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Individual Decision Making
Outline for Today ,[object Object],[object Object],[object Object]
Why economics? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Course Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Review of subject structure Subject Situation Description Tool Analysis Individual Decision-Making Single-agent decision One agent must make an optimal decision. The actions of others have no direct influence. Uncertainty and the time-value of money may be concerns. Decision tree Roll-back Strategic Decision-Making Simultaneous moves Both agents know everything about the game and each other. Decisions happen simultaneously. Matrix Game Nash Equilibrium Sequential moves Both agents know everything about the game and each other. Decisions happen sequentially. Sequential Game Roll-back Negotiations Bilateral Two agents who are active negotiations. Try to maximise value and appropriate their share Bargaining Theory Nash bargaining Monopoly One seller versus many buyers. All agents are active negotiators. Bargain over value split. VAUC  (Value-Appropriation Under Competition) CDV & added value Many sellers Many sellers versus many buyers. All agents are active negotiators. Bargain over value split. VAUC CDV, added value,  Market-clearing prices Mass-Market Pricing Monopoly One seller versus many buyers. Firm sets price. Supply (cost) & Demand (revenue) Functions MC = MR Oligopoly Many sellers vs. many buyers. Firms set prices. Bertrand, Cournot Nash, undercut-proofness
Skills required for subject ,[object Object],[object Object],[object Object],The main thinking tool  = MODELS Reduce complex situations to their fundamentals to develop  general  principles
Subject Organisation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Individual decision making:  ā€œnon-strategicā€ decisions Topic 1
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decisions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of Decisions Degree of Uncertainty Low High Degree of Interdependence  with othersā€™ actions High Low Market entry Extreme sports What to wear What to have for lunch How hard to study Whether to do an MBA Which MBA What to bid
Decision Trees ,[object Object],[object Object],R L
Example (non-strategic under certainty) =  Decision Node: indicates a point at which an action must be taken (one path for each possible action) CBD $120,000 $150,000 Brunswick open a restaurant donā€™t $0 Entry decision Location decision
Changā€™s Dilemma in 2003 ,[object Object],[object Object],[object Object],[object Object]
Framing the Decision: Step I ,[object Object],Abandon Continue Perhaps make proposal with inferior technology at an additional cost of $50,000 Take risk on developing the new technology at an additional cost of $200,000 and reconsider proposal
Step II Abandon Continue Not Proposal $0 Expend $50,000 and perhaps win
Uncertainty in a decision tree ,[object Object],[object Object],[object Object],[object Object],[object Object],=  Random Event Node: point at which ā€œNatureā€ takes an action of her own (one path for each possible outcome)
Step III Abandon Continue Not Proposal $0 Win Lose 0.05 0.95 $800,000 -$50,000
Step IV Abandon Continue Not Proposal $0 Win Lose 0.05 0.95 -$50,000 Succeed Fail 0.5 0.5 $800,000 Expend $50,000 and perhaps win Expend $50,000 and have a good chance of winning
Step V No Prop No Prop Abandon Continue $0 W L 0.05 0.95 -$50,000 Succeed Fail 0.5 0.5 $800,000 No Prop -$200,000 W L 0.9 0.1 -$250,000 $600,000 -$200,000 W L 0.05 0.95 -$250,000 $600,000
Optimal decision plan ,[object Object],[object Object],[object Object]
Example (non-strategic under certainty) ,[object Object],[object Object],[object Object],[object Object],[object Object],CBD $120,000 $150,000 Brunswick open a restaurant donā€™t $0
Solving at a node with uncertainty:  Expected value ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Solving the Tree Abandon Continue Not Proposal $0 Win Lose 0.05 0.95 $800,000 -$50,000 ? Expected value = 0.05 ($800,000) + 0.95 (-$50,000) = - $7,500
Solving the Tree Abandon Continue Not Proposal $0 -$7,500 ? Choose the branch with the best payoff
Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 Not Proposal -$200,000 Win Lose 0.9 0.1 -$250,000 $600,000 Not Proposal -$200,000 Win Lose 0.05 0.95 -$250,000 $600,000
Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 Not Proposal -$200,000 Win Lose 0.9 0.1 -$250,000 $600,000 Not Proposal -$200,000 -$207,500
Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 Not Proposal -$200,000 $515,000 -$200,000
Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 $515,000 -$200,000
Solving the Tree Abandon Continue $0 $157,500 ,[object Object],[object Object]
Indyā€™s Choice ,[object Object],[object Object]
Framing the Decision ,[object Object],Give drink to Snr Drink himself
Framing the Decision ,[object Object],Give drink to Snr Drink himself Right Wrong Jnr & Snr Live Jnr & Snr Die Right Wrong Jnr & Snr Live Snr Dies but Jnr Lives
Uses of Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Economic Cost = opportunity foregone ,[object Object],[object Object],[object Object],[object Object]
Consider this situation ,[object Object],[object Object],[object Object]
Definition:  Sunk Cost ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Definition:  *** Economic Profit *** ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Individual decision making ppt @ becdoms

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  • 5. Review of subject structure Subject Situation Description Tool Analysis Individual Decision-Making Single-agent decision One agent must make an optimal decision. The actions of others have no direct influence. Uncertainty and the time-value of money may be concerns. Decision tree Roll-back Strategic Decision-Making Simultaneous moves Both agents know everything about the game and each other. Decisions happen simultaneously. Matrix Game Nash Equilibrium Sequential moves Both agents know everything about the game and each other. Decisions happen sequentially. Sequential Game Roll-back Negotiations Bilateral Two agents who are active negotiations. Try to maximise value and appropriate their share Bargaining Theory Nash bargaining Monopoly One seller versus many buyers. All agents are active negotiators. Bargain over value split. VAUC (Value-Appropriation Under Competition) CDV & added value Many sellers Many sellers versus many buyers. All agents are active negotiators. Bargain over value split. VAUC CDV, added value, Market-clearing prices Mass-Market Pricing Monopoly One seller versus many buyers. Firm sets price. Supply (cost) & Demand (revenue) Functions MC = MR Oligopoly Many sellers vs. many buyers. Firms set prices. Bertrand, Cournot Nash, undercut-proofness
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  • 8. Individual decision making: ā€œnon-strategicā€ decisions Topic 1
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  • 11. Types of Decisions Degree of Uncertainty Low High Degree of Interdependence with othersā€™ actions High Low Market entry Extreme sports What to wear What to have for lunch How hard to study Whether to do an MBA Which MBA What to bid
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  • 13. Example (non-strategic under certainty) = Decision Node: indicates a point at which an action must be taken (one path for each possible action) CBD $120,000 $150,000 Brunswick open a restaurant donā€™t $0 Entry decision Location decision
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  • 16. Step II Abandon Continue Not Proposal $0 Expend $50,000 and perhaps win
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  • 18. Step III Abandon Continue Not Proposal $0 Win Lose 0.05 0.95 $800,000 -$50,000
  • 19. Step IV Abandon Continue Not Proposal $0 Win Lose 0.05 0.95 -$50,000 Succeed Fail 0.5 0.5 $800,000 Expend $50,000 and perhaps win Expend $50,000 and have a good chance of winning
  • 20. Step V No Prop No Prop Abandon Continue $0 W L 0.05 0.95 -$50,000 Succeed Fail 0.5 0.5 $800,000 No Prop -$200,000 W L 0.9 0.1 -$250,000 $600,000 -$200,000 W L 0.05 0.95 -$250,000 $600,000
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  • 24. Solving the Tree Abandon Continue Not Proposal $0 Win Lose 0.05 0.95 $800,000 -$50,000 ? Expected value = 0.05 ($800,000) + 0.95 (-$50,000) = - $7,500
  • 25. Solving the Tree Abandon Continue Not Proposal $0 -$7,500 ? Choose the branch with the best payoff
  • 26. Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 Not Proposal -$200,000 Win Lose 0.9 0.1 -$250,000 $600,000 Not Proposal -$200,000 Win Lose 0.05 0.95 -$250,000 $600,000
  • 27. Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 Not Proposal -$200,000 Win Lose 0.9 0.1 -$250,000 $600,000 Not Proposal -$200,000 -$207,500
  • 28. Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 Not Proposal -$200,000 $515,000 -$200,000
  • 29. Solving the Tree Abandon Continue Succeed Fail 0.5 0.5 $0 $515,000 -$200,000
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