One of the greatest challenges in the modern world is dealing with rapid change, in technologies, finance, and society. This presentation addresses how to manage surprise: how systems really change (not gradually), how to identify shifts, and how to improve your skills at managing change.
This document outlines the steps for conducting a Bayesian analysis to estimate default probabilities using both empirical data and expert elicitation. It presents three statistical models of increasing complexity to model default, applies the analysis to Moody's corporate bond default data from 1999-2009, and elicits expert opinions to specify prior distributions. The results provide posterior distributions over model parameters and show that the data favors a lower level of default rate autocorrelation than assumed priorly. The Bayesian approach allows formal incorporation of both hard data and soft expert knowledge.
Decision making techniques ppt @ mba opreatiop mgmt Babasab Patil
This document discusses various decision making techniques including decision analysis, linear programming, and simulation. Decision analysis involves representing decision problems as decision trees and using expected monetary value to evaluate choices. Linear programming optimizes objectives subject to constraints. Simulation models systems to test decisions without real-world risks. These techniques help decision makers evaluate options systematically and optimize outcomes.
Meerkats watch for predators and other threats to warn their group. In the workplace, risks like problems must be evaluated and mitigated. An IT risk management methodology should be adopted to assess which problems need prioritized solutions. The Toyota logo represents customers, products, and technological progress, relevant areas in any risk landscape. A matrix model analyzes the intersections of people, processes, and technologies where strengths, weaknesses, threats, and opportunities reside.
This document discusses concepts related to decision making including axioms, normative analysis, decision problems, objectives, attributes, and decision making methods. It provides descriptions of the SMART method and key stages in SMART analysis including identifying decision makers and alternatives, attributes, assigning values, determining weights, and sensitivity analysis. Criteria for accurate value trees and conditions for decision making under certainty, uncertainty, and risk are also outlined.
Role of Scaling in Developing an Understanding of How Systems Work OR the dan...Norman Johnson
A presentation to the Santa Fe Institute's Business Nework.
The method of scaling data is often used to understand patterns and discover explanations how systems work. We find that scaling methods, while maybe useful for data interpolation, may provide a false predictions. Also we present how scaling is just one step in a growing ability to describe a system. Applications include finance, general science, data analysis, Big Data. Detailed notes contained in the comments of the PPT file.
This document discusses the concept of high order complexity and its implications for management and leadership. It provides examples of complex systems including electricity distribution and cybersecurity. Some key lessons discussed are:
- Understanding and embracing complexity is essential for success in management.
- Decision making is important under conditions of high complexity, as an endeavor can be defined by the aggregation of its decisions.
- Leadership must be distributed to influence sound decision making across all parts of a complex system, especially as complexity increases. Without good decision making processes, the prospects of success are jeopardized.
Gigamap example by Manuela Aguirre: https://www.slideshare.net/ManuelaAguirre/policy-support-full-presentation
In this presentation you will learn about design tools and techniques to solve wicked problems, using Systems Thinking.
Systems Thinking looks at the whole of a system rather than focusing on its individual parts, to better understand complex phenomena. Systems Thinking contrasts with analytic thinking: you solve problems by going deeper, by looking at the greater whole of a system and the relations between its elements, rather than solving individual problems in a linear way via simple cause and effect explanations.
You can apply Systems Thinking principles in different situations: to understand how large organisations function and design for the enterprise (e.g. when you are trying to revamp a large intranet), but also to solve social problems and issues (e.g. unemployment with disadvantaged youth or mobility in larger cities). So basically whenever there is complexity and conflict (of interest) in your project, Systems Thinking will be helpful.
After an introduction to Systems Thinking and its core concepts, we will first explain and practice a few techniques that you as a designer can apply to better understand complex systems, for example creating a System Map and drawing Connection Circles. In the second part of the workshop, we will introduce techniques that help you shape solutions, for example using Paradoxical Thinking for ideation and writing ‘What-if’ Scenarios.
Presented at EuroIA 2015 with Koen Peters.
This document outlines the steps for conducting a Bayesian analysis to estimate default probabilities using both empirical data and expert elicitation. It presents three statistical models of increasing complexity to model default, applies the analysis to Moody's corporate bond default data from 1999-2009, and elicits expert opinions to specify prior distributions. The results provide posterior distributions over model parameters and show that the data favors a lower level of default rate autocorrelation than assumed priorly. The Bayesian approach allows formal incorporation of both hard data and soft expert knowledge.
Decision making techniques ppt @ mba opreatiop mgmt Babasab Patil
This document discusses various decision making techniques including decision analysis, linear programming, and simulation. Decision analysis involves representing decision problems as decision trees and using expected monetary value to evaluate choices. Linear programming optimizes objectives subject to constraints. Simulation models systems to test decisions without real-world risks. These techniques help decision makers evaluate options systematically and optimize outcomes.
Meerkats watch for predators and other threats to warn their group. In the workplace, risks like problems must be evaluated and mitigated. An IT risk management methodology should be adopted to assess which problems need prioritized solutions. The Toyota logo represents customers, products, and technological progress, relevant areas in any risk landscape. A matrix model analyzes the intersections of people, processes, and technologies where strengths, weaknesses, threats, and opportunities reside.
This document discusses concepts related to decision making including axioms, normative analysis, decision problems, objectives, attributes, and decision making methods. It provides descriptions of the SMART method and key stages in SMART analysis including identifying decision makers and alternatives, attributes, assigning values, determining weights, and sensitivity analysis. Criteria for accurate value trees and conditions for decision making under certainty, uncertainty, and risk are also outlined.
Role of Scaling in Developing an Understanding of How Systems Work OR the dan...Norman Johnson
A presentation to the Santa Fe Institute's Business Nework.
The method of scaling data is often used to understand patterns and discover explanations how systems work. We find that scaling methods, while maybe useful for data interpolation, may provide a false predictions. Also we present how scaling is just one step in a growing ability to describe a system. Applications include finance, general science, data analysis, Big Data. Detailed notes contained in the comments of the PPT file.
This document discusses the concept of high order complexity and its implications for management and leadership. It provides examples of complex systems including electricity distribution and cybersecurity. Some key lessons discussed are:
- Understanding and embracing complexity is essential for success in management.
- Decision making is important under conditions of high complexity, as an endeavor can be defined by the aggregation of its decisions.
- Leadership must be distributed to influence sound decision making across all parts of a complex system, especially as complexity increases. Without good decision making processes, the prospects of success are jeopardized.
Gigamap example by Manuela Aguirre: https://www.slideshare.net/ManuelaAguirre/policy-support-full-presentation
In this presentation you will learn about design tools and techniques to solve wicked problems, using Systems Thinking.
Systems Thinking looks at the whole of a system rather than focusing on its individual parts, to better understand complex phenomena. Systems Thinking contrasts with analytic thinking: you solve problems by going deeper, by looking at the greater whole of a system and the relations between its elements, rather than solving individual problems in a linear way via simple cause and effect explanations.
You can apply Systems Thinking principles in different situations: to understand how large organisations function and design for the enterprise (e.g. when you are trying to revamp a large intranet), but also to solve social problems and issues (e.g. unemployment with disadvantaged youth or mobility in larger cities). So basically whenever there is complexity and conflict (of interest) in your project, Systems Thinking will be helpful.
After an introduction to Systems Thinking and its core concepts, we will first explain and practice a few techniques that you as a designer can apply to better understand complex systems, for example creating a System Map and drawing Connection Circles. In the second part of the workshop, we will introduce techniques that help you shape solutions, for example using Paradoxical Thinking for ideation and writing ‘What-if’ Scenarios.
Presented at EuroIA 2015 with Koen Peters.
This document discusses quantitative techniques for managers, specifically operations research and decision theory. It provides an overview of operations research, describing its scope, characteristics, and that it takes a systems approach. It then discusses decision theory and models for decision making under certainty, risk, and uncertainty. It outlines the steps in the decision theory approach and describes various criteria that can be used under uncertainty, like maximin, maximax, regret, equal probability, and Hurwicz criteria. It concludes with an example decision making problem and asks to indicate the decision that would be taken under different criteria like pessimistic, optimistic, equal probability, regret, and Hurwicz.
This document provides an overview of key concepts in sampling and descriptive statistics. It defines populations, samples, parameters, and statistics. It explains why samples are used instead of whole populations for research. Common sampling methods like simple random and systematic sampling are also described. The document then covers descriptive statistics, including frequency distributions, measures of central tendency, and measures of dispersion. It discusses the normal distribution and how the central limit theorem applies. Key terms are defined, such as standard deviation, variance, and standardized scores.
Anticipation 2017 Assembling Requisite Stakeholder VarietyPeter Jones
This document discusses ensuring variety in stakeholder representation in foresight practices to reduce cognitive biases. It notes that foresight methods often mix to reduce reliance on one, but variety is also needed in stakeholder perspectives represented. Without accounting for cognitive and temporal biases in who is selected, four points of failure can occur: biased framing, biased content selection, horizon bias in stakeholders, and insufficient variety. The document advocates for evolutionary sampling to map categories related to the issue and minimize influence of biases, expanding variety both within the issue and beyond the future system. It also discusses accounting for individuals' temporal preferences to avoid horizon biases within groups.
Workshop Trade-off Analysis - CGIAR_21 Feb 2013_Group discussion_1.Theory of ...LotteKlapwijk
1. Theories of change need to be dynamic and set against plausible future scenarios to account for underlying trends and potential shocks. They should acknowledge non-linear change and tipping points.
2. Outcomes should be articulated honestly and realistically in terms of contributions rather than strict attribution, and consider trade-offs between different goals and timescales. Process-level indicators are also important.
3. Theories of change will need revising as realities emerge, and should embrace system-level definitions to influence key actors and institutions driving change. Building resilience to mitigate anticipated and unanticipated shocks at different scales should also be included.
The United Nations uses a risk management process that involves assessing the criticality of programs to balance security risks. It uses a risk matrix to determine risk levels and requires a program criticality assessment for activities with high or very high residual risks. The assessment evaluates the contribution of activities to strategic results and their likelihood of implementation against criteria to designate them as Priority 1 activities that are lifesaving or directed by the Secretary-General. Risk level and program criticality are determined separately without consideration of each other.
The presentation discusses evaluating complex interventions and programs. It notes that current evaluation approaches do not adequately address complex settings characterized by emergent outcomes, dynamic contexts, and uncertainty. Two new UK centers explore new evaluation approaches for complex systems. Such systems have multiple interacting components, self-organization, feedback loops, unpredictability, and tipping points. This poses challenges for evaluations having long causal chains, changing circumstances, openness to context, and multiple perspectives. New approaches discussed include participatory, realist, case study, system mapping and modeling methods. Key is understanding complexity, allowing flexibility, engaging stakeholders, and managing uncertainty.
Systems analysis involves determining what needs to be accomplished and how best to do so. It is the study of designing, specifying, and implementing computer systems for business. A systems analyst identifies objectives, constraints, alternatives and examines costs, benefits, and risks to help decision makers choose better options. Systems analysis is used to guide decisions regarding plans, programs, policies, research, and other complex issues through an interdisciplinary approach. It involves stages like feasibility studies, requirements specification, and physical design to transform logical specifications into a real system. Systems analysis impacts education by examining instructional, project management, information, and other systems to aid decision making.
The document discusses topics related to knowledge, complex systems, decisions under uncertainty, and risks. It covers how to understand and manage unpredictable change, knowledge production in chaotic systems, and tools for analyzing complex problems. The goal is to facilitate decision making on complex issues and discuss perspectives on uncertainty and risk that may be unfamiliar to non-scientists and decision makers.
The document discusses two approaches to policy formulation: the rational comprehensive method that thoroughly analyzes goals and alternatives, and the successive limited comparison method that focuses on incremental changes. It analyzes the merits and limitations of each approach, and proposes a normative optimum model that seeks to balance rational analysis with practical policymaking constraints. The discussion provides insights into real-world policymaking processes versus theoretical models.
Problem Solving means by definition that something is being changed. The best ways to solve a problem often get canonized as best practices. Yet debate rages on about best practices with long histories (such as ITIL) and ultra-high promotion (such as Design Thinking). How can consensus "bests" remain in perpetual debate?
Session 5_Sampling strategy_Intake Dr Emmanuel.pdfmuhirwaSamuel
This study examined the risk of tuberculosis (TB) among people infected with HIV compared to those not infected with HIV. A cohort of 215 people infected with HIV and 298 not infected were followed for 2 years. During this time, 8 people in the HIV-infected cohort developed TB, compared to 1 person in the HIV-uninfected cohort. The incidence of TB was 3.72 times higher among those with HIV compared to those without (11 times higher risk). However, the study is limited by potential selection bias since 41 participants selectively dropped out of the study.
An overview of "resilience thinking" for participants at a meeting on resilience in the electricity space organized by the Electric Power Research Institute (EPRI).
Exploratory research is useful when researchers lack clarity on problems and objectives. It allows researchers to develop clearer concepts, establish priorities, define operations, and improve research design. The primary goal is to gain insights and understanding of the problem. Information needs are loosely defined and the process is flexible. Descriptive research assumes prior knowledge of the problem situation. The goals are to describe characteristics or functions, with clearly defined information needs and a pre-planned, structured design. Survey research encompasses any measurement using questions, from short forms to in-depth interviews. Diagnostic research aims to describe characteristics or frequencies of groups, with a rigid design similar to surveys to describe, analyze, interpret, and suggest remedies. Experimental research tests hypotheses through replication,
1The Nature of SuccessClass SeventeenREVIEW!!!!.docxvickeryr87
1
The Nature of Success
Class Seventeen
REVIEW!!!!
Midterm Exam
1. 55 multiple choice questions
2. Testing your fund of knowledge
3. Mainly from lectures, readings that are directly relevant
4. An ‘A’ means an ‘A’
5. Understand the concepts
November 6
3
The Nature of Success
Class One
Introduction and Course Overview
4
Reality is Amorphous
Draw a line around the system boundary
Indicate the most important challenges the system must face
Indicate how the system interacts to face these challenges
What it means to draw that boundary line
You have defined the domain of success/failure that you want to understand.
You have identified the entities inside the boundary that are needed to achieve success (through their interactions). Thus, you have defined your system.
You have identified the entities outside the boundary that will pose the challenges/opportunities that must be managed by the system for the achievement of success.
You understand that it is the information that comes in from the outside entities and is processed by the inside entities – according to an established set of rules – that defines the functioning of the system.
The systems use of this established set of rules is based on the system’s working model of reality.
Core Ideas
Once a system’s purpose/aims and boundaries are known, then we have to understand the system’s structure and function.
A system’s structure describes the entities contained by the system and the particular way they are organized.
A system’s function describes how the entities interact with each other and how these interactions form the emergent properties of the system.
Emergent properties: The whole is greater than the sum of its parts.
Remarkably, a great variety of different systems have similar structural and functional characteristics.
Understanding these commonalities will make our work much easier.
Once we get all this we will see that Complex Systems – no matter how complex – usually follow a small number of simple rules.
If we can understand the rules of the Complex System containing a domain of success we care about, then we understand the rules that lead to the domain of success we care about.
6
7
The Nature of Success
Class Two
System Observations
8
The Nature of Success
Class Three
What is a System?
Our Basic System Model
Pattern of Emergent
Behavior
Observed Regularities
Behavior of System Elements
Positive
Feedback
Negative
Feedback
Responding to Ever-Changing
Environment
Key Points re Systems
System Boundaries: what’s in and what’s out
System components: what are the entities that comprise the inside of the system?
System interactions: what governs the behavior about how the systems entities interact with each other?
System purpose: What is the system ‘trying’ to accomplish? What does success and failure mean related to this definition of purpose?
System information pr.
This document discusses key concepts in epidemiology for comparing disease frequency between groups with different exposures. It explains that epidemiological studies measure associations between exposures and outcomes, but not necessarily causal effects. An association indicates how likely past events were, while a causal effect shows how probabilities change if exposures change. Studies aim to minimize bias and chance error to help infer causality from significant associations.
This document discusses power and decision making in public organizations. It covers several key points:
1) Power comes from various sources like control over resources or expertise, and influences decision outcomes and organizational effectiveness. Understanding power dynamics is important.
2) Decision making is complex due to unclear goals, constraints, and political factors. Models include fully rational approaches, incrementalism, and garbage can theory where problems and solutions are loosely connected.
3) Strategic management tools like environmental scans, SWOT analyses, and Miles and Snow typologies can help organizations develop strategies to achieve goals given their context. However, applying theories faces challenges due to variations in public sector settings.
This document provides an overview of experimental design and sampling techniques in statistics. It defines key terms like population, sample, census, bias, and experimental units. It describes different sampling methods like simple random sampling, stratified sampling, cluster sampling, and multistage sampling. It also covers principles of experimental design like control, replication, and randomization. Specific experimental designs discussed include completely randomized design, block design, and matched pairs design. The document cautions about potential issues like nonresponse bias, response bias, and lack of realism in experiments.
Decision-making Support System for climate change adaptation_yin v2Chonghua Yin
GENIES is a decision support system for climate change adaptation that uses a system dynamics approach. It provides an open framework platform where users can build modular system dynamic models by linking existing model components and applications. GENIES helps users visualize complex systems, predict outcomes, and identify problems by simulating processes. It provides tools for risk assessment, cost-benefit analysis, and climate change uncertainty analysis to support decision-making for climate change adaptation. GENIES is being developed as a collaborative community of practice between research institutions, organizations, practitioners, and other stakeholders.
Enhancing Asset Quality: Strategies for Financial Institutionsshruti1menon2
Ensuring robust asset quality is not just a mere aspect but a critical cornerstone for the stability and success of financial institutions worldwide. It serves as the bedrock upon which profitability is built and investor confidence is sustained. Therefore, in this presentation, we delve into a comprehensive exploration of strategies that can aid financial institutions in achieving and maintaining superior asset quality.
University of North Carolina at Charlotte degree offer diploma Transcripttscdzuip
办理美国UNCC毕业证书制作北卡大学夏洛特分校假文凭定制Q微168899991做UNCC留信网教留服认证海牙认证改UNCC成绩单GPA做UNCC假学位证假文凭高仿毕业证GRE代考如何申请北卡罗莱纳大学夏洛特分校University of North Carolina at Charlotte degree offer diploma Transcript
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This document discusses quantitative techniques for managers, specifically operations research and decision theory. It provides an overview of operations research, describing its scope, characteristics, and that it takes a systems approach. It then discusses decision theory and models for decision making under certainty, risk, and uncertainty. It outlines the steps in the decision theory approach and describes various criteria that can be used under uncertainty, like maximin, maximax, regret, equal probability, and Hurwicz criteria. It concludes with an example decision making problem and asks to indicate the decision that would be taken under different criteria like pessimistic, optimistic, equal probability, regret, and Hurwicz.
This document provides an overview of key concepts in sampling and descriptive statistics. It defines populations, samples, parameters, and statistics. It explains why samples are used instead of whole populations for research. Common sampling methods like simple random and systematic sampling are also described. The document then covers descriptive statistics, including frequency distributions, measures of central tendency, and measures of dispersion. It discusses the normal distribution and how the central limit theorem applies. Key terms are defined, such as standard deviation, variance, and standardized scores.
Anticipation 2017 Assembling Requisite Stakeholder VarietyPeter Jones
This document discusses ensuring variety in stakeholder representation in foresight practices to reduce cognitive biases. It notes that foresight methods often mix to reduce reliance on one, but variety is also needed in stakeholder perspectives represented. Without accounting for cognitive and temporal biases in who is selected, four points of failure can occur: biased framing, biased content selection, horizon bias in stakeholders, and insufficient variety. The document advocates for evolutionary sampling to map categories related to the issue and minimize influence of biases, expanding variety both within the issue and beyond the future system. It also discusses accounting for individuals' temporal preferences to avoid horizon biases within groups.
Workshop Trade-off Analysis - CGIAR_21 Feb 2013_Group discussion_1.Theory of ...LotteKlapwijk
1. Theories of change need to be dynamic and set against plausible future scenarios to account for underlying trends and potential shocks. They should acknowledge non-linear change and tipping points.
2. Outcomes should be articulated honestly and realistically in terms of contributions rather than strict attribution, and consider trade-offs between different goals and timescales. Process-level indicators are also important.
3. Theories of change will need revising as realities emerge, and should embrace system-level definitions to influence key actors and institutions driving change. Building resilience to mitigate anticipated and unanticipated shocks at different scales should also be included.
The United Nations uses a risk management process that involves assessing the criticality of programs to balance security risks. It uses a risk matrix to determine risk levels and requires a program criticality assessment for activities with high or very high residual risks. The assessment evaluates the contribution of activities to strategic results and their likelihood of implementation against criteria to designate them as Priority 1 activities that are lifesaving or directed by the Secretary-General. Risk level and program criticality are determined separately without consideration of each other.
The presentation discusses evaluating complex interventions and programs. It notes that current evaluation approaches do not adequately address complex settings characterized by emergent outcomes, dynamic contexts, and uncertainty. Two new UK centers explore new evaluation approaches for complex systems. Such systems have multiple interacting components, self-organization, feedback loops, unpredictability, and tipping points. This poses challenges for evaluations having long causal chains, changing circumstances, openness to context, and multiple perspectives. New approaches discussed include participatory, realist, case study, system mapping and modeling methods. Key is understanding complexity, allowing flexibility, engaging stakeholders, and managing uncertainty.
Systems analysis involves determining what needs to be accomplished and how best to do so. It is the study of designing, specifying, and implementing computer systems for business. A systems analyst identifies objectives, constraints, alternatives and examines costs, benefits, and risks to help decision makers choose better options. Systems analysis is used to guide decisions regarding plans, programs, policies, research, and other complex issues through an interdisciplinary approach. It involves stages like feasibility studies, requirements specification, and physical design to transform logical specifications into a real system. Systems analysis impacts education by examining instructional, project management, information, and other systems to aid decision making.
The document discusses topics related to knowledge, complex systems, decisions under uncertainty, and risks. It covers how to understand and manage unpredictable change, knowledge production in chaotic systems, and tools for analyzing complex problems. The goal is to facilitate decision making on complex issues and discuss perspectives on uncertainty and risk that may be unfamiliar to non-scientists and decision makers.
The document discusses two approaches to policy formulation: the rational comprehensive method that thoroughly analyzes goals and alternatives, and the successive limited comparison method that focuses on incremental changes. It analyzes the merits and limitations of each approach, and proposes a normative optimum model that seeks to balance rational analysis with practical policymaking constraints. The discussion provides insights into real-world policymaking processes versus theoretical models.
Problem Solving means by definition that something is being changed. The best ways to solve a problem often get canonized as best practices. Yet debate rages on about best practices with long histories (such as ITIL) and ultra-high promotion (such as Design Thinking). How can consensus "bests" remain in perpetual debate?
Session 5_Sampling strategy_Intake Dr Emmanuel.pdfmuhirwaSamuel
This study examined the risk of tuberculosis (TB) among people infected with HIV compared to those not infected with HIV. A cohort of 215 people infected with HIV and 298 not infected were followed for 2 years. During this time, 8 people in the HIV-infected cohort developed TB, compared to 1 person in the HIV-uninfected cohort. The incidence of TB was 3.72 times higher among those with HIV compared to those without (11 times higher risk). However, the study is limited by potential selection bias since 41 participants selectively dropped out of the study.
An overview of "resilience thinking" for participants at a meeting on resilience in the electricity space organized by the Electric Power Research Institute (EPRI).
Exploratory research is useful when researchers lack clarity on problems and objectives. It allows researchers to develop clearer concepts, establish priorities, define operations, and improve research design. The primary goal is to gain insights and understanding of the problem. Information needs are loosely defined and the process is flexible. Descriptive research assumes prior knowledge of the problem situation. The goals are to describe characteristics or functions, with clearly defined information needs and a pre-planned, structured design. Survey research encompasses any measurement using questions, from short forms to in-depth interviews. Diagnostic research aims to describe characteristics or frequencies of groups, with a rigid design similar to surveys to describe, analyze, interpret, and suggest remedies. Experimental research tests hypotheses through replication,
1The Nature of SuccessClass SeventeenREVIEW!!!!.docxvickeryr87
1
The Nature of Success
Class Seventeen
REVIEW!!!!
Midterm Exam
1. 55 multiple choice questions
2. Testing your fund of knowledge
3. Mainly from lectures, readings that are directly relevant
4. An ‘A’ means an ‘A’
5. Understand the concepts
November 6
3
The Nature of Success
Class One
Introduction and Course Overview
4
Reality is Amorphous
Draw a line around the system boundary
Indicate the most important challenges the system must face
Indicate how the system interacts to face these challenges
What it means to draw that boundary line
You have defined the domain of success/failure that you want to understand.
You have identified the entities inside the boundary that are needed to achieve success (through their interactions). Thus, you have defined your system.
You have identified the entities outside the boundary that will pose the challenges/opportunities that must be managed by the system for the achievement of success.
You understand that it is the information that comes in from the outside entities and is processed by the inside entities – according to an established set of rules – that defines the functioning of the system.
The systems use of this established set of rules is based on the system’s working model of reality.
Core Ideas
Once a system’s purpose/aims and boundaries are known, then we have to understand the system’s structure and function.
A system’s structure describes the entities contained by the system and the particular way they are organized.
A system’s function describes how the entities interact with each other and how these interactions form the emergent properties of the system.
Emergent properties: The whole is greater than the sum of its parts.
Remarkably, a great variety of different systems have similar structural and functional characteristics.
Understanding these commonalities will make our work much easier.
Once we get all this we will see that Complex Systems – no matter how complex – usually follow a small number of simple rules.
If we can understand the rules of the Complex System containing a domain of success we care about, then we understand the rules that lead to the domain of success we care about.
6
7
The Nature of Success
Class Two
System Observations
8
The Nature of Success
Class Three
What is a System?
Our Basic System Model
Pattern of Emergent
Behavior
Observed Regularities
Behavior of System Elements
Positive
Feedback
Negative
Feedback
Responding to Ever-Changing
Environment
Key Points re Systems
System Boundaries: what’s in and what’s out
System components: what are the entities that comprise the inside of the system?
System interactions: what governs the behavior about how the systems entities interact with each other?
System purpose: What is the system ‘trying’ to accomplish? What does success and failure mean related to this definition of purpose?
System information pr.
This document discusses key concepts in epidemiology for comparing disease frequency between groups with different exposures. It explains that epidemiological studies measure associations between exposures and outcomes, but not necessarily causal effects. An association indicates how likely past events were, while a causal effect shows how probabilities change if exposures change. Studies aim to minimize bias and chance error to help infer causality from significant associations.
This document discusses power and decision making in public organizations. It covers several key points:
1) Power comes from various sources like control over resources or expertise, and influences decision outcomes and organizational effectiveness. Understanding power dynamics is important.
2) Decision making is complex due to unclear goals, constraints, and political factors. Models include fully rational approaches, incrementalism, and garbage can theory where problems and solutions are loosely connected.
3) Strategic management tools like environmental scans, SWOT analyses, and Miles and Snow typologies can help organizations develop strategies to achieve goals given their context. However, applying theories faces challenges due to variations in public sector settings.
This document provides an overview of experimental design and sampling techniques in statistics. It defines key terms like population, sample, census, bias, and experimental units. It describes different sampling methods like simple random sampling, stratified sampling, cluster sampling, and multistage sampling. It also covers principles of experimental design like control, replication, and randomization. Specific experimental designs discussed include completely randomized design, block design, and matched pairs design. The document cautions about potential issues like nonresponse bias, response bias, and lack of realism in experiments.
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GENIES is a decision support system for climate change adaptation that uses a system dynamics approach. It provides an open framework platform where users can build modular system dynamic models by linking existing model components and applications. GENIES helps users visualize complex systems, predict outcomes, and identify problems by simulating processes. It provides tools for risk assessment, cost-benefit analysis, and climate change uncertainty analysis to support decision-making for climate change adaptation. GENIES is being developed as a collaborative community of practice between research institutions, organizations, practitioners, and other stakeholders.
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Ensuring robust asset quality is not just a mere aspect but a critical cornerstone for the stability and success of financial institutions worldwide. It serves as the bedrock upon which profitability is built and investor confidence is sustained. Therefore, in this presentation, we delve into a comprehensive exploration of strategies that can aid financial institutions in achieving and maintaining superior asset quality.
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办理美国UNCC毕业证书制作北卡大学夏洛特分校假文凭定制Q微168899991做UNCC留信网教留服认证海牙认证改UNCC成绩单GPA做UNCC假学位证假文凭高仿毕业证GRE代考如何申请北卡罗莱纳大学夏洛特分校University of North Carolina at Charlotte degree offer diploma Transcript
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OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.
[4:55 p.m.] Bryan Oates
OJPs are becoming a critical resource for policy-makers and researchers who study the labour market. LMIC continues to work with Vicinity Jobs’ data on OJPs, which can be explored in our Canadian Job Trends Dashboard. Valuable insights have been gained through our analysis of OJP data, including LMIC research lead
Suzanne Spiteri’s recent report on improving the quality and accessibility of job postings to reduce employment barriers for neurodivergent people.
Decoding job postings: Improving accessibility for neurodivergent job seekers
Improving the quality and accessibility of job postings is one way to reduce employment barriers for neurodivergent people.
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
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Fabular Frames and the Four Ratio ProblemMajid Iqbal
Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.
Managing surprise and risk from a systems and diversity perspective
1. Managing Strategic Surprise and Risk:
• How adversarial systems evolve
• Addressing “don’t know what you don’t know”
Norman L Johnson
norman@santafe.edu
2. Strategy Evolution: Issues and
Considerations
Core focus: Eliminating surprise in planning (later for training and response)
Background: Evolutionary theory (old and new and …), Fitness Landscapes
Application
• Apply to changes or evolution in ecologies, stock markets, consumer
markets, battles, epidemics, organizations
• Any system where there competing or cooperating or synergistic “agents” –
with or without centralized coordination, who’s options are limited by
temporary or long term restrictions
Considerations
• Diversity of resources: in selection and synergy
• Robustness of strategy versus optimization of performance
• Co-evolution in equilibrium – strategies of opposing sides balance each other
• Changing evolutionary paths – when surprise drastically changes strategies
• Local versus global risk (battle versus campaign)
• Types of structures - Knowing what you can change and at what price
3. - graphically representing different strategies
Expected value
Strategy plots
Strategy measure
Expected value = (probability that a strategy will be successful or
will occur) * (“payoff” or desirability of the strategy)
Assume an objective assessment; payoffs can be negative; like fitness landscapes
Strategy measure = a way of differentiating one strategy from
another - from simple (resources required) to complex
(combination of many factors) - Likely multiple axes
4. Expected value
The Distribution of Strategies
Strategy measure
Strategies tend to be more numerous around the optimal strategy
- because we populate our beliefs and plans around known actions
5. Expected value
The Distribution of Strategies
Strategy measure
The greater the vertical distribution of expected values:
•The lower the predictability of the outcome
•The greater the uncertainty in a region of strategy
•The inability to “control” the situation (the “complexity barrier”)
• The greater the influence of uncontrollable exogenous influences
6. Expected value
The Distribution of Strategies
Strategy measure
Strategies of lower payoff tend to be less numerous (populated)
- because we explore regions of known successes
- because we ignore options of likely “perceived” failure
7. Expected value
The Distribution of Strategies
Strategy measure
Some regions of the strategy space may be empty, because “structures” in the
system make strategies in this region improbable or inaccessible
• For example, the “structural” restriction of deployment of the military within the US.
or unacceptable.
• Many cultural barriers are of this type , because cultural barriers are not always based
on logistic barriers, these are likely candidates for strategic surprise.
8. Expected value
Overall Distribution of Strategy Plots
Strategy measure
For less complex systems:
• Overall shapes tend to have “normal” distributions or “mono-modal” - highly
populated around the peak and lower populated at the edges
• With few gaps and smooth variations in expected value from one strategy
measure to another (distribution is well populated)
9. Expected value
What the Shape of Strategy Plots Tell You
Strategy measure
The less the breadth of the overall shape (relative to other arenas)
• The more optimized the system or refined the strategies
• The less change in the environment of the system (or opponent)
10. Expected value
“Traditional” View of How Strategy Plots Change
Strategy measure
If there is no environmental (exogenous) change, then the only
change is from within – by optimizing or refining strategies.
This process will tend to make the distribution more peaked.
11. Expected value
“Traditional” View of How Strategy Plots Change
Strategy measure
If there is environmental change, then strategies will incrementally
adjust to accommodate the change. The transition is from peaked
(optimized) to broad (transition) to peaked (optimized).
This would describe a situation where a known vulnerability is
exploited, countermeasures deployed, and adversary adjusts.
12. Diversity: Optimization and Robustness
Expected value
Expected value
Which strategy collection is more optimized? More robust?
Strategy measure
Strategy measure
13. Diversity: Optimization and Robustness
Expected value
Expected value
Which strategy collection is more optimized? More robust?
Strategy measure
More optimized
Strategy measure
More robust
But both of these will likely return the same rewards for
a unchanging environment (the peak). You will only see
a difference when the system undergoes change.
14. Failure of the traditional approach when:
• Existing structures prevent adaptation to change
• The system has “calcified” - internal or external structures become fixed
(see structures VG).
• Habitual or peer copying behavior dominates rational choices
• Low diversity of viewpoints/solutions limits exploration
• Limited synergy between existing diversity
• Change affects planning and outcomes
• New structures (e.g., technology changes) introduce new options
• System is “out of equilibrium” - in transition
• Complexity prevents planning or predictability
• The complexity barrier is active (when good plans go bad because of
complexity) - strategies (even past successful ones) may not lead to
desired outcomes
• Subjective or cultural evaluation dominates rather than objective
evaluation (often a consequence of complexity)
15. Strategy Evolution and Rate of Change
collective
More change
Expected value
Expected value
Little change
Strategy measure
Strategy measure
More change
Strategy measure
Expected value
Expected value
Extreme change
innovators
innovators
innovators
collective
Strategy measure
The “rate of change” refers to rates of change from internal, system or external sources. Because collectives
require more time to respond to change, high rates of change increases the effectiveness of innovators.
16. Strategy Evolution and Role of Outliers
Strategy measure
Outlier
discovery
Expected value
Expected value
Alternative View of How Strategy Plots Change
Strategy measure
Strategy measure
Outlier
exploitation
&
Resource
transfer
Expected value
Expected value
Outlier
propagation
Strategy measure
Instead of the gradual change shown in the previous viewgraph ( “Traditional” View of How Strategy Plots Change),
change often occurs in unexpected regions of the strategy - largely because of structural restrictions.
17. Where do outliers come from?
• Combination of diverse information from an existing set of solutions
(connecting paths that weren’t obvious)- this addresses the complexity barrier.
These unused options often appear as “weak” signals. What most people do
not realize is that ultimately all “strong” strategies have their origins as weak
signals.
The questions is how “weak” signals are identified and reinforced and become
strong signals.
The critical process is differentiating the “good” weak signals from the poor
weak signals.
The poor weak signals are often taken to be noise or random exploration.
Often they can contain significant information on what will be “good” weak
signals.
18. Collective Intelligence in complex environments
In complex domains:
• Beginning points differ
• End points differ
• But pieces of paths can overlay and find
synergy
begin
end
Options in infrastructure, societal structure, economies, etc.
•
•
•
•
•
•
•
•
19. Where do outliers or weak signals come from?
• Combination of diverse information in an existing set of solutions
(connecting paths that weren’t obvious)
From the previous viewgraph, commonalities of path can reinforce weak signals,
but only if the awareness of the commonality exists. Communication and boarder
awareness is essential (See Einstein quote on next page).
• Transferring a solution from another situation to the current problem –
requires diverse information sources across problem areas (usually considered
to be the greatest source of outliers).
• Change in environment opens up new opportunities – these may be
difficult to populate (exploit) if there are no solutions being explore in this
previously “poor” space (the diversity exploration issue). Environmental
change is often the consequence of introduction of a new technology, in either
the existing system or, more often, in a less important subsystem.
• Serendipity (often some combination of the above, but not recognizable as
such - often attributed to “luck”, but in a proper problem-solving environment,
there is no “luck” and they will be discovered.)
Note: not all “good” outliers work (you can have “a good idea before its
20. Defender vs. Adversary Views
The following is an example of how a
defender (blue team) and an attacker
(red team) address uncertainty, surprise,
and risk differently.
Lesson: There is a formal approach to
address unknowns in vulnerabilities and
threats, from both perspectives.
21. Landscape of Blue Team Threat Planning
Two areas of knowledge required: the vulnerability to attack and the threat scenario that
exploits the vulnerability (there could be many ways to exploit a vulnerability).
Threat Identified, either from intel
or from analysis.
yes
• Is the vulnerability well
characterized?
• Are there other threat scenarios
that exploit the vulnerability?
Is the Threat
identified?
Nature of threat NOT Identified.
no
• Can you know or can you
discover the vulnerability?
• Do you have what you need to
do identify the threat?
A real threat exploits a vulnerability.
Vulnerabilities can be known or undiscovered by the defender.
22. Landscape of Blue Team Threat Planning
Now consider Blue vulnerabilities:
Does Blue have knowledge of the
vulnerability?
yes
Threat and vulnerability
identified
no
Threat identified but
vulnerability uncertain.
(e.g, intel identifies threat)
yes
Action: Close gap
Action: Discover and
close gap.
Discovery of threat
required
Action: gap closure and
discovery of threat based on vulnerability
analysis
Threat and vulnerability
not known
Don’t have knowledge
to look for threat or
discover vulnerability
Action: ??
Has Blue
identified
the threat?
no
23. Actions of Blue Team Threat Planning
Have knowledge of the vulnerability?
yes
Develop operational
plan
yes
Lowest Risk
Is the
Threat
identified?
no
Based on vulnerability Develop threat
scenarios;
Move to box above.
Moderate Risk
no
Based on threat:
Do vulnerability
analysis Move to box at left
Moderate Risk
Discover threat or
vulnerability:
then move to left or
Highest
above.
Highest
Risk
Risk
24. Landscape of Red Team Planning
Same matrix, but from Red
perspective of planning and
knowledge of Blue
preparedness and response
options. Initially Red
guesses Blue’s knowledge of
vulnerability and plans an
optimal threat scenario.
They learn Blue’s
preparedness as they execute
different threats.
Has Red
identified a
attackthreat
scenario?
Red asks: Does Blue appear to have
knowledge of the Blue’s vulnerability?
yes
yes
no
Blue likely
addressed attackthreat scenario:
Highest risk Red avoids
Blue may have
addressed
vulnerability
Red develops
scenario at moderate
risk
no
Blue appears not to
know vulnerabilities:
Red exploits hidden
vulnerability Moderate-Low risk
Red exploits Blue’s
unknown
Lowest
Lowest
vulnerability by
risk
risk
developing scenario
25. Summary - Evolution: Issues and Considerations
Core focus: Eliminating surprise in planning (later for training and response)
Application
• Any system where there competing or cooperating or synergistic “agents”
whose options are limited by temporary or long term restrictions
Issues and Considerations
• Diversity of strategies: in selection, synergy and collaboration
• Robustness versus optimization - interplay with uncertainty
• Emergent properties
• Connection between command (control level) versus performance (selection level)
• Managing emergent properties and the complexity barrier
• Local versus global risk (battle versus campaign)
• Co-evolution (opposing or parallel evolution)
• Discovery of outliers and creation of disequilibrium by either side
• Change in evolutionary paths – when surprise drastically changes strategies
• Role of Structures
Structures create and limit vulnerabilities & limit ability to change
Editor's Notes
No general proof of why a shortest path is found.
Complexity: You know what it is when you see it, but you can’t define it.
Fundamental concepts
Structure in chaos
Emergent properties
Chaotic behavior or non-linear response