The contrast between single thinking and diversity is long since inherent to the search for ‘truth’ in science—and beyond. This Presentation aims at summarizing the reasons why scientists should be humble in contending methods for expressing experimental knowledge. However, we suppose that there must be reasons for the present trend toward selection of a single solution rather than using diversity as the approach to increase confidence that we are pointing to the correct answers: some examples are listed. Concern is expressed that this trend could lead to ‘political’ decisions, hindering rather than promoting, scientific understanding, and even potentially threatening scientific integrity.
This document discusses the difference between probability and exposure/payoff when assessing risks. It argues that risk management is more about understanding how random events affect us through exposure rather than trying to precisely define or understand probability alone. It provides examples showing how exposure/payoff can be very different from the underlying random variable being assessed. The document advocates focusing on risks we understand through modifying exposure over trying to understand all risks precisely. It also discusses how contract theory is more relevant to risk management than attempts to verbally define probability.
A scientific theory, according to Popper, can be legitimately saved from falsification by introducing an auxiliary hypothesis to generate new, falsifiable predictions. Also, if there are suspicions of bias or error, the researchers might introduce an auxiliary falsifiable hypothesis that would allow testing. But this technique can not solve the problem in general, because any auxiliary hypothesis can be challenged in the same way, ad infinitum. To solve this regression, Popper introduces the idea of a basic statement, an empirical statement that can be used both to determine whether a given theory is falsifiable and, if necessary, to corroborate falsification assumptions.
DOI: 10.13140/RG.2.2.22162.09923
Diversity state of_infor_beliefs_rubric fall 2011carolbillingcwi
This document outlines a rubric for scoring diversity statements according to Illinois state teaching standards. It evaluates candidates on expressing responsibility for student learning, understanding social contexts, using culturally responsive teaching strategies, addressing factors like poverty and divorce, and advocating for fair education. Candidates are scored on demonstrating, explaining, and giving examples to show their understanding and application of these concepts.
This document summarizes the key differences between affirmative action and diversity initiatives and provides suggestions to avoid conflicts between the two. Affirmative action is mandated for federal contractors and focuses on prohibited discrimination, while diversity metrics are voluntarily set up and defined more broadly. Conflicts can arise from inconsistent groupings, methodologies, and lack of management accountability across the two programs. The document recommends basing diversity metrics on affirmative action plans and clear communication to avoid appearances of quotas.
Metrics Matter: Measuring the Success of Your Company's Diversity Efforts
Learning Objective: To understand how to effectively measure the success of organizational diversity initiatives
Throughout corporate history diversity and inclusion have been two sensitive and highly controversial topics, which have shaped and molded organizational cultures. Misperceptions of diversity and inclusion efforts in organizations often lead to generalizations of initiatives that lack substance and measurable outcomes. Many HR and diversity practitioners still struggle with connecting diversity efforts to their organization’s bottom-line—and effectively communicating the return on investment of such efforts. This session will help attendees understand the steps it takes to measure the success of their diversity initiatives, how to create diversity scorecards, and the importance of performing self-audits of current diversity practices to ensure inclusion.
At the end of this seminar, participants will be able to:
a. Identify the steps it takes to measure their diversity initiatives
b. Understand the relationship between affirmative action plans (AAPs) and diversity initiatives
c. How to use traditional metrics to create diversity scorecards
d. How to self-audit HR practices to ensure inclusion
Measurement Memo Re: Measuring the Impact of Student Diversity Programandrejohnson034
This is a Measurement Memo that I developed for graduate course PAD 745 (Program Development and Evaluation). Addressed to the NYC Department of Education, it details baselines and benchmarks to measure my imaginary non-profit, Advocates for Student Diversity in Specialized High Schools (ASDSHS) against.
The organization was seeking funding from the NYC DOE in order to carry out its mission of expanding public and legislative support for the use of a holistic admissions approach in the city's specialized high school admissions process.
Are your senior leaders leading the charge to realizing a bottom-line payoff from diversity and inclusion? We are all aware of the need for top management “buy-in” for D&I. But turning head nods into consistent, visible and impactful actions by senior leaders is often a much greater challenge. This session will explore the missing links between verbal endorsement and active role modeling and ownership for D&I accountability. It will present ways to increase the likelihood that senior managers will make inclusive, culturally competent behaviors part of their leadership style and a “diversity lens” part of their business decision-making. We’ll suggest approaches to increase hands-on participation in strategy development, in-depth dialogue with diverse constituencies and expectation setting for their own subordinates. Potential measures of progress for this aspect of D&I change will also be discussed.
What Participants Will Learn:
What senior leader behaviors have the greatest impact on D&I progress.
How to more fully engage leaders in creating and implementing D&I strategy and in role modeling of inclusive behaviors.
What cultural competence is and why it’s important for leaders.
Approaches to measuring progress in increasing top management’s D&I leadership.
The Four Maturity Stages of Diversity and Inclusion ProgramsHuman Capital Media
Join us as we detail the four different stages of maturity — undeveloped, beginning, intermediate and advanced/vanguard — that maps to each company’s diversity program.
Join us to identify the stage of your organization and gain a deeper understanding of the following:
What do the four stages of the maturity development mean to an organization?
The primary goals of each maturity stage and how those change over time.
How to foresee challenges in a diversity function and develop plans to overcome them.
How measurement techniques can help support communication and ROI calculations.
This document discusses the difference between probability and exposure/payoff when assessing risks. It argues that risk management is more about understanding how random events affect us through exposure rather than trying to precisely define or understand probability alone. It provides examples showing how exposure/payoff can be very different from the underlying random variable being assessed. The document advocates focusing on risks we understand through modifying exposure over trying to understand all risks precisely. It also discusses how contract theory is more relevant to risk management than attempts to verbally define probability.
A scientific theory, according to Popper, can be legitimately saved from falsification by introducing an auxiliary hypothesis to generate new, falsifiable predictions. Also, if there are suspicions of bias or error, the researchers might introduce an auxiliary falsifiable hypothesis that would allow testing. But this technique can not solve the problem in general, because any auxiliary hypothesis can be challenged in the same way, ad infinitum. To solve this regression, Popper introduces the idea of a basic statement, an empirical statement that can be used both to determine whether a given theory is falsifiable and, if necessary, to corroborate falsification assumptions.
DOI: 10.13140/RG.2.2.22162.09923
Diversity state of_infor_beliefs_rubric fall 2011carolbillingcwi
This document outlines a rubric for scoring diversity statements according to Illinois state teaching standards. It evaluates candidates on expressing responsibility for student learning, understanding social contexts, using culturally responsive teaching strategies, addressing factors like poverty and divorce, and advocating for fair education. Candidates are scored on demonstrating, explaining, and giving examples to show their understanding and application of these concepts.
This document summarizes the key differences between affirmative action and diversity initiatives and provides suggestions to avoid conflicts between the two. Affirmative action is mandated for federal contractors and focuses on prohibited discrimination, while diversity metrics are voluntarily set up and defined more broadly. Conflicts can arise from inconsistent groupings, methodologies, and lack of management accountability across the two programs. The document recommends basing diversity metrics on affirmative action plans and clear communication to avoid appearances of quotas.
Metrics Matter: Measuring the Success of Your Company's Diversity Efforts
Learning Objective: To understand how to effectively measure the success of organizational diversity initiatives
Throughout corporate history diversity and inclusion have been two sensitive and highly controversial topics, which have shaped and molded organizational cultures. Misperceptions of diversity and inclusion efforts in organizations often lead to generalizations of initiatives that lack substance and measurable outcomes. Many HR and diversity practitioners still struggle with connecting diversity efforts to their organization’s bottom-line—and effectively communicating the return on investment of such efforts. This session will help attendees understand the steps it takes to measure the success of their diversity initiatives, how to create diversity scorecards, and the importance of performing self-audits of current diversity practices to ensure inclusion.
At the end of this seminar, participants will be able to:
a. Identify the steps it takes to measure their diversity initiatives
b. Understand the relationship between affirmative action plans (AAPs) and diversity initiatives
c. How to use traditional metrics to create diversity scorecards
d. How to self-audit HR practices to ensure inclusion
Measurement Memo Re: Measuring the Impact of Student Diversity Programandrejohnson034
This is a Measurement Memo that I developed for graduate course PAD 745 (Program Development and Evaluation). Addressed to the NYC Department of Education, it details baselines and benchmarks to measure my imaginary non-profit, Advocates for Student Diversity in Specialized High Schools (ASDSHS) against.
The organization was seeking funding from the NYC DOE in order to carry out its mission of expanding public and legislative support for the use of a holistic admissions approach in the city's specialized high school admissions process.
Are your senior leaders leading the charge to realizing a bottom-line payoff from diversity and inclusion? We are all aware of the need for top management “buy-in” for D&I. But turning head nods into consistent, visible and impactful actions by senior leaders is often a much greater challenge. This session will explore the missing links between verbal endorsement and active role modeling and ownership for D&I accountability. It will present ways to increase the likelihood that senior managers will make inclusive, culturally competent behaviors part of their leadership style and a “diversity lens” part of their business decision-making. We’ll suggest approaches to increase hands-on participation in strategy development, in-depth dialogue with diverse constituencies and expectation setting for their own subordinates. Potential measures of progress for this aspect of D&I change will also be discussed.
What Participants Will Learn:
What senior leader behaviors have the greatest impact on D&I progress.
How to more fully engage leaders in creating and implementing D&I strategy and in role modeling of inclusive behaviors.
What cultural competence is and why it’s important for leaders.
Approaches to measuring progress in increasing top management’s D&I leadership.
The Four Maturity Stages of Diversity and Inclusion ProgramsHuman Capital Media
Join us as we detail the four different stages of maturity — undeveloped, beginning, intermediate and advanced/vanguard — that maps to each company’s diversity program.
Join us to identify the stage of your organization and gain a deeper understanding of the following:
What do the four stages of the maturity development mean to an organization?
The primary goals of each maturity stage and how those change over time.
How to foresee challenges in a diversity function and develop plans to overcome them.
How measurement techniques can help support communication and ROI calculations.
This document discusses the history and foundations of probability theory. It covers key thinkers such as Laplace, Maxwell, Keynes, Jeffreys, de Finetti, Kolmogorov, and Cox. Cox's 1946 work generalizing Boolean logic to degrees of belief is identified as inspiring significant further investigation due to leaving conceptual issues to be explored. Developments inspired by Cox's work are then briefly mentioned, such as investigations into alternate axioms, efficiently employing logical operations, and deriving Feynman rules for quantum mechanics.
This document provides an introduction to the author's paper on objectivity in science. It begins by outlining the debate around whether objectivity exists in science. The author then defines key terms like objectivity and science. The main body discusses the problem of underdetermination, which questions objectivity by showing that multiple hypotheses can be consistent with the evidence. The author argues this problem strikes a "death blow" to the idea of objective science. They intend to later argue that using perspectives and context, an intellectual consensus can be reached that approaches objectivity, though true objectivity cannot be achieved.
This document discusses the philosophical concepts of a priori and a posteriori knowledge. It begins by explaining that a priori knowledge is derived from pure reason independent of sense experience, while a posteriori knowledge depends on empirical observations and sense experiences. It then explores ongoing debates around these concepts, including whether experience plays any role in justifying a priori beliefs and whether a priori knowledge truly exists independent of experience. The document traces the concepts back to Aristotle and discusses how prominent philosophers like Kant have further developed the distinction between a priori and a posteriori justification of knowledge.
The document discusses how argumentation can function like scientific hypothesis testing to generate reliable knowledge about topics that cannot be empirically observed or proven. It argues that placing the burden of proof on the affirmative team, by presuming the proposition is false until proven otherwise, introduces rigor to the argumentative process and allows the outcomes to be considered knowledge. The document also notes several implications this view has for current forensic practices, such as emphasizing the specific wording of the proposition over implementation plans and avoiding debates over minor differences between positions as long as the negative still opposes the proposition.
1) The document discusses different interpretations of probability, as it relates to causal modeling in the social sciences, including both generic and single-case causal claims.
2) It argues that objective Bayesianism, which treats probabilities as degrees of rational belief that incorporate empirical constraints, provides the best interpretation for probabilistic causal modeling.
3) Under an objective Bayesian view, probabilities can be used to evaluate hypotheses in both generic and single cases, allowing for rational decision making in areas like hypothesis testing and policy decisions.
Hypothesis is a proposed explanation for an observable phenomenon. A scientific hypothesis must be testable and falsifiable. It provides a provisional idea that requires evaluation through experimentation to either confirm or disprove it. Evaluating hypotheses involves testing them rigorously and considering factors like simplicity, scope, and fruitfulness. Statistical hypothesis testing compares a null hypothesis of no relationship between phenomena to an alternative hypothesis proposing a specific relationship.
This document provides an overview of the contents of a CD produced by Dialogue Education for use by teachers in the classroom. It contains 17 pages covering various topics in the philosophy of science like definitions of key terms, theories of demarcation, induction, and the theory-dependence of observation. The CD also includes videos, games, and a bibliography for further reading. It is intended solely for use by schools that have purchased the CD from Dialogue Education.
The document discusses interpreting probability in causal modelling approaches in the social sciences. It argues that objective Bayesianism provides the best interpretation of probability for causal modelling by treating probabilities as frequency-driven epistemic probabilities that make use of empirical constraints. This allows probabilities to be applied to both generic and single-case causal claims and helps guide decisions through rational degrees of belief informed by evidence and experience.
The document discusses interpreting probability in causal modelling approaches in the social sciences. It argues that objective Bayesianism provides the best interpretation of probability for causal modelling by treating generic and single-case causal claims probabilistically. Objective Bayesian probabilities are "frequency-driven epistemic probabilities" that make sense of both types of causal claims and allow learning from experience by incorporating empirical constraints. This approach guides both individual decisions and policy making by using probability as a guide based on available evidence rather than notions of error.
Mathematical Reasoning (unit-5) UGC NET Paper-1 Study Notes (E-books) Down...DIwakar Rajput
This document provides a summary of mathematical reasoning and aptitude topics that are important for the UGC NET exam. It mentions that mathematical reasoning is a very important section that can play a role in whether candidates are selected. It provides summaries, tips, and 200 practice questions for each topic, such as profit and loss, ratios, and more. It is intended to help students practice topics, increase accuracy, and develop a strong understanding to do well on the exam.
Spanos lecture 7: An Introduction to Bayesian Inference jemille6
This document provides an introduction to Bayesian inference through lecture notes on probability and statistics. It discusses three interpretations of probability: classical, degrees of belief, and frequency. The classical interpretation relies on an explicit chance mechanism with equally likely outcomes, which is too restrictive for empirical modeling. The degrees of belief interpretation considers probability as subjective beliefs, while the frequency interpretation views probability as the limit of relative frequencies in repeated experiments, as justified by the Strong Law of Large Numbers.
Inductive Approach
Mills Inductive Reasoning Essay
Essay On Induction
Induction Reasoning
Inductive Argument Paper
Inductive & Deductive Research
Inductive Argument
This document discusses the scientific method and its application to technical analysis. It explains that the scientific method involves making observations, developing hypotheses to explain those observations, making predictions based on the hypotheses, testing those predictions through further observation or experimentation, and drawing conclusions about the validity of the hypotheses based on how well they fit with the observations. The document then outlines the five stages of applying the scientific method to technical analysis: observation, hypothesis, prediction, verification, and conclusion. It provides examples of how each stage would work in practice when analyzing financial market data.
This document discusses the scientific method and its application to technical analysis. It explains that the scientific method involves making observations, developing hypotheses to explain those observations, making predictions based on the hypotheses, testing those predictions through further observation or experimentation, and drawing conclusions about the validity of the hypotheses based on how well they fit with the observations. The document then outlines the five stages of applying the scientific method to technical analysis: observation, hypothesis, prediction, verification, and conclusion. It provides examples of how each stage would work in practice when analyzing financial market data.
The document discusses the hypothetico-deductive method of science. It notes that previously induction was seen as the method of science but was later criticized. The hypothetico-deductive method involves:
1) Scientists making hypotheses to explain observations and phenomena, which involves creativity and synthesis.
2) Logically deducing consequences from the hypotheses.
3) Empirically testing the deduced consequences to validate or falsify the hypotheses.
Through this process, hypotheses are refined and scientific explanations are developed in an iterative manner.
This document discusses the traditional problem of induction and attempts to justify the inductive method. It presents Hume's view that induction cannot be justified since we cannot infer general laws from specific cases. Two options are considered: obtain knowledge non-inductively or accept induction is irrational. Popper argues for the first option, proposing scientific theories are conjectures subject to falsification, not verification. He claims induction is not needed if we tentatively accept the best theories until falsified. While this avoids Hume's problem, critics argue falsifiability is too weak a criterion and background assumptions are needed for tests. Overall, the document examines Hume's skepticism of induction and Popper's attempt to justify scientific reasoning without relying on induction
The document discusses the application of the scientific method to technical analysis. It describes the key stages of the scientific method as observation, hypothesis formation, prediction, verification through new observations or experiments, and drawing a conclusion about the hypothesis based on how well predictions matched observations. The document also discusses the development of the scientific method over time and key philosophical views that shaped it, such as Bacon's emphasis on experiments, Descartes' emphasis on doubt, and Popper's view of falsification.
The document discusses the scientific method and its application to process improvement. It begins by discussing key thinkers who helped establish the scientific method, such as Einstein, Pearson, Broad, Popper, Dewey, Simon, and Ackoff. It then covers concepts like the scientific method, theory development and testing, bounded rationality in decision making, and systems thinking. The document concludes by discussing statistical process control pioneers like Shewhart, Juran, and their contributions to using statistics and understanding process dominance to analyze and improve processes, setting up the DMAIC method as a strategic approach.
The document discusses three theories of truth:
1. Correspondence theory proposes that a proposition is true if it corresponds to the facts in reality. It has strengths in simplicity and appealing to common sense but weaknesses in linguistic issues and circular reasoning.
2. Coherence theory states that a proposition is true if it coheres with other propositions taken to be true. It has strengths in explaining mathematical truths but weaknesses in also falling victim to circular reasoning.
3. Pragmatism holds that a proposition is true if believing it has practical consequences and "works". William James defined truth as ideas that help us get into satisfactory relations with our experiences.
This document discusses the history and foundations of probability theory. It covers key thinkers such as Laplace, Maxwell, Keynes, Jeffreys, de Finetti, Kolmogorov, and Cox. Cox's 1946 work generalizing Boolean logic to degrees of belief is identified as inspiring significant further investigation due to leaving conceptual issues to be explored. Developments inspired by Cox's work are then briefly mentioned, such as investigations into alternate axioms, efficiently employing logical operations, and deriving Feynman rules for quantum mechanics.
This document provides an introduction to the author's paper on objectivity in science. It begins by outlining the debate around whether objectivity exists in science. The author then defines key terms like objectivity and science. The main body discusses the problem of underdetermination, which questions objectivity by showing that multiple hypotheses can be consistent with the evidence. The author argues this problem strikes a "death blow" to the idea of objective science. They intend to later argue that using perspectives and context, an intellectual consensus can be reached that approaches objectivity, though true objectivity cannot be achieved.
This document discusses the philosophical concepts of a priori and a posteriori knowledge. It begins by explaining that a priori knowledge is derived from pure reason independent of sense experience, while a posteriori knowledge depends on empirical observations and sense experiences. It then explores ongoing debates around these concepts, including whether experience plays any role in justifying a priori beliefs and whether a priori knowledge truly exists independent of experience. The document traces the concepts back to Aristotle and discusses how prominent philosophers like Kant have further developed the distinction between a priori and a posteriori justification of knowledge.
The document discusses how argumentation can function like scientific hypothesis testing to generate reliable knowledge about topics that cannot be empirically observed or proven. It argues that placing the burden of proof on the affirmative team, by presuming the proposition is false until proven otherwise, introduces rigor to the argumentative process and allows the outcomes to be considered knowledge. The document also notes several implications this view has for current forensic practices, such as emphasizing the specific wording of the proposition over implementation plans and avoiding debates over minor differences between positions as long as the negative still opposes the proposition.
1) The document discusses different interpretations of probability, as it relates to causal modeling in the social sciences, including both generic and single-case causal claims.
2) It argues that objective Bayesianism, which treats probabilities as degrees of rational belief that incorporate empirical constraints, provides the best interpretation for probabilistic causal modeling.
3) Under an objective Bayesian view, probabilities can be used to evaluate hypotheses in both generic and single cases, allowing for rational decision making in areas like hypothesis testing and policy decisions.
Hypothesis is a proposed explanation for an observable phenomenon. A scientific hypothesis must be testable and falsifiable. It provides a provisional idea that requires evaluation through experimentation to either confirm or disprove it. Evaluating hypotheses involves testing them rigorously and considering factors like simplicity, scope, and fruitfulness. Statistical hypothesis testing compares a null hypothesis of no relationship between phenomena to an alternative hypothesis proposing a specific relationship.
This document provides an overview of the contents of a CD produced by Dialogue Education for use by teachers in the classroom. It contains 17 pages covering various topics in the philosophy of science like definitions of key terms, theories of demarcation, induction, and the theory-dependence of observation. The CD also includes videos, games, and a bibliography for further reading. It is intended solely for use by schools that have purchased the CD from Dialogue Education.
The document discusses interpreting probability in causal modelling approaches in the social sciences. It argues that objective Bayesianism provides the best interpretation of probability for causal modelling by treating probabilities as frequency-driven epistemic probabilities that make use of empirical constraints. This allows probabilities to be applied to both generic and single-case causal claims and helps guide decisions through rational degrees of belief informed by evidence and experience.
The document discusses interpreting probability in causal modelling approaches in the social sciences. It argues that objective Bayesianism provides the best interpretation of probability for causal modelling by treating generic and single-case causal claims probabilistically. Objective Bayesian probabilities are "frequency-driven epistemic probabilities" that make sense of both types of causal claims and allow learning from experience by incorporating empirical constraints. This approach guides both individual decisions and policy making by using probability as a guide based on available evidence rather than notions of error.
Mathematical Reasoning (unit-5) UGC NET Paper-1 Study Notes (E-books) Down...DIwakar Rajput
This document provides a summary of mathematical reasoning and aptitude topics that are important for the UGC NET exam. It mentions that mathematical reasoning is a very important section that can play a role in whether candidates are selected. It provides summaries, tips, and 200 practice questions for each topic, such as profit and loss, ratios, and more. It is intended to help students practice topics, increase accuracy, and develop a strong understanding to do well on the exam.
Spanos lecture 7: An Introduction to Bayesian Inference jemille6
This document provides an introduction to Bayesian inference through lecture notes on probability and statistics. It discusses three interpretations of probability: classical, degrees of belief, and frequency. The classical interpretation relies on an explicit chance mechanism with equally likely outcomes, which is too restrictive for empirical modeling. The degrees of belief interpretation considers probability as subjective beliefs, while the frequency interpretation views probability as the limit of relative frequencies in repeated experiments, as justified by the Strong Law of Large Numbers.
Inductive Approach
Mills Inductive Reasoning Essay
Essay On Induction
Induction Reasoning
Inductive Argument Paper
Inductive & Deductive Research
Inductive Argument
This document discusses the scientific method and its application to technical analysis. It explains that the scientific method involves making observations, developing hypotheses to explain those observations, making predictions based on the hypotheses, testing those predictions through further observation or experimentation, and drawing conclusions about the validity of the hypotheses based on how well they fit with the observations. The document then outlines the five stages of applying the scientific method to technical analysis: observation, hypothesis, prediction, verification, and conclusion. It provides examples of how each stage would work in practice when analyzing financial market data.
This document discusses the scientific method and its application to technical analysis. It explains that the scientific method involves making observations, developing hypotheses to explain those observations, making predictions based on the hypotheses, testing those predictions through further observation or experimentation, and drawing conclusions about the validity of the hypotheses based on how well they fit with the observations. The document then outlines the five stages of applying the scientific method to technical analysis: observation, hypothesis, prediction, verification, and conclusion. It provides examples of how each stage would work in practice when analyzing financial market data.
The document discusses the hypothetico-deductive method of science. It notes that previously induction was seen as the method of science but was later criticized. The hypothetico-deductive method involves:
1) Scientists making hypotheses to explain observations and phenomena, which involves creativity and synthesis.
2) Logically deducing consequences from the hypotheses.
3) Empirically testing the deduced consequences to validate or falsify the hypotheses.
Through this process, hypotheses are refined and scientific explanations are developed in an iterative manner.
This document discusses the traditional problem of induction and attempts to justify the inductive method. It presents Hume's view that induction cannot be justified since we cannot infer general laws from specific cases. Two options are considered: obtain knowledge non-inductively or accept induction is irrational. Popper argues for the first option, proposing scientific theories are conjectures subject to falsification, not verification. He claims induction is not needed if we tentatively accept the best theories until falsified. While this avoids Hume's problem, critics argue falsifiability is too weak a criterion and background assumptions are needed for tests. Overall, the document examines Hume's skepticism of induction and Popper's attempt to justify scientific reasoning without relying on induction
The document discusses the application of the scientific method to technical analysis. It describes the key stages of the scientific method as observation, hypothesis formation, prediction, verification through new observations or experiments, and drawing a conclusion about the hypothesis based on how well predictions matched observations. The document also discusses the development of the scientific method over time and key philosophical views that shaped it, such as Bacon's emphasis on experiments, Descartes' emphasis on doubt, and Popper's view of falsification.
The document discusses the scientific method and its application to process improvement. It begins by discussing key thinkers who helped establish the scientific method, such as Einstein, Pearson, Broad, Popper, Dewey, Simon, and Ackoff. It then covers concepts like the scientific method, theory development and testing, bounded rationality in decision making, and systems thinking. The document concludes by discussing statistical process control pioneers like Shewhart, Juran, and their contributions to using statistics and understanding process dominance to analyze and improve processes, setting up the DMAIC method as a strategic approach.
The document discusses three theories of truth:
1. Correspondence theory proposes that a proposition is true if it corresponds to the facts in reality. It has strengths in simplicity and appealing to common sense but weaknesses in linguistic issues and circular reasoning.
2. Coherence theory states that a proposition is true if it coheres with other propositions taken to be true. It has strengths in explaining mathematical truths but weaknesses in also falling victim to circular reasoning.
3. Pragmatism holds that a proposition is true if believing it has practical consequences and "works". William James defined truth as ideas that help us get into satisfactory relations with our experiences.
Gamify it until you make it Improving Agile Development and Operations with ...Ben Linders
So many challenges, so little time. While we’re busy developing software and keeping it operational, we also need to sharpen the saw, but how? Gamification can be a way to look at how you’re doing and find out where to improve. It’s a great way to have everyone involved and get the best out of people.
In this presentation, Ben Linders will show how playing games with the DevOps coaching cards can help to explore your current development and deployment (DevOps) practices and decide as a team what to improve or experiment with.
The games that we play are based on an engagement model. Instead of imposing change, the games enable people to pull in ideas for change and apply those in a way that best suits their collective needs.
By playing games, you can learn from each other. Teams can use games, exercises, and coaching cards to discuss values, principles, and practices, and share their experiences and learnings.
Different game formats can be used to share experiences on DevOps principles and practices and explore how they can be applied effectively. This presentation provides an overview of playing formats and will inspire you to come up with your own formats.
• For a full set of 530+ questions. Go to
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The importance of sustainable and efficient computational practices in artificial intelligence (AI) and deep learning has become increasingly critical. This webinar focuses on the intersection of sustainability and AI, highlighting the significance of energy-efficient deep learning, innovative randomization techniques in neural networks, the potential of reservoir computing, and the cutting-edge realm of neuromorphic computing. This webinar aims to connect theoretical knowledge with practical applications and provide insights into how these innovative approaches can lead to more robust, efficient, and environmentally conscious AI systems.
Webinar Speaker: Prof. Claudio Gallicchio, Assistant Professor, University of Pisa
Claudio Gallicchio is an Assistant Professor at the Department of Computer Science of the University of Pisa, Italy. His research involves merging concepts from Deep Learning, Dynamical Systems, and Randomized Neural Systems, and he has co-authored over 100 scientific publications on the subject. He is the founder of the IEEE CIS Task Force on Reservoir Computing, and the co-founder and chair of the IEEE Task Force on Randomization-based Neural Networks and Learning Systems. He is an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
This presentation by Katharine Kemp, Associate Professor at the Faculty of Law & Justice at UNSW Sydney, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
1.) Introduction
Our Movement is not new; it is the same as it was for Freedom, Justice, and Equality since we were labeled as slaves. However, this movement at its core must entail economics.
2.) Historical Context
This is the same movement because none of the previous movements, such as boycotts, were ever completed. For some, maybe, but for the most part, it’s just a place to keep your stable until you’re ready to assimilate them into your system. The rest of the crabs are left in the world’s worst parts, begging for scraps.
3.) Economic Empowerment
Our Movement aims to show that it is indeed possible for the less fortunate to establish their economic system. Everyone else – Caucasian, Asian, Mexican, Israeli, Jews, etc. – has their systems, and they all set up and usurp money from the less fortunate. So, the less fortunate buy from every one of them, yet none of them buy from the less fortunate. Moreover, the less fortunate really don’t have anything to sell.
4.) Collaboration with Organizations
Our Movement will demonstrate how organizations such as the National Association for the Advancement of Colored People, National Urban League, Black Lives Matter, and others can assist in creating a much more indestructible Black Wall Street.
5.) Vision for the Future
Our Movement will not settle for less than those who came before us and stopped before the rights were equal. The economy, jobs, healthcare, education, housing, incarceration – everything is unfair, and what isn’t is rigged for the less fortunate to fail, as evidenced in society.
6.) Call to Action
Our movement has started and implemented everything needed for the advancement of the economic system. There are positions for only those who understand the importance of this movement, as failure to address it will continue the degradation of the people deemed less fortunate.
No, this isn’t Noah’s Ark, nor am I a Prophet. I’m just a man who wrote a couple of books, created a magnificent website: http://www.thearkproject.llc, and who truly hopes to try and initiate a truly sustainable economic system for deprived people. We may not all have the same beliefs, but if our methods are tried, tested, and proven, we can come together and help others. My website: http://www.thearkproject.llc is very informative and considerably controversial. Please check it out, and if you are afraid, leave immediately; it’s no place for cowards. The last Prophet said: “Whoever among you sees an evil action, then let him change it with his hand [by taking action]; if he cannot, then with his tongue [by speaking out]; and if he cannot, then, with his heart – and that is the weakest of faith.” [Sahih Muslim] If we all, or even some of us, did this, there would be significant change. We are able to witness it on small and grand scales, for example, from climate control to business partnerships. I encourage, invite, and challenge you all to support me by visiting my website.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
This presentation by Professor Giuseppe Colangelo, Jean Monnet Professor of European Innovation Policy, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by Tim Capel, Director of the UK Information Commissioner’s Office Legal Service, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
The Intersection between Competition and Data Privacy – CAPEL – June 2024 OEC...
Fostering Diversity (of thinking) in Science
1. Fostering Diversity (of thinking)
in Measurement Science
Franco Pavese*, Paul De Bièvre**
* Former Research Director at the
National Research Council,
Istituto di Metrologia “G.Colonnetti”
(from 2006 INRIM)
IMEKO TC21, Chair
Torino, Italy
E-mail: frpavese@gmail.com
This presentation is licensed under a Creative Commons Attribution 3.0 License
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** Independent Consultant on Metrology in
Chemistry (MiC)
Former Unit Head, Stable Isotope
Measurements IRMM,
Founding Editor and Editor-in-Chief until
2011 of the Journal “Accreditation and
Quality Assurance”
Kasterlee, Belgium
E-mail: paul.de.bievre@skynet.be
St. Petersburg, September 1
2. we observe increasing symptoms of a trend:
the prevalence of single-path thinking, maybe
fostered by either the anxiety to take a decision
by the intention to attempt to ‘force’ a conclusion
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Prologue
In many fields of science
or
St. Petersburg, September 2
3. Truth: the gnoseological* dilemma
* ”of the philosophy of knowledge and the human faculties for learning”
“Five fundamental aspects can be attributed to ‘truth’ …
by correspondence
by revelation (disclosure)
by conformity to a rule
by consistency (coherence)
by benefit
They are not reciprocally alternative, are diverse and not-reducible
“Consistency is indifferent to truth. Once can be entirely
consistent and still be entirely wrong” [Steven G. Vick]
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to each other.” [N. Abbagnano, Dictionary of Philosophy]
St. Petersburg, September 3
4. Truth: the gnoseological dilemma
The history of thinking shows that, in the search for ‘truth’,
general principles are typically subject to contrasting
positions, leading to irresolvable criticism.
“Reason alone is incapable of resolving the various
philosophical problems” [D. Hume]
Actually, it is impossible to demonstrate any position,
(“Relativism is the traditional epithet applied to pragmatism
by realists” [R. Rorty])
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including “relativism”—or similar categories
St. Petersburg, September 4
5. Truth ⇒ Certainty
The epistemological dilemma
Modern science, basically founded on empirism, as
opposed to metaphysics, is usually considered exempt from
the previous weakness.
Considering doubt as a shortcoming, science reasoning
aims at reaching, if not truth, at least certainties,
and many scientists tend to believe that this goal can
be fulfilled in their field. However, let us listen to Fr Bacon:
“If we begin with certainties, we shall end in doubts; but if
we begin with doubts, and are patient with them, we shall
end with certainties.” [Sir Francis Bacon 1605]
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(…still an optimistic viewpoint …)
St. Petersburg, September 5
6. Truth ⇒ Certainty ⇒ Objectivity
The illusion of objective knowledge
As alerted by philosophers, however, the previous belief
simply arises from the illusion of science being able to attain
objectivity, as a consequence of being based on information
drawn from the observation of natural phenomena, taken as
‘facts’.
Fact:
“A thing that is known or proven to be true” [Oxford Dictionary]
“A piece of information presented as having objective reality”
[Merriam-Webster Dictionary]
Objectivity and cause-effect-cause chain are the pillars of
single-path scientific reasoning.
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St. Petersburg, September 6
7. Truth ⇒ Certainty ⇒ Objectivity
The illusion of objective knowledge
Should these pillars stand firm, the theories developed for
systematically interlocking the empirical experience would,
similarly, consist of a single building block, with the
occasional addition of ancillary building blocks
accommodating specific new knowledge.
“Verification” [L. Wittgenstein] would become unnecessary…
the road toward the next “Scientific revolution” [T. Kuhn]
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(a static vision)
“Falsification” [K. Popper] a paradox …
impossible.
St. Petersburg, September 7
8. Truth ⇒ Certainty ⇒ Objectivity
Remedy 1: Uncertainty (& Imprecision)
Confronted with the evidence available since long, and
reconfirmed everyday, that the previous scenario does not apply,
However, strictly speaking, it applies only if the object of the
observations (the ‘measurand’ in measurement science) is the
same. Hence the issue is not fully resolved, the problem is shifted
to another concept, :
the uniqueness of the measurand, a concept of non-random
“Concerning non-precise data, uncertainty is called imprecision …
is not of stochastic nature … can be modelled by the so-called
non-precise numbers” [R. Viertl, EOLSS UNESCO Encyclopoedia]
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the concept of ‘uncertainty’ came in.
nature, leading to imprecision.
St. Petersburg, September 8
9. Certainty ⇒ Uncertainty ⇒ Chance
Confronted with the evidence of diverse results of observations,
modern science’s way-out was to introduce the concept of
‘chance’—replacing ‘certainty’.
This was done with the illusion of reaching firmer conclusions by
establishing a hierarchy in measurement results (e.g. based on the
frequency of occurrence), in order to take a ‘decision’ (i.e. for
choosing from various measurement results).
Chance concept initiated the framework of ‘probability’, but expanded later into
several other streams, e.g., possibility, fuzzy, cause-effect, interval, non-parametric,
… reasoning frames depending on the type of information available
or on the approach to it.
“With the idol of certainty (including that of degrees of imperfect
certainty or probability) there falls one of the defences of
obscurantism which bar the way of scientific advance.” [K. Popper]
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Remedy 2: Decision
St. Petersburg, September 9
10. The illusions of chance –1
Chance ⇒ (Prediction) ⇒ Decision
The ultimate common goal of any branch of science is
to communicate measurement results and perform robust
prediction.
In the probability frame, any decision strategy requires the
choice of an expected value as well of the limits of the
dispersion interval of the observations.
The choice of the expected value (‘expectation’: “a strong belief
that something will happen or be the case” [from Oxford Dictionary])
is not unequivocal, since several location parameters are
offered by probability theory—with a ‘true value’ still standing
in the shade, deviations from which are called ‘errors’.
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St. Petersburg, September 10
11. Chance ⇒ (Prediction) ⇒ Decision
The illusions of chance –2
As to data dispersion, most theoretical frameworks tend to lack
general reasons for bounding a probability distribution, whose tails
thus extend without limits to infinitum.
However, without a limit, no decision is possible; and,
the wider the limit, the less meaningful a decision is.
Stating a limit becomes itself a decision, assumed to fit the
intended use of the data.
The terms used in this frame clearly indicate the difficulty and
the meaning that is applicable in this context:
‘confidence level’ (confidence: “the feeling or belief that one can have
faith in or rely on someone or something” [from Oxford Dictionary]), or
‘degree of belief’ (belief: “trust, faith, or confidence in (someone or
something)” or “an acceptance that something exists or is true, especially
one without proof” [ibidem])
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St. Petersburg, September 11
12. Chance ⇒ (Prediction) ⇒ Decision
The illusions of chance –3
As to data dispersion, alternatively, one can believe in using
truncated (finite tail-width) distributions.
However, reasons for truncation are generally supported
by uncertain information.
In rare cases it may be justified by theory, e.g. a bound to
zero –itself not normally reachable exactly (experimental
limit of detection)
Stating limits becomes itself again a decision, also in this
case assumed to be fit for the intended use of the data.
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St. Petersburg, September 12
13. Uncertainty ⇒ Chance ⇒ Decision ⇒ Risk
But … what about ‘decision’?
When (objective) reasoning is replaced by choice,
a decision can only be based on
• a priori assumptions (for hypotheses), or
• inter-subjectively accepted conventions (predictive, for
subsequent action),
However, hypotheses cannot be proved, and inter-subjective
agreements are strictly relative to a community and for a
given period of time.
The loss of certainty resulted in the loss of uniqueness of
decisions, and the concept of ‘risk’ emerged as a remedy.
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Remedy 3:
St. Petersburg, September 13
14. Uncertainty ⇒ Chance ⇒ Decision ⇒ Risk
Any parameter chosen to represent a set of observations
becomes ‘uncertain’,
not because it must be expressed with a dispersion attribute
associated to an expected value, but
because the choice of both parameters is the result of
decisions, and a decision cannot be ‘exact’ (unequivocal).
Any decision is fuzzy.
The use of risk does not alleviate the issue:
if a decision cannot be exact, the risk cannot be null.
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The illusions of risk –1
St. Petersburg, September 14
15. Uncertainty ⇒ Chance ⇒ Decision ⇒ Risk
In other words:
• The association of a ‘risk’ to a decision, a recent
popular issue, does not add any real benefit in respect
to the fundamental issue.
• Risk is only zero for certainty, so zero risk is
unreachable.
“The relations between probability and experience are also still in
need of clarification. In investigating this problem we shall discover
what will at first seem an almost insuperable objection to my
methodological views. For although probability statements play such
a vitally important role in empirical science, they turn out to be in
principle impervious to strict falsification.” [K. Popper 1936]
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The illusions of risk –2
St. Petersburg, September 15
16. Chance is a bright prescription for working on symptoms of the
disease, but is not a therapy for its deep origin, subjectivity.
In fact, the very origin of the problem is related to our
knowledge interface—human being.
It is customary to make a distinction between the
‘outside’ and the ‘inside’ of the observer, i.e. between
the ‘real world’ and the ‘mind’.
Note: we are not fostering here a vision of the world as a ‘dream’.
There are solid arguments for conceiving a structured and
reasonably stable reality outside us (objectivity of the “true value”).
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The failure of remedies:
deeper origin –1
St. Petersburg, September 16
17. This distinction is one of the reasons generating a dichotomy since
at least a couple of centuries, between ‘exact sciences’ and other
branches, often called ‘soft sciences’, like psychology, sociology,
economy…
For ‘soft’ science we are ready to admit that the objects of
observations tend to be dissimilar, because every human
individual is dissimilar from any other.
In ‘exact’ science we are usually not readily admitting that
the human interface between our ‘mind’ and the ‘real
world’ is a factor of influence affecting our knowledge.
Mathematics stays in between, not being based on observations but
on a ‘exact’ construction of concepts based on the thinking
mechanisms in our mind.
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The failure of remedies:
deeper origin –2
St. Petersburg, September 17
18. • All of the above should suggest scientists to be
humble about contending on methods for expressing
experimental knowledge— apart from gross
mistakes (“blunders”).
• Different from the theoretical context, experience can be
only shared to a certain degree, leading, at best, to a
shared decision. The association of
a ‘risk’ to a decision does not add any real benefit with
respect to the fundamental issue.
• One cannot expect a single decision to be valid in all
cases, i.e. without exceptions. Risk is
only zero for certainty, so zero risk is unreachable.
• Similarly, no single frame of reasoning leading to a
specific type of decision can be expected to be valid in
all cases.
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Consequences
St. Petersburg, September 18
19. • Also in science, ‘diversity’ is not always a synonym of
‘confusion’, a popular term used to contrast it, rather it is an
invaluable additional resource leading to better
understanding.
• Should this be the case, diversity rather becomes richness, by
deserving a higher degree of confidence in our pointing to the
correct answers
(but, obviously, “nothing that has been or will be said makes it a
process of evolution toward anything” [T. Kuhn]).
• This fact is already well understood in experimental science,
where the main way to detect systematic effects is to
diversify the experimental methods and procedures used.
Why not accepting it also in reasoning?
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Diversity: a resource
St. Petersburg, September 19
20. Sparse examples of exclusive choices
in measurement science
The Guide for the Expression of Uncertainty in Measurement (GUM) in favour to
choose a single framework, with the ‘error approach’ discontinued in favour of an
‘uncertainty approach’;
The Guide for the Expression of Uncertainty in Measurement (GUM) in favour to
choose for its future edition the single approach—‘Bayesian’— replacing
‘frequentist’ parts;
The International System of Measurement Units (SI) proposed to change, with
“fundamental constants” replacing ‘physical states or conditions’ in definitions of
base units;
The singled ‘official’ set of “recommended values” used for the numerical values
of quantities (fundamental constants, atomic masses, differences in scales, …);
The pretended permanent validity of numerical value stipulations;
The traditional exclusive classification of the errors/effects in random and
systematic, with the concept of “correction” associated to the latter; …
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St. Petersburg, September 20
21. • At its origin, the indicated trend might be due to
a wrong assignment to a relevant Commission or
Task Group, with the request of a single
‘consensus’ outcome, instead of a rationally-compounded
information/knowledge.
• However, the consequence risks to be
politics (needing decisions) leaking into science
(seeking understanding),
a trend carrying the danger of potentially
threatening scientific integrity.
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General conclusions
digest of the best available
St. Petersburg, September 21