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.).
Towards Integrating Everything (well at least: ABM, data-mining, qual&quant d...Bruce Edmonds
A talk given at the SKIN3 workshop in Budapest, May 2014 (http://cress.soc.surrey.ac.uk/SKIN/events/third-skin-workshop)
Innovation or other policy-orientated research has tended to take one of two strategies: (a) work with high-level abstractions of macro-level variables or (b) focus on micro-level aspects/areas with simpler mechanisms. Whilst (a) may provide some comfort in the form of forecasts, these are almost useless for policy since they can only be relied upon if nothing much has changed. Although approach (b) may produce some interesting studies which show how complex even small aspects of the involved processes are, with maybe interesting emergent effects, it provides only a small part of the overall picture and little to guide decision making.
Rather, I (with others) suggest a different approach. Instead of aiming to produce some kind of "adequate" theory (usually in the form of a model along with its interpretation), that instead we aim at integrating different kinds of evidence and find the best ways to present these to policy makers in order to help policy-makers 'drive' by providing views of what is happening. Thus (1) utilising the greatest possible range of evidence and (2) providing rich, relevant but synthetic views of this evidence to the policy makers. Any projections should be 'possibilistic' rather than 'probabilistic' - showing the different ways in which social processes might unfold, and help inform the analysis of risks. The talk looks at some of the ways in which this might be done, to integrate micro-level narrative data, time-series data, survey data, network data, big data using a variety of techniques. In this view, models do not disappear, but rather have a different purpose and hence be developed and checked differently.
This shift will involve a change in attitude and approach from both researchers and those in the policy world. Researchers will have to give up the playing for general or abstract theory, satisfying themselves with more gentle and incremental abstraction, whilst also accepting and working with a greater variety of kinds of evidence. They will also have to stop 'conning' the policy world with forecasts, and refuse to provide these as more dangerous than helpful. The policy world will have to stop looking for a magic 'crutch' that will reduce uncertainty (or provide justification for chosen policies) and move towards greater openness with both data and models.
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.
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.
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
The Complexity of Data: Computer Simulation and “Everyday” Social ScienceEdmund Chattoe-Brown
Although the existence of various forms of complexity in social systems is now widely recognised, this approach to explanation faces two major challenges that turn out to be intimately connected. The first is the existing conflict in social science between “micro” and “macro” styles of social explanation. The second is the relationship of complexity to the kind of data routinely collected in social science. In order to be accepted, complexity approaches need simultaneously to dodge the first conflict while making much better use of existing forms of data.
The first part of the talk will provide an introduction to the simulation approach and a discussion of various concepts in complexity with reference to simulation as a distinctive theory-building tool and methodology. The second part of the talk will develop these ideas in more depth using simulations by the author as case studies.
Using Data Integration Modelsfor Understanding Complex Social SystemsBruce Edmonds
Describing the use of complex, descriptive simulations to integrate the maximum amount of evidence in a staged manner. With an example from the SCID project (http://www.scid-project.org).
Towards Integrating Everything (well at least: ABM, data-mining, qual&quant d...Bruce Edmonds
A talk given at the SKIN3 workshop in Budapest, May 2014 (http://cress.soc.surrey.ac.uk/SKIN/events/third-skin-workshop)
Innovation or other policy-orientated research has tended to take one of two strategies: (a) work with high-level abstractions of macro-level variables or (b) focus on micro-level aspects/areas with simpler mechanisms. Whilst (a) may provide some comfort in the form of forecasts, these are almost useless for policy since they can only be relied upon if nothing much has changed. Although approach (b) may produce some interesting studies which show how complex even small aspects of the involved processes are, with maybe interesting emergent effects, it provides only a small part of the overall picture and little to guide decision making.
Rather, I (with others) suggest a different approach. Instead of aiming to produce some kind of "adequate" theory (usually in the form of a model along with its interpretation), that instead we aim at integrating different kinds of evidence and find the best ways to present these to policy makers in order to help policy-makers 'drive' by providing views of what is happening. Thus (1) utilising the greatest possible range of evidence and (2) providing rich, relevant but synthetic views of this evidence to the policy makers. Any projections should be 'possibilistic' rather than 'probabilistic' - showing the different ways in which social processes might unfold, and help inform the analysis of risks. The talk looks at some of the ways in which this might be done, to integrate micro-level narrative data, time-series data, survey data, network data, big data using a variety of techniques. In this view, models do not disappear, but rather have a different purpose and hence be developed and checked differently.
This shift will involve a change in attitude and approach from both researchers and those in the policy world. Researchers will have to give up the playing for general or abstract theory, satisfying themselves with more gentle and incremental abstraction, whilst also accepting and working with a greater variety of kinds of evidence. They will also have to stop 'conning' the policy world with forecasts, and refuse to provide these as more dangerous than helpful. The policy world will have to stop looking for a magic 'crutch' that will reduce uncertainty (or provide justification for chosen policies) and move towards greater openness with both data and models.
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.
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.
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
The Complexity of Data: Computer Simulation and “Everyday” Social ScienceEdmund Chattoe-Brown
Although the existence of various forms of complexity in social systems is now widely recognised, this approach to explanation faces two major challenges that turn out to be intimately connected. The first is the existing conflict in social science between “micro” and “macro” styles of social explanation. The second is the relationship of complexity to the kind of data routinely collected in social science. In order to be accepted, complexity approaches need simultaneously to dodge the first conflict while making much better use of existing forms of data.
The first part of the talk will provide an introduction to the simulation approach and a discussion of various concepts in complexity with reference to simulation as a distinctive theory-building tool and methodology. The second part of the talk will develop these ideas in more depth using simulations by the author as case studies.
Using Data Integration Modelsfor Understanding Complex Social SystemsBruce Edmonds
Describing the use of complex, descriptive simulations to integrate the maximum amount of evidence in a staged manner. With an example from the SCID project (http://www.scid-project.org).
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.
The Scandal of Generic Models in the Social SciencesBruce Edmonds
Despite overwhelming evidence that many aspects of human cognition are highly context-dependent, generic (that is models that are supposed to hold across different contexts) abound, including: most models of rationality and decision making, and most models that are based on statistically fitting equations to data. Context itself, especially social context, has been systematically by-passed by both quantitative and qualitative researchers. Quantitative researchers claim to be only interested in those patterns that are cross-context. Qualitative researchers only deal with accounts within context. Neither tackle the nature of context itself: how it works, in what ways it impacts upon behaviour.
Dealing with context is notoriously hard: the concept is slippery and its effects hard to identify. However, I claim it is not impossible to research. A combination of rich datasets and newer computational methods could help (a) identify some social contexts and (b) relate what happens within a context to how contexts are collectively constructed. Such a step could help relate quantitative and qualitative evidence in a way that is better founded and hence, perhaps, open the way to the unification of the social sciences as a coherent discipline.
Mundane Rationality as a basis for modelling and understanding behaviour wit...Bruce Edmonds
The paper starts out by pointing out the context-dependency of human cognition and behaviour, pointing out that (a) human behaviour can change sharply across contexts but also that (b) behaviour within a given context can sometimes be described in relatively simple terms . It thus argues against a grand theory of rationality that seeks to explain and/or generate human behaviour across of contexts. Rather it suggests an alternative approach whereby "mundane" accounts of rationality are used which are specific to a limited number of contexts. Such an approach has its particular difficulties, but allows the integration of narrative accounts of possible behaviours using a variety of social mechanisms at the micro level with comparisons with aggregate macro data. It is noted that in the resulting simulations that equilibria are simply not relevant within plausible timescales.
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.
Personal understanding and publically useful knowledge in Social SimulationBruce Edmonds
There are two different ways in which social simulation can help a researcher - by honing their intution about how certain models and mechanisms (roughly what Polanyi meant by "Personal Knowledge") and in demonstrating hypotheses that might be interesting and relevant to other researchers in the field (roughly what Popper meant by "Objective Knowledge"). Both are valid goals and useful, indeed I would argue both are essential to real progress in social simulation. However, too often, these are conflated and confused, to the detriment of social simulation. This talk aims to clearly distringuish between the two modes, including the different ways of obtaining them, their different (and complementary) uses as well as when and how these are appropriate to communicate to others. In short a "model" of simulation usefullness is outlined with implications for the method of social simulation.
Context dependency and the development of social institutionsBruce Edmonds
A talk at the 1st Constructed Complexities workshop on "" at the University of Surrey, July 2013. http://constructedcomplexities.wordpress.com/
-----------------------
It is well established that many aspects of human cognition are context-dependent, including: memory, preferences, language, perception, reasoning and emotion. What seems to occur is that the kind of situation is recognised and information stored with respect to that. This means that when faced with a similar situation, beliefs, expectations, habits, defaults, norms, procedures etc. that are relevant to the context can be brought to bear. I will call this mental correlate of the kind of situation the “context”. Thus the mental context frames conscious thinking by preferentially providing the relevant information making learning and reasoning practical, as well as allowing relatively “crisp” and logical thought within this frame. This is the “context heuristic” that seems to have been built into us by the process of evolution.
This recognition seems to occur in a rich, fuzzy and largely unconscious manner, which means that it can be hard to give distinct identities and talk about these contexts. It can thus be problematic to talk about “the” context in many cases, and indeed one cannot assume that different people are thinking about the same situation as (effectively) the same context from a third party perspective. Indeed one of the powerful aspects of the context heuristic is that it allows us flip between mental contexts allowing us to thing about a situation or problem from different contextual frames. Due to our facility at automatically identifying context and the indefinable way it is recognised it is hard for people to retrieve what is or signals a context (in contrast to what is relevant when recognised). However, they do seem to be sensitive to when they have the wrong context.
Thus learning is not just a matter of recording beliefs, expectations, habits, defaults, norms, procedures etc. but also a matter of learning to recognise the kinds of situation to organise their remembrance. A large part of our world is humanly constructed, or common (e.g. shared human emotions or a shared environment). Our classification of these kinds of situation is thus heavily coordinated among people of the same society – we learn to recognise situations in effectively the same way and hence remember the relevant beliefs, expectations, habits, defaults, norms, procedures etc. for the same kinds of situation. A shared body of knowledge (in its wisest sense) that constitutes a culture does not only include the foreground beliefs, norms etc. but also how the world is divided into kinds of situation. Some of these contexts will have universal roots, such as the emotion of fear or being hungry, and thus might be approximately the same across cultures (without transmission), others will be specific to cultures.
The
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.
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
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.
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.
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.
Presentation given at the HEA Social Sciences learning and teaching summit 'Exploring the implications of ‘the era of big data’ for learning and teaching'.
A blog post outlining the issues discussed at the summit is available via: http://bit.ly/1lCBUIB
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.
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.
Urban Neighbourhood Analysis (UNA) using Mixed Method Research DesignProf Ashis Sarkar
This presentation emphasizes on identification and analysis of 'urban neighbourhood'. Of the several methods of research, the 'mixed method' design has been discussed with examples.
Staging Model Abstraction – an example about political participationBruce Edmonds
A presentation at the workshop on ABM and Theory (From Cases to General Principles), Hannover, July 2019
This reports on work where we started with a complex, but evidence driven model, and then modelled that model sto understand and abstract from it. As reported in the paper:
Lafuerza LF, Dyson L, Edmonds B, McKane AJ (2016) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261
Some supporting slides on modelling purposes and pitfalls when using ABM in policy contexts to accompany discussion on Modelling Pitfalls at the ESSA Summer School, Aberdeen, June 2019
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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.
The Scandal of Generic Models in the Social SciencesBruce Edmonds
Despite overwhelming evidence that many aspects of human cognition are highly context-dependent, generic (that is models that are supposed to hold across different contexts) abound, including: most models of rationality and decision making, and most models that are based on statistically fitting equations to data. Context itself, especially social context, has been systematically by-passed by both quantitative and qualitative researchers. Quantitative researchers claim to be only interested in those patterns that are cross-context. Qualitative researchers only deal with accounts within context. Neither tackle the nature of context itself: how it works, in what ways it impacts upon behaviour.
Dealing with context is notoriously hard: the concept is slippery and its effects hard to identify. However, I claim it is not impossible to research. A combination of rich datasets and newer computational methods could help (a) identify some social contexts and (b) relate what happens within a context to how contexts are collectively constructed. Such a step could help relate quantitative and qualitative evidence in a way that is better founded and hence, perhaps, open the way to the unification of the social sciences as a coherent discipline.
Mundane Rationality as a basis for modelling and understanding behaviour wit...Bruce Edmonds
The paper starts out by pointing out the context-dependency of human cognition and behaviour, pointing out that (a) human behaviour can change sharply across contexts but also that (b) behaviour within a given context can sometimes be described in relatively simple terms . It thus argues against a grand theory of rationality that seeks to explain and/or generate human behaviour across of contexts. Rather it suggests an alternative approach whereby "mundane" accounts of rationality are used which are specific to a limited number of contexts. Such an approach has its particular difficulties, but allows the integration of narrative accounts of possible behaviours using a variety of social mechanisms at the micro level with comparisons with aggregate macro data. It is noted that in the resulting simulations that equilibria are simply not relevant within plausible timescales.
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.
Personal understanding and publically useful knowledge in Social SimulationBruce Edmonds
There are two different ways in which social simulation can help a researcher - by honing their intution about how certain models and mechanisms (roughly what Polanyi meant by "Personal Knowledge") and in demonstrating hypotheses that might be interesting and relevant to other researchers in the field (roughly what Popper meant by "Objective Knowledge"). Both are valid goals and useful, indeed I would argue both are essential to real progress in social simulation. However, too often, these are conflated and confused, to the detriment of social simulation. This talk aims to clearly distringuish between the two modes, including the different ways of obtaining them, their different (and complementary) uses as well as when and how these are appropriate to communicate to others. In short a "model" of simulation usefullness is outlined with implications for the method of social simulation.
Context dependency and the development of social institutionsBruce Edmonds
A talk at the 1st Constructed Complexities workshop on "" at the University of Surrey, July 2013. http://constructedcomplexities.wordpress.com/
-----------------------
It is well established that many aspects of human cognition are context-dependent, including: memory, preferences, language, perception, reasoning and emotion. What seems to occur is that the kind of situation is recognised and information stored with respect to that. This means that when faced with a similar situation, beliefs, expectations, habits, defaults, norms, procedures etc. that are relevant to the context can be brought to bear. I will call this mental correlate of the kind of situation the “context”. Thus the mental context frames conscious thinking by preferentially providing the relevant information making learning and reasoning practical, as well as allowing relatively “crisp” and logical thought within this frame. This is the “context heuristic” that seems to have been built into us by the process of evolution.
This recognition seems to occur in a rich, fuzzy and largely unconscious manner, which means that it can be hard to give distinct identities and talk about these contexts. It can thus be problematic to talk about “the” context in many cases, and indeed one cannot assume that different people are thinking about the same situation as (effectively) the same context from a third party perspective. Indeed one of the powerful aspects of the context heuristic is that it allows us flip between mental contexts allowing us to thing about a situation or problem from different contextual frames. Due to our facility at automatically identifying context and the indefinable way it is recognised it is hard for people to retrieve what is or signals a context (in contrast to what is relevant when recognised). However, they do seem to be sensitive to when they have the wrong context.
Thus learning is not just a matter of recording beliefs, expectations, habits, defaults, norms, procedures etc. but also a matter of learning to recognise the kinds of situation to organise their remembrance. A large part of our world is humanly constructed, or common (e.g. shared human emotions or a shared environment). Our classification of these kinds of situation is thus heavily coordinated among people of the same society – we learn to recognise situations in effectively the same way and hence remember the relevant beliefs, expectations, habits, defaults, norms, procedures etc. for the same kinds of situation. A shared body of knowledge (in its wisest sense) that constitutes a culture does not only include the foreground beliefs, norms etc. but also how the world is divided into kinds of situation. Some of these contexts will have universal roots, such as the emotion of fear or being hungry, and thus might be approximately the same across cultures (without transmission), others will be specific to cultures.
The
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.
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
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.
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.
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.
Presentation given at the HEA Social Sciences learning and teaching summit 'Exploring the implications of ‘the era of big data’ for learning and teaching'.
A blog post outlining the issues discussed at the summit is available via: http://bit.ly/1lCBUIB
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.
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.
Urban Neighbourhood Analysis (UNA) using Mixed Method Research DesignProf Ashis Sarkar
This presentation emphasizes on identification and analysis of 'urban neighbourhood'. Of the several methods of research, the 'mixed method' design has been discussed with examples.
Similar to How social simulation could help social science deal with context (20)
Staging Model Abstraction – an example about political participationBruce Edmonds
A presentation at the workshop on ABM and Theory (From Cases to General Principles), Hannover, July 2019
This reports on work where we started with a complex, but evidence driven model, and then modelled that model sto understand and abstract from it. As reported in the paper:
Lafuerza LF, Dyson L, Edmonds B, McKane AJ (2016) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261
Some supporting slides on modelling purposes and pitfalls when using ABM in policy contexts to accompany discussion on Modelling Pitfalls at the ESSA Summer School, Aberdeen, June 2019
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.
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.
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.
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.
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.
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.
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
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.
Risk-aware policy evaluation using agent-based simulationBruce Edmonds
A talk about how modelling of complex issues of policy relevance. It covers some of the tensions and difficulties, as well as some of the unrealistic expectations of this kind of modelling. Rather it is suggested these kinds of model should be used as a kind of risk-analysis. Two examples of this are given.
Talk given in Reykjavik at University of Iceland, 30th Nov 2016.
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)
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
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's entangled aventures in wonderlandRichard 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.
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.
Nucleic Acid-its structural and functional complexity.
How social simulation could help social science deal with context
1. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 1
How social simulation could help
social science deal with context
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
2. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 2
…but first an Advertisement!
3. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 3
Talk Outline
1. Some personal motivation
2. Talking about “Context”
3. How Social Science effectively avoids
dealing with context
4. Approaching context from narrative accounts
5. Implementing context-sensitive behaviour in
social simulation
6. Concluding Discussion
4. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 4
Some personal motivation
Part 1:
5. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 5
The Complexity of Vision
• Although easy to do, vision is very complex
• Done using processes that we are not aware of
• E.g. we are not (usually) aware of shifting focus
6. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 6
The Complexity of Thought
• Although easy to do, thought is
very complex
• Done using some processes
that we are not aware of
• E.g. we are not (usually)
aware of shifting context
7. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 7
Talking about “Context”
Part 2:
8. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 8
The Difficulty of Talking about
Context
• The word “context” is used in many different
senses across different fields
• Somewhat of a “dustbin” concept resorted to
when more immediate explanations fail (like
the other “c-words”: complexity & creativity)
• Problematic to talk about, as it is not clear that
“contexts” are usually identifiably distinct
• Mentioning “context” is often a signal for a
more “humanities oriented” or
“participatory/involved” approach and hence
resisted by “scientists” who are seeking
general laws
9. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 9
A (simplistic) illustration of context from the
point of view of an actor
10. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 10
Situational Context
• The situation in which an event takes place
• This is indefinitely extensive, it could include
anything relevant or coincident
• The time and place specify it, but relevant
details might not be retrievable from this
• It is almost universal to abstract to what is
relevant about these to a recognised type
when communicating about this
• Thus the question “What was the context?”
often effectively means “What about the
situation do I need to know to understand?
11. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 11
Cognitive Context (CC)
• Many aspects of human cognition are context-
dependent, including: memory, visual perception,
choice making, reasoning, emotion, and language
• The brain somehow deals with situational context
effectively, abstracting kinds of situations so
relevant information can be easily and
preferentially accessed
• The relevant correlate of the situational context
will be called the cognitive context
• It is not known how the brain does this, and
probably does this in a rich and complex way that
might prevent easy labeling/reification of contexts
12. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 12
Social Context
• Since humans are fundamentally social beings…
• …social context is often most important
• e.g. an interview, a party or a lecture
• But social context may be co-determined, since:
– Special rules, norms, habits, terms, dress will be
developed for particular social contexts
– The presence of special features, rules etc. make the
social context recognisable distinct
• Over time social contexts plus their features
become entrenched and passed down
• Social Context arises and is so recognisable as a
result of cognitive and external features (e.g.
building a lecture hall)
13. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 13
How Social Science effectively avoids
dealing with context
Part 3:
14. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 14
Why context is unavoidable
• Many aspects of human cognition are known to
be highly context-sensitive, including: memory,
preferences, language, visual perception,
reasoning and emotion
• There is much qualitative research that has
documented instances where a specific context
is essential to understanding observed behaviour
• Simple observation and introspection tells us that
behaviour in different kinds of situation is not
only different but decided on in different ways
(e.g. in a lecture and a football game)
15. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 15
However despite this, in quantitative
social science….
• Almost all formal models of human behaviour
(mathematical, logical or computational) are
generic – they do not exhibit this sharp context-
dependency
• Another stream of models (models fitted to or
tested against data) consider a single model (at a
time) against a set of data that derives from many
different contexts – only interested in what
behaviour is “context independent”
• This seems to me to be a case of massive
“wishful thinking”
16. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 16
Context-Dependency and
Randomness
Lots of
information
lost if
randomness
used to
“model”
contextual
variation
17. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 17
Qualitative research…
• Does take context seriously, but has (largely) often
limits iteself to description within specific contexts
• Knowledge is only useful if it is to some extent
applicable in a new situation (even if only slightly new)
• Analogical reasoning can project knowledge from one
context upon another, and this can give insights
(interesting hypotheses) but not reliable knowledge
• This kind of research often avoids responsibility for
the application of knowledge gained from its studies
(necessarily in a different situation to where it was
observed) whilst implying it is somehow useful
18. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 18
Context in the Social Sciences
– the elephant in the room
To summarise:
• We have one set of researchers who are ignoring context,
optimistically hoping to find general patterns even though
they must know context must be crucial in many cases
• Another set of researchers refuse to look at what is
general across contexts or how contexts might
systematically affect behaviour
• Few are seriously trying to study social context itself – how
it works, what regularities there are, how to identify it, how
to model its impact, when we can generalise across a set
of contexts
Social context is central to human behaviour but
effectively not researched much
19. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 19
Some ways forward
• Keeping the data and simply NOT summarising it (at
least not prematurely)
• Data mining local patterns to detect commonality of
multiple models/measurements across similar contexts
• More complex simulation models with context-dependent
cognitive models
• Context-oriented microsimulation models
• Context-sensitive visualisation techniques
• Integrating personal/anecdotal accounts of behaviour –
making use of qualitative evidence with its context
• Not leaving the context(s) – understanding and acting
within the sphere of a shared context
• Staging abstraction more gradually
• Clusters of related models covering different contexts
1
2
3
20. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 20
Approaching context from qualitative
narratives
Part 4:
21. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 21
Integrating Aspects of Qualitative
Evidence into ABMs
• Identifying kinds of context (those over which we
might expect some regularity in terms of shared
norms, expectations etc.) might allow suggestions
from qualitative evidence to be incorporated into
agent-based models
• For example by providing the repertoire of possible
strategies in the context which are decided between
• This could greatly enrich agent-based models
allowing in some of the social “mess” we observe
• However this requires new methods to analyse
narrative evidence in a context-depenent manner
22. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 22
Identifying the ‘right’ contexts
• It is hard since people assume context, it is
usually left implicit, indeed people are often not
aware of the context they are assuming
• However…
– Socially entrenched contexts can be identified
– When giving examples (could you imagine doing that in
situation X) people are relatively good at recognising
when the context is wrong
– Our intuitions are a good starting point, as long as we
are aware they might be wrong
• No well-developed methods – this needs further
research
23. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 23
Different Aspects Illustrated
Universe of Knowledge
Knowledge indicated by current cognitive context
Knowledge that is possible to
apply given circumstances
Cause1 & Cause2…
Result1 & Result2…
Event1, event2, etc.
24. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 24
CSNE Analysis Framework
1. Context: the kind of situation one is in that
determines the ‘bundle’ of knowledge that is
relevant to that kind of situation
2. Scope: what is and is not possible given the
current situation and observations
3. Narrative Elements: the narrative elements that
are mentioned assuming the context and scope
25. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 25
About Scope
• By “scope” I mean the reasoning as to which
knowledge is possible given the circumstances
• For example, if all the seats are taken in a lecture,
then some of the norms, habits and patterns as to
where one sits might not apply
• Reasoning about scope can be complex and is
done consciously
• However once judgments about scope are made
then they tend to be assumed (i.e. are fixed),
unless the situation changes critically
26. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 26
Scope vs. Cognitive Context
• Both scope and cognitive context determine which
knowledge is useful for any particular situation
that is encountered
• However, they play different roles:
– CC is learnt using pattern recognition over a long time,
but then is largely a ‘given’, is almost impossible to
change when learnt, is quick and automatic and is
socially rooted
– Scope is largely reasoned afresh each time, taking
effort to do so, is possible to re-evaluate but only if
needed, and is more individually oriented
27. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 27
Narrative Elements
A variety of narrative structure elements are
possible, including:
– Causal stories: A … resulted in … B
– Sequences: A … then … B … then C
– Choices: had to choose between … A and B
– End points: which resulted in A which was a
disaster/really good/…
– Parallelism: A … happens at the same time as ….B
Some possible structures for these suggested by:
(Abell 1992) or (Toulmin 2003)
28. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 28
Narrative
Text
Identifying
Contexts
Identifying
Scope
Identifying
Narrative
Elements
Micro-Level
Specification for
Agent
Behaviour
Agent Program
Code
Using an CSNE analysis in ABM
29. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 29
Some Example Analyses
using narrative examples from:
Bhawani, S. (2004) Adaptive Knowledge Dynamics and Emergent
Artificial Societies: Ethnographically Based Multi-Agent Simulations
of Behavioural Adaptation in Agro-Climatic Systems. Doctoral
Thesis, University of Kent, Canterbury, UK.
(thesis linked from ‘Relevant Papers’ at http://cfpm.org/qual2rule)
30. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 30
Hypotheses about relevant contexts
for the interviewed stakeholder
Different perspectives from which the narratives
seem to be told:
• “survival” – things are continually getting worse
and the primary goal is to keep in farming, battle
against nature etc. to avoid bankrupcy
• “comfort” – conditions are comfortable with no
immediate survival threat, one could stop worrying
so much and take things a little easy
• “entrepreneur” – one is looking for big profit,
taking risks if necessary
31. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 31
Survival ComfortStart Entrepreneur
Conditions Stable
and ensuring no
foreseeable threat
An opportunity
arises to make
more money
Opportunity
disappears
Existential threat
becomes
feasible
An illustration of the relevant cognitive
contexts
32. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 32
Quote 1 (p. 113) and CSNE Analysis
“The one conundrum here is that there are more people in
the East who want to … upgrade to more wheat allied
products, that may alter the value of the end product to us.
You see the worst thing that has happened to us worldwide
is the collapse of the Eastern economy... but it is coming
back again now and that actually may help us again. It is a
great shame because we were getting into the Eastern
markets and it was beginning to grow and suddenly it
collapsed.”
33. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 33
Quote 2 (p. 127) and CSNE Analysis
“…we have often had this conversation around this table.
Some people don't want to maximize profit.... They are
happier to take a slightly easier, lower level approach and
have an easier life, and not make quite so much money....
And I can relate to that... But because I'm a tenant I don't
own my own land... Everything we farm is rented and
therefore we have an immediate cost, the first cost we meet
is to our landlord and that tends to go up.”
34. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 34
Narrative
Text
Identifying
Contexts
Identifying
Scope
Identifying
Narrative
Elements
Agent context
structure
What agent
reasoning
about scope
occurs
Specification
of sequences,
plans,
branches
Agent context
recognition
and retrieval
rules
Reasoning
rules about
scope
Specific code
for narrative
elements
Micro-level
specification
Agent
architecture
structuring
program code
35. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 35
Implementing Context-Sensitive
Agents in Social Simulations
Part 5:
36. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 36
Ignoring Context
• Some social simulation simple ignores that their
agents are making decisions in different contexts
and hopes that does not change outcomes much
• Other modelling is conceived to represent within a
single context, in which case it can be ignored but
only if
– everyone is using the same idea of this context
– there is no significant “leakage” of causation from
outside the background, that is the scope is wide
enough to include all significant influencing factors
– The actors/organisms don’t deal with the same situation
as different cognitive contexts
37. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 37
Some Simulation Work addressing
Context-Dependency in Cognition
• (Schlosser & al 2005) argue that reputation is context
dependent
• (Edmonds & Norling 2007) looks at difference that
context-dependent learning and reasoning in an
artificial stock market
• (Andrighetto & al 2008) show context-dependent
learning of norms is different form a generic method
• (Tykhonov & al 2008) argue that trust is context
dependent
• (Fieldhouse & al 2016) have different social networks
and behaviours for some different contexts
38. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 38
Comparison in an Artificial Stock
Market
Environment:
• Traders (n context, n straight GP)
• 1 Market maker: prices and deals: 5 stocks
• Traders buy and sell shares at current market
price, but do not have to do so
• Traders have memories, can observe actions of
others, index, etc.
• Modelling ‘arms-race’
• Actions change environment
39. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 39
Basic Cognitive Model
• Rich, automatic, imprecise, messy cognitive
context recognition using many inputs (including
maybe internal ones)
• Crisp, costly, conscious, explicit cognitive
processes using material indicated by cognitive
context
Context
Recognition
Context-Structured
Memory
Reasoning/plan
ning/belief
revision/etc.
40. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 40
Example – models in the cognition of a
trading agent
700
750
800
850
900
950
750 850 950
Volume - past 5 periods
Volatility-past5periods
41. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 41
The model contents in snapshot of
one trader
model-256 priceLastWeek [stock-4]
model-274 priceLastWeek [stock-5]
model-271 doneByLast [normTrader-5] [stock-4]
model-273 IDidLastTime [stock-2]
model-276 IDidLastTime [stock-5]
model-399
minus
[divide
[priceLastWeek [stock-2]]
[priceLastWeek [stock-5]]]
[times
[priceLastWeek [stock-4]]
[priceNow [stock-5]]]
42. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 42
0
5000
10000
15000
20000
25000
30000
0 100 200 300 400 500
Time
TotalValueofAssetsTotal Assets in a Typical Run
Black=context, White= non-context
43. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 43
Implications for Simulation Modelling
• Simulations which represent agents, that in the
real world would be acting with respect to different
contexts but who are represented with an
essentially uniform behaviour need to justify this
• Social simulation might be missing a class of
phenomena that is essentially context-dependent
– How social contexts emerge
– Cross-cultural interaction where different contexts
assumed
• Context-dependent cognitive models in
simulations are feasible but are more work
44. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 44
Concluding Discussion
Part 6:
45. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 45
What is Essential to (empirical)
Science?
• Validity: agreement (in some way) of models to what we
observe, not science otherwise
• Formality: formal models (maths, simulation, etc.) are
precise and replicable – essential to being able to build
knowledge within a community of researchers
• Simplicity: ability to analyse/understand our models,
nice to have but unattainable in general
• Generality: the extent of the applicability of a single
model (i.e. its scope), there needs to be some small
generality to apply models in places other than where
developed, but wide generality not necessary
This talk has suggested the following trade-off:
reducing the generality of each behaviour,
and tolerating complexity
to achieve more validity in the face of complexity
46. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 46
Context-Dependency
and “Being Scientific”
• If the relevant context can be reliably identified
then context-dependency is not the same as
subjectivity (even if there are a some hard cases
that escape definition)
• Generality is nice if you can get it, but its no good
pretending to have it if it is inaccessible
• Science should adapt to what it wishes to
understand, not the other way around
• Useful, validated models of context-dependent
phenomena are more scientific since they reflect
more of what is actually happening, not less
47. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 47
Don’t Prematurely Summarise!
• Traditionally Science has summarised its data
and conclusions using averages, linear regression
models, aggregate graphs, etc. etc.
• …and thus has missed some of the complexity,
the fundamental variety and context-dependency
of social phenomena
• We no longer have to do this!
• Agent-based modelling (along with other
advances, such as the ability to store lots of
original data etc.) means we can preserve, model
and explore this richness
48. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 48
Conclusions
• Social science has largely ignored or by-passed
social context
• …not becuase it is not important – it is central to
many social phenomena
• ...but becuase it is hard to deal with using
simplistic models
• ...and because context-dependency has been
associated with subjectivity and not scientific
• Social simulation has the tools to address
these problems, and could thus allow it to take
a central role in the social sciences
49. How social simulation could help social science deal with context, Bruce Edmonds, Social Simulation, Stockholm, August 2018, 49
These slides will be at: http://slideshare.net/BruceEdmonds
Bruce Edmonds: http://bruce.edmonds.name
Centre for Policy Modelling: http://cfpm.org
Collected papers and slides of mine on context at:
http://bruce.edmonds.name/context
The End!
Editor's Notes
AI, NL, Sociology, Philosophy, Mobile devices, Psychology, Cognitive Science
For detailed argument seem my previous papers on this
Dustbin Like complexity
will talk about this problem later
Social Intelligence Hypothesis
Wittgenstein, Vygotsky, Tomasello
Contexts are often described using their social features “I was talking to my mother”
leakage noise
not the case where un-modelled aspects are effectively random
discuss random gas example
different modelling goals and kinds of validity
schrodinger’s equation – we dont understand its analytic consequences but its still useful