Extended version of my Complexity and Context-Dependency Talk given at the IOP seminar on "Complexity of Complexity" in Bath 19th Dec 2011.
This version talks more about looking for context in data.
Towards a Context-Sensitive Structure for Behavioural Rules (Context, Scope,...Bruce Edmonds
Slides given at an informal workshop on "Using Qualitative Evidence to inform Behavioural Rules suitable for an agent-based simulation" see http://cfpm.org/qual2rule/
This document discusses contested modelling approaches for sustainability interventions. It begins by noting that over-reliance on mathematical models has led to a loss of narrative and limited debate. It then examines different ontologies for understanding social-technical transitions, and questions whether modelling projects make their underlying ontologies explicit. The document also considers the purpose and enactment of modelling, and whether projects support reflexivity and stakeholder engagement. It analyzes several case studies along these dimensions and concludes by discussing challenges around validation and the need for wider stakeholder engagement in modelling to better support sustainability action.
Why symbol-grounding is both impossible and unnecessary, and why theory-tethe...Aaron Sloman
Introduction to key ideas of semantic models,
implicit definitions and symbol tethering, using ideas from philosophy of science and model theoretic semantics to explain why symbol ground theory is misguided: there is no need for all symbols used by an intelligent agent to be 'grounded' in terms of experience, or sensory-motor patterns. Rather, most of the meaning of a symbol may come from its role in a powerful explanatory theory, though the theory should have some connection with experiments and observations in order to be applicable to the world. That is not the same as requiring every symbol to be linked to experiences, experiments or measurements.
Symbol grounding theory is a modern version of the philosophical theory of 'concept empiricism', which was refuted by the philosopher Immanuel Kant in the 18th century.
The document discusses Gestalt theory and its application to website interface design. It provides an overview of Gestalt theory, outlining 10 laws of Gestalt including similarity, proximity, closure, and simplicity. The document then explains each law and provides an example of how it can be applied to website interface design, such as using similar buttons to group related functions or placing closely spaced buttons to indicate they serve the same purpose. The goal is to help designers create intuitive interfaces that allow users to understand how to use the site without instructions.
This document discusses the DynaLearn project, which aims to develop an interactive learning environment allowing learners to construct conceptual knowledge about systems individually or collaboratively. The project addresses declining interest in science education by focusing on conceptual understanding over surface knowledge. Learners will acquire knowledge through conceptual modeling using diagrams and virtual characters. Semantic technologies will provide individualized feedback and recommendations. The system will be evaluated based on how well it engages learners and improves their conceptual understanding and motivation in science curricula.
The document discusses what a knowledge representation is. It argues that a knowledge representation plays five distinct roles:
1. It acts as a surrogate for real-world entities, allowing reasoning to be done internally rather than through direct interaction. All representations are imperfect surrogates.
2. It embodies a set of ontological commitments about how to conceptualize the world. Selecting a representation means deciding what aspects to focus on and what to ignore.
3. It provides a fragmentary theory of intelligent reasoning, specifying what inferences are sanctioned and recommended.
4. It serves as a pragmatic and efficient computational environment for thinking.
5. It acts as a medium for human expression, a language
This book examines the theoretical foundations of planning and design processes. It explores the work of Horst Rittel, who developed theories on "wicked problems" that are widely used today in fields like product design, architecture, planning, government, and software design. The book collects Rittel's previously unpublished work and discusses his philosophical influences and theories. It aims to continue teaching Rittel's work, which the author Jean-Pierre Protzen collaborated on for over 20 years with Rittel before his death.
Towards a Context-Sensitive Structure for Behavioural Rules (Context, Scope,...Bruce Edmonds
Slides given at an informal workshop on "Using Qualitative Evidence to inform Behavioural Rules suitable for an agent-based simulation" see http://cfpm.org/qual2rule/
This document discusses contested modelling approaches for sustainability interventions. It begins by noting that over-reliance on mathematical models has led to a loss of narrative and limited debate. It then examines different ontologies for understanding social-technical transitions, and questions whether modelling projects make their underlying ontologies explicit. The document also considers the purpose and enactment of modelling, and whether projects support reflexivity and stakeholder engagement. It analyzes several case studies along these dimensions and concludes by discussing challenges around validation and the need for wider stakeholder engagement in modelling to better support sustainability action.
Why symbol-grounding is both impossible and unnecessary, and why theory-tethe...Aaron Sloman
Introduction to key ideas of semantic models,
implicit definitions and symbol tethering, using ideas from philosophy of science and model theoretic semantics to explain why symbol ground theory is misguided: there is no need for all symbols used by an intelligent agent to be 'grounded' in terms of experience, or sensory-motor patterns. Rather, most of the meaning of a symbol may come from its role in a powerful explanatory theory, though the theory should have some connection with experiments and observations in order to be applicable to the world. That is not the same as requiring every symbol to be linked to experiences, experiments or measurements.
Symbol grounding theory is a modern version of the philosophical theory of 'concept empiricism', which was refuted by the philosopher Immanuel Kant in the 18th century.
The document discusses Gestalt theory and its application to website interface design. It provides an overview of Gestalt theory, outlining 10 laws of Gestalt including similarity, proximity, closure, and simplicity. The document then explains each law and provides an example of how it can be applied to website interface design, such as using similar buttons to group related functions or placing closely spaced buttons to indicate they serve the same purpose. The goal is to help designers create intuitive interfaces that allow users to understand how to use the site without instructions.
This document discusses the DynaLearn project, which aims to develop an interactive learning environment allowing learners to construct conceptual knowledge about systems individually or collaboratively. The project addresses declining interest in science education by focusing on conceptual understanding over surface knowledge. Learners will acquire knowledge through conceptual modeling using diagrams and virtual characters. Semantic technologies will provide individualized feedback and recommendations. The system will be evaluated based on how well it engages learners and improves their conceptual understanding and motivation in science curricula.
The document discusses what a knowledge representation is. It argues that a knowledge representation plays five distinct roles:
1. It acts as a surrogate for real-world entities, allowing reasoning to be done internally rather than through direct interaction. All representations are imperfect surrogates.
2. It embodies a set of ontological commitments about how to conceptualize the world. Selecting a representation means deciding what aspects to focus on and what to ignore.
3. It provides a fragmentary theory of intelligent reasoning, specifying what inferences are sanctioned and recommended.
4. It serves as a pragmatic and efficient computational environment for thinking.
5. It acts as a medium for human expression, a language
This book examines the theoretical foundations of planning and design processes. It explores the work of Horst Rittel, who developed theories on "wicked problems" that are widely used today in fields like product design, architecture, planning, government, and software design. The book collects Rittel's previously unpublished work and discusses his philosophical influences and theories. It aims to continue teaching Rittel's work, which the author Jean-Pierre Protzen collaborated on for over 20 years with Rittel before his death.
DMIL week 3: Cognitive authority and academic textsDrew Whitworth
How do academic texts manifest cognitive authority? Why do we give credibility to papers written in certain ways and not others? This presentation addresses these questions in ways that focus on the question of how you, the MA student, are asked to do this in essays; and, importantly, why we ask you to do so. The issue is a case study of cognitive authority in a specific setting but should therefore also provide practical guidance to you when it comes to thinking about essay writing. I also cover the issue of academic malpractice.
On Slideshare, the audio track embedded in this presentation will be missing.
Toward Radical Information Literacy: Invited talk at ECIL 2014, DubrovnikDrew Whitworth
Presentation for Andrew Whitworth's invited talk at the European Conference on Information Literacy conference, Dubrovnik, Croatia, 2014. The presentation outlines the theoretical core of the book 'Radical Information Literacy' -- a synthesis of sociocultural practice theory, phenomenography and discourse analysis, applying this to the field of information literacy. 'Radical' IL is defined as teaching that helps redistribute authority over information practice, among members of target populations.
Sherrington's view of cognition resulting from computations performed by individual neurons passing signals over connections does not fully capture neural dynamics. The presentation discusses:
1) Evidence of population-level phenomena like cell assemblies that challenge the importance of neuron-to-neuron connections.
2) Sherrington's view represents cognition with dedicated circuits, but population activity can represent information in high-dimensional state spaces mapped to lower dimensional manifolds.
3) reconciling descriptions of neural activity as mass action in space but flows in time may lead to the emergence of spatiotemporal scales of organization across the brain.
The document provides an overview of knowledge processing concepts including knowledge representation methods, reasoning, and important terms. It discusses knowledge processing as a core AI paradigm and its relationship to knowledge management. Key representation methods are described like logic, rules, semantic networks and frames. Reasoning and the Knowledge Query and Manipulation Language (KQML) are also summarized.
The document discusses Gestalt principles of design which describe how the human brain naturally organizes visual elements. It outlines six key principles: focal point, figure/ground relationship, similarity, continuity, closure, and proximity/alignment. These principles can be leveraged in visual design to guide a viewer's attention and perception by exploiting the brain's innate tendencies towards pattern recognition and completion.
This document discusses different types of long-term memory including declarative and non-declarative memory. Declarative memory includes episodic memory for autobiographical experiences and semantic memory for general knowledge. Non-declarative memory includes procedural memory, conditioning, priming and other forms of implicit memory which do not require conscious recollection. The document also examines models of semantic memory including hierarchical networks and prototype theories.
This document provides an overview of computational social neuroscience and the use of dynamical models to study social behavior. It discusses how dynamical models have been expanded over time to capture increasingly complex social phenomena by incorporating elements like broken symmetry, discreteness, adaptation, directedness, and multi-agent interactions. Models have been used to study behavioral coordination and related neural oscillations. The goal is to develop a "nesting doll" modeling strategy to describe social behavior across multiple scales from molecules to culture.
Mike C Jackson and Postmodern systems thinking by Mohammad Ali JaafarMohammad Ali Jaafar
Postmodern systems approach aims to help managers improve organizations by promoting diversity. Postmodernists would classify all of the various systems approaches considered so far, whether their aim is to improve goal seeking and viability, to explore purposes, or to ensure fairness, as being ‘modernist’ in character.
This document discusses new trends in cognitive science related to embodied cognition and the concept of presence. It summarizes key theories of embodied cognition that see cognition as emerging from the interaction between the mind and body in a physical environment. The document also explores how the concept of presence may help address issues with differentiating self from other and internal from external processes. Presence is discussed as a potential way for cognitive robots to separate themselves from the external world.
Smart Coach is a video analysis software that allows coaches to analyze player technique, capture video from cameras or camcorders, and create reports. Key features include drawing and measurement tools for technical analysis, audio recording of coach comments, screen shots with notes, and comparisons of pre- and post-correction videos. Reports can include video, slides, audio commentary, and be edited by head coaches. The software helps enhance player performance by identifying technical flaws and measuring the impact of corrective measures through comparative analysis.
The document provides an overview of the role of an instructional coach, outlining key responsibilities and approaches. It discusses that coaching should not be a way to enforce programs, fix people, or act as therapy. It then outlines the teacher learning cycle that coaches support, including learning, planning, applying lessons, reflecting, refining practice, and evaluating. Coaches can take on consultant, collaborator, or coach roles depending on the teacher's needs, using questioning, active listening, and evidence-based feedback to support teachers' growth and change.
This document discusses managing complexity using the Cynefin framework. It begins with the author's background and reflections on past project performance. It then discusses that complexity is the norm for many projects and initiatives due to interconnected systems and emergent behaviors. Traditional reductionist approaches are insufficient for complex problems. The Cynefin framework categorizes problems into obvious, complicated, complex, and chaotic domains and suggests sensemaking and appropriate strategies for each. The document advocates understanding complexity, influencing rather than controlling systems, and learning over rigid planning when managing complex initiatives and environments.
Dr. Marshall Goldsmith, CEO coach and best-selling author reveals why coaching is the must have skill for managers who want to engage their people and succeed in today's business environment.
This document discusses why managers should take on a coaching role. It provides tips for managers to build a coaching culture, including believing in their team, managing conflict, being employee-focused, and building the team. Effective coaching requires commitment to scrimmaging, recognition, developing presentation skills, logical thinking, and learning. Common coaching traps to avoid are thinking coaching is difficult or redundant, allowing bottom performers to leave on their own, and not believing in continual improvement. To be an effective coach, managers should prioritize coaching activities, embrace accountability, and mandate a winning attitude.
The document discusses the changing role of managers from one of command and control to that of a coach. Coaching involves an ongoing dialogue between manager and employee to develop skills, performance, and potential. It focuses on encouraging and motivating the employee to achieve higher goals, unlike performance assessments. Coaching is important for reinforcing formal training and sustaining new skills. It indicates that the highest reason employees leave organizations is dissatisfaction with their direct supervisor. Effective coaching involves managers asking open-ended questions to draw solutions from employees rather than being prescriptive. This approach increases innovation, learning, thinking, and team productivity.
This document provides guidance on how to coach and develop others effectively. It discusses behaviors good coaches exhibit such as helping people understand themselves, facilitating goal setting, and providing encouragement. It also outlines behaviors coaches should avoid, like giving answers or imposing their own opinions. The document then reviews skills coaches need like asking thought-provoking questions, active listening, and motivating action. It introduces the G.R.O.W. model for structuring coaching conversations around setting goals, discussing reality, exploring options, and determining willingness. The coaching process involves building trust with the coachee, using the G.R.O.W. model, and following up to check on progress.
ReadySetPresent (Coaching PowerPoint Presentation Content): 100+ PowerPoint presentation content slides. Being capable of coaching is an important skill that can transform a manager’s scope of influence. 100+ PowerPoint presentation content slides. Coaching PowerPoint Presentation Content slides include topics such as: 25 slides on the characteristics and skills of coaches, Benefits of coaching, techniques for coaching, 8 slides on the "we need to talk" coaching meeting, 10 slides on dealing with poor performance, avoiding coaching pitfalls, 20 slides on the 6- step coaching model, a 1 minute guide to praise/reprimands, discussing recurring problems, 20+ slides on modeling coaching behavior, building a coaching atmosphere and assessing your coaching style. Learn how to utilize open and closed questions, how to's and more!
Computing the Sociology of Survival – how to use simulations to understand c...Bruce Edmonds
This document summarizes Bruce Edmonds' presentation on using simulations to understand complex socio-ecological systems and address ecological survival issues. The presentation discusses using an integrated socio-ecological modeling approach, where an individual-based ecological model is combined with agent-based human societies to analyze their interactions over long time periods. The model simulates evolving food webs and the impacts of introducing human agents who can learn and innovate. Preliminary results show humans rapidly impacting ecosystem populations and diversity by becoming top predators and filling multiple niches. The approach aims to better understand how societal structures influence environmental decision-making.
Why the "hard" problem of consciousness is easy and the "easy" problem hard....Aaron Sloman
The "hard" problem of concsiousness can be shown to be a non-problem because it is formulated using a seriously defective concept (the concept of "phenomenal consciousness" defined so as to rule out cognitive functionality and causal powers).
So the hard problem is an example of a well known type of philosophical problem that needs to be dissolved (fairly easily) rather than solved. For other examples, and a brief introduction to conceptual analysis, see http://www.cs.bham.ac.uk/research/projects/cogaff/misc/varieties-of-atheism.html
In contrast, the so-called "easy" problem requires detailed analysis of very complex and subtle features of perceptual processes, introspective processes and other mental processes, sometimes labelled "access consciousness": these have cognitive functions, but their complexity (especially the way details change as the environment changes or the perceiver moves) is considerable and very hard to characterise.
"Access consciousness" is complex also because it takes many different forms, since what individuals are conscious of and what uses being conscious of things can be put to, can vary hugely, from simple life forms, through many other animals and human infants, to sophisticated adult humans,
Finding ways of modelling these aspects of consciousness, and explaining how they arise out of physical mechanisms, requires major advances in the science of information processing systems -- including computer science and neuroscience.
There are empirical facts about introspection that have generated theories of consciousness but some of the empirical facts go unnoticed by philosophers.
The notion of a virtual machine is introduced briefly and illustrated using Conway's "Game of life" and other examples of virtual machinery that explain how contents of consciousness can have causal powers and can have intentionality (be able to refer to other things).
The beginnings of a research program are presented, showing how more examples can be collected and how notions of virtual machinery may need to be developed to cope with all the phenomena.
This document discusses social complexity and presents two simple agent-based models. It explains that social systems are complex due to cognitive individuals, downward causation between micro and macro levels, social embeddedness, and context-dependent behavior. Two simple models are described: one on homophily-driven altruism and one on ape dominance interactions. While simple, the models demonstrate emergent phenomena from micro-level rules and help explore social processes, but more complex and empirically-validated models are needed to truly understand social systems.
The Modelling of Context-Dependent Causal ProcessesA Recasting of Robert Ros...Bruce Edmonds
Bruce Edmonds discusses recasting some of Robert Rosen's ideas about modeling complex systems in light of context-dependency. Rosen argued against reductionist approaches and sought alternative modeling methods. Edmonds acknowledges Rosen's influence but argues that formal systems can model complex systems if their generality is reduced to improve validity. Edmonds advocates dividing the world into meaningful contexts to develop clustered, layered models that are valid within their scopes. This approach accepts limits to simplicity and generality due to complexity.
DMIL week 3: Cognitive authority and academic textsDrew Whitworth
How do academic texts manifest cognitive authority? Why do we give credibility to papers written in certain ways and not others? This presentation addresses these questions in ways that focus on the question of how you, the MA student, are asked to do this in essays; and, importantly, why we ask you to do so. The issue is a case study of cognitive authority in a specific setting but should therefore also provide practical guidance to you when it comes to thinking about essay writing. I also cover the issue of academic malpractice.
On Slideshare, the audio track embedded in this presentation will be missing.
Toward Radical Information Literacy: Invited talk at ECIL 2014, DubrovnikDrew Whitworth
Presentation for Andrew Whitworth's invited talk at the European Conference on Information Literacy conference, Dubrovnik, Croatia, 2014. The presentation outlines the theoretical core of the book 'Radical Information Literacy' -- a synthesis of sociocultural practice theory, phenomenography and discourse analysis, applying this to the field of information literacy. 'Radical' IL is defined as teaching that helps redistribute authority over information practice, among members of target populations.
Sherrington's view of cognition resulting from computations performed by individual neurons passing signals over connections does not fully capture neural dynamics. The presentation discusses:
1) Evidence of population-level phenomena like cell assemblies that challenge the importance of neuron-to-neuron connections.
2) Sherrington's view represents cognition with dedicated circuits, but population activity can represent information in high-dimensional state spaces mapped to lower dimensional manifolds.
3) reconciling descriptions of neural activity as mass action in space but flows in time may lead to the emergence of spatiotemporal scales of organization across the brain.
The document provides an overview of knowledge processing concepts including knowledge representation methods, reasoning, and important terms. It discusses knowledge processing as a core AI paradigm and its relationship to knowledge management. Key representation methods are described like logic, rules, semantic networks and frames. Reasoning and the Knowledge Query and Manipulation Language (KQML) are also summarized.
The document discusses Gestalt principles of design which describe how the human brain naturally organizes visual elements. It outlines six key principles: focal point, figure/ground relationship, similarity, continuity, closure, and proximity/alignment. These principles can be leveraged in visual design to guide a viewer's attention and perception by exploiting the brain's innate tendencies towards pattern recognition and completion.
This document discusses different types of long-term memory including declarative and non-declarative memory. Declarative memory includes episodic memory for autobiographical experiences and semantic memory for general knowledge. Non-declarative memory includes procedural memory, conditioning, priming and other forms of implicit memory which do not require conscious recollection. The document also examines models of semantic memory including hierarchical networks and prototype theories.
This document provides an overview of computational social neuroscience and the use of dynamical models to study social behavior. It discusses how dynamical models have been expanded over time to capture increasingly complex social phenomena by incorporating elements like broken symmetry, discreteness, adaptation, directedness, and multi-agent interactions. Models have been used to study behavioral coordination and related neural oscillations. The goal is to develop a "nesting doll" modeling strategy to describe social behavior across multiple scales from molecules to culture.
Mike C Jackson and Postmodern systems thinking by Mohammad Ali JaafarMohammad Ali Jaafar
Postmodern systems approach aims to help managers improve organizations by promoting diversity. Postmodernists would classify all of the various systems approaches considered so far, whether their aim is to improve goal seeking and viability, to explore purposes, or to ensure fairness, as being ‘modernist’ in character.
This document discusses new trends in cognitive science related to embodied cognition and the concept of presence. It summarizes key theories of embodied cognition that see cognition as emerging from the interaction between the mind and body in a physical environment. The document also explores how the concept of presence may help address issues with differentiating self from other and internal from external processes. Presence is discussed as a potential way for cognitive robots to separate themselves from the external world.
Smart Coach is a video analysis software that allows coaches to analyze player technique, capture video from cameras or camcorders, and create reports. Key features include drawing and measurement tools for technical analysis, audio recording of coach comments, screen shots with notes, and comparisons of pre- and post-correction videos. Reports can include video, slides, audio commentary, and be edited by head coaches. The software helps enhance player performance by identifying technical flaws and measuring the impact of corrective measures through comparative analysis.
The document provides an overview of the role of an instructional coach, outlining key responsibilities and approaches. It discusses that coaching should not be a way to enforce programs, fix people, or act as therapy. It then outlines the teacher learning cycle that coaches support, including learning, planning, applying lessons, reflecting, refining practice, and evaluating. Coaches can take on consultant, collaborator, or coach roles depending on the teacher's needs, using questioning, active listening, and evidence-based feedback to support teachers' growth and change.
This document discusses managing complexity using the Cynefin framework. It begins with the author's background and reflections on past project performance. It then discusses that complexity is the norm for many projects and initiatives due to interconnected systems and emergent behaviors. Traditional reductionist approaches are insufficient for complex problems. The Cynefin framework categorizes problems into obvious, complicated, complex, and chaotic domains and suggests sensemaking and appropriate strategies for each. The document advocates understanding complexity, influencing rather than controlling systems, and learning over rigid planning when managing complex initiatives and environments.
Dr. Marshall Goldsmith, CEO coach and best-selling author reveals why coaching is the must have skill for managers who want to engage their people and succeed in today's business environment.
This document discusses why managers should take on a coaching role. It provides tips for managers to build a coaching culture, including believing in their team, managing conflict, being employee-focused, and building the team. Effective coaching requires commitment to scrimmaging, recognition, developing presentation skills, logical thinking, and learning. Common coaching traps to avoid are thinking coaching is difficult or redundant, allowing bottom performers to leave on their own, and not believing in continual improvement. To be an effective coach, managers should prioritize coaching activities, embrace accountability, and mandate a winning attitude.
The document discusses the changing role of managers from one of command and control to that of a coach. Coaching involves an ongoing dialogue between manager and employee to develop skills, performance, and potential. It focuses on encouraging and motivating the employee to achieve higher goals, unlike performance assessments. Coaching is important for reinforcing formal training and sustaining new skills. It indicates that the highest reason employees leave organizations is dissatisfaction with their direct supervisor. Effective coaching involves managers asking open-ended questions to draw solutions from employees rather than being prescriptive. This approach increases innovation, learning, thinking, and team productivity.
This document provides guidance on how to coach and develop others effectively. It discusses behaviors good coaches exhibit such as helping people understand themselves, facilitating goal setting, and providing encouragement. It also outlines behaviors coaches should avoid, like giving answers or imposing their own opinions. The document then reviews skills coaches need like asking thought-provoking questions, active listening, and motivating action. It introduces the G.R.O.W. model for structuring coaching conversations around setting goals, discussing reality, exploring options, and determining willingness. The coaching process involves building trust with the coachee, using the G.R.O.W. model, and following up to check on progress.
ReadySetPresent (Coaching PowerPoint Presentation Content): 100+ PowerPoint presentation content slides. Being capable of coaching is an important skill that can transform a manager’s scope of influence. 100+ PowerPoint presentation content slides. Coaching PowerPoint Presentation Content slides include topics such as: 25 slides on the characteristics and skills of coaches, Benefits of coaching, techniques for coaching, 8 slides on the "we need to talk" coaching meeting, 10 slides on dealing with poor performance, avoiding coaching pitfalls, 20 slides on the 6- step coaching model, a 1 minute guide to praise/reprimands, discussing recurring problems, 20+ slides on modeling coaching behavior, building a coaching atmosphere and assessing your coaching style. Learn how to utilize open and closed questions, how to's and more!
Computing the Sociology of Survival – how to use simulations to understand c...Bruce Edmonds
This document summarizes Bruce Edmonds' presentation on using simulations to understand complex socio-ecological systems and address ecological survival issues. The presentation discusses using an integrated socio-ecological modeling approach, where an individual-based ecological model is combined with agent-based human societies to analyze their interactions over long time periods. The model simulates evolving food webs and the impacts of introducing human agents who can learn and innovate. Preliminary results show humans rapidly impacting ecosystem populations and diversity by becoming top predators and filling multiple niches. The approach aims to better understand how societal structures influence environmental decision-making.
Why the "hard" problem of consciousness is easy and the "easy" problem hard....Aaron Sloman
The "hard" problem of concsiousness can be shown to be a non-problem because it is formulated using a seriously defective concept (the concept of "phenomenal consciousness" defined so as to rule out cognitive functionality and causal powers).
So the hard problem is an example of a well known type of philosophical problem that needs to be dissolved (fairly easily) rather than solved. For other examples, and a brief introduction to conceptual analysis, see http://www.cs.bham.ac.uk/research/projects/cogaff/misc/varieties-of-atheism.html
In contrast, the so-called "easy" problem requires detailed analysis of very complex and subtle features of perceptual processes, introspective processes and other mental processes, sometimes labelled "access consciousness": these have cognitive functions, but their complexity (especially the way details change as the environment changes or the perceiver moves) is considerable and very hard to characterise.
"Access consciousness" is complex also because it takes many different forms, since what individuals are conscious of and what uses being conscious of things can be put to, can vary hugely, from simple life forms, through many other animals and human infants, to sophisticated adult humans,
Finding ways of modelling these aspects of consciousness, and explaining how they arise out of physical mechanisms, requires major advances in the science of information processing systems -- including computer science and neuroscience.
There are empirical facts about introspection that have generated theories of consciousness but some of the empirical facts go unnoticed by philosophers.
The notion of a virtual machine is introduced briefly and illustrated using Conway's "Game of life" and other examples of virtual machinery that explain how contents of consciousness can have causal powers and can have intentionality (be able to refer to other things).
The beginnings of a research program are presented, showing how more examples can be collected and how notions of virtual machinery may need to be developed to cope with all the phenomena.
This document discusses social complexity and presents two simple agent-based models. It explains that social systems are complex due to cognitive individuals, downward causation between micro and macro levels, social embeddedness, and context-dependent behavior. Two simple models are described: one on homophily-driven altruism and one on ape dominance interactions. While simple, the models demonstrate emergent phenomena from micro-level rules and help explore social processes, but more complex and empirically-validated models are needed to truly understand social systems.
The Modelling of Context-Dependent Causal ProcessesA Recasting of Robert Ros...Bruce Edmonds
Bruce Edmonds discusses recasting some of Robert Rosen's ideas about modeling complex systems in light of context-dependency. Rosen argued against reductionist approaches and sought alternative modeling methods. Edmonds acknowledges Rosen's influence but argues that formal systems can model complex systems if their generality is reduced to improve validity. Edmonds advocates dividing the world into meaningful contexts to develop clustered, layered models that are valid within their scopes. This approach accepts limits to simplicity and generality due to complexity.
Context-dependency, risk analysis and policy modellingBruce Edmonds
The document discusses the gap between research and policy worlds due to their different goals and perspectives. Researchers aim to include caveats and complexities while policymakers want simple recommendations. Context-dependency of human behavior further complicates modeling for policy. Cognitive context influences memory, reasoning and more. Social embedding means behavior depends on networks, not just individuals. The researcher responses include focusing on a single context or seeking general patterns. Policymakers may want narratives for policies. Truly general models are difficult, and context-sensitive approaches are suggested.
The Nature of Language Learning TheoriesDr Shamim Ali
The document discusses several key concepts related to theories in second language acquisition (SLA). It begins by explaining observations that theories need to account for, such as input being necessary but not sufficient for SLA. It also discusses that SLA occurs incidentally through exposure to language and that learners acquire unconscious knowledge beyond what is present in the input. The document also notes that learners' output develops in predictable stages and that SLA outcomes can vary between learners and linguistic subsystems. Key terms like phenomenon, construct, and theory are defined as they relate to researching and explaining SLA.
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.
This document is Timothy Joseph Murphy's final paper for an epistemology seminar at the University of Cincinnati. It examines the concept of simplicity and its relationship to symmetry. Murphy argues that simplicity is a cognitive virtue realized through the application of symmetry in neural representation and processing. The paper discusses previous approaches to justifying simplicity, defines key concepts like symmetry, and proposes experiments to better understand how the brain represents and processes symmetry.
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.
Agroforestry SystemsComplex or worse? by Clas Andersson, Dept. of Energy and...SIANI
This document discusses the complexity of agroforestry systems. It notes that agroforestry involves interactions between many different fields, like social, ecological, economic and political factors. It is difficult to consider any specific context of agroforestry without involving these other interconnected factors. The document suggests that while agroforestry is complicated due to these interactions, it also exhibits emergent properties and patterns that are characteristics of complex systems. It proposes that agroforestry may represent a new class of "wicked" systems that defy characterization by existing theories of complexity or simplicity.
I am very fond of complexity thinking these days. It provides a refreshing alternative for people planning interventions and conducting evaluation in humanitarian and development aid.
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.
Knowledge representation is a field of artificial intelligence that represents information about the world in a way that a computer system can understand to perform complex tasks. It simplifies complex systems through modeling human psychology and problem-solving. Examples of knowledge representation include semantic nets, frames, rules, and ontologies. Knowledge representation allows for automated reasoning about represented knowledge and asserting new knowledge. While first-order logic provides powerful and compact representation, it lacks ease of use and practical implementation for real-world problems. Effective knowledge representation requires balancing expressive power with practical considerations like execution efficiency.
Learning with technology as coordinated sociomaterial practice: digital liter...Martin Oliver
This document discusses conceptualizing educational technology through a sociomaterial lens. It argues that technology is often theorized as having effects on learning, but not how those effects are achieved through sociomaterial relationships. The document advocates analyzing digital literacies as situated practices that coordinate people and technologies in different ways, producing multiple realities. It provides examples analyzing how technologies shape bodies and medical understandings of conditions like atherosclerosis. The overall aim is a praxiological study of digital literacies as networked 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.
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.
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.).
"Objective fiction: the semantic construction of web reality" talks about current challenges for semantic technologies, and the Semantic Web in particular, focusing on cognitive and social dimensions of human semantics.
Complexity theories and language teaching practice – A compatible pairing? Paper presented by Sarah Mercer at the Manchester Roundtable on Complexity and ELT. The University of Manchester, 15 April 2015
Luke Naismith - Workshop - KM Middle East 2011KMMiddleEast
The document describes an upcoming workshop on applying knowledge management in the Middle East context. The workshop will use interactive techniques like anecdote circles and group work to identify factors that promote or inhibit adoption of KM practices in the Middle East and discuss if the region's context is significantly different than others. Participants will explore the issues, share experiences, and identify themes, characters, and traits. If time allows, they will develop archetypes. A mix of sensemaking, narrative inquiry, and strategic foresight applications will guide the workshop's interactive approach.
Similar to Complexity and Context-Dependency (version for Bath IOP Seminar) (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
Modelling Pitfalls - introduction and some casesBruce Edmonds
This document discusses several case studies of agent-based models (ABMs) and some of the potential pitfalls of ABMs. It begins by outlining Bruce Edmonds' approach to discussing pitfalls, which is to start with the purpose of the simulation and think about how it could go wrong. Several examples of ABMs are then summarized, including Schelling's model of racial segregation, opinion dynamics models, a water distribution model of Bali, models for predicting US elections, and models of cooperation evolution. The document concludes by providing a summary of different ABM purposes and some of the particular risks associated with each purpose.
A talk at the workshop on "Agent-Based Models in Philosophy: Prospects and Limitations", Rurh University, Bochum, Germany
Abstract:
ABMs (like other kinds of model) can be used in a purely abstract way, as a kind of thought experiment - a way of thinking about some aspect of the world that is too complicated to hold in our mind (in all its detail). In this way it both informs and complements discursive thought. However there is another set of uses for ABMs - empirical uses - where the mapping between the model and sets of observation-derived data are crucial. For these uses, one has to (a) use the mapping to get from some data to the model (b) use the model for some inference and (c) use the mapping again back to data. This includes both predictive and explanatory uses of ABMs. These are easily distinguishable from abstact uses becuase there is a fixed and well-defined relationship between the model and the data, this is not flexible on a case by case basis. In these cases the reliability comes from the composite (a)-(b)-(c) mapping, so that simplifying step (b) can be counterproductive if that means weakening steps (a) and (c) because it is the strength of the overall chain that is important. Taking the use of models in quantum mechanics as an example, one can see that sometimes the evolution of the formal models driven by empirical adequacy can be more important than the attendent abstract models used to get a feel for what is happening. Although using ABM's for empirical purposes is more challenging than for purely abstract purposes, they are being increasingly used for empirical explanation rather than thought experiments, and there is no reason to suppose that robust empirical adequacy is unachievable.
Mixing fat data, simulation and policy - what could possibly go wrong?Bruce Edmonds
A talk given at the CECAN workshop on "What Good Data could do for Evaluation" at the Alan Turing Institute, 25th Feb. 2019.
Abstract:
In complex situations (which includes most where humans are involved) it is infeasible to predict the impact of any particular policy (or even what is probable). Randomised Control Trials do not tell one: what kinds of situation a policy might work in, what are enablers and inhibitors of the effectiveness of a policy. Here I suggest that using 'fat' data and simulation might allow a possibilistic analysis of policy impact - namely an exploration of what could go surprisingly wrong (or indeed right). Whilst this does not allow the optimisation of policy, it does inform the effective monitoring of policy, and basic contingency planning. However, this requires a different approach to policy - from planning and optimisation to an adaptive approach, with richer continual monitoring and a readiness to tune or adapt policy as data comes in. Examples of this are given concerning domestic water consumption (in the main talk), and in supplementary slides: voter turnout and fishing.
Social Context
An invited talk at the 2018 Surrey Sociology Conference, Barnett Hill, Surrey, November 2018.
Although there is much evidence that context is crucial to much human cognition and social behaviour, it remains a difficult area to research. In much social science research it is either by-passed or ignored. In some qualitative research context is almost deified with any level of generalisation across contexts being left to the reader. At the other extreme, some qualitative research restricts itself to patterns that are generally detectable - that is the patterns that are left when one aggregates over many different contexts. Context is often used as a 'dustbin concept' to which otherwise unexplained variation is attributed.
This talk looks at some of the ways social context might be actively represented, understood and researched. Firstly the ideas of cognitive then social context are distinguished. Then some possible approaches to researching this are discussed, including: agent-based simulation, a context-sensitive analysis of narrative data and machine learning.
Using agent-based simulation for socio-ecological uncertainty analysisBruce Edmonds
A talk given in the MMU Big Data Centrem, 30th October 2018.
Both social and ecological systems can be highly complex, but the interaction between these two worlds - a socio-ecological system (SES) - can add even greater levels. However, the maintenance of SES are vital to our well being and the health of the planet. We do not know how such systems work in practice and we lack good data about them (especially the ecological side) so predicting the effect of any particular policy is infeasible. Here we present an approach which tries to understand some of the ways in which SES may go wrong, but constructing different complex simulation models and analysing the emergent outcomes. These, in silico, examples can allow for the institution of targeted data gathering instruments that give the earliest possible warning of deleterious outcomes, and thus allow for timely remedial responses. An example of this approach applied to fisheries is described.
Finding out what could go wrong before it does – Modelling Risk and UncertaintyBruce Edmonds
The document discusses challenges with using models to predict outcomes for complex systems and situations. It describes how classic policy modeling approaches often make simplifying assumptions that may not accurately capture real-world complexity. Specifically, statistical models assume fixed relationships that don't account for heterogeneity; microsimulations and CGE models only model simple systems and make strong assumptions; and system dynamics and simulation models have limitations due to unknown parameters and structural changes. The document advocates approaches that rapidly sense and react to complex, uncertain systems instead of trying to predict them, as predicting complex, unpredictable situations can provide false confidence and lead to unforeseen consequences.
Agent-based modelling,laboratory experiments,and observation in the wildBruce Edmonds
An invited talk at the workshop on "Social complexity and laboratory experiments – testing assumptions and predictions of social simulation models with experiments" at Social Simulation 2018, Stockholm
Culture trumps ethnicity!– Intra-generational cultural evolution and ethnoce...Bruce Edmonds
Essential to understanding the impact of in-group bias on society is the micro-macro link and the complex dynamics involved. Agent-based modelling (ABM) is the only technique that can formally represent this and thus allow for the more rigorous exploration of possi-ble processes and their comparison with observed social phenomena. This talk discusses these issues, providing some examples of some relevant ABMs.
A talk given at the BIGSSS summer school on conflict, Bremen, Jul/Aug 2018.
An Introduction to Agent-Based ModellingBruce Edmonds
An introduction to the technique with two example models of in-group bias and voter turnout.
An invited talk at the BIGSSS Summer Schools in Computational Social Science, at the Jacobs Bremen University, July 2018.
Mixing ABM and policy...what could possibly go wrong?Bruce Edmonds
Invited talk at 19th International Workshop on Multi-Agent Based Simulation at Stockholm on 14th July 2018.
Mixing ABM and Policy ... what could possibly go wrong?
This talk looks at a number of ways in which using ABM in the context of influencing policy can go wrong: during model construction, with model application and other.
It is related to the book chapter:
Aodha, L. and Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity - a handbook, 2nd edition. Springer, 801-822.
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
This document summarizes Bruce Edmonds' presentation on different purposes of modeling. It discusses how models can be used for prediction, explanation, theoretical exploration, and analogy, but each model must be justified for its specific purpose. It argues against vague notions of "theory" and advocates for developing more general models through a staged process of building on empirical models in gentler steps of abstraction, rather than through large leaps. The goal is to justify models and their relationships through empirical work before claiming general theories.
Socio-Ecological Simulation - a risk-assessment approachBruce Edmonds
An invited talk in Tromsoe, 5 June 2018.
Both social and ecological systems are complex, but when they combine (as when human societies farm/hunt) there is a double complexity. This complexity means it is infeasible to predict the outcome of their interaction and unwise to rely on any prediction. An alternative approach is to use complex simulations to try and discover some possible ways that such systems can go wrong. This can reveal risks that other approaches might miss, due to the fact that more of the complexity is included within the model. Once a risk is identified then measures to monitor its emergence can be implemented, allowing the earliest possible warning of this. An example of this approach applied to a fisheries ecosystem is described.
A talk at the workshop on "Thinking toys (or games) for commoning, Basel, 5/6 April, Switzerland.
This describes a simple model of anonymous donation of resources, with minimal group structuring.
Am open-access paper on this model is at: http://cfpm.org/discussionpapers/152
The model can be freely downloaded from:
http://openABM.org/model/4744
A talk at ESSA@Work, TUHH (Technical University of Hamburg), 24th Nov 2017.
Abstract: Simulation models can only be justified with respect to the models purpose or aim. The talk looks at six common purposes for modelling: prediction, explanation, analogy, theoretical exposition, description, and illustration. Each of these is briefly described, with an example and an brief analysis of the risks to achieving these, and hence how they should be demonstrated. The importance of being explicitly clear about the model purpose is repeatedly emphasised.
The Post-Truth Drift in Social SimulationBruce Edmonds
Bruce Edmonds discusses the potential for "post-truth drift" in social simulation, where models are presented in a way that prioritizes impact over truth. He identifies several ways this can occur: 1) not clearly stating a model's purpose; 2) overly relying on assumptions of simplicity without evidence; 3) over reliance on existing theories without empirical validation; 4) using analogical reasoning to claim false generality; and 5) using ambiguous language. Edmonds advocates clearly communicating a model's limitations and intended purpose to avoid misrepresentation.
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.
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
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
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تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
2. Talk Outline
1. Accepting Complexity
2. Context-Dependency
3. Social Context
4. Looking for Context-Dependency
5. Consequences for Science
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-2
3. Part 1:
Complexity
– everything that is not simple
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-3
4. The Anti-Anthropocentric Assumption
• That the universe is not arranged for our
benefit (as academics)
• e.g. that assumptions such as the following are
likely to be wrong:
– Our planet is the centre of the universe
– Planetary orbits are circles
– Risky events follow a normal distribution
– Humans act as if they followed a simple utility
optimisation algorithm
• The one that I am particularly arguing against
here is that our brains happen to have evolved
so as to be able to understand models
adequate to the phenomena we observe
5. Versions of this assumption
• Whilst other animals have severe limitations and
biases in their cognition, we don‟t
• That our tools (writing, computers etc.) allow us to
escape our limitations and biases to achieve
general intelligence
• That simplicity (that which is easier for us to
analyse) is any guide to truth (other things being
equal etc.)
• If your model is not simple enough to analyse and
understand, you are: (1) not clever enough, (2) lazy
(have not worked hard enough), (3) premature
(don‟t yet have the formal tools to crack it) or (4)
mistaken
• We would simply prefer it were so, e.g. that there
are “no miracles”
6. Living with the AAA
• Accepting that that much of the world around us is
fundamentally beyond modeling that is both
adequate and sufficiently simple and general for us
to cope with
• Acknowledging our (brain+tools) biases and
limitations and so considering how we might extend
our scientific understanding as much as possible
• Phenomena that are simple enough for us to
scientifically understand are the exception – the
exception to be sought and struggled for
• Simplicity is the exception – a science of non-simple
systems makes no more sense than a science of
non-red things
7. Possible modelling trade-offs
• Some desiderata for simplicity
models: validity,
formality, simplicity and Analogy
generality
• these are difficult to Abstract
generality
obtain simultaneously Simulation What
(for complex systems)
Policy
• there is some sort of formality Makers
complicated trade-off Want
between them (for each Data
modelling exercise) validity
8. What is Essential to an Empirical Science?
• Validity: agreement of models to what we observe (the
evidence), not science otherwise
• Formality: formal models (maths, simulation) are
precise and replicable – essential to being able to build
knowledge within a community of researchers
• Simplicity: ability to analyse/understand our models,
good to have but unattainable in general (AAA)
• Generality: the extent of the applicability/scope of a
single model, there needs to be some small generality
to apply models in places other than where developed,
but wide generality not necessary
This talk argues for the following strategy:
weakening the it another way, against the following strategy:
… or, to put generality of our formal models to achieve
weakening validity (e.g. the face of the preserve (the
more validity in to analogy) to AAA
illusion of) generality
9. Consequences of accepting less
generality…
• A lack of models that cover all systems
• Islands and layers of local consistency
• Rather than a reduction between layers, a
modelling relation
• Rather than a “ladder” (total order) of sciences
with the most fundamental at the base, a
patchy network (partial order) of models
• Rather than a neat system of “theories” and
“models”, related clusters of models of
different abstractions and generality
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-9
10. From this…
Geography
Social Sciences Ecology
Reduction
Inference
Psychology Zoology
Biology
Chemistry
Physics
11. …to this!
Weaker Modelling
Relations
Islands of Local
Consistency
Clusters of
Related
Models
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-11
12. This raises some questions…
• Does this make us all relativists?
• Does this mean that scientific knowledge is
just the same as other kinds of belief?
• Does this mean that we should abandon
formal models?
• Does this mean that we cannot attain useful
understanding of complex systems?
• Does this mean that interdisciplinary
science is hopeless?
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-12
13. … to which I answer “No”
• The picture I paint already represents the
reality of understanding non-simple systems
• It just differs from some of the rhetoric of
science, and hence the picture and beliefs
many have about science
• It does have some consequences for how we
do science (to come in following slides)
• Rather, accepting these realities will help us do
better science by being aware of:
– hidden assumptions
– over-generalisations
– reliance on single simple models
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-13
14. Part 2:
Context-Dependency
– and its cognitive roots
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-14
15. 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 situation 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 of contexts
16. The Context “Heuristic” I
• A strategy for dealing with the world by
cognitively limited beings (humans)
• CCs are associated with sets of relevant
„background‟ assumptions, terms, norms,
knowledge, etc. against which the explicit
„foreground‟ learning, reasoning, events, etc.
are conceptualised as occurring
• To be useful kinds of situation needs to be
reliably recognisable as a CC…
• … and packages of foreground features need
to be retrievable from this CC
17. The Context “Heuristic” II
• Integrates the rich, “fuzzy”, and unconscious pattern
recognition of CC with relatively “thin”, crisp and
conscious learning and reasoning within a CC
• Makes within CC reasoning, belief update feasible
• Is dependent on the world being usefully separable
into CCs – which is not necessarily the case
• Also that learnt CC can be reliably recognised later
• CC are learned flexibly – what counts as a
meaningful CC but it is essential that relevant CC
are reliably recognisable later
• Considering the same situation w.r.t. different CC is
often a useful cognitive tool
18. Talking about Context
• What corresponds to CC in the brain may well not
be: accessible to consiousness, introspection,
clearly identifiable, or simple enough to defineThus
it may not be meaningful to talk about “the” context
of any particular event
• Indeed people may consider the same situation
using very different CCs
• However sometimes, in retrospect, the CC can be
roughly identified (especially if socially entrenched)
• Even then it is frequently not explicitly described
• For the sake of the discussion I talk about CC as if it
were definite, given you understand that this is a
simplification
19. About Context-Dependency
• Context-dependency is not relativity since contexts
can be reliably recognised (and/or corrected if
wrongly recognised)
• It is also not merely the “current audience” since
contexts are developed, taught and reinforced over
time (e.g. within academic fields)
• But since it might be recognised in a “fuzzy” and
unconscious manner the bounds of the context may
not be reifiable in crisp terms
• This is a heuristic – a strategy that may help push
forward the boundaries of formal empirical science
• There is some evidence that our cognition is
context-dependent in many ways which means that
to a considerable extent it may be unavoidable
20. Why might the world we study be
usefully split into such “contexts”
• In some cases (e.g. in ecology or social
science) contexts might be co-developed
over time between the entities (e.g. a niche,
or social context like a lecture)
• In some others it may be the only practical
way to proceed
• In yet others our cognitive, unconscious
tendency to deal with the world in terms of
contexts might lead us to try and divide the
world along less useful lines
21. Context and Causality
• In almost all situations (and all social situations)
there are an unlimited number of things that could
be attributed as a cause
• Related to “Causal Spread” (Wheeler); “Wild
Disjunction” (Fodor); and “Embeddedness”
(Granovetter)
• Without a limitation as to the scope causation
makes no sense
• However given a context there are many factors
that can be assumed to be insignificantly relevant
and/or constant
• Thus causality makes sense given a context, since
it excludes most possibilities
22. Transcending Contexts
• It is often desired that a model be
generalised to a broader scope
– From: M holds in context A & M’ holds in
context B if A then M if B then M‟
– However A and B rarely precisely reifiable
• Simplifying does not necessarily lead to
greater generality (by leaving out what is
essential for the case)
• What one can leave out is a hypothesis only
determinable by evidence and experiment
23. Context and Analogical Thinking
• Humans are good at using analogies, relating an
idea or example from one context to another
• They build the mapping from the analogy to the a
context “on the fly” largely unconsciously
• The mappings are different each time an analogy is
applied, thus not a reliable source of knowledge and
each person might build a different mapping but can
yield new insights and can guide research direction
• Many published models do not have an explicit
mapping to a domain, but are used as analogy
• This is sometimes hidden, so when a simulation (or
analytic model) models an idea which applies as an
analogy to a domain and not directly, given a
spurious impression of generality
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-23
24. Part 3:
Social Context
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-24
25. Social Intelligence Hypothesis (SIH)
• Kummer, H., Daston, L., Gigerenzer, G. and Silk, J. (1997)
• The crucial evolutionary advantages that
human intelligence gives are due to the social
abilities and structures it facilitates
• This explains the prevalence of specific
abilities such as: imitation, language, social
norms, lying, alliances, gossip, politics etc.
• Social intelligence is not a result of general
intelligence applied to social organisation, but
the essential core of human intelligence
• in fact our “general” intelligence could be
merely a side-effect of social intelligence
26. An Evolutionary Story
Social intelligence implies that:
• Groups of humans can develop their own,
very different, (sub)cultures of technologies,
norms etc. (Boyd and Richerson 1985)
• These allow the group with their culture to
inhabit a variety of ecological niches (e.g.
the Kalahari, Polynesia) (Reader 1980)
• Thus humans, as a species, are able to
survive catastrophes that effect different
niches in different ways (specialisation)
Social Intelligence and Multi-Agent Systems, Bruce Edmonds, MALLOW 2010, Lyon, 31st August, slide 26
27. 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)
28. Implications of Context-
Dependency in Social Science
• Behaviour of observed actors might need to change
sharply across different social contexts
• The relevant behaviour, norms, kinds of interaction
etc. might also need to change
• Social contexts might need to be co-developed,
changing and sometimes instituted (e.g. a lecture)
• These may need to be different for different groups
• Some kinds of social behaviour are necessarily
context-dependent (compliance)
• It is unlikely that a lot of key social knowledge,
behaviour etc. will be generic and hence amenable
to explicit programming
29. Part 4:
Looking for Context
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-29
30. Kaneko (1990)
• Exhibited a system of parallel chaotic but weakly
coupled processes
• Each process seems chaotic and independent
• But as system size increases, variance as a
proportion of size does not disappear
• Law of large numbers does not apply
Globally coupled
Variance
(scaled by size)
Model with random noise
Size
31. An Illustration of Masked Context-
Dependency
Global
models are
simply
uninformative
when the
phenomena
is context-
dependent
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-31
32. Cleveland Heart Disease Data Set – the
processed sub-set used
In processed sub-set:
• 281 entries
• 14 numeric or numerically coded attributes
• Attribute 14 is the outcome (0, 1, 2, 3, 4)
• Some attributes: age, sex, resting blood
pressure (trestpbs), cholesterol (chol),
fasting blood sugar (fbs), maximum heart
rate (thalach), number of major vessels (0-
3) colored by flourosopy (ca)
• From the Machine Learning Repository
33. General Correlations (1% Sig)
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-33
34. Fitting a Global Model (R=56%)
Num = -0.01*age + 0.17*sex + 0.20*cp + 0.00*trestbps + 0.10*restecg + -
0.01*thalach + 0.23*exang + 0.18*oldpeak + 0.16*slope + 0.43*ca + 0.14*thal + -
0.60 (+/- 0.83)
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-34
35. Looking for Clusters in HD Data Set
(Start of Process)
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-35
36. After Solutions Locally Evolve
Speciation of
Solutions
In some areas
no solution
dominates
Some
Solutions
Spread over
area of
applicability
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-36
37. Final Set of Clustered Solutions
• Final solution
set after some
time.
• Still complex but
some structure
is revealed
• Note presence
of “fbs” despite
not being
globally
correlated and
that “chol”
helped define
the context
space
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-37
38. Part 5:
Consequences for Science
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-38
39. Consequences of Context-
Dependency I: not ignoring context
• Much modelling happens with a single
context in mind, in which it can be 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
• Unfortunately the indication of the intended
scope is often only implicit
40. Implications for Modelling Complex
Systems
• It is very useful to describe, as far as
possible, the intended scope of a model
• Applying a model developed within one
context elsewhere (including a more
general scope) is difficult
• No easy way to transcend context
• Difficult to reify contexts to get generality
Ignoring context may have the result that our
models are either (a) subtly and critically misleading
or (b) merely analogies in computational form
41. Consequences of Context-
Dependency II: clusters of models
• Instead of having one model for one
phenomena we may end up with a cluster
of loosely-related models that each
represent different aspects of it
• Given that we want to understand our
models and only a few models are
analytically tractable this happens anyway
• Separate but related models may avoid
over-generalised models that do not directly
relate to anything observed (or another
model) and rely on imprecise interpretation
42. Example: an Ideal Gas
Ideal Gas Laws Macro Data
Models
Analytical
Derivations using
simplifying
assumptions
Simulation Models
Atomic Model of Micro data and
an Ideal Gas understanding
43. Consequences of Context-
Dependency III: layers of models
• Given we need both rigour (understanding our
models) and relevance (clear mapping to what is
observed) in our models...
• We might have complicated, descriptive simulations
that relate in a more direct way to data models of
what we observe: a Data-Integration Model (DIM)
• But then need to model the DIM with simpler
simulations to understand it and check its
programmed correctly
• As well as (maybe, hopefully) be able to generalise
from it (and other similar DIMs) to a model which
generalises certain aspects but to a wider scope
44. The Approach in the SCID Project
Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-44
45. Consequences of Context-
Dependency IV: noise
• Whilst some noise comes from an identified
source within a system (e.g. heat noise)
• Other noise comes from without (e.g. the babble
of a crowd around a conversation)
• This kind of noise is recognisable as an extra-
contextual “leak”, due to the imperfection of the
context heuristic (or poorly chosen context)
• Such noise is not necessarily random…
• …indeed it may well be more productive to look
for the context-dependency rather than “dumping”
the lack of fit as random “noise”
• (Possibilistic as opposed to probabilistic models)
• e.g. Peter Allen‟s “Invaders” in Evolution
46. Conclusions
• In the face of complexity, context-dependency is
unavoidable
• Accepting and thinking about context-dependency
need not lead to sloppy, relativistic or bad science
• In fact it is desirable, since simple systems are the
special case and other trade-offs are worse…
• …such as gaining generality at the loss of validity
• It may lead to pushing the boundaries of science
forward a bit more and avoiding some pitfalls
• It could motivate a move to a more complex and
structural understanding of what is observed
• We may end up with a “patch work” of locally
coherent model clusters of a lot of different kinds –
science may have to be more like zoology
• Science being imperfect or incomplete does not
invalidate its project
47. The End
Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org
The SCID Project
http://scid-project.org
Editor's Notes
3 parts to talk
Elsewhere I have argued about simplicity as a guide to truth
rest of the talk I
I am not claiming that such trade-offs are fixed, universal or simpleComes from modelling experienceTalk about validity, formality, complexity, generality
different modelling goals and kinds of validityschrodinger’s equation – we dont understand its analytic consequences but its still usefulnot john symonds “reasons” for not abandoning a fundermenatlist approach were a simple wish for no micarcles
e.g. thinking of a problem as an opportunity
although not reifiable, we may be able to recognise when we have got the context wrong
Example of someone who broke their leg for unlimited number of causesin the broken leg example we can exclude that gravity was too strongFormalisms such as Pearl are only applicable given a context
no reason to suppose that our brains happen to be evolved to directly understand a model adequate to much social phenomenaIt may be that we have to make do with lots of different context-specific simulations
Whilst fish live inhabit, we (as humans) inhabit society
Reader 1980, Man on Earth
Social Intelligence HypothesisWittgenstein, Vygotsky, TomaselloContexts are often described using their social features “I was talking to my mother”
leakage noisenot the case where un-modelled aspects are effectively randomdiscuss random gas example
not possible to describe context entirely, but any hints are better than nonedifficult to represent many as contexts explicitly specified, but could be emergentit is standard practice to try and indicate context in social sciences, it should become the practice in SS
in fact we are generally only happy when a lot of different bridges are made between different models
We should abandon meta-physical commitments such as the theoretical completeness of science and emphasise its strong points – that it worksWhy is it reluctant to do so, because of the foundational status it gives science, but remember philsophy used to have this kind of status, and look what happened to it