OD&VC.pptx
•Download as PPTX, PDF•
0 likes•16 views
This document discusses the importance and applications of differential equations in data science. It explains that differential equations are used to model complex systems in science and engineering by relating a function to its derivatives. Some examples given include using differential equations to model population growth, heat transfer, fluid dynamics, and chemical reactions. While exact solutions are difficult to find, numerical methods can be used to approximate solutions to differential equations.
Report
Share
Report
Share
Recommended
Application of ordinary differential equations.pptx
Application of ordinary differential equations...
F O R U MComplexity and TransitionManagementJan Rotman.docx
F O R U M
Complexity and Transition
Management
Jan Rotmans and Derk Loorbach
Keywords:
complex adaptive systems
emergence
governance
industrial ecology
sustainable development
transitions
Summary
This article presents a framework, transition management, for
managing complex societal systems. The principal contribu-
tion of this article is to articulate the relationship between
transition management and complex systems theory. A better
understanding of the dynamics of complex, adaptive systems
provides insight into the opportunities, limitations, and con-
ditions under which it is possible to influence such systems.
Transition management is based on key notions of complex
systems theory, such as variation and selection, emergence,
coevolution, and self-organization. It involves a cyclical process
of phases at various scale levels: stimulating niche develop-
ment at the micro level, finding new attractors at the macro
level by developing a sustainability vision, creating diversity by
setting out experiments, and selecting successful experiments
that can be scaled up.
Address correspondence to:
Jan Rotmans
DRIFT: Dutch Research Institute For
Transitions
Erasmus University Rotterdam
P.O. Box 1738
3000 DR Rotterdam
[email protected]
www.drift.eur.nl
c© 2009 by Yale University
DOI: 10.1111/j.1530-9290.2009.00116.x
Volume 13, Number 2
184 Journal of Industrial Ecology www.blackwellpublishing.com/jie
F O R U M
Introduction
Our society faces a number of persistent prob-
lems whose symptoms are becoming more and
more apparent. Persistent problems are complex
because they are deeply embedded in our societal
structures; uncertain due to the hardly reducible
structural uncertainty they include; difficult to
manage, with a variety of actors with diverse in-
terests involved; and hard to grasp in the sense
that they are difficult to interpret and ill struc-
tured (Dirven et al. 2002). Persistent problems
are the superlative form of what Rittel and Web-
ber (1973, 160) refer to as “wicked problems.”
An example of a persistent problem is the energy
problem, with anthropogenic climate change as a
manifestation (Energy Council 2004). Persistent
problems cannot be solved through only current
policies (Ministry of Housing, Spatial Planning
and Environment 2002; Social and Economic
Council of the Netherlands 2001). Persistent
problems are related to the system failures that
crept into our societal systems and that, contrary
to market failures, cannot be corrected by the
market or current policies. System failures are
locked-in flaws in our societal structures, such as
technological bias, weak or dominant networks,
institutional barriers, and path dependencies.
Combating system failures requires a restruc-
turing of societal systems—that is, a transition. A
transition is a radical, structural change of a so-
cietal (sub)system that is the result of a coevolu-
tion of economic, cultural, technological, ecolog-
ical, and institutional developments at ...
Recommended
Application of ordinary differential equations.pptx
Application of ordinary differential equations...
F O R U MComplexity and TransitionManagementJan Rotman.docx
F O R U M
Complexity and Transition
Management
Jan Rotmans and Derk Loorbach
Keywords:
complex adaptive systems
emergence
governance
industrial ecology
sustainable development
transitions
Summary
This article presents a framework, transition management, for
managing complex societal systems. The principal contribu-
tion of this article is to articulate the relationship between
transition management and complex systems theory. A better
understanding of the dynamics of complex, adaptive systems
provides insight into the opportunities, limitations, and con-
ditions under which it is possible to influence such systems.
Transition management is based on key notions of complex
systems theory, such as variation and selection, emergence,
coevolution, and self-organization. It involves a cyclical process
of phases at various scale levels: stimulating niche develop-
ment at the micro level, finding new attractors at the macro
level by developing a sustainability vision, creating diversity by
setting out experiments, and selecting successful experiments
that can be scaled up.
Address correspondence to:
Jan Rotmans
DRIFT: Dutch Research Institute For
Transitions
Erasmus University Rotterdam
P.O. Box 1738
3000 DR Rotterdam
[email protected]
www.drift.eur.nl
c© 2009 by Yale University
DOI: 10.1111/j.1530-9290.2009.00116.x
Volume 13, Number 2
184 Journal of Industrial Ecology www.blackwellpublishing.com/jie
F O R U M
Introduction
Our society faces a number of persistent prob-
lems whose symptoms are becoming more and
more apparent. Persistent problems are complex
because they are deeply embedded in our societal
structures; uncertain due to the hardly reducible
structural uncertainty they include; difficult to
manage, with a variety of actors with diverse in-
terests involved; and hard to grasp in the sense
that they are difficult to interpret and ill struc-
tured (Dirven et al. 2002). Persistent problems
are the superlative form of what Rittel and Web-
ber (1973, 160) refer to as “wicked problems.”
An example of a persistent problem is the energy
problem, with anthropogenic climate change as a
manifestation (Energy Council 2004). Persistent
problems cannot be solved through only current
policies (Ministry of Housing, Spatial Planning
and Environment 2002; Social and Economic
Council of the Netherlands 2001). Persistent
problems are related to the system failures that
crept into our societal systems and that, contrary
to market failures, cannot be corrected by the
market or current policies. System failures are
locked-in flaws in our societal structures, such as
technological bias, weak or dominant networks,
institutional barriers, and path dependencies.
Combating system failures requires a restruc-
turing of societal systems—that is, a transition. A
transition is a radical, structural change of a so-
cietal (sub)system that is the result of a coevolu-
tion of economic, cultural, technological, ecolog-
ical, and institutional developments at ...
F O R U MComplexity and TransitionManagementJan Rotman.docx
F O R U M
Complexity and Transition
Management
Jan Rotmans and Derk Loorbach
Keywords:
complex adaptive systems
emergence
governance
industrial ecology
sustainable development
transitions
Summary
This article presents a framework, transition management, for
managing complex societal systems. The principal contribu-
tion of this article is to articulate the relationship between
transition management and complex systems theory. A better
understanding of the dynamics of complex, adaptive systems
provides insight into the opportunities, limitations, and con-
ditions under which it is possible to influence such systems.
Transition management is based on key notions of complex
systems theory, such as variation and selection, emergence,
coevolution, and self-organization. It involves a cyclical process
of phases at various scale levels: stimulating niche develop-
ment at the micro level, finding new attractors at the macro
level by developing a sustainability vision, creating diversity by
setting out experiments, and selecting successful experiments
that can be scaled up.
Address correspondence to:
Jan Rotmans
DRIFT: Dutch Research Institute For
Transitions
Erasmus University Rotterdam
P.O. Box 1738
3000 DR Rotterdam
[email protected]
www.drift.eur.nl
c© 2009 by Yale University
DOI: 10.1111/j.1530-9290.2009.00116.x
Volume 13, Number 2
184 Journal of Industrial Ecology www.blackwellpublishing.com/jie
F O R U M
Introduction
Our society faces a number of persistent prob-
lems whose symptoms are becoming more and
more apparent. Persistent problems are complex
because they are deeply embedded in our societal
structures; uncertain due to the hardly reducible
structural uncertainty they include; difficult to
manage, with a variety of actors with diverse in-
terests involved; and hard to grasp in the sense
that they are difficult to interpret and ill struc-
tured (Dirven et al. 2002). Persistent problems
are the superlative form of what Rittel and Web-
ber (1973, 160) refer to as “wicked problems.”
An example of a persistent problem is the energy
problem, with anthropogenic climate change as a
manifestation (Energy Council 2004). Persistent
problems cannot be solved through only current
policies (Ministry of Housing, Spatial Planning
and Environment 2002; Social and Economic
Council of the Netherlands 2001). Persistent
problems are related to the system failures that
crept into our societal systems and that, contrary
to market failures, cannot be corrected by the
market or current policies. System failures are
locked-in flaws in our societal structures, such as
technological bias, weak or dominant networks,
institutional barriers, and path dependencies.
Combating system failures requires a restruc-
turing of societal systems—that is, a transition. A
transition is a radical, structural change of a so-
cietal (sub)system that is the result of a coevolu-
tion of economic, cultural, technological, ecolog-
ical, and institutional developments at .
Application of Differential Equation
Differential Equation is a very important topic of Mathematics. We tried our best to describes applications of differential equation in this presentation.
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...
The main recommendations of this article mainly analyzing the rate of harmful elements the period of exploitation of the automobile implements and its services to develop activity of automobile implements of the exploitation period. Shavkat Giyazov "Analysis of Existing Models in Relation to the Problems of Mass Exchange between Autotransport Complex and the Environment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38681.pdf Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/38681/analysis-of-existing-models-in-relation-to-the-problems-of-mass-exchange-between-autotransport-complex-and-the-environment/shavkat-giyazov
General Linear Model | Statistics
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Qualitative Analysis of a Discrete SIR Epidemic Model
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Science - Part IV - Regression Analysis & ANOVA
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
linear model multiple predictors.pdf
This is a lecture of prof dr Akhter given in domain of quantatative methods for decision making.
A researcher in attempting to run a regression model noticed a neg.docx
A researcher in attempting to run a regression model noticed a negative beta sign for an explanatory variable when s/he was expecting a positive sign based on theoretical considerations. What advice would you give to the researcher as to what is going on and what specific diagnostics would you look at? Explain conceptually and statisticallythe different ways you cancorrect for this problem.
Reason
One of the most common and important reasons for such situations is the existence of multicollinearity. Multicollinearity can happen if some of the independent variables are highly correlated to each other or to another variable that is not in the model.
Multicollinearity also has other symptoms such as
· Large variance for regression coefficients
· Non-significant individual coefficients while the general model is significant
· Change of marginal contributions depending on the variables in the model
· Large correlation coefficients in the correlation matrix of variables
It should however be noted that the general model can preserve its predictive ability and it is only the explanatory power that is lost
Before going to the solutions and measures the researcher can take it is wise to take a step back and see the underlying reason for the multicollinearity. An extreme case where two variables are identical gives the best understanding of problem
In this case we are trying to define y as a function of and while in reality . Therefore any linear combination of and is replaceable by infinite other linear combinations (ie )
It is simply understandable that while the y is predicted correctly in all the instances individual coefficients for and are meaningless.
Diagnosis
One of the most common diagnoses for multicollinearity is the variance inflation factor (VIF)
Where
And is the coefficient of multiple determination of regression of on other variables
The variance inflation factor therefore determines how much the variance of each coefficient inflates. when equals zero VIF equals 1 which suggests zero multicollinearity heuristic is that any value of VIF larger than 10 is alerting and a case of strong multicollinearity exists.
Solution
s
There are a few solutions for the multi Collinearity problem:
1- Ignoring the problem completely is possible for cases where we only care about the final model fit and prediction capability rather than individual coefficients and explanation power
2- Removing some of the correlated variables from the model, this can be justified since we can argue the effect of variable is however seen by similar highly correlated variables that are kept in the model
3- Principle component analysis (or any orthogonal transformation) can reduce the number of factors to a few orthogonal factors with no collinearity; however we should note that the interpretation of variables after a PC transformation is hard.
4- For cases where we intend to keep all the variables in the model without any major transformation, the Ridge regr.
Unit I-B
Why Operations Research?
Introduction
Origin of operations research
Definition of operations research
Characteristics of operations research
Role of operations research in decision-making
Methods of solving operations research problem
Phases in solving operations research problems
Typical problems in operations research
Scope of operations research
Why to study operations research
Nita H.Shah Ravi M. Gor Hardik Soni
Not sure how to do this case analysis please help me do it!1.Are t.pdf
Not sure how to do this case analysis please help me do it!
1.Are the regions similar?
2.What would be a good forecasting method to use?
3.Are there more advanced methods that might be considered?
Here is the data
https://docs.google.com/spreadsheets/d/1LYZHLx9f0Av9FJ6NLfULMU61cZPV3voqrVzbJNfV
jKk/edit?usp=sharing
Solution
Most people view the world as consisting of a large number of alternatives. Futures research
evolved as a way of examining the alternative futures and identifying the most probable.
Forecasting is designed to help decision making and planning in the present.
Forecasts empower people because their use implies that we can modify variables now to alter
(or be prepared for) the future. A prediction is an invitation to introduce change into a system.
There are several assumptions about forecasting:
1. There is no way to state what the future will be with complete certainty. Regardless of the
methods that we use there will always be an element of uncertainty until the forecast horizon has
come to pass.
2. There will always be blind spots in forecasts. We cannot, for example, forecast completely
new technologies for which there are no existing paradigms.
3. Providing forecasts to policy-makers will help them formulate social policy. The new social
policy, in turn, will affect the future, thus changing the accuracy of the forecast.
Many scholars have proposed a variety of ways to categorize forecasting methodologies. The
following classification is a modification of the schema developed by Gordon over two decades
ago:
Genius forecasting - This method is based on a combination of intuition, insight, and luck.
Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts
are based exclusively on intuition. Science fiction writers have sometimes described new
technologies with uncanny accuracy.
There are many examples where men and women have been remarkable successful at predicting
the future. There are also many examples of wrong forecasts. The weakness in genius forecasting
is that its impossible to recognize a good forecast until the forecast has come to pass.
Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream
science generally ignores this fact because the implications are simply to difficult to accept. Our
current understanding of reality is not adequate to explain this phenomena.
Trend extrapolation - These methods examine trends and cycles in historical data, and then use
mathematical techniques to extrapolate to the future. The assumption of all these techniques is
that the forces responsible for creating the past, will continue to operate in the future. This is
often a valid assumption when forecasting short term horizons, but it falls short when creating
medium and long term forecasts. The further out we attempt to forecast, the less certain we
become of the forecast.
The stability of the environment is the key factor in determining whether tren.
In mathematics, a partial differential equation (.pdf
In mathematics, a partial differential equation (PDE) is a differential equation that
contains unknown multivariable functions and their partial derivatives. (This is in contrast to
ordinary differential equations, which deal with functions of a single variable and their
derivatives.) PDEs are used to formulate problems involving functions of several variables, and
are either solved by hand, or used to create a relevant computer model. PDEs can be used to
describe a wide variety of phenomena such as sound, heat, electrostatics, electrodynamics, fluid
flow, or elasticity. These seemingly distinct physical phenomena can be formalised identically in
terms of PDEs, which shows that they are governed by the same underlying dynamic. Just as
ordinary differential equations often model one-dimensional dynamical systems, partial
differential equations often model multidimensional systems. PDEs find their generalisation in
stochastic partial differential equations.
Solution
In mathematics, a partial differential equation (PDE) is a differential equation that
contains unknown multivariable functions and their partial derivatives. (This is in contrast to
ordinary differential equations, which deal with functions of a single variable and their
derivatives.) PDEs are used to formulate problems involving functions of several variables, and
are either solved by hand, or used to create a relevant computer model. PDEs can be used to
describe a wide variety of phenomena such as sound, heat, electrostatics, electrodynamics, fluid
flow, or elasticity. These seemingly distinct physical phenomena can be formalised identically in
terms of PDEs, which shows that they are governed by the same underlying dynamic. Just as
ordinary differential equations often model one-dimensional dynamical systems, partial
differential equations often model multidimensional systems. PDEs find their generalisation in
stochastic partial differential equations..
Blue property assumptions.
This presentation briefly discusses about Linear Regression:Best Linear Unbiased Estimator(BLUE) property assumptions in Data Analytics
What price will pi network be listed on exchanges
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the telegram contact of my personal pi vendor to trade with.
@Pi_vendor_247
what is a pi whale and how to access one.
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
More Related Content
Similar to OD&VC.pptx
F O R U MComplexity and TransitionManagementJan Rotman.docx
F O R U M
Complexity and Transition
Management
Jan Rotmans and Derk Loorbach
Keywords:
complex adaptive systems
emergence
governance
industrial ecology
sustainable development
transitions
Summary
This article presents a framework, transition management, for
managing complex societal systems. The principal contribu-
tion of this article is to articulate the relationship between
transition management and complex systems theory. A better
understanding of the dynamics of complex, adaptive systems
provides insight into the opportunities, limitations, and con-
ditions under which it is possible to influence such systems.
Transition management is based on key notions of complex
systems theory, such as variation and selection, emergence,
coevolution, and self-organization. It involves a cyclical process
of phases at various scale levels: stimulating niche develop-
ment at the micro level, finding new attractors at the macro
level by developing a sustainability vision, creating diversity by
setting out experiments, and selecting successful experiments
that can be scaled up.
Address correspondence to:
Jan Rotmans
DRIFT: Dutch Research Institute For
Transitions
Erasmus University Rotterdam
P.O. Box 1738
3000 DR Rotterdam
[email protected]
www.drift.eur.nl
c© 2009 by Yale University
DOI: 10.1111/j.1530-9290.2009.00116.x
Volume 13, Number 2
184 Journal of Industrial Ecology www.blackwellpublishing.com/jie
F O R U M
Introduction
Our society faces a number of persistent prob-
lems whose symptoms are becoming more and
more apparent. Persistent problems are complex
because they are deeply embedded in our societal
structures; uncertain due to the hardly reducible
structural uncertainty they include; difficult to
manage, with a variety of actors with diverse in-
terests involved; and hard to grasp in the sense
that they are difficult to interpret and ill struc-
tured (Dirven et al. 2002). Persistent problems
are the superlative form of what Rittel and Web-
ber (1973, 160) refer to as “wicked problems.”
An example of a persistent problem is the energy
problem, with anthropogenic climate change as a
manifestation (Energy Council 2004). Persistent
problems cannot be solved through only current
policies (Ministry of Housing, Spatial Planning
and Environment 2002; Social and Economic
Council of the Netherlands 2001). Persistent
problems are related to the system failures that
crept into our societal systems and that, contrary
to market failures, cannot be corrected by the
market or current policies. System failures are
locked-in flaws in our societal structures, such as
technological bias, weak or dominant networks,
institutional barriers, and path dependencies.
Combating system failures requires a restruc-
turing of societal systems—that is, a transition. A
transition is a radical, structural change of a so-
cietal (sub)system that is the result of a coevolu-
tion of economic, cultural, technological, ecolog-
ical, and institutional developments at .
Application of Differential Equation
Differential Equation is a very important topic of Mathematics. We tried our best to describes applications of differential equation in this presentation.
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...
The main recommendations of this article mainly analyzing the rate of harmful elements the period of exploitation of the automobile implements and its services to develop activity of automobile implements of the exploitation period. Shavkat Giyazov "Analysis of Existing Models in Relation to the Problems of Mass Exchange between Autotransport Complex and the Environment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38681.pdf Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/38681/analysis-of-existing-models-in-relation-to-the-problems-of-mass-exchange-between-autotransport-complex-and-the-environment/shavkat-giyazov
General Linear Model | Statistics
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Qualitative Analysis of a Discrete SIR Epidemic Model
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Science - Part IV - Regression Analysis & ANOVA
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
linear model multiple predictors.pdf
This is a lecture of prof dr Akhter given in domain of quantatative methods for decision making.
A researcher in attempting to run a regression model noticed a neg.docx
A researcher in attempting to run a regression model noticed a negative beta sign for an explanatory variable when s/he was expecting a positive sign based on theoretical considerations. What advice would you give to the researcher as to what is going on and what specific diagnostics would you look at? Explain conceptually and statisticallythe different ways you cancorrect for this problem.
Reason
One of the most common and important reasons for such situations is the existence of multicollinearity. Multicollinearity can happen if some of the independent variables are highly correlated to each other or to another variable that is not in the model.
Multicollinearity also has other symptoms such as
· Large variance for regression coefficients
· Non-significant individual coefficients while the general model is significant
· Change of marginal contributions depending on the variables in the model
· Large correlation coefficients in the correlation matrix of variables
It should however be noted that the general model can preserve its predictive ability and it is only the explanatory power that is lost
Before going to the solutions and measures the researcher can take it is wise to take a step back and see the underlying reason for the multicollinearity. An extreme case where two variables are identical gives the best understanding of problem
In this case we are trying to define y as a function of and while in reality . Therefore any linear combination of and is replaceable by infinite other linear combinations (ie )
It is simply understandable that while the y is predicted correctly in all the instances individual coefficients for and are meaningless.
Diagnosis
One of the most common diagnoses for multicollinearity is the variance inflation factor (VIF)
Where
And is the coefficient of multiple determination of regression of on other variables
The variance inflation factor therefore determines how much the variance of each coefficient inflates. when equals zero VIF equals 1 which suggests zero multicollinearity heuristic is that any value of VIF larger than 10 is alerting and a case of strong multicollinearity exists.
Solution
s
There are a few solutions for the multi Collinearity problem:
1- Ignoring the problem completely is possible for cases where we only care about the final model fit and prediction capability rather than individual coefficients and explanation power
2- Removing some of the correlated variables from the model, this can be justified since we can argue the effect of variable is however seen by similar highly correlated variables that are kept in the model
3- Principle component analysis (or any orthogonal transformation) can reduce the number of factors to a few orthogonal factors with no collinearity; however we should note that the interpretation of variables after a PC transformation is hard.
4- For cases where we intend to keep all the variables in the model without any major transformation, the Ridge regr.
Unit I-B
Why Operations Research?
Introduction
Origin of operations research
Definition of operations research
Characteristics of operations research
Role of operations research in decision-making
Methods of solving operations research problem
Phases in solving operations research problems
Typical problems in operations research
Scope of operations research
Why to study operations research
Nita H.Shah Ravi M. Gor Hardik Soni
Not sure how to do this case analysis please help me do it!1.Are t.pdf
Not sure how to do this case analysis please help me do it!
1.Are the regions similar?
2.What would be a good forecasting method to use?
3.Are there more advanced methods that might be considered?
Here is the data
https://docs.google.com/spreadsheets/d/1LYZHLx9f0Av9FJ6NLfULMU61cZPV3voqrVzbJNfV
jKk/edit?usp=sharing
Solution
Most people view the world as consisting of a large number of alternatives. Futures research
evolved as a way of examining the alternative futures and identifying the most probable.
Forecasting is designed to help decision making and planning in the present.
Forecasts empower people because their use implies that we can modify variables now to alter
(or be prepared for) the future. A prediction is an invitation to introduce change into a system.
There are several assumptions about forecasting:
1. There is no way to state what the future will be with complete certainty. Regardless of the
methods that we use there will always be an element of uncertainty until the forecast horizon has
come to pass.
2. There will always be blind spots in forecasts. We cannot, for example, forecast completely
new technologies for which there are no existing paradigms.
3. Providing forecasts to policy-makers will help them formulate social policy. The new social
policy, in turn, will affect the future, thus changing the accuracy of the forecast.
Many scholars have proposed a variety of ways to categorize forecasting methodologies. The
following classification is a modification of the schema developed by Gordon over two decades
ago:
Genius forecasting - This method is based on a combination of intuition, insight, and luck.
Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts
are based exclusively on intuition. Science fiction writers have sometimes described new
technologies with uncanny accuracy.
There are many examples where men and women have been remarkable successful at predicting
the future. There are also many examples of wrong forecasts. The weakness in genius forecasting
is that its impossible to recognize a good forecast until the forecast has come to pass.
Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream
science generally ignores this fact because the implications are simply to difficult to accept. Our
current understanding of reality is not adequate to explain this phenomena.
Trend extrapolation - These methods examine trends and cycles in historical data, and then use
mathematical techniques to extrapolate to the future. The assumption of all these techniques is
that the forces responsible for creating the past, will continue to operate in the future. This is
often a valid assumption when forecasting short term horizons, but it falls short when creating
medium and long term forecasts. The further out we attempt to forecast, the less certain we
become of the forecast.
The stability of the environment is the key factor in determining whether tren.
In mathematics, a partial differential equation (.pdf
In mathematics, a partial differential equation (PDE) is a differential equation that
contains unknown multivariable functions and their partial derivatives. (This is in contrast to
ordinary differential equations, which deal with functions of a single variable and their
derivatives.) PDEs are used to formulate problems involving functions of several variables, and
are either solved by hand, or used to create a relevant computer model. PDEs can be used to
describe a wide variety of phenomena such as sound, heat, electrostatics, electrodynamics, fluid
flow, or elasticity. These seemingly distinct physical phenomena can be formalised identically in
terms of PDEs, which shows that they are governed by the same underlying dynamic. Just as
ordinary differential equations often model one-dimensional dynamical systems, partial
differential equations often model multidimensional systems. PDEs find their generalisation in
stochastic partial differential equations.
Solution
In mathematics, a partial differential equation (PDE) is a differential equation that
contains unknown multivariable functions and their partial derivatives. (This is in contrast to
ordinary differential equations, which deal with functions of a single variable and their
derivatives.) PDEs are used to formulate problems involving functions of several variables, and
are either solved by hand, or used to create a relevant computer model. PDEs can be used to
describe a wide variety of phenomena such as sound, heat, electrostatics, electrodynamics, fluid
flow, or elasticity. These seemingly distinct physical phenomena can be formalised identically in
terms of PDEs, which shows that they are governed by the same underlying dynamic. Just as
ordinary differential equations often model one-dimensional dynamical systems, partial
differential equations often model multidimensional systems. PDEs find their generalisation in
stochastic partial differential equations..
Blue property assumptions.
This presentation briefly discusses about Linear Regression:Best Linear Unbiased Estimator(BLUE) property assumptions in Data Analytics
Similar to OD&VC.pptx (20)
F O R U MComplexity and TransitionManagementJan Rotman.docx
F O R U MComplexity and TransitionManagementJan Rotman.docx
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...
Qualitative Analysis of a Discrete SIR Epidemic Model
Qualitative Analysis of a Discrete SIR Epidemic Model
Data Science - Part IV - Regression Analysis & ANOVA
Data Science - Part IV - Regression Analysis & ANOVA
A researcher in attempting to run a regression model noticed a neg.docx
A researcher in attempting to run a regression model noticed a neg.docx
Not sure how to do this case analysis please help me do it!1.Are t.pdf
Not sure how to do this case analysis please help me do it!1.Are t.pdf
Perspective of feature selection in bioinformatics
Perspective of feature selection in bioinformatics
In mathematics, a partial differential equation (.pdf
In mathematics, a partial differential equation (.pdf
Zuur et al 2010 methods in ecology and evolution a protocol for data explorat...
Zuur et al 2010 methods in ecology and evolution a protocol for data explorat...
PO WER - XX LO Gdańsk - Mathematics and treatment of cancer
PO WER - XX LO Gdańsk - Mathematics and treatment of cancer
Recently uploaded
What price will pi network be listed on exchanges
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the telegram contact of my personal pi vendor to trade with.
@Pi_vendor_247
what is a pi whale and how to access one.
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
The European Unemployment Puzzle: implications from population aging
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
when will pi network coin be available on crypto exchange.
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
what is the best method to sell pi coins in 2024
The best way to sell your pi coins safely is trading with an exchange..but since pi is not launched in any exchange, and second option is through a VERIFIED pi merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and pioneers and resell them to Investors looking forward to hold massive amounts before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade pi coins with.
@Pi_vendor_247
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth CapitalWebinar Exploring DORA for Fintechs - Simont Braun
Webinar Exploring DORA for Fintechs - Simont Braun
US Economic Outlook - Being Decided - M Capital Group August 2021.pdf
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
The WhatsPump Pseudonym Problem and the Hilarious Downfall of Artificial Enga...
The WhatsPump Pseudonym Problem and the Hilarious Downfall of Artificial Engagement
How to get verified on Coinbase Account?_.docx
t's important to note that buying verified Coinbase accounts is not recommended and may violate Coinbase's terms of service. Instead of searching to "buy verified Coinbase accounts," follow the proper steps to verify your own account to ensure compliance and security.
This assessment plan proposal is to outline a structured approach to evaluati...
This assessment plan proposal is to outline a structured approach to evaluati...lamluanvan.net Viết thuê luận văn
Luận Văn Group hỗ trợ viết luận văn thạc sĩ,chuyên đề,khóa luận tốt nghiệp, báo cáo thực tập, Assignment, Essay
Zalo/Sdt 0967 538 624/ 0886 091 915 Website:lamluanvan.net
Tham gia nhóm hỗ trợ viết bài fb: https://www.facebook.com/groups/285625754522599?locale=vi_VNThe new type of smart, sustainable entrepreneurship and the next day | Europe...
europeanbusinessreview.eu, 27-05-24
what is the future of Pi Network currency.
The future of the Pi cryptocurrency is uncertain, and its success will depend on several factors. Pi is a relatively new cryptocurrency that aims to be user-friendly and accessible to a wide audience. Here are a few key considerations for its future:
Message: @Pi_vendor_247 on telegram if u want to sell PI COINS.
1. Mainnet Launch: As of my last knowledge update in January 2022, Pi was still in the testnet phase. Its success will depend on a successful transition to a mainnet, where actual transactions can take place.
2. User Adoption: Pi's success will be closely tied to user adoption. The more users who join the network and actively participate, the stronger the ecosystem can become.
3. Utility and Use Cases: For a cryptocurrency to thrive, it must offer utility and practical use cases. The Pi team has talked about various applications, including peer-to-peer transactions, smart contracts, and more. The development and implementation of these features will be essential.
4. Regulatory Environment: The regulatory environment for cryptocurrencies is evolving globally. How Pi navigates and complies with regulations in various jurisdictions will significantly impact its future.
5. Technology Development: The Pi network must continue to develop and improve its technology, security, and scalability to compete with established cryptocurrencies.
6. Community Engagement: The Pi community plays a critical role in its future. Engaged users can help build trust and grow the network.
7. Monetization and Sustainability: The Pi team's monetization strategy, such as fees, partnerships, or other revenue sources, will affect its long-term sustainability.
It's essential to approach Pi or any new cryptocurrency with caution and conduct due diligence. Cryptocurrency investments involve risks, and potential rewards can be uncertain. The success and future of Pi will depend on the collective efforts of its team, community, and the broader cryptocurrency market dynamics. It's advisable to stay updated on Pi's development and follow any updates from the official Pi Network website or announcements from the team.
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...
The financial landscape in India has witnessed a significant development with the recent collaboration between Poonawalla Fincorp and IndusInd Bank.
The launch of the co-branded credit card, the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card, marks a major milestone for both entities.
This strategic move aims to redefine and elevate the banking experience for customers.
how to sell pi coins in South Korea profitably.
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
Recently uploaded (20)
The European Unemployment Puzzle: implications from population aging
The European Unemployment Puzzle: implications from population aging
when will pi network coin be available on crypto exchange.
when will pi network coin be available on crypto exchange.
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...
Isios-2024-Professional-Independent-Trustee-Survey.pdf
Isios-2024-Professional-Independent-Trustee-Survey.pdf
Webinar Exploring DORA for Fintechs - Simont Braun
Webinar Exploring DORA for Fintechs - Simont Braun
US Economic Outlook - Being Decided - M Capital Group August 2021.pdf
US Economic Outlook - Being Decided - M Capital Group August 2021.pdf
The WhatsPump Pseudonym Problem and the Hilarious Downfall of Artificial Enga...
The WhatsPump Pseudonym Problem and the Hilarious Downfall of Artificial Enga...
This assessment plan proposal is to outline a structured approach to evaluati...
This assessment plan proposal is to outline a structured approach to evaluati...
The new type of smart, sustainable entrepreneurship and the next day | Europe...
The new type of smart, sustainable entrepreneurship and the next day | Europe...
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...
OD&VC.pptx
- 1. Applications Of Differential Equations in Data Science
- 2. Introduction Modeling Complex Systems is crucial for understanding many real-world phenomena in science and engineering. Differential Equations play a key role in modeling such systems.This presentation will describe the importance of differential equations in data science.
- 3. What are Differential Equations? A differential equation is an equation that relates a function to its derivatives.They are used to model many physical and biological systems. There are two types:ordinary differential equations and partial differential equations.
- 4. Applications of Differential Equations Differential equations are used to model a wide variety of systems,including population growth, heat transfer, fluid dynamics, and chemical reactions. Solving these equations allows us to make predictions and understand the underlying mechanisms of these systems.
- 5. Numerical Methods for Solving Differential Equations Exact solutions to differential equations are often difficult or impossible to find. Instead, numerical methods are used to approximate solutions. These methods include Euler's method, Runge- Kutta methods, and finite difference methods.
- 6. E amples of Differential Equation Models Differential equations can be used to model a wide range of phenomena, from the spread of infectious diseases to the behavior of financial markets. Examples include the SIR model, which models the spread of disease, and the Black-Scholes model, which models stock prices.
- 7. Conclusion Differential equations are a powerful tool for modeling complex systems. They allow us to make predictions and understand the underlying mechanisms of these systems. While exact solutions are often difficult or impossible to find, numerical methods provide a way to approximate solutions. Differential equations will continue to play a key role in data science and other fields.