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Decision making fundamentals
- Decision trees can be used to model decision making problems and show possible decision options and outcomes in a visual, branching structure. They contain decision nodes, representing choices, and chance nodes, representing uncertain outcomes. - Branches from decision nodes represent available choices, and branches from chance nodes represent possible outcomes. The ends of branches specify consequences. Decision trees help evaluate problems systematically by "rolling back" from outcomes to choices.
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Decision making fundamentals
- 1. ©2013 LHST sarl Workingin the Digital Age http://DSign4Change.com Decision Making Fundamentals November 5 2016 Introduction ©2016 L. SCHLENKER
- 2. ©2013 LHST sarl Introduction ©2016L. SCHLENKER Introduction Modeling Rationality Decision making Decision Trees
- 3. ©2013 LHST sarl Introduction –Intelligence: The identification of a challenge that requires data collection and a relevant decision Design: Exploring, planning, and analyzing alternative courses of action – Choice: Selecting the appropriate course of action H. A. Simon Jack is looking at Anne, but Anne is looking at George. Jack will be successful, but George will not. Is a successful person looking at an unsuccessful one?
- 4. ©2013 LHST sarl©2016L. SCHLENKER Introduction INTELLIGENCE • Fact Finding • Problem/Opportunity Sensing • Analysis/Exploration DESIGN • Formulation of Solutions • Generation of Alternatives • Modeling/Simulation CHOICE • Alternative Selection • Goal Maximization • Decision Making • Implementation
- 5. ©2013 LHST sarl Identify Problem Formulate& Implement Model Analyze Model Test Results Implement Solution unsatisfactory results ©2016 L. SCHLENKER Introduction • Programmed Decisions – Situations in which past experience permits decision rules to be developed and applied in the future • Non_programmed Decisions – responses to unique, poorly defined challenges that have important consequences to the organization
- 6. ©2013 LHST sarl©2016L. SCHLENKER Introduction Information Technology Business Analytics Decision Making INDIVIDUALS GROUPS ORGANIZATIONS
- 7. ©2013 LHST sarl •Structured data - refers to information with a high degree of organization – Tables – Spreadsheets – Databases • Non structured data – data that is not organized in a pre-defined manner – Music – Videos • Quantitative data - numeric values that indicate how much or how many – Production quantities – Rate of return – Cash flows • Qualitative data - labels or concepts used to identify an attribute – Performance – Effectiveness Introduction
- 8. ©2013 LHST sarl UNCERTAINTY •Facts not known • Look for Information • Fact Finding /.Analysis DATA BASED COMPLEXITY • Too many facts • Produce Information • Simulation/Synt hesis MODEL BASED EQUIVOCALITY • Facts not Clear • Analyse Information • Application of Expertise KNOWLEDGE BASED Introduction
- 9. ©2013 LHST sarl 1.Formulation. 2. Solution. 3. Interpretation. Introduction
- 10. ©2013 LHST sarl •Models are at the heart of decision-making • Types of models: – Mental (language, mnemonics) – Visual (blueprints, graphics) – Physical/Scale (maps, buildings) – Mathematical (analytics) Models Models are usually simplified versions of the artefacts they represent A valid model portrays the the relevant characteristics of the object or decision being studied
- 11. ©2013 LHST sarl •Cost - It is often less costly to analyze decision problems using models. • Time - Models often deliver needed information more quickly than their real-world counterparts. • Comprehension - Models can be used to do things that would be impossible. • Models give us insight & understanding that improves decision making. Models
- 12. ©2013 LHST sarl •Deterministic Models - all the input data are available with complete certainty • Stochastic Models – some of the input data values are uncertain ©2016 L. SCHLENKER Models
- 13. ©2013 LHST sarl ClassicalDecision Theory ©2016 L. SCHLENKER Models – Underlying assumptions • Decision makers have all the information they need • Decision makers can make the best decision • Decision makers agree about what needs to be done When faced with a decision situation, managers should. . . . . . and end up with a decision that best serves the interests of the organization. • obtain complete and perfect information • eliminate uncertainty • evaluate everything rationally and logically The rational model: decision making is a straightforward, three- stage process - list, rank, select Copyright © by Houghton Mifflin Company.
- 14. ©2013 LHST sarl BehavioralDecision Making ©2016 L. SCHLENKER Models• Bounded rationality introduces a set of more realistic assumptions about the decision-making process – Satisficing: limited information searches to identify problems and alternative solutions – : a limited capacity to process information When faced with a decision situation managers actually… . . . and end up with a decision that may or may not serve the interests of the organization. • use incomplete and imperfect information • are constrained by bounded rationality • tend to satisfice – Organizational coalitions: solution chosen is a result of compromise, bargaining, and accommodation between coalitions
- 15. ©2013 LHST sarl Viewsmain components of decision process (problems, solutions, participants, choice situations) as all mixed up together in the garbage can of the organization – Dynamic situation means often more acting than thinking, solutions used even if can’t be linked to particular problem, many problems going unsolved – Highlights fact that often decision and implementation done by different people ©2016 L. SCHLENKER Models
- 16. ©2013 LHST sarl Prescriptiveknown or under LP, Networks, IP, decision maker’s CPM, EOQ, NLP, control GP, MOLP Predictive known or under Regression Analysis, decision maker’s Time Series Analysis, control Discriminant Analysis Descriptive unknown or Simulation, PERT, uncertain Queueing, Inventory Models Model Independent OR/MS Category Variables Techniques Models www.dezyre.com ©2016 L. SCHLENKER
- 17. ©2013 LHST sarl •Models can be used for structurable aspects of decision problems. • Other aspects cannot be structured easily, requiring intuition and judgment. • Caution: Human judgment and intuition is not always rational! Bounded Rationality
- 18. ©2013 LHST sarl •Arise when trivial factors influence initial thinking about a problem. • Decision-makers usually under-adjust from their initial “anchor”. Bounded Rationality A mental shortcut or rule of thumb the brain uses to simplify complex problems
- 19. ©2013 LHST sarl©2016L. SCHLENKER • Refers to how decision-makers view a problem from a win-loss perspective. • The way a problem is framed often influences choices in irrational ways… • Suppose you’ve been given $200 000 for sure. Which of the following two options would your prefer to lose 500 for sure, to 1000 with a a probability of 50%? Bounded Rationality An example of cognitive bias, in which people react to a particular choice in different ways depending on how it is presented
- 20. ©2013 LHST sarl •Decision making is affected by a lack of information • Decision-makers usually prefer to make “safe bets” rather than test the unknown Bounded Rationality The tendency to avoid options for which missing information makes the probability seem "unknown".[ ©2016 L. SCHLENKER
- 21. ©2013 LHST sarl Gooddecisions do not always lead to good outcomes... A structured, modeling approach to decision making helps us make good decisions, but can’t guarantee good outcomes. Good decisions vs good outcomes Bounded Rationality Define the Problem Identify the Alternatives Determine the Criteria Identify the Alternatives Choose an Alternative Structuring the Problem Analyzing the Problem
- 22. ©2013 LHST sarl Whydo we take poor decisions? • The object of measurement (i.e., the thing being measured) is not understood. • The concept or the meaning of measurement is not understood. • The methods of measurement are not well understood Decision Making ©2016 L. SCHLENKER
- 23. ©2013 LHST sarl •What does productivity mean (faster, more impressive, more precise) ? • Is it observable – how is something more precise answer to a problem? • The challenge is deciding what we want to measure Lewis Mumford, Technics and Civilization Decision Making ©2016 L. SCHLENKER
- 24. ©2013 LHST sarl©2016L. SCHLENKER • Is management an art or a science ? • Mesaurement is the reduction of uncertainty through putting a number on it • In science , engineering, actuarial science, economics, - we talk of putting a number on it www.google.com/dashboard ©2014 L. SCHLENKER "Although this may seem a paradox, all exact science is based on the idea of approximation” Bertrand Russel Decision Making ©2016 L. SCHLENKER
- 25. ©2013 LHST sarl •Reducing the number of potential outcomes is the key to better decision-making • Develop unambiguous definitions and measurement • What data do I have, Choose the appropriate measure • Understand how people react to the data www.google.com/dashboard ©2016 L. SCHLENKER Ask Examples Resources Is it possible that this may already have been researched? The average cost of IT training for given type of user Go to the library (Internet) Could it be projected from past experience? Growth in product demand Research the market Does it leave a trail of some kind? Current level of customer retention Look for the data Could it be observed in real-time? The amount of time an equipment operator spends filling out forms Unsupervised learning Can it be tested? The effect of a new system on the productivity of a sales clerk Supervised learning Decision Making
- 26. ©2013 LHST sarl AnalysisPhase of Decision-Making Process • analyst will concentrate on the quantitative data associated with the problem • analyst will develop mathematical models that describe the objectives, constraints, and other relationships that exist in the problem • analyst will use quantitative methods to make a recommendation Business analysis that aims to understand or predict behavior or events through the use of mathematical measurements Decision Making Potential Reasons for a Quantitative Analysis Approach to Decision Making – The problem is complex. – The problem is very important. – The problem is new. – The problem is repetitive.
- 27. ©2013 LHST sarl •Decision-tree models offer a visual tool that can represent the key elements in a model for decision making • Decision trees are a comprehensive tool for modeling all possible decision options. • While influence diagrams produce a compact summary of a problem, decision trees can show the problem in greater detail. Decision Trees Witten, Frank and Hall 2010 Supervised Categorical Do we plan when it’s sunny, hot, normal and windy ?
- 28. ©2013 LHST sarl •Decision trees utilize a network of two types of nodes: decision (choice) nodes, and states of nature (chance) nodes • Square represents decisions to be made. • Circles represents chance events. Chance nodes, are random variables and they represent uncertain quantities that are relevant to the decision problem. • Branches from a square correspond to the choices available to the decision maker. • Branches from a circle represent the possible outcome of a chance event. • The consequence is specified at the ends of the branches. Simple Probability Tree Decision Trees ©2016 L. SCHLENKER Supervised Categorical
- 29. ©2013 LHST sarl •A good decision tree should be short and ask only a few meaningful questions. • Decision trees are used mostly to answer relatively simple binary decisions. • Decision trees use machine learning algorithms to abstract knowledge from data • The more training data is provided, the more accurate its knowledge extraction will be, and thus, it will make more accurate decisions. • The more variables the tree can choose from, the greater is the likely of the accuracy of the decision tree. split. Decision Trees ©2016 L. SCHLENKER Supervised Categorical
- 30. ©2013 LHST sarl •The options represented by branches from a decision node must be such that the decision maker can choose only one option. • Each chance node must have branches that correspond to a set of mutually exclusive outcomes; • Whereas we build the tree left to right, we evaluate the tree in the reverse direction (“rolling back the tree”). • At each chance node, we can calculate the expected payoff represented by the probability distribution at the node. Decision Trees ©2016 L. SCHLENKER Supervised Categorical