Decision theory as the name would imply is concerned with the process of making decisions. The extension to statistical decision theory includes decision making in the presence of statistical knowledge which provides some information where there is uncertainty. The elements of decision theory are quite logical and even perhaps intuitive. The classical approach to decision theory facilitates the use of sample information in making inferences about the unknown quantities. Other relevant information includes that of the possible consequences which is quantified by loss and the prior information which arises from statistical investigation. The use of Bayesian analysis in statistical decision theory is natural. Their unification provides a foundational framework for building and solving decision problems. The basic ideas of decision theory and of decision theoretic methods lend themselves to a variety of applications and computational and analytic advances.
5.DECISION MAKING PROCESS :-
Recognizing & defining the situation
Identifying the alternatives
Evaluating the alternatives
Apply the model
Selecting the best alternatives
Conduct a sensitivity of the solution
Implementing the chosen alternatives
Following up & evaluating the result
6.TYPE OF DECISION MAKING ENVIRONMENT
Decision making under certainty
Decision making under uncertainty
Decision making under risk
23.DECISION TREE :
Instances describable by attribute-value pairs
e.g Humidity: High, Normal
Target function is discrete valued
e.g Play tennis; Yes, No
Disjunctive hypothesis may be required
e.g Outlook=Sunny Wind=Weak
Possibly noisy training data
Missing attribute values
Application Examples:
Medical diagnosis
Credit risk analysis
Object classification for robot manipulator (Tan 1993)
25.Bayesian analysis
26.Utility theory :
Step for determine the utility for money :
Develop a payoff table using monetary values
Identify the best and worst payoff value
For every other monetary value in the original payoff table
Convert the payoff table from monetary value to calculate utility value.
Apply the expected utility criterion to the utility table and select the decision alternative with the best expected utility.
A partnership of funders invites applications for proposals to support networking of researchers from different disciplines relating to the topic of decision making under uncertainty. The theme of the call builds on a number of events held by the funding partners and Research Councils UK (RCUK).
There is a budget of up to £750,000 to support this activity, and we expect to fund a maximum of two networks, which will include support for feasibility projects, for two years.
Proposals will need to consider & seek to involve a wide breadth of relevant communities and build on current RCUK funded activities (see Annex I for examples).
The purpose of this call is to develop & build widespread linkages between disciplines related to decision making under uncertainty and grow a multidisciplinary community in this space. The network(s) will be expected to work with user organisations (policy-makers, industry, and/or civil society organisations) to analyse real-world systems and identify where multi-disciplinary research can develop new approaches to improve decision-making under uncertainty.
Decision theory as the name would imply is concerned with the process of making decisions. The extension to statistical decision theory includes decision making in the presence of statistical knowledge which provides some information where there is uncertainty. The elements of decision theory are quite logical and even perhaps intuitive. The classical approach to decision theory facilitates the use of sample information in making inferences about the unknown quantities. Other relevant information includes that of the possible consequences which is quantified by loss and the prior information which arises from statistical investigation. The use of Bayesian analysis in statistical decision theory is natural. Their unification provides a foundational framework for building and solving decision problems. The basic ideas of decision theory and of decision theoretic methods lend themselves to a variety of applications and computational and analytic advances.
5.DECISION MAKING PROCESS :-
Recognizing & defining the situation
Identifying the alternatives
Evaluating the alternatives
Apply the model
Selecting the best alternatives
Conduct a sensitivity of the solution
Implementing the chosen alternatives
Following up & evaluating the result
6.TYPE OF DECISION MAKING ENVIRONMENT
Decision making under certainty
Decision making under uncertainty
Decision making under risk
23.DECISION TREE :
Instances describable by attribute-value pairs
e.g Humidity: High, Normal
Target function is discrete valued
e.g Play tennis; Yes, No
Disjunctive hypothesis may be required
e.g Outlook=Sunny Wind=Weak
Possibly noisy training data
Missing attribute values
Application Examples:
Medical diagnosis
Credit risk analysis
Object classification for robot manipulator (Tan 1993)
25.Bayesian analysis
26.Utility theory :
Step for determine the utility for money :
Develop a payoff table using monetary values
Identify the best and worst payoff value
For every other monetary value in the original payoff table
Convert the payoff table from monetary value to calculate utility value.
Apply the expected utility criterion to the utility table and select the decision alternative with the best expected utility.
A partnership of funders invites applications for proposals to support networking of researchers from different disciplines relating to the topic of decision making under uncertainty. The theme of the call builds on a number of events held by the funding partners and Research Councils UK (RCUK).
There is a budget of up to £750,000 to support this activity, and we expect to fund a maximum of two networks, which will include support for feasibility projects, for two years.
Proposals will need to consider & seek to involve a wide breadth of relevant communities and build on current RCUK funded activities (see Annex I for examples).
The purpose of this call is to develop & build widespread linkages between disciplines related to decision making under uncertainty and grow a multidisciplinary community in this space. The network(s) will be expected to work with user organisations (policy-makers, industry, and/or civil society organisations) to analyse real-world systems and identify where multi-disciplinary research can develop new approaches to improve decision-making under uncertainty.
Decision analysis
DEFINITION of 'Decision Analysis - DA'
A systematic, quantitative and visual approach to addressing and evaluating important choices confronted by businesses. Decision analysis utilizes a variety of tools to evaluate all relevant information to aid in the decision making process. A graphical representation of alternatives and possible solutions, as well as challenges and uncertainties, can be created on a decision tree or influence diagram.
Decision analysis (DA) has been applied to business problems in management, marketing, operations, accounting, and finance. In addition, it has had an impact on the fields of medicine, law, military science, environmental sciences, and public policy more generally.
Markov chain:
Markov models are useful when a decision problem involves risk that is continuous over time, when the timing of events is important, and when important events may happen more than once. Markov models assume that a patient is always in one of a finite number of discrete health states, called Markov states. The ability of the Markov model to represent repetitive events and the time dependence of both probabilities and utilities allows for more accurate representation of clinical settings that involve these issues.
In your routine laboratory works, do you have to issue a statement of conformity after testing a product sample, such as stating a Pass or fail, a compliance or non-compliance? What is your decision rule as required by this ISO standard? What is the risk to make a wrong decision in rejecting the test result based on the product specification limit when it is actually in conformance? Are you able to control such a risk in order to make an informed decision?
If you have all these questions in mind, I have got the answers for you.
This presentation explores the challenges, opportunities and available tools in developing a safety case regime for operators of major hazardous installations.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
2. DECISION THEORY
Steps involved in decision theory approach:
•Determine the various alternative courses of actions from which the final
decision has to be made.
•Identify the possible outcomes, called the states of nature or events
for the decision problem.
•Construct a pay off table.
•The decision maker chooses the criterion which results in largest pay off.
OPERATION RESEARCH 2
3. Decision making environments:
•Decision under certainty
Whenever there exists only one outcome for a decision, we are
dealing with this category
•Decisions under uncertainty:
When more than one outcome can result from any single
decision i.e. more than one state of nature exists.
•Decision under risk:
The decision maker chooses from among several possible
outcomes where the probability of occurrence can be stated
objectively from the past data.
•Decision under conflict:
Neither states of nature are completely known nor are they
completely uncertain.
OPERATION RESEARCH 3
4. DECISION UNDER UNCERTAINTY:
There are five criterion on the basis of which rules for making a decision is
Formulated:
Criterion of pessimism:
• Minimax or Maximin
• Maximin is a conservative approach to assume worst possible outcomes
• Steps involved:
Find minimum assured pay off for each alternative
Choose the maximum of minimum values.
• Minimax involves two steps:
Determine maximum possible cost for each alternative
Choose the alternative minimum of above costs
OPERATION RESEARCH 4
5. Criterion of optimism:
•Mimimin or maximax
•Two extreme optimism
•Decision makes ensures that he should not miss the opportunity to achieve the
the greatest possible pay off or lowest possible cost
•Steps involved:
Determine maximum possible payoff
Select a alterative which corresponds to maximum of above
maximum pay off
•Minimin of cost is done in similar manner
OPERATION RESEARCH 5
6. Laplace criterion:
• It is assumed that all states of nature will occur with equal probability
•Probabilities of each state of nature is given by 1/( number of states of nature)
•Steps involved:
i. Assign equal probabilities to each payoff of a strategy
ii. Determine the expected pay off value for each alternative.
iii. Select the alternative which corresponds to the maximum payoff or
minimum cost
OPERATION RESEARCH 6
7. Criterion of realism or Hurwicz criterion:
•Coefficient of optimism α
•0<α<1 where o signifies total pessimism and 1 total optimism
•Steps involved:
i. Decide the coefficient of optimism and the coefficient of pessimism
ii. Determine the maximum as well as minimum pay off for each alterative
h= α x maximum for each alterative + (1-α) x minimum for each alterative
iii. Select the alternative with highest value of h.
OPERATION RESEARCH 7
8. Example:
A farmer wants to decide which of the three crops he should plant on his 100
Acre farm. The profit from each is dependent on the rainfall during the growing
seasons. The farmer has categorized the amount of rainfall as high, medium,
low. His estimated profit for each is show in the table:
Rainfall Crop A Crop B Crop C
High 8000 3500 5000
Medium 4500 4500 5000
Low 2000 5000 4000
If the farmer wishes to plant only one crop, decide which will be his choice using
•Maximax criterion
•Maximin criterion
•Hurwicz criterion
•Laplace criterion
OPERATION RESEARCH 8
9. Rainfall Crop A Crop B Crop C
High 8000 3500 5000
Medium 4500 4500 5000
Low 2000 5000 4000
i. Maximax criterion:
From table we observe that maximum pay off for each alternative are
8000, 5000 ad 5000 respectively. Maximum among these is 8000
corresponding to crop A. So this strategy chooses crop A .
ii. Maximin criterion selects crop C
iii. Hurwicz criterion:
Assuming degree of optimism α = 0.6 ad therefore 1-α = 0.4 , the value of
h is calculated in the table:
Alternativ
e
Maximum
pay off
Minimum
pay off
OPERATION RESEARCH 9
h
Crop A 8000 2000 5600
Crop B 5000 3500 4400
Crop C 5000 4000 4600
10. The maximum value is 5600 so this criterion selects crop A.
iv. Laplace criterion:
Assign equal probabilities i.e. 1/3. The expected pay off is calculated
for each alterative:
E (Crop A)=1/3(8000)+1/3(4500)+1/3(2000)= 4833
E (Crop B)=4333
E (Crop C)=4666
Hence this criterion also selects crop A.
OPERATION RESEARCH 10
