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
An Introduction to Agent-Based ModellingBruce Edmonds
An introduction to the technique with two example models of in-group bias and voter turnout.
An invited talk at the BIGSSS Summer Schools in Computational Social Science, at the Jacobs Bremen University, July 2018.
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
Models are a tool, not a picture of reality. There are many different uses for models. The intended use of a model - its 'purpose' - affects how it is judged, checked and developed. Much confusion and bad practice in modelling can be attributed to not clearly identifying the intended 'purpose' for a model. Neo-classical Economics is used to illustrate some of these confusions. In some (but not all) uses the model stands in for a theory (at least key aspects of it), but this can happen in different ways and at different levels of abstraction. The talk looks at some of these different ways and advocates a staged, inductive methodology for theory development instead of one that jumps to high generality and simple models which confuse different uses.
A talk given at the Workshop on "From Cases To General Principles - Theory Development Through Agent-Based Modeling" see http://abm-theory.org
A talk at ESSA@Work, TUHH (Technical University of Hamburg), 24th Nov 2017.
Abstract: Simulation models can only be justified with respect to the models purpose or aim. The talk looks at six common purposes for modelling: prediction, explanation, analogy, theoretical exposition, description, and illustration. Each of these is briefly described, with an example and an brief analysis of the risks to achieving these, and hence how they should be demonstrated. The importance of being explicitly clear about the model purpose is repeatedly emphasised.
The Post-Truth Drift in Social SimulationBruce Edmonds
A talk at the Social Simulation Conference, Dublin, September 2017.
Abstract
The paper identifies a danger in the field of social simulation a danger of using weasel words to give a false impression to the world about the achievements of our field. Whether this is intentional or unintentional, the effect might be to damage the reputation of the field and impair its development. At the root of this is a need for brutal honesty and openness, something that can be personally difficult and that needs social support. The paper considers some of the subtle ways that this kind of post-truth drift might occur, including: confusion/conflation of modelling purpose, wishing to justify pragmatic limitations in our work, falling back to unvalidated theory, confusing using a model for a way of looking at the world for something more reliable, and seeking protection from critique in vagueness. It calls on social simulation researchers to firmly reject such a drift.
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
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.
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.
An Introduction to Agent-Based ModellingBruce Edmonds
An introduction to the technique with two example models of in-group bias and voter turnout.
An invited talk at the BIGSSS Summer Schools in Computational Social Science, at the Jacobs Bremen University, July 2018.
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
Models are a tool, not a picture of reality. There are many different uses for models. The intended use of a model - its 'purpose' - affects how it is judged, checked and developed. Much confusion and bad practice in modelling can be attributed to not clearly identifying the intended 'purpose' for a model. Neo-classical Economics is used to illustrate some of these confusions. In some (but not all) uses the model stands in for a theory (at least key aspects of it), but this can happen in different ways and at different levels of abstraction. The talk looks at some of these different ways and advocates a staged, inductive methodology for theory development instead of one that jumps to high generality and simple models which confuse different uses.
A talk given at the Workshop on "From Cases To General Principles - Theory Development Through Agent-Based Modeling" see http://abm-theory.org
A talk at ESSA@Work, TUHH (Technical University of Hamburg), 24th Nov 2017.
Abstract: Simulation models can only be justified with respect to the models purpose or aim. The talk looks at six common purposes for modelling: prediction, explanation, analogy, theoretical exposition, description, and illustration. Each of these is briefly described, with an example and an brief analysis of the risks to achieving these, and hence how they should be demonstrated. The importance of being explicitly clear about the model purpose is repeatedly emphasised.
The Post-Truth Drift in Social SimulationBruce Edmonds
A talk at the Social Simulation Conference, Dublin, September 2017.
Abstract
The paper identifies a danger in the field of social simulation a danger of using weasel words to give a false impression to the world about the achievements of our field. Whether this is intentional or unintentional, the effect might be to damage the reputation of the field and impair its development. At the root of this is a need for brutal honesty and openness, something that can be personally difficult and that needs social support. The paper considers some of the subtle ways that this kind of post-truth drift might occur, including: confusion/conflation of modelling purpose, wishing to justify pragmatic limitations in our work, falling back to unvalidated theory, confusing using a model for a way of looking at the world for something more reliable, and seeking protection from critique in vagueness. It calls on social simulation researchers to firmly reject such a drift.
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
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.
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.
The Past, Present and Future of ABM: How To Cope With A New Research Method Edmund Chattoe-Brown
This talk considers the challenges of developing a "canon" for ABM based on research (some of which has been forgotten), the present problem situation of many non comparable models and a possible future based on greater interdisciplinary and more systematic development of methodology.
Why Do We CollaborateWhy do human beings collaborate.docxalanfhall8953
Why Do We Collaborate?
Why do human beings collaborate
Steve Denning
Why Do We Collaborate?
Comment Now
Follow Comments
Every man must decide whether he will walk in the light of creative altruism or in the darkness of destructive selfishness.
–Martin Luther King Jr.
Why do human beings collaborate? Ever since Darwin, biologists have been vexed by the question, because in evolutionary terms, self-less behavior makes no sense. We would expect altruists who act contrary to their own interest to be systematically eliminated from the species.
In an interesting new book, The Social Conquest of Earth, Edward O. Wilson argues that altruism is a result not of individual selection (as biologists have thought), but ofgroup selection. Wilson argues that a tribe with many members willing to contribute to or sacrifice themselves for the common good will be victorious over other tribes that are less collaborative. His book draws from social psychology, archaeology and evolutionary biology and examines those species that have developed advanced social lives, or what biologists call “eusociality”—bees, ants, termites and human beings. These species have been extraordinarily successful and are extremely rare.
“Our ancestors,” Wilson writes, “were one of only two dozen or so animal lines ever to evolve eusociality, the next major level of biological organization above the organismic. There, group members across two or more generations stay together, cooperate, care for the young, and divide labor in a way favoring reproduction of some individuals over that in others.”
Wilson argues that evolutionary competition among ants is best understood not at the individual level but at the level of the colony. The battle of fitness is waged at the level of the hive, not the individual bee.
Humans, Wilson argues, have become genetically hard-wired to join groups. Once having joined a group, members tend to see the group as superior to competing groups. Our groups—tribes, teams, communities, nations—compete with one another for dominance, but as individuals, we also compete for survival and reproduction within groups via individual selection.
Overall, selfish individuals might defeat altruistic individuals, but groups of collaborators are victorious over groups of selfish people. The human condition, Wilson concludes, is largely a product of the tension between the two impulses.
Human beings thus experience multilevel selection: individual selection and group selection. The two modes operate together on the same individual, but largely in opposition to each other. Individual selection shapes selfish instincts in each member while group selection shapes instincts that encourage collaboration within the group, but not toward members of other groups.
Individual selection is responsible for much of what ethicists label morally reprehensible conduct, while group selection is responsible for the greater part of ethical or good conduct. Together, the two .
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.).
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.
Getting Beyond Groupthink and Ineffective BrainstormingSteven Martin
Even in the best of circumstances, chances are on any project or program, there can be some amount of alternate opinions that are not heard. Some recent research suggests that the traditional group brainstorming techniques are actually not very effective. And when it comes to generating ideas, groupthink can severely limit diversity of thought. In the end, there are better ways to generate and sustain ideas.
5.6 Gestalt Principles of PerceptionLearning Objectives.docxblondellchancy
5.6 Gestalt Principles of Perception
Learning Objectives
By the end of this section, you will be able to:
• Explain the figure-ground relationship
• Define Gestalt principles of grouping
• Describe how perceptual set is influenced by an individual’s characteristics and mental state
In the early part of the 20th century, Max Wertheimer published a paper demonstrating that individuals
perceived motion in rapidly flickering static images—an insight that came to him as he used a child’s toy
tachistoscope. Wertheimer, and his assistants Wolfgang Köhler and Kurt Koffka, who later became his
partners, believed that perception involved more than simply combining sensory stimuli. This belief led to
a new movement within the field of psychology known as Gestalt psychology. The word gestalt literally
means form or pattern, but its use reflects the idea that the whole is different from the sum of its parts. In
other words, the brain creates a perception that is more than simply the sum of available sensory inputs,
and it does so in predictable ways. Gestalt psychologists translated these predictable ways into principles
by which we organize sensory information. As a result, Gestalt psychology has been extremely influential
in the area of sensation and perception (Rock & Palmer, 1990).
One Gestalt principle is the figure-ground relationship. According to this principle, we tend to segment
our visual world into figure and ground. Figure is the object or person that is the focus of the visual
field, while the ground is the background. As Figure 5.23 shows, our perception can vary tremendously,
depending on what is perceived as figure and what is perceived as ground. Presumably, our ability to
interpret sensory information depends on what we label as figure and what we label as ground in any
particular case, although this assumption has been called into question (Peterson & Gibson, 1994; Vecera
& O’Reilly, 1998).
Figure 5.23 The concept of figure-ground relationship explains why this image can be perceived either as a vase or
as a pair of faces.
Another Gestalt principle for organizing sensory stimuli into meaningful perception is proximity. This
principle asserts that things that are close to one another tend to be grouped together, as Figure 5.24
illustrates.
172 Chapter 5 | Sensation and Perception
This OpenStax book is available for free at https://cnx.org/content/col11629/1.5
Figure 5.24 The Gestalt principle of proximity suggests that you see (a) one block of dots on the left side and (b)
three columns on the right side.
How we read something provides another illustration of the proximity concept. For example, we read this
sentence like this, notl iket hiso rt hat. We group the letters of a given word together because there are no
spaces between the letters, and we perceive words because there are spaces between each word. Here are
some more examples: Cany oum akes enseo ft hiss entence? What doth es e wor dsmea n?
We might also use the pr ...
Sustainable Practices, Art and Design Thinkingdrbastiaan
Building a foundation for the future requires radical thinking, creative solutions, and collaborative action to navigate beyond today’s economic and global challenges.
ARE YOU SOLVING THE RIGHT PROBLEMSREFRAMING THEM CA.docxfestockton
ARE YOU
SOLVING
THE RIGHT
PROBLEMS?
REFRAMING THEM CAN REVEAL
UNEXPECTED SOLUTIONS.
BY THOMAS WEDELL-WEDELLSBORG
PHOTOGRAPHY BY FREDRIK BRODEN
FEATURE ARE YOU SOLVING THE RIGHT PROBLEMS?
76 HARVARD BUSINESS REVIEW JANUARY–FEBRUARY 2017
emphasized the importance of properly diagnosing
your problems. So why do organizations still struggle
to get it right?
Part of the reason is that we tend to overengineer
the diagnostic process. Many existing frameworks—
TRIZ, Six Sigma, Scrum, and others—are quite com-
prehensive. When properly applied, they can be tre-
mendously powerful. But their very thoroughness
also makes them too complex and time-consuming to
fit into a regular workday. The setting in which people
most need to be better at problem diagnosis is not the
annual strategy seminar but the daily meeting—so we
need tools that don’t require the entire organization
to undergo weeks-long training programs.
But even when people apply simpler problem-
diagnosis frameworks, such as root cause analy-
sis and the related 5 Whys questioning technique,
they often find themselves digging deeper into the
how good
is your
company
at problem
solving?
Probably quite good, if your managers are like
those at the companies I’ve studied. What they strug-
gle with, it turns out, is not solving problems but fig-
uring out what the problems are. In surveys of 106
C-suite executives who represented 91 private and
public-sector companies in 17 countries, I found that a
full 85% strongly agreed or agreed that their organiza-
tions were bad at problem diagnosis, and 87% strongly
agreed or agreed that this flaw carried significant
costs. Fewer than one in 10 said they were unaffected
by the issue. The pattern is clear: Spurred by a pen-
chant for action, managers tend to switch quickly into
solution mode without checking whether they really
understand the problem.
It has been 40 years since Mihaly Csikszentmihalyi
a n d Ja c o b G e t ze l s e m p i r i c a l l y d e m o n s t r ate d
the central role of problem framing in creativity.
Thinkers from Albert Einstein to Peter Drucker have
78 HARVARD BUSINESS REVIEW JANUARY–FEBRUARY 2017
FEATURE ARE YOU SOLVING THE RIGHT PROBLEMS?
problem they’ve already defined rather than arriving
at another diagnosis. That can be helpful, certainly.
But creative solutions nearly always come from an
alternative definition of your problem.
Through my research on corporate innovation,
much of it conducted with my colleague Paddy
Miller, I have spent close to 10 years working with
and studying reframing—first in the narrow context
of organizational change and then more broadly. In
the following pages I offer a new approach to prob-
lem diagnosis that can be applied quickly and, I’ve
found, frequently leads to creative solutions by un-
earthing radically different framings of familiar and
persistent problems. To put reframing in context,
I’ll explain more precisel ...
What Is An Expository Essay. What Is an Expository Essay? Types, Structure, E...Ladonna Mayer
Expository Essay Masterclass: The Art of Perfect Writing - Wr1ter.com. How to Write an Expository Essay: Examples and 25 Topic Ideas - How to .... How To Write An Expository Essay in 6 Steps CustomEssayMeister.com. Expository Essay: Definition, Outline, Topics amp; Examples of Expository .... How To Write An Expository Essay 7 Best Tips. Expository Essay - 6 Examples, Format, Pdf Examples. PPT - Expository Essay PowerPoint Presentation, free download - ID:1429673. Free Expository Essay Writing Tips and Guidelines. What Is an Expository Essay? Types, Structure, Examples. Expository Essay Essays Cognition. How to Write an Expository Essay. What Is an Expository Essay? Examples and Guide YourDictionary. What Is an Expository Essay and How to Write It CustomEssayMeister.com. Example of expository paragraph. Expository Essays: Types .... Writing Workshop: Expository/College Essay - Mrs. Guillorys English Class. Expository Essay. Expository Essay ExamplesGreat Topic Ideas Pro Essay Help. Expository essay for kids. Expository Writing for Children: Tips .... How to Write an Expository Essay Step by Step. Expository essay writing - College Homework Help and Online Tutoring.. Example Of Expository Essay Expository Writing. ️ Whats an expository essay. Best Expository Essay Topics 2018 For .... How To Write An Expository Essay Total Assignment Help. Expository Essay Sample: Academic Guide. Expository Essays Exposed for Students. The Expository Essay Essays. Expository essay template. Expository Essay. 2022-10-19. FREE 8 Sample Expository Essay Templates in MS Word PDF. Example of expository paragraph about love. How to Write an Essay in .... Descriptive Essay: Expository essay definition and examples. Expository Essays: A Complete Writing Guide for Beginners. How To Write Expository Essay Sketsa. How to write Excellent Expository Essays What Is An Expository Essay What Is An Expository Essay. What Is an Expository Essay? Types, Structure, Examples
Going Horizontal: The path to better organizations and a better society - Sam...Spark the Change Montréal
Collective leadership, self-management, employee led organization, non-hierarchical or horizontal ways of working – no matter what we call it, these ways of working together are what allow organizations to tap into their full potential and respond to their most current and relevant challenges.
When organizations begin to shift their culture towards more explicit forms of shared power, mutual responsibility and care, the journey forward is neither clear nor easy, and we often get stuck.
But what if organizations themselves are the ideal training grounds for developing the most critical personal and collective leadership skills required for our shared future?
Based on a unique anthropological lens, Samantha has uncovered a practical approach to going horizontal that can help anyone, no matter the domain of your activity, the size of your organization or your role within it.
PLUS THE DECISION MAKING PROCESS12LikeLikeTweet 4.docxLeilaniPoolsy
PLUS: THE DECISION MAKING PROCESS
12LikeLike
Tweet 4
2
MAY 29, 2009
Document
We selected a six step decision making process that synthesized the decision making models
used in existing training, not just ethics training.
The model is descriptive of how people intuitively make decisions and makes the steps
explicit.
The six steps of this natural, intuitive decision-making process are:
• Step 1:
Define the problem (#1)
• Step 2:
Identify available alternative solutions to the problem (#2)
• Step 3:
Evaluate the identified alternatives (#3)
• Step 4:
Make the decision (#4)
• Step 5:
Implement the decision (#5)
• Step 6:
Evaluate the decision (#6)
Step 1: Define the problem
The most significant step in any decision making process is describing why a decision is called for and identifying the
most desired outcome(s) of the decision making process.
One way of deciding if a problem exists is to couch the problem in terms of what one wanted or expected and the actual
situation. In this way a problem is defined as the difference between expected and/or desired outcomes and actual
outcomes.
This careful attention to definition in terms of outcomes allows one to clearly state the problem. This is a critical
consideration because how one defines a problem determines how one defines causes and where one searches for
solutions.
The limiting aspect of the problem definition step is not widely appreciated. Consider this example.
Your company owns an old, downtown office building. Tenants are complaining that their employees are getting angry
and frustrated because there is always a long delay getting an elevator to the lobby at rush hour.
You are asked for a reaction on how to solve this problem. As with most problem situations there are several ways to
define the situation and several solutions that suggest themselves.
This scenario has been presented to over 200 groups in a training environment. The most common alternatives these
groups offered were:
• Flexible hours- so all the tenants' employees wouldn't be at the elevators at the same time.
• Faster elevators - so each elevator could carry more people in a given time period.
• Bigger elevators - so each elevator could carry more people per trip.
• Elevator banks- so each elevator would only stop on certain floors, increasing efficiency.
• Better elevator controls - so each eltor would be used more efficiently.
• More elevators - so that overall carrying capacity could be increased.
• Improved elevator maintenance - so each elevator would be more efficient.
• Encourage employees to use the stairs - so fewer people would use the elevators.
PLUS: The Decision Making Process | Ethics Resource Center
If you examine each alternative you will see that several different definitions of the problem must have existed.
• If the solution is "flexible hours" the problem must have been defined as, "Too many people getting off work at a
given ti.
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
The Past, Present and Future of ABM: How To Cope With A New Research Method Edmund Chattoe-Brown
This talk considers the challenges of developing a "canon" for ABM based on research (some of which has been forgotten), the present problem situation of many non comparable models and a possible future based on greater interdisciplinary and more systematic development of methodology.
Why Do We CollaborateWhy do human beings collaborate.docxalanfhall8953
Why Do We Collaborate?
Why do human beings collaborate
Steve Denning
Why Do We Collaborate?
Comment Now
Follow Comments
Every man must decide whether he will walk in the light of creative altruism or in the darkness of destructive selfishness.
–Martin Luther King Jr.
Why do human beings collaborate? Ever since Darwin, biologists have been vexed by the question, because in evolutionary terms, self-less behavior makes no sense. We would expect altruists who act contrary to their own interest to be systematically eliminated from the species.
In an interesting new book, The Social Conquest of Earth, Edward O. Wilson argues that altruism is a result not of individual selection (as biologists have thought), but ofgroup selection. Wilson argues that a tribe with many members willing to contribute to or sacrifice themselves for the common good will be victorious over other tribes that are less collaborative. His book draws from social psychology, archaeology and evolutionary biology and examines those species that have developed advanced social lives, or what biologists call “eusociality”—bees, ants, termites and human beings. These species have been extraordinarily successful and are extremely rare.
“Our ancestors,” Wilson writes, “were one of only two dozen or so animal lines ever to evolve eusociality, the next major level of biological organization above the organismic. There, group members across two or more generations stay together, cooperate, care for the young, and divide labor in a way favoring reproduction of some individuals over that in others.”
Wilson argues that evolutionary competition among ants is best understood not at the individual level but at the level of the colony. The battle of fitness is waged at the level of the hive, not the individual bee.
Humans, Wilson argues, have become genetically hard-wired to join groups. Once having joined a group, members tend to see the group as superior to competing groups. Our groups—tribes, teams, communities, nations—compete with one another for dominance, but as individuals, we also compete for survival and reproduction within groups via individual selection.
Overall, selfish individuals might defeat altruistic individuals, but groups of collaborators are victorious over groups of selfish people. The human condition, Wilson concludes, is largely a product of the tension between the two impulses.
Human beings thus experience multilevel selection: individual selection and group selection. The two modes operate together on the same individual, but largely in opposition to each other. Individual selection shapes selfish instincts in each member while group selection shapes instincts that encourage collaboration within the group, but not toward members of other groups.
Individual selection is responsible for much of what ethicists label morally reprehensible conduct, while group selection is responsible for the greater part of ethical or good conduct. Together, the two .
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.).
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.
Getting Beyond Groupthink and Ineffective BrainstormingSteven Martin
Even in the best of circumstances, chances are on any project or program, there can be some amount of alternate opinions that are not heard. Some recent research suggests that the traditional group brainstorming techniques are actually not very effective. And when it comes to generating ideas, groupthink can severely limit diversity of thought. In the end, there are better ways to generate and sustain ideas.
5.6 Gestalt Principles of PerceptionLearning Objectives.docxblondellchancy
5.6 Gestalt Principles of Perception
Learning Objectives
By the end of this section, you will be able to:
• Explain the figure-ground relationship
• Define Gestalt principles of grouping
• Describe how perceptual set is influenced by an individual’s characteristics and mental state
In the early part of the 20th century, Max Wertheimer published a paper demonstrating that individuals
perceived motion in rapidly flickering static images—an insight that came to him as he used a child’s toy
tachistoscope. Wertheimer, and his assistants Wolfgang Köhler and Kurt Koffka, who later became his
partners, believed that perception involved more than simply combining sensory stimuli. This belief led to
a new movement within the field of psychology known as Gestalt psychology. The word gestalt literally
means form or pattern, but its use reflects the idea that the whole is different from the sum of its parts. In
other words, the brain creates a perception that is more than simply the sum of available sensory inputs,
and it does so in predictable ways. Gestalt psychologists translated these predictable ways into principles
by which we organize sensory information. As a result, Gestalt psychology has been extremely influential
in the area of sensation and perception (Rock & Palmer, 1990).
One Gestalt principle is the figure-ground relationship. According to this principle, we tend to segment
our visual world into figure and ground. Figure is the object or person that is the focus of the visual
field, while the ground is the background. As Figure 5.23 shows, our perception can vary tremendously,
depending on what is perceived as figure and what is perceived as ground. Presumably, our ability to
interpret sensory information depends on what we label as figure and what we label as ground in any
particular case, although this assumption has been called into question (Peterson & Gibson, 1994; Vecera
& O’Reilly, 1998).
Figure 5.23 The concept of figure-ground relationship explains why this image can be perceived either as a vase or
as a pair of faces.
Another Gestalt principle for organizing sensory stimuli into meaningful perception is proximity. This
principle asserts that things that are close to one another tend to be grouped together, as Figure 5.24
illustrates.
172 Chapter 5 | Sensation and Perception
This OpenStax book is available for free at https://cnx.org/content/col11629/1.5
Figure 5.24 The Gestalt principle of proximity suggests that you see (a) one block of dots on the left side and (b)
three columns on the right side.
How we read something provides another illustration of the proximity concept. For example, we read this
sentence like this, notl iket hiso rt hat. We group the letters of a given word together because there are no
spaces between the letters, and we perceive words because there are spaces between each word. Here are
some more examples: Cany oum akes enseo ft hiss entence? What doth es e wor dsmea n?
We might also use the pr ...
Sustainable Practices, Art and Design Thinkingdrbastiaan
Building a foundation for the future requires radical thinking, creative solutions, and collaborative action to navigate beyond today’s economic and global challenges.
ARE YOU SOLVING THE RIGHT PROBLEMSREFRAMING THEM CA.docxfestockton
ARE YOU
SOLVING
THE RIGHT
PROBLEMS?
REFRAMING THEM CAN REVEAL
UNEXPECTED SOLUTIONS.
BY THOMAS WEDELL-WEDELLSBORG
PHOTOGRAPHY BY FREDRIK BRODEN
FEATURE ARE YOU SOLVING THE RIGHT PROBLEMS?
76 HARVARD BUSINESS REVIEW JANUARY–FEBRUARY 2017
emphasized the importance of properly diagnosing
your problems. So why do organizations still struggle
to get it right?
Part of the reason is that we tend to overengineer
the diagnostic process. Many existing frameworks—
TRIZ, Six Sigma, Scrum, and others—are quite com-
prehensive. When properly applied, they can be tre-
mendously powerful. But their very thoroughness
also makes them too complex and time-consuming to
fit into a regular workday. The setting in which people
most need to be better at problem diagnosis is not the
annual strategy seminar but the daily meeting—so we
need tools that don’t require the entire organization
to undergo weeks-long training programs.
But even when people apply simpler problem-
diagnosis frameworks, such as root cause analy-
sis and the related 5 Whys questioning technique,
they often find themselves digging deeper into the
how good
is your
company
at problem
solving?
Probably quite good, if your managers are like
those at the companies I’ve studied. What they strug-
gle with, it turns out, is not solving problems but fig-
uring out what the problems are. In surveys of 106
C-suite executives who represented 91 private and
public-sector companies in 17 countries, I found that a
full 85% strongly agreed or agreed that their organiza-
tions were bad at problem diagnosis, and 87% strongly
agreed or agreed that this flaw carried significant
costs. Fewer than one in 10 said they were unaffected
by the issue. The pattern is clear: Spurred by a pen-
chant for action, managers tend to switch quickly into
solution mode without checking whether they really
understand the problem.
It has been 40 years since Mihaly Csikszentmihalyi
a n d Ja c o b G e t ze l s e m p i r i c a l l y d e m o n s t r ate d
the central role of problem framing in creativity.
Thinkers from Albert Einstein to Peter Drucker have
78 HARVARD BUSINESS REVIEW JANUARY–FEBRUARY 2017
FEATURE ARE YOU SOLVING THE RIGHT PROBLEMS?
problem they’ve already defined rather than arriving
at another diagnosis. That can be helpful, certainly.
But creative solutions nearly always come from an
alternative definition of your problem.
Through my research on corporate innovation,
much of it conducted with my colleague Paddy
Miller, I have spent close to 10 years working with
and studying reframing—first in the narrow context
of organizational change and then more broadly. In
the following pages I offer a new approach to prob-
lem diagnosis that can be applied quickly and, I’ve
found, frequently leads to creative solutions by un-
earthing radically different framings of familiar and
persistent problems. To put reframing in context,
I’ll explain more precisel ...
What Is An Expository Essay. What Is an Expository Essay? Types, Structure, E...Ladonna Mayer
Expository Essay Masterclass: The Art of Perfect Writing - Wr1ter.com. How to Write an Expository Essay: Examples and 25 Topic Ideas - How to .... How To Write An Expository Essay in 6 Steps CustomEssayMeister.com. Expository Essay: Definition, Outline, Topics amp; Examples of Expository .... How To Write An Expository Essay 7 Best Tips. Expository Essay - 6 Examples, Format, Pdf Examples. PPT - Expository Essay PowerPoint Presentation, free download - ID:1429673. Free Expository Essay Writing Tips and Guidelines. What Is an Expository Essay? Types, Structure, Examples. Expository Essay Essays Cognition. How to Write an Expository Essay. What Is an Expository Essay? Examples and Guide YourDictionary. What Is an Expository Essay and How to Write It CustomEssayMeister.com. Example of expository paragraph. Expository Essays: Types .... Writing Workshop: Expository/College Essay - Mrs. Guillorys English Class. Expository Essay. Expository Essay ExamplesGreat Topic Ideas Pro Essay Help. Expository essay for kids. Expository Writing for Children: Tips .... How to Write an Expository Essay Step by Step. Expository essay writing - College Homework Help and Online Tutoring.. Example Of Expository Essay Expository Writing. ️ Whats an expository essay. Best Expository Essay Topics 2018 For .... How To Write An Expository Essay Total Assignment Help. Expository Essay Sample: Academic Guide. Expository Essays Exposed for Students. The Expository Essay Essays. Expository essay template. Expository Essay. 2022-10-19. FREE 8 Sample Expository Essay Templates in MS Word PDF. Example of expository paragraph about love. How to Write an Essay in .... Descriptive Essay: Expository essay definition and examples. Expository Essays: A Complete Writing Guide for Beginners. How To Write Expository Essay Sketsa. How to write Excellent Expository Essays What Is An Expository Essay What Is An Expository Essay. What Is an Expository Essay? Types, Structure, Examples
Going Horizontal: The path to better organizations and a better society - Sam...Spark the Change Montréal
Collective leadership, self-management, employee led organization, non-hierarchical or horizontal ways of working – no matter what we call it, these ways of working together are what allow organizations to tap into their full potential and respond to their most current and relevant challenges.
When organizations begin to shift their culture towards more explicit forms of shared power, mutual responsibility and care, the journey forward is neither clear nor easy, and we often get stuck.
But what if organizations themselves are the ideal training grounds for developing the most critical personal and collective leadership skills required for our shared future?
Based on a unique anthropological lens, Samantha has uncovered a practical approach to going horizontal that can help anyone, no matter the domain of your activity, the size of your organization or your role within it.
PLUS THE DECISION MAKING PROCESS12LikeLikeTweet 4.docxLeilaniPoolsy
PLUS: THE DECISION MAKING PROCESS
12LikeLike
Tweet 4
2
MAY 29, 2009
Document
We selected a six step decision making process that synthesized the decision making models
used in existing training, not just ethics training.
The model is descriptive of how people intuitively make decisions and makes the steps
explicit.
The six steps of this natural, intuitive decision-making process are:
• Step 1:
Define the problem (#1)
• Step 2:
Identify available alternative solutions to the problem (#2)
• Step 3:
Evaluate the identified alternatives (#3)
• Step 4:
Make the decision (#4)
• Step 5:
Implement the decision (#5)
• Step 6:
Evaluate the decision (#6)
Step 1: Define the problem
The most significant step in any decision making process is describing why a decision is called for and identifying the
most desired outcome(s) of the decision making process.
One way of deciding if a problem exists is to couch the problem in terms of what one wanted or expected and the actual
situation. In this way a problem is defined as the difference between expected and/or desired outcomes and actual
outcomes.
This careful attention to definition in terms of outcomes allows one to clearly state the problem. This is a critical
consideration because how one defines a problem determines how one defines causes and where one searches for
solutions.
The limiting aspect of the problem definition step is not widely appreciated. Consider this example.
Your company owns an old, downtown office building. Tenants are complaining that their employees are getting angry
and frustrated because there is always a long delay getting an elevator to the lobby at rush hour.
You are asked for a reaction on how to solve this problem. As with most problem situations there are several ways to
define the situation and several solutions that suggest themselves.
This scenario has been presented to over 200 groups in a training environment. The most common alternatives these
groups offered were:
• Flexible hours- so all the tenants' employees wouldn't be at the elevators at the same time.
• Faster elevators - so each elevator could carry more people in a given time period.
• Bigger elevators - so each elevator could carry more people per trip.
• Elevator banks- so each elevator would only stop on certain floors, increasing efficiency.
• Better elevator controls - so each eltor would be used more efficiently.
• More elevators - so that overall carrying capacity could be increased.
• Improved elevator maintenance - so each elevator would be more efficient.
• Encourage employees to use the stairs - so fewer people would use the elevators.
PLUS: The Decision Making Process | Ethics Resource Center
If you examine each alternative you will see that several different definitions of the problem must have existed.
• If the solution is "flexible hours" the problem must have been defined as, "Too many people getting off work at a
given ti.
Staging Model Abstraction – an example about political participationBruce Edmonds
A presentation at the workshop on ABM and Theory (From Cases to General Principles), Hannover, July 2019
This reports on work where we started with a complex, but evidence driven model, and then modelled that model sto understand and abstract from it. As reported in the paper:
Lafuerza LF, Dyson L, Edmonds B, McKane AJ (2016) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261
Some supporting slides on modelling purposes and pitfalls when using ABM in policy contexts to accompany discussion on Modelling Pitfalls at the ESSA Summer School, Aberdeen, June 2019
A talk at the workshop on "Agent-Based Models in Philosophy: Prospects and Limitations", Rurh University, Bochum, Germany
Abstract:
ABMs (like other kinds of model) can be used in a purely abstract way, as a kind of thought experiment - a way of thinking about some aspect of the world that is too complicated to hold in our mind (in all its detail). In this way it both informs and complements discursive thought. However there is another set of uses for ABMs - empirical uses - where the mapping between the model and sets of observation-derived data are crucial. For these uses, one has to (a) use the mapping to get from some data to the model (b) use the model for some inference and (c) use the mapping again back to data. This includes both predictive and explanatory uses of ABMs. These are easily distinguishable from abstact uses becuase there is a fixed and well-defined relationship between the model and the data, this is not flexible on a case by case basis. In these cases the reliability comes from the composite (a)-(b)-(c) mapping, so that simplifying step (b) can be counterproductive if that means weakening steps (a) and (c) because it is the strength of the overall chain that is important. Taking the use of models in quantum mechanics as an example, one can see that sometimes the evolution of the formal models driven by empirical adequacy can be more important than the attendent abstract models used to get a feel for what is happening. Although using ABM's for empirical purposes is more challenging than for purely abstract purposes, they are being increasingly used for empirical explanation rather than thought experiments, and there is no reason to suppose that robust empirical adequacy is unachievable.
Mixing fat data, simulation and policy - what could possibly go wrong?Bruce Edmonds
A talk given at the CECAN workshop on "What Good Data could do for Evaluation" at the Alan Turing Institute, 25th Feb. 2019.
Abstract:
In complex situations (which includes most where humans are involved) it is infeasible to predict the impact of any particular policy (or even what is probable). Randomised Control Trials do not tell one: what kinds of situation a policy might work in, what are enablers and inhibitors of the effectiveness of a policy. Here I suggest that using 'fat' data and simulation might allow a possibilistic analysis of policy impact - namely an exploration of what could go surprisingly wrong (or indeed right). Whilst this does not allow the optimisation of policy, it does inform the effective monitoring of policy, and basic contingency planning. However, this requires a different approach to policy - from planning and optimisation to an adaptive approach, with richer continual monitoring and a readiness to tune or adapt policy as data comes in. Examples of this are given concerning domestic water consumption (in the main talk), and in supplementary slides: voter turnout and fishing.
Social Context
An invited talk at the 2018 Surrey Sociology Conference, Barnett Hill, Surrey, November 2018.
Although there is much evidence that context is crucial to much human cognition and social behaviour, it remains a difficult area to research. In much social science research it is either by-passed or ignored. In some qualitative research context is almost deified with any level of generalisation across contexts being left to the reader. At the other extreme, some qualitative research restricts itself to patterns that are generally detectable - that is the patterns that are left when one aggregates over many different contexts. Context is often used as a 'dustbin concept' to which otherwise unexplained variation is attributed.
This talk looks at some of the ways social context might be actively represented, understood and researched. Firstly the ideas of cognitive then social context are distinguished. Then some possible approaches to researching this are discussed, including: agent-based simulation, a context-sensitive analysis of narrative data and machine learning.
Using agent-based simulation for socio-ecological uncertainty analysisBruce Edmonds
A talk given in the MMU Big Data Centrem, 30th October 2018.
Both social and ecological systems can be highly complex, but the interaction between these two worlds - a socio-ecological system (SES) - can add even greater levels. However, the maintenance of SES are vital to our well being and the health of the planet. We do not know how such systems work in practice and we lack good data about them (especially the ecological side) so predicting the effect of any particular policy is infeasible. Here we present an approach which tries to understand some of the ways in which SES may go wrong, but constructing different complex simulation models and analysing the emergent outcomes. These, in silico, examples can allow for the institution of targeted data gathering instruments that give the earliest possible warning of deleterious outcomes, and thus allow for timely remedial responses. An example of this approach applied to fisheries is described.
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 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
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.
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)
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.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
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.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
insect taxonomy importance systematics and classification
A Simple Model of Group Commoning
1. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 1
A Simple Model of Group Commoning
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
2. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 2
Tags and Groups
THE SNEETCHES
by Theodor Geisel (1961)
(aka. Dr. Seuss)
Now, the Star-Belly Sneetches
Had bellies with stars.
The Plain-Belly Sneetches
Had none upon thars.
Those stars weren't so big. They were really so small
You might think such a thing wouldn't matter at all.
But, because they had stars, all the Star-Belly Sneetches
Would brag, "We're the best kind of Sneetch on the beaches."
With their snoots in the air, they would sniff and they'd snort
“We'll have nothing to do with the Plain-Belly sort!"
And whenever they met some, when they were out walking,
They'd hike right on past them without even talking.
…
3. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 3
In-groups
• There is a lot of research that humans cooperate
more with what they perceive is their group
• But they are flexible about how they define who is
in their ‘in group’ and who is not (the ‘out-group’)
• Even when people are arbitrarily divided into
groups, they still give preference to their group
• The effect is even stronger when there is some
commonality (culture, occupation, location etc.)
• With strangers, one can only guess who is in ones
in-group through observable signals (accent,
clothes, etc), these we call ‘tags’
• Tags need not be hard-wired to behaviour
4. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 4
A simple simulation model…
Here I present a ‘simple’ agent-based simulation
which shows a process by which cooperation and
free donation can flourish, even when agents:
• Are selfish, trying to increase their wealth
• Are mutually anonymous, and so can not decide
behaviour from their past dealings with them
• Agents can choose not to help others without
others knowing this directly
But which assumes that:
• Different groups are recognisable
• One can learn from anyone
5. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 5
Model Processes
Each tick all agents do the following as follows:
1. Partner Selection: (with probability GB) select
another in its in-group as self (if there is one)
otherwise another agent at random
2. Game Interaction: donate or not depending on their
strategy and whether other is in- or out-group
3. Imitation: (with probability LB) select another in its
in-group as self (if exists) otherwise another at
random, if other’s payoff > its own, copy other’s
selector, cultural tag and strategies
4. Innovation: with small probabilities change (a)
cultural tag to a random other and (b) change one of
its strategies (or kind of in-group)
6. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 6
The view of agents in the simulation
Each ‘spoke’ is a
group (those with
the same tag)
give to in-group
give to nobody
give to anybody
give to out-group
Colours indicate
type of agent
7. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 7
Dominating Model Process
On inspection of many runs the following processes
seem to dominate the dynamics:
• By chance a new “seed group” of cooperators
appears, these have a high mutual donation and
hence are imitated by others so the group grows
• A cooperative group is “infected” by individuals
who receive but do not give, the payoff of agents
in diminishes and eventually individuals leave
• There are no cooperative groups, everyone is
selfish, the system ‘stagnates’ until a new seed
cooperative group appears
8. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 8
Warnings!
• This is a very simple and abstract model that
shows possibilities only
– It does not show that humans behave this way
• There are many ways in which humans make
cooperate/not decisions (or decide which group
they are in) that are not included in this simulation
• Humans have many other mechanisms to
facilitate cooperation (social norms, memory,
enforceable contracts, social exclusion etc.)
– This just demonstrates some minimal conditions for
cooperative groups to emerge
9. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 9
Conclusions
Free donation can flourish and be sustainable via a dynamic
system of groups – continually forming, growing, dying
…even when agents are selfish, deal with strangers and
non-cooperation can be hidden
But the conditions for this include that agents can:
• See the success of individuals in other groups – openness
• Change groups, if this seems better for them – personal
choice of membership
• Can form completely new groups fluidly – freedom of
association
• Turning selfish within a group is relatively slow – some
group pressure to conform
10. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 10
Acknowledgements
Most of this work was
done by my friend and
long-time collaborator,
David Hales.
For more about him and
his work on tag models
and cooperation see:
http://davidhales.com
Some of this work was
funded by the EPSRC
11. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 11
Other, more complex, models
1D world of
complex artifacts
whose creation
steps has to be
discovered shared
between agents
12. A Simple Model of Group Commoning, Bruce Edmonds, Basel, Switzerland, April 2018, 12
Centre for Policy Modelling: http://cfpm.org
Bruce Edmonds: http://bruce.edmonds.name
A version of these slides are at: http://slideshare.net/BruceEdmonds
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