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Data Collection Philosophy Applied to Decision Making:
Chaos Theory vs. Thin-Slicing
Data Collection Philosophy Applied to Decision Making:
Chaos Theory vs. Thin-Slicing
Gregory E. ThompsonElectrical Engineer
ThomTech Design, Inc.Minneapolis, MN
Gregory E. ThompsonElectrical Engineer
ThomTech Design, Inc.Minneapolis, MN
A New Approach to Collecting Data for Decision Support Systems – August 2006A New Approach to Collecting Data for
Decision Support Systems – August 2006
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 2Slide Number: 2
Subtitles – Help in UnderstandingSubtitles – Help in Understanding
• A New Approach to Collecting Data for Decision Support Systems.
• The Power of Thinking Without Thinking - Blink.• Making a New Science - Chaos.• Revolutionizing the Way We Understand the
World Within & Without – Chaos & Blink.• A Way of Seeing Order & Pattern where
Formerly Only the Random, the Erratic, the Unpredictable – in Short, the Chaotic – had been Observed.
• Many More – Perhaps You’ll Think of Others
• A New Approach to Collecting Data for Decision Support Systems.
• The Power of Thinking Without Thinking - Blink.• Making a New Science - Chaos.• Revolutionizing the Way We Understand the
World Within & Without – Chaos & Blink.• A Way of Seeing Order & Pattern where
Formerly Only the Random, the Erratic, the Unpredictable – in Short, the Chaotic – had been Observed.
• Many More – Perhaps You’ll Think of Others
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 3Slide Number: 3
Goals & ObjectivesGoals & Objectives
• Provide Brief Overview of Chaos Theory• Gain a Basic Understanding of Thin-Slicing• Relate Chaos/Blink to Information
Gathering• Examine Data Collection Analysis Methods• Expand Our Views on Present Day
Methods of Data-Based Decision Making• Explore Additional Topics• Spark Additional Discussion
• Provide Brief Overview of Chaos Theory• Gain a Basic Understanding of Thin-Slicing• Relate Chaos/Blink to Information
Gathering• Examine Data Collection Analysis Methods• Expand Our Views on Present Day
Methods of Data-Based Decision Making• Explore Additional Topics• Spark Additional Discussion
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 4Slide Number: 4
AgendaAgenda
• Introduction• Caveats• Approaches to Data
Collection• Chaos Theory• Thin-Slicing• Examples• Comparison• Results• Questions• References
• Introduction• Caveats• Approaches to Data
Collection• Chaos Theory• Thin-Slicing• Examples• Comparison• Results• Questions• References
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 5Slide Number: 5
CaveatsCaveats
• Information boom era• We all use chaos• We all thin-slice• News gathering• Decide by comfort level• Study• Smarter• Decide on a Plan• Act/Execute• Evaluate Performance• Reap Benefits ☻
• Information boom era• We all use chaos• We all thin-slice• News gathering• Decide by comfort level• Study• Smarter• Decide on a Plan• Act/Execute• Evaluate Performance• Reap Benefits ☻
45 Minutes
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 6Slide Number: 6
Introduction – We Need InformationIntroduction – We Need Information
• The concept of knowing “What is going on?” becomes increasingly important as service providers: – compete for resources– increase service areas– expand customer options– solve budgetary constraints.
• The concept of knowing “What is going on?” becomes increasingly important as service providers: – compete for resources– increase service areas– expand customer options– solve budgetary constraints.
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 7Slide Number: 7
Data Collection System ElementsData Collection System Elements
• Data Collectors• Means of Transmission• Employ Information – Make Decisions
• Data Collectors• Means of Transmission• Employ Information – Make Decisions
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 8Slide Number: 8
Challenge – Monstrous Amounts of DataChallenge – Monstrous Amounts of Data
• However, the collection of data has become an epidemic. Lots of data is being collected – is it being used appropriately?
• There are several examples of information collected & its availability to others.
• However, the collection of data has become an epidemic. Lots of data is being collected – is it being used appropriately?
• There are several examples of information collected & its availability to others.
DATA DATA
Chaos TheoryChaos Theory
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 10Slide Number: 10
Chaos TheoryChaos Theory
• Chaos Theory has been around for four decades.
• Opposite of entropy, purports that random systems become more ordered over time.
• Premise - the more data we collect and the more times we stir the pot, the better our result.
• Popularized during the last decade – the 90s.
• Chaos Theory has been around for four decades.
• Opposite of entropy, purports that random systems become more ordered over time.
• Premise - the more data we collect and the more times we stir the pot, the better our result.
• Popularized during the last decade – the 90s.
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 11Slide Number: 11
Butterfly Effect - ChaosButterfly Effect - Chaos
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 12Slide Number: 12
Life Will Find a Way –Jurassic Park
Life Will Find a Way –Jurassic Park
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 13Slide Number: 13
More Data & More Stirring = Better OutputMore Data & More Stirring = Better Output
DATA
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 14Slide Number: 14
Obsession with Data Creates DilemmaObsession with Data Creates Dilemma
• Why collect data?– Increase efficiency– Improve safety– Reduce paperwork– Etc.
• Examples– News– Sports– Gambling– Credit– Schools– Weather– Others
• Why collect data?– Increase efficiency– Improve safety– Reduce paperwork– Etc.
• Examples– News– Sports– Gambling– Credit– Schools– Weather– Others
DataCost
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 15Slide Number: 15
Data Collection is CostlyData Collection is Costly
• Data collection systems are costly, especially real-time systems.– How do we know we’re
collecting the right data?– What is too much data?– Can we meet our decision
making needs with only a few items of data?
– All the things that surround a data system.
• Examine the relationship between data versus cost.
• Data collection systems are costly, especially real-time systems.– How do we know we’re
collecting the right data?– What is too much data?– Can we meet our decision
making needs with only a few items of data?
– All the things that surround a data system.
• Examine the relationship between data versus cost.
Thin-Slicing TheoryThin-Slicing Theory
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 17Slide Number: 17
Thin-SlicingThin-Slicing
• Thin-slicing became popular over the last few years.– We make decisions very
quickly based on a few pieces of data.
– Additional data actually clouds the picture and makes decision making more difficult or even incorrect.
– Thin-slicing is not an exotic gift, it is a central part of what it means to be human.
• Thin-slicing became popular over the last few years.– We make decisions very
quickly based on a few pieces of data.
– Additional data actually clouds the picture and makes decision making more difficult or even incorrect.
– Thin-slicing is not an exotic gift, it is a central part of what it means to be human.
DANGER
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 18Slide Number: 18
Thin-SlicingThin-Slicing
1 2 3
Information Block
Blin
k
Information Block
Thin-Sliced
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 19Slide Number: 19
Thin-Slicinganother view
Thin-Slicinganother view
Information& Data
Information& Data
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 20Slide Number: 20
Chicks & Red StripeChicks & Red Stripe
chicksfeeding
mamabird
evolution naturally opts for thin-slicing
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 21Slide Number: 21
2 x 2 Venn Diagram2 x 2 Venn Diagram• Chaos is complex.• Blink or thin-slicing is
simple, simple does not imply frivolous.
• Chaos has high simplicity & high complexity.
• Blink is simple.• This Venn diagram
illustrates the difference between Chaos Theory and Thin-Slicing.
• Different yet related ☻
• Chaos is complex.• Blink or thin-slicing is
simple, simple does not imply frivolous.
• Chaos has high simplicity & high complexity.
• Blink is simple.• This Venn diagram
illustrates the difference between Chaos Theory and Thin-Slicing.
• Different yet related ☻
CHAOSCHAOS BLINKBLINK
CO
MP
LEX
CO
MP
LEX
SIM
PLE
SIM
PLE
VennVenn
Decision MakingDecision Making
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 23Slide Number: 23
Over Simplified Decision MakingOver Simplified Decision Making
• “…truly successful decision making relies on a balance between deliberate & instinctive thinking”
• “…in good decision making, frugality matters.”
• “a general never knows anything with certainty, never sees his enemy clearly, & never knows positively where he is”(Napoleon Bonaparte)
• “…truly successful decision making relies on a balance between deliberate & instinctive thinking”
• “…in good decision making, frugality matters.”
• “a general never knows anything with certainty, never sees his enemy clearly, & never knows positively where he is”(Napoleon Bonaparte)
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 24Slide Number: 24
Premise & PropositionPremise & Proposition
1. Data collection is important2. Data items need to be selected carefully3. Deliberately keep data items to a narrow few4. Attempt to focus on only those items that affect decision
making5. Reduce costs & limit resources on data collection by
starting small & expanding deliberately
1. Data collection is important2. Data items need to be selected carefully3. Deliberately keep data items to a narrow few4. Attempt to focus on only those items that affect decision
making5. Reduce costs & limit resources on data collection by
starting small & expanding deliberately
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 25Slide Number: 25
How Do We Do This?How Do We Do This?
• Even the most complicated of relationships and problems have an identifiable underlying pattern (Chaos Theory)
• In picking up these patterns, less is more• Overloading decision makers with information
makes picking up that signature harder (Decision Support Systems)
• Successful decision makers have to edit ☻• Trouble begins when editing process is
disrupted – create an environment that let’s edit
• Even the most complicated of relationships and problems have an identifiable underlying pattern (Chaos Theory)
• In picking up these patterns, less is more• Overloading decision makers with information
makes picking up that signature harder (Decision Support Systems)
• Successful decision makers have to edit ☻• Trouble begins when editing process is
disrupted – create an environment that let’s edit
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 26Slide Number: 26
ConclusionsConclusions
• The essence of Blink is that snap judgments are often based on fairly deep knowledge, freed from the constraints imposed by consideration of too much information.
• In a world of overwhelming perceptual stimulation, it seems that reducing the data and allowing intuition to guide us may be a useful coping strategy.
• We want to know everything• Chessboard – you can see everything, win?
• The essence of Blink is that snap judgments are often based on fairly deep knowledge, freed from the constraints imposed by consideration of too much information.
• In a world of overwhelming perceptual stimulation, it seems that reducing the data and allowing intuition to guide us may be a useful coping strategy.
• We want to know everything• Chessboard – you can see everything, win?
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 27Slide Number: 27
Quote from BlinkQuote from Blink
• It doesn't seem like we have much control over whatever bubbles to the surface from our unconscious. But we do, and if we can control the environment in which rapid cognition takes place, then we can control rapid cognition. We can protect people fighting wars, or manning emergency rooms, or policing the streets from making mistakes. (Gladwell)
• It doesn't seem like we have much control over whatever bubbles to the surface from our unconscious. But we do, and if we can control the environment in which rapid cognition takes place, then we can control rapid cognition. We can protect people fighting wars, or manning emergency rooms, or policing the streets from making mistakes. (Gladwell)
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 28Slide Number: 28
Quote from BlinkQuote from Blink
• It’s not life threatening where a wrong answer means immediate death, then the answer you get will be right most of the time and is extremely energy efficient.
• Given that the situations where such strategies arise are not often situations where the wrong answer means immediate death, it’s not surprising that our brains are optimized for efficiency rather than 100% accuracy. (Gladwell)
• It’s not life threatening where a wrong answer means immediate death, then the answer you get will be right most of the time and is extremely energy efficient.
• Given that the situations where such strategies arise are not often situations where the wrong answer means immediate death, it’s not surprising that our brains are optimized for efficiency rather than 100% accuracy. (Gladwell)
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 29Slide Number: 29
Examples to Demonstrate UnderstandingExamples to Demonstrate Understanding
• Baseball Manager– Deep into the count
• Military General– Fog of war
• Snowfighter– Storm white-out
• You can’t see what the other guy is thinking whether that’s an opposing team, an enemy, or mother nature.
• Baseball Manager– Deep into the count
• Military General– Fog of war
• Snowfighter– Storm white-out
• You can’t see what the other guy is thinking whether that’s an opposing team, an enemy, or mother nature.
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 30Slide Number: 30
Management StylesManagement Styles
• Just in Time Management• Total Quality Management• Business Process Re-engineering• Decentralized versus Centralized• Information boom• Communications overload• World is Flat• Leadership versus Management• Decision Making has never been harder or
never been easier
• Just in Time Management• Total Quality Management• Business Process Re-engineering• Decentralized versus Centralized• Information boom• Communications overload• World is Flat• Leadership versus Management• Decision Making has never been harder or
never been easier
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 31Slide Number: 31
ReferencesReferences
• Williams, Garnett; “Chaos Theory Tamed”; National Academies Press, September 1997. ISBN 0-309-06351-5
• Glieck, James; “Chaos: Making a New Science”; Penguin (Non-Classics); Reprint edition December 1, 1988. ISBN 0-140-09250-1
• Gladwell, Malcolm; “The Tipping Point”; Little Brown & Company, March 2000. ISBN 0-316-31696-2
• Gladwell, Malcolm; “blink, the power of thinking without thinking”; Little Brown & Company, January 2005. ISBN 0-316-17232-4
• Williams, Garnett; “Chaos Theory Tamed”; National Academies Press, September 1997. ISBN 0-309-06351-5
• Glieck, James; “Chaos: Making a New Science”; Penguin (Non-Classics); Reprint edition December 1, 1988. ISBN 0-140-09250-1
• Gladwell, Malcolm; “The Tipping Point”; Little Brown & Company, March 2000. ISBN 0-316-31696-2
• Gladwell, Malcolm; “blink, the power of thinking without thinking”; Little Brown & Company, January 2005. ISBN 0-316-17232-4
August 2006August 2006 Rural ITS Big Sky, MTRural ITS Big Sky, MT Slide Number: 32Slide Number: 32
More ReferencesMore References
• Surowiecki, James; “The Wisdom of Crowds”; Doubleday; June 2004. ISBN 0-385-50386-5
• Schwartz, Barry; “The Paradox of Choice”; Harper Collins; 2004. ISBN 0-06-000568-8
• Levitt, Steven D. and Dubner, Stephen J.; “Freakonomics”; Harper Collins; 2005. ISBN 0-06-073132-X
• Surowiecki, James; “The Wisdom of Crowds”; Doubleday; June 2004. ISBN 0-385-50386-5
• Schwartz, Barry; “The Paradox of Choice”; Harper Collins; 2004. ISBN 0-06-000568-8
• Levitt, Steven D. and Dubner, Stephen J.; “Freakonomics”; Harper Collins; 2005. ISBN 0-06-073132-X
Questions & DiscussionQuestions & Discussion
Gregory Thompson2817 South Anthony Lane #107
Minneapolis, MN 55418Tel: 612 788-0835
Gregory Thompson2817 South Anthony Lane #107
Minneapolis, MN 55418Tel: 612 788-0835