers!
• Because they have to make decisions on grounds
they understand and know the reliability of
• They can not (and should not) delegate this to
‘experts’ and their inscrutable models
• Rather modellers should use their modelling to
understand the key emergent kinds of outcome
• To inform:
– the consideration of these kinds of outcome
– the design of appropriate data visalisations
– the design of ‘earl warning indicators
• …So that policy can adapt to changing trends and
events as quickly and fluidly as possible
50. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 50
Conclusions
• Modelling of complex phenomena is not cheap or
quick and requires iterative development
• It will not forecast the impact of potential policies
or events, but can anticipate possible future
outcomes in a way intuition can not
• There will always be a ‘scope’ – a set of
conditions/assumptions a model depends upon
• But a good model can repay its investment in
terms of cost and improving people’s lives many,
many times over
51. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 51
Summary
It is no good wishing that the world or
modelling is simple and trying to ‘force’ it to
be so, one has to adapt to suit reality…
…this includes how models and modelling
are used by the policy process
52. Risk-aware policy evaluation using agent-based simulation, Bruce Edmonds, November 2016. slide 52
The End
The Centre for Policy Modelling:
http://cfpm.org
These slides will be available at: http://slideshare.net/BruceEdmonds
Stefano’s model of
housing was
developed under this
project, funded by the
EPSRC, grant
number EP/H02171X
Social Science Aspects
of Fisheries for the 21st
Century – with two
Icelandic partners:
MATIS and the
University of Iceland