logging in or signing up Making Risky Decisions Visually shai_seattle Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Copy Does not support media & animations WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 1754 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: June 12, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Making Risky Decisions Visually : Making Risky Decisions Visually A purely visual approach to decision theory and risk analysis Risk Analysis : Risk Analysis Assess factors that jeopardize success Strike a balance between: Potential for success Associated costs Make informed decisions based on reasonable estimates Risk Analysis : Risk Analysis Assess factors that jeopardize success Strike a balance between: Potential for success Associated costs Make informed decisions based on reasonable estimates Decision Tree Visualization : Decision Tree Visualization FUND? PROJECT PROFIT $100 $10 -$10 $0 Success .2 Failure .8 Yes No High .5 Low .5 Decision Tree Visualization : Decision Tree Visualization FUND? PROJECT PROFIT $100 $10 -$10 $0 Success .2 Failure .8 Yes No High .5 Low .5 Decision Tree Visualization : Decision Tree Visualization FUND? PROJECT PROFIT $100 $10 -$10 $0 Success .2 Failure .8 Yes No High .5 Low .5 $55 $3 $3 Decision Tree Limitations : Decision Tree Limitations The tree becomes unwieldy for all but the most simple cases While the tree is very good at visually representing qualitative relationships, it does not allow visual representation of quantitative information Decision trees are not easily represented on a PC without special software Decision Tree Limitations : Decision Tree Limitations The tree becomes unwieldy for all but the most simple cases While the tree is very good at visually representing qualitative relationships, it does not allow visual representation of quantitative information Decision trees are not easily represented on a PC without special software Goals of Decision Visualization : Goals of Decision Visualization Easy data interpretation A visualization helps to convey important information Familiarity with statistics not required Visualization do not require that an audience be familiar with statistics Convey reasoning, data, and assumptions Skeptical of “black boxes” Goals of Decision Visualization : Goals of Decision Visualization Easy data interpretation A visualization helps to convey important information Familiarity with statistics not required Visualization do not require that an audience be familiar with statistics Convey reasoning, data, and assumptions Skeptical of “black boxes” Slide 11: FUND? YES NO SUCCESS FAILURE HIGH LOW An Alternative: Risk Blocks Project Profit Slide 12: FUND? YES NO SUCCESS FAILURE HIGH LOW An Alternative: Risk Blocks Project Profit Slide 13: FUND? YES NO SUCCESS FAILURE HIGH LOW An Alternative: Risk Blocks A More Realistic Example : A More Realistic Example Actual R&D problems are considerably more complicated: Multiple competing ways of designing a project Known technology (low technical risk) New technology (high technical risk) Previously-used technology (moderate technical risk) Project must transition through several development phases Multiple commercialization phases Project Management : Project Management Organize and manage resources to reach goals Time Cost Scope Typical project phases: Initiation Planning Execution Closing Product Development Phases : Product Development Phases Proof of Concept Demonstrate the feasibility of a design Detailed Design In-depth design of all aspects of the project Manufacture The manufacturing process which creates the product Sales Initial sales estimate for each design Competitors Estimated response of competitors Slide 18: Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Fast .2 Slow .8 Pass .8 Pass .9 Pass .5 Proof of Concept Proof of Concept Proof of Concept Fund? Detailed Design Detailed Design Detailed Design Manufacture Manufacture Manufacture Sales Sales Sales Competitor Competitor Competitor Competitor Competitor Competitor Pass .8 Pass .8 Pass .9 Pass .9 Pass .5 Pass .9 200 1000 50 200 200 1000 50 200 200 1000 50 200 -5 -25 -125 -15 -30 -130 -15 -30 -130 Design A Design C Design B Slide 19: Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Fast .2 Slow .8 Pass .8 Pass .9 Pass .5 Proof of Concept Proof of Concept Proof of Concept Fund? Detailed Design Detailed Design Detailed Design Manufacture Manufacture Manufacture Sales Sales Sales Competitor Competitor Competitor Competitor Competitor Competitor Pass .8 Pass .8 Pass .9 Pass .9 Pass .5 Pass .9 200 1000 50 200 200 1000 50 200 200 1000 50 200 -5 -25 -125 -15 -30 -130 -15 -30 -130 280 80 840 170 840 170 773 324 289 258 773 683 612 298 120 71 52 40 Design A Design C Design B Slide 20: Fast Slow Design B Design C Pass Fail Pass Pass High Pass Fail Pass Fail Pass Fail High Fast Slow Design A Fail Pass Pass Pass Fail High Low Fast Fast Slow Fail Competitor Sales Manufacture Detailed Design Proof of Concept Product Slide 21: Competitor Sales Manufacture Detailed Design Proof of Concept Product Fail Fail Fast Fast Slow Fail Fail Fail Fail Fail Fast Slow Fast Slow Design B Design C Pass Pass Pass High Pass Pass Pass High Design A Pass Pass Pass High Low Slide 22: Competitor Sales Manufacture Detailed Design Proof of Concept Product Design A Fail Pass Pass Pass Fail High Low Fast Fast Slow Fail Design B Design C Pass Fail Pass Pass High Pass Fail Pass Fail Pass Fail High Fast Slow Fast Slow Conclusion : Conclusion Proposed Risk Blocks approach: Requires less mental processing Many decision makers decide without mathematics No formal mathematical training required You do not have the permission to view this presentation. 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Making Risky Decisions Visually shai_seattle Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Copy Does not support media & animations WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 1754 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: June 12, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Making Risky Decisions Visually : Making Risky Decisions Visually A purely visual approach to decision theory and risk analysis Risk Analysis : Risk Analysis Assess factors that jeopardize success Strike a balance between: Potential for success Associated costs Make informed decisions based on reasonable estimates Risk Analysis : Risk Analysis Assess factors that jeopardize success Strike a balance between: Potential for success Associated costs Make informed decisions based on reasonable estimates Decision Tree Visualization : Decision Tree Visualization FUND? PROJECT PROFIT $100 $10 -$10 $0 Success .2 Failure .8 Yes No High .5 Low .5 Decision Tree Visualization : Decision Tree Visualization FUND? PROJECT PROFIT $100 $10 -$10 $0 Success .2 Failure .8 Yes No High .5 Low .5 Decision Tree Visualization : Decision Tree Visualization FUND? PROJECT PROFIT $100 $10 -$10 $0 Success .2 Failure .8 Yes No High .5 Low .5 $55 $3 $3 Decision Tree Limitations : Decision Tree Limitations The tree becomes unwieldy for all but the most simple cases While the tree is very good at visually representing qualitative relationships, it does not allow visual representation of quantitative information Decision trees are not easily represented on a PC without special software Decision Tree Limitations : Decision Tree Limitations The tree becomes unwieldy for all but the most simple cases While the tree is very good at visually representing qualitative relationships, it does not allow visual representation of quantitative information Decision trees are not easily represented on a PC without special software Goals of Decision Visualization : Goals of Decision Visualization Easy data interpretation A visualization helps to convey important information Familiarity with statistics not required Visualization do not require that an audience be familiar with statistics Convey reasoning, data, and assumptions Skeptical of “black boxes” Goals of Decision Visualization : Goals of Decision Visualization Easy data interpretation A visualization helps to convey important information Familiarity with statistics not required Visualization do not require that an audience be familiar with statistics Convey reasoning, data, and assumptions Skeptical of “black boxes” Slide 11: FUND? YES NO SUCCESS FAILURE HIGH LOW An Alternative: Risk Blocks Project Profit Slide 12: FUND? YES NO SUCCESS FAILURE HIGH LOW An Alternative: Risk Blocks Project Profit Slide 13: FUND? YES NO SUCCESS FAILURE HIGH LOW An Alternative: Risk Blocks A More Realistic Example : A More Realistic Example Actual R&D problems are considerably more complicated: Multiple competing ways of designing a project Known technology (low technical risk) New technology (high technical risk) Previously-used technology (moderate technical risk) Project must transition through several development phases Multiple commercialization phases Project Management : Project Management Organize and manage resources to reach goals Time Cost Scope Typical project phases: Initiation Planning Execution Closing Product Development Phases : Product Development Phases Proof of Concept Demonstrate the feasibility of a design Detailed Design In-depth design of all aspects of the project Manufacture The manufacturing process which creates the product Sales Initial sales estimate for each design Competitors Estimated response of competitors Slide 18: Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Fast .2 Slow .8 Pass .8 Pass .9 Pass .5 Proof of Concept Proof of Concept Proof of Concept Fund? Detailed Design Detailed Design Detailed Design Manufacture Manufacture Manufacture Sales Sales Sales Competitor Competitor Competitor Competitor Competitor Competitor Pass .8 Pass .8 Pass .9 Pass .9 Pass .5 Pass .9 200 1000 50 200 200 1000 50 200 200 1000 50 200 -5 -25 -125 -15 -30 -130 -15 -30 -130 Design A Design C Design B Slide 19: Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Slow .8 Fast .2 Fast .2 Slow .8 Pass .8 Pass .9 Pass .5 Proof of Concept Proof of Concept Proof of Concept Fund? Detailed Design Detailed Design Detailed Design Manufacture Manufacture Manufacture Sales Sales Sales Competitor Competitor Competitor Competitor Competitor Competitor Pass .8 Pass .8 Pass .9 Pass .9 Pass .5 Pass .9 200 1000 50 200 200 1000 50 200 200 1000 50 200 -5 -25 -125 -15 -30 -130 -15 -30 -130 280 80 840 170 840 170 773 324 289 258 773 683 612 298 120 71 52 40 Design A Design C Design B Slide 20: Fast Slow Design B Design C Pass Fail Pass Pass High Pass Fail Pass Fail Pass Fail High Fast Slow Design A Fail Pass Pass Pass Fail High Low Fast Fast Slow Fail Competitor Sales Manufacture Detailed Design Proof of Concept Product Slide 21: Competitor Sales Manufacture Detailed Design Proof of Concept Product Fail Fail Fast Fast Slow Fail Fail Fail Fail Fail Fast Slow Fast Slow Design B Design C Pass Pass Pass High Pass Pass Pass High Design A Pass Pass Pass High Low Slide 22: Competitor Sales Manufacture Detailed Design Proof of Concept Product Design A Fail Pass Pass Pass Fail High Low Fast Fast Slow Fail Design B Design C Pass Fail Pass Pass High Pass Fail Pass Fail Pass Fail High Fast Slow Fast Slow Conclusion : Conclusion Proposed Risk Blocks approach: Requires less mental processing Many decision makers decide without mathematics No formal mathematical training required