Overview and introduction of ways to think about fads and their diffusion in ready-to-wear fashion. Consideration is given to identifying metrics for modeling massive amounts of data.
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In his abstract, Scriffignano summarizes as follows:
l explore some of the ways in which the massive availability of data is changing and the types of questions we must ask in the context of making business decisions. Truth be told, nearly all organizations struggle to make sense out of the mounting data already within the enterprise. At the same time, businesses, individuals, and governments continue to try to outpace one another, often in ways that are informed by newly-available data and technology, but just as often using that data and technology in alarmingly inappropriate or incomplete ways. Multiple “solutions” exist to take data that is poorly understood, promising to derive meaning that is often transient at best. A tremendous amount of “dark” innovation continues in the space of fraud and other bad behavior (e.g. cyber crime, cyber terrorism), highlighting that there are very real risks to taking a fast-follower strategy in making sense out of the ever-increasing amount of data available. Tools and technologies can be very helpful or, as Scriffignano puts it, “they can accelerate the speed with which we hit the wall.” Drawing on unstructured, highly dynamic sources of data, fascinating inference can be derived if we ask the right questions (and maybe use a bit of different math!). This session will cover three main themes: The new normal (how the data around us continues to change), how are we reacting (bringing data science into the room), and the path ahead (creating a mindset in the organization that evolves). Ultimately, what we learn is governed as much by the data available as by the questions we ask. This talk, both relevant and occasionally irreverent, will explore some of the new ways data is being used to expose risk and opportunity and the skills we need to take advantage of a world awash in data.
Presentation on Modernization Theory for PS 212 Culture and Politics in the Third World at the University of Kentucky, Summer 2007. Dr. Christopher S. Rice, Instructor.
Riel Miller educacao a distancia sociedade da informação
Connecting Research and Policy in the Digital Economy: Possibility Space Scenarios & 21st Century Transitions
As transformações oportunizadas pelo século XXI
João Jose Saraiva da Fonseca
http://joaojosefonseca1.blogspot.com/
1. Provide your position on what theorist is most relatable to you.docxjeremylockett77
1. Provide your position on what theorist is most relatable to your ideology and values. Respond to two other students’ comments seeking further explanation of their position and consequences of their thoughts.
· Dr. Thomas Sowell - Imperfections of the Market
· POLITICAL THEORY - John Maynard Keynes
· POLITICAL THEORY – Friedrich Hayek
2. Every decision has an Opportunity Cost due to the nature of scarcity, there is always a better alternative not chosen, therefore, there is always an opportunity cost. “The opportunity cost of an alternative is what you give up to pursue it” (Froeb, McCann,Shor & Ward, 2016). When you go to a Maroon 5 concert, you give up $100 of benefits you would have received if you had gone to a Beyoncé concert. Also, you would also avoid $80 of cost for the Beyoncé concert. According to the definition below, the opportunity cost of seeing Maroon 5 concert is $100 - $80 = $20. Please delve into the statement there are always opportunity costs. How can an individual make the best decision? Is there a best decision? Would one miss an opportunity not attending one of the concerts? Include a minimum of one reference.
3. Millennials are renting offices sharing costs to reduce their overhead expenditures and overall efficiency. What are the disadvantages and advantages of economies of scales? Give examples of your local establishments that use shared locations to decrease costs, i.e., Taco Bell and KFC. Include a minimum of one reference.
4. Article: Understanding the Impact of Transportation on Economic DevelopmentHow can the growth of intermodal transportation affect the product’s supply and demand? Discuss the major points of the article. How do transportation costs affect others? Please be specific. Discuss increases and decreases in supply and demand. Include a minimum of one reference
5. Behavior economics is a relatively new concept that was developed by Daniel Kahneman and Amos Tversky and is known as the prospect theory. The prospect theory posits that consumers are inspired by the comparison of prices to the reference price rather than the actual price. Please discuss why managing price expectations is as important as managing price. Please give three examples of local restaurants using prospect theory. Include a minimum of one reference.
6. This link explicitly discusses the theme behind the game theory. Please discuss the principles associated with this theory, as well as, how the classical game theory can be contained. Does the game theory in your opinion support the corporate’s strategy? When should the prisoner dilemma be used? Include a minimum of one reference.
7. Will there be a global economic crisis in a world of significant uncertainty? Please review the article from Goldman Sachs, Landing the Plane. Where are we headed the next few years of uncertainty and risks? What are the five greatest current global economic challenges? How will they affect the US economy? Include a minimum of one ...
Cultural Contradictions of Scanning in an Evidence-based Policy EnvironmentWendy Schultz
An overview of the tensions that arise when attempting to embed a futures perspective, in the form of horizon scanning, in organisations with an evidence-based culture.
Making Decisions in a World Awash in Data: We’re going to need a different bo...Micah Altman
In his abstract, Scriffignano summarizes as follows:
l explore some of the ways in which the massive availability of data is changing and the types of questions we must ask in the context of making business decisions. Truth be told, nearly all organizations struggle to make sense out of the mounting data already within the enterprise. At the same time, businesses, individuals, and governments continue to try to outpace one another, often in ways that are informed by newly-available data and technology, but just as often using that data and technology in alarmingly inappropriate or incomplete ways. Multiple “solutions” exist to take data that is poorly understood, promising to derive meaning that is often transient at best. A tremendous amount of “dark” innovation continues in the space of fraud and other bad behavior (e.g. cyber crime, cyber terrorism), highlighting that there are very real risks to taking a fast-follower strategy in making sense out of the ever-increasing amount of data available. Tools and technologies can be very helpful or, as Scriffignano puts it, “they can accelerate the speed with which we hit the wall.” Drawing on unstructured, highly dynamic sources of data, fascinating inference can be derived if we ask the right questions (and maybe use a bit of different math!). This session will cover three main themes: The new normal (how the data around us continues to change), how are we reacting (bringing data science into the room), and the path ahead (creating a mindset in the organization that evolves). Ultimately, what we learn is governed as much by the data available as by the questions we ask. This talk, both relevant and occasionally irreverent, will explore some of the new ways data is being used to expose risk and opportunity and the skills we need to take advantage of a world awash in data.
Presentation on Modernization Theory for PS 212 Culture and Politics in the Third World at the University of Kentucky, Summer 2007. Dr. Christopher S. Rice, Instructor.
Riel Miller educacao a distancia sociedade da informação
Connecting Research and Policy in the Digital Economy: Possibility Space Scenarios & 21st Century Transitions
As transformações oportunizadas pelo século XXI
João Jose Saraiva da Fonseca
http://joaojosefonseca1.blogspot.com/
1. Provide your position on what theorist is most relatable to you.docxjeremylockett77
1. Provide your position on what theorist is most relatable to your ideology and values. Respond to two other students’ comments seeking further explanation of their position and consequences of their thoughts.
· Dr. Thomas Sowell - Imperfections of the Market
· POLITICAL THEORY - John Maynard Keynes
· POLITICAL THEORY – Friedrich Hayek
2. Every decision has an Opportunity Cost due to the nature of scarcity, there is always a better alternative not chosen, therefore, there is always an opportunity cost. “The opportunity cost of an alternative is what you give up to pursue it” (Froeb, McCann,Shor & Ward, 2016). When you go to a Maroon 5 concert, you give up $100 of benefits you would have received if you had gone to a Beyoncé concert. Also, you would also avoid $80 of cost for the Beyoncé concert. According to the definition below, the opportunity cost of seeing Maroon 5 concert is $100 - $80 = $20. Please delve into the statement there are always opportunity costs. How can an individual make the best decision? Is there a best decision? Would one miss an opportunity not attending one of the concerts? Include a minimum of one reference.
3. Millennials are renting offices sharing costs to reduce their overhead expenditures and overall efficiency. What are the disadvantages and advantages of economies of scales? Give examples of your local establishments that use shared locations to decrease costs, i.e., Taco Bell and KFC. Include a minimum of one reference.
4. Article: Understanding the Impact of Transportation on Economic DevelopmentHow can the growth of intermodal transportation affect the product’s supply and demand? Discuss the major points of the article. How do transportation costs affect others? Please be specific. Discuss increases and decreases in supply and demand. Include a minimum of one reference
5. Behavior economics is a relatively new concept that was developed by Daniel Kahneman and Amos Tversky and is known as the prospect theory. The prospect theory posits that consumers are inspired by the comparison of prices to the reference price rather than the actual price. Please discuss why managing price expectations is as important as managing price. Please give three examples of local restaurants using prospect theory. Include a minimum of one reference.
6. This link explicitly discusses the theme behind the game theory. Please discuss the principles associated with this theory, as well as, how the classical game theory can be contained. Does the game theory in your opinion support the corporate’s strategy? When should the prisoner dilemma be used? Include a minimum of one reference.
7. Will there be a global economic crisis in a world of significant uncertainty? Please review the article from Goldman Sachs, Landing the Plane. Where are we headed the next few years of uncertainty and risks? What are the five greatest current global economic challenges? How will they affect the US economy? Include a minimum of one ...
Big Data for Development: Opportunities and Challenges, Summary SlidedeckUN Global Pulse
Summary points from UN Global Pulse White Paper "Big Data for Development: Opportunities & Challenges." See: http://www.unglobalpulse.org/BigDataforDevelopment
The presentatio offers an overview on big data in/for global development - i.e. how big data & data science are being developed in emerging and developing regions.
It is divided in three main sections:
(1) what is big data (as of today) & what is big data in/for development?
(2) Who is actually doing «big data for development»? Who are the main intrnational actors/stakeholders? What are main experiences?
(3) Why are we doing this? - i.e. are we doing this right? What are the main access, capacity / interpretation / ethical issues?
Michael Edson @ Potomac Forum: Relevance is in the Eyes of the BeholderMichael Edson
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Presentation - Understanding the Landscape of Disinformation in the Media Ag...Amir Jahangir
Unraveling the Web: Mastering Narratives to Counter Disinformation and Shape a Resilient Future for Pakistan
Introduction
Disinformation as a Tool of Influence
Defining Disinformation
Its Impact on the Environment
The Current Global Landscape (2023)
Understanding the Usage Paradox
Examining the Pakistan Landscape (2023)
The Changing Face of Reality
Reality in the Physical World
Reality in the Digital World
Mixed Reality: Blurring Boundaries
The Changing Pakistan
Embracing Change
Evolving Realities
Exploring Different Realms of Reality
Relevance to Audience
Generating Information in the Evolving Landscape
Importance of Credible Sources of Information
Dealing with Information
Information Disorder and its Implications
The Role of Credibility in News Consumption
The Fourth Industrial Revolution
Shifting Industry Dynamics
Balancing Information Accessibility with Credibility
The Biggest Risks for 2024
Projecting Risks Faced by 4 Billion People in 60 Countries
Conclusion
Importance of Navigating Disinformation in Shaping a Resilient Future
Encouraging Audience Engagement and Inquiry
Thank You
Backup Slides
Information vs. News: Understanding Credibility
Prevailing and Emerging Trends in Media Consumption
Industry Realignment: Adapting to Changing Media Consumption Patterns
Quality vs. Access case study Complete a full paper outline incl.docxmakdul
Quality vs. Access case study
Complete a full paper outline including each of the headings below. Make sure to touch upon the following items in your outline:
· Introduction: Briefly introduce the case study-Quality vs. Access (details attached). In addition, clearly state the purpose of the analysis and what you hope to prove in the report.
· Stakeholders: Identify the stakeholders who are involved in your case study. Discuss the entities who have an interest in the situation. How do their interests affect your ability to find a solution
· Overview: Provide a succinct overview of the current situation relating to your case study.
· Analysis: Provide an analysis of the situation. Make sure to discuss the incentives or lack thereof. How have the current incentives caused the problem? Address the specific questions posed in your chosen case study. Apply the concepts you have been exposed to throughout the course to aid in your analysis.
· Recommendations: Based upon your analysis, make appropriate recommendations that could alleviate or solve the presented problem.
· Conclusion
· References: Make sure to support your claims with reputable resources. All citation should follow the most current version of AMA style.
Background info:
Case Study: Quality vs. Access
The Affordable Care Act raised the Medicaid reimbursement levels to Medicare levels, resulting in improved appointment availability for Medicaid recipients. One of the components of the Affordable Care Act now coming into effect is the reporting of quality measurements and tying these into reimbursement. Some of the measurements are subjective, such as patient satisfaction, while others are quantitative, such as percentage of patients with their diabetes under control. Patient adherence to treatment plans has been shown to be as low as 40%. Opponents of the rating system say this system will result in more difficult and low socio-economic group patients being turned away by providers.
· How could the payment system be modified to reward quality of care but not result in reduced access to those in lower socio-economic groups or with poorer health?
Resources:
Wherry, Laura R., and Sarah Miller. "Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid expansions: A quasi-experimental study." Annals of internal medicine (2016). http://annals.org.une.idm.oclc.org/aim/article/2513980/early-coverage-access-utili zation-health-effects-associated-affordable-care-act
Martin, Leslie R., et al. "The challenge of patient adherence." Ther Clin Risk Manag 1.3 (2005): 189-199.
https://www-ncbi-nlm-nih-gov.une.idm.oclc.org/pmc/articles/PMC1661624/
Rubric
Introduction
Meets the
“Satisfactory” criteria and utilizes course concepts and reputable resources to support claims
Stakeholders
Meets the
“Satisfactory” criteria and utilizes course concepts and reputable resources to support claims
Overview
Meets the
“Satisfactory” crit ...
Why aren't Evaluators using Digital Media Analytics?CesToronto
Whether it’s through blogs, tweets, or even the comments section of an online newspaper, the world is increasingly talking online. However, the potential uses for the massive amounts of information available on the internet remain largely untapped in the sphere of evaluation.
This presentation will explore innovative methods to extract these insights from the large and complex collections of digital data publicly available online. In particular, we will examine the unprecedented uses, and potential limitations, of digital media analytics to:
• Measure the outcomes of public outreach, advocacy, communications, and information sharing programs;
• Establish current and retroactive baselines;
• Conduct “borderless” data collection to gain insights from other countries, as well as disapora communities in Canada;
• Identify unknown stakeholder groups and create detailed stakeholder maps; and,
• Provide context and insight to inform further data collection.
Breakout 3. AI for Sustainable Development and Human Rights: Inclusion, Diver...Saurabh Mishra
This group reviewed data and measurements indicating the positive potential of AI to serve Sustainable Development Goals (SDG’s). Alongside these optimistic inquiries, this group also investigated the risks of AI in areas such as privacy, vulnerable populations, human rights, workplace and organizational policy. The socio-political consequences of AI raise many complex questions which require continued rigorous examination.
An overview of key activities in a complete futures / foresight study, with a 'shopper's guide' to relevant tools and methods to suit each activity. Use it to compose an integrated futures research project, soup to nuts.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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Big Data for Development: Opportunities and Challenges, Summary SlidedeckUN Global Pulse
Summary points from UN Global Pulse White Paper "Big Data for Development: Opportunities & Challenges." See: http://www.unglobalpulse.org/BigDataforDevelopment
The presentatio offers an overview on big data in/for global development - i.e. how big data & data science are being developed in emerging and developing regions.
It is divided in three main sections:
(1) what is big data (as of today) & what is big data in/for development?
(2) Who is actually doing «big data for development»? Who are the main intrnational actors/stakeholders? What are main experiences?
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Presentation - Understanding the Landscape of Disinformation in the Media Ag...Amir Jahangir
Unraveling the Web: Mastering Narratives to Counter Disinformation and Shape a Resilient Future for Pakistan
Introduction
Disinformation as a Tool of Influence
Defining Disinformation
Its Impact on the Environment
The Current Global Landscape (2023)
Understanding the Usage Paradox
Examining the Pakistan Landscape (2023)
The Changing Face of Reality
Reality in the Physical World
Reality in the Digital World
Mixed Reality: Blurring Boundaries
The Changing Pakistan
Embracing Change
Evolving Realities
Exploring Different Realms of Reality
Relevance to Audience
Generating Information in the Evolving Landscape
Importance of Credible Sources of Information
Dealing with Information
Information Disorder and its Implications
The Role of Credibility in News Consumption
The Fourth Industrial Revolution
Shifting Industry Dynamics
Balancing Information Accessibility with Credibility
The Biggest Risks for 2024
Projecting Risks Faced by 4 Billion People in 60 Countries
Conclusion
Importance of Navigating Disinformation in Shaping a Resilient Future
Encouraging Audience Engagement and Inquiry
Thank You
Backup Slides
Information vs. News: Understanding Credibility
Prevailing and Emerging Trends in Media Consumption
Industry Realignment: Adapting to Changing Media Consumption Patterns
Quality vs. Access case study Complete a full paper outline incl.docxmakdul
Quality vs. Access case study
Complete a full paper outline including each of the headings below. Make sure to touch upon the following items in your outline:
· Introduction: Briefly introduce the case study-Quality vs. Access (details attached). In addition, clearly state the purpose of the analysis and what you hope to prove in the report.
· Stakeholders: Identify the stakeholders who are involved in your case study. Discuss the entities who have an interest in the situation. How do their interests affect your ability to find a solution
· Overview: Provide a succinct overview of the current situation relating to your case study.
· Analysis: Provide an analysis of the situation. Make sure to discuss the incentives or lack thereof. How have the current incentives caused the problem? Address the specific questions posed in your chosen case study. Apply the concepts you have been exposed to throughout the course to aid in your analysis.
· Recommendations: Based upon your analysis, make appropriate recommendations that could alleviate or solve the presented problem.
· Conclusion
· References: Make sure to support your claims with reputable resources. All citation should follow the most current version of AMA style.
Background info:
Case Study: Quality vs. Access
The Affordable Care Act raised the Medicaid reimbursement levels to Medicare levels, resulting in improved appointment availability for Medicaid recipients. One of the components of the Affordable Care Act now coming into effect is the reporting of quality measurements and tying these into reimbursement. Some of the measurements are subjective, such as patient satisfaction, while others are quantitative, such as percentage of patients with their diabetes under control. Patient adherence to treatment plans has been shown to be as low as 40%. Opponents of the rating system say this system will result in more difficult and low socio-economic group patients being turned away by providers.
· How could the payment system be modified to reward quality of care but not result in reduced access to those in lower socio-economic groups or with poorer health?
Resources:
Wherry, Laura R., and Sarah Miller. "Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid expansions: A quasi-experimental study." Annals of internal medicine (2016). http://annals.org.une.idm.oclc.org/aim/article/2513980/early-coverage-access-utili zation-health-effects-associated-affordable-care-act
Martin, Leslie R., et al. "The challenge of patient adherence." Ther Clin Risk Manag 1.3 (2005): 189-199.
https://www-ncbi-nlm-nih-gov.une.idm.oclc.org/pmc/articles/PMC1661624/
Rubric
Introduction
Meets the
“Satisfactory” criteria and utilizes course concepts and reputable resources to support claims
Stakeholders
Meets the
“Satisfactory” criteria and utilizes course concepts and reputable resources to support claims
Overview
Meets the
“Satisfactory” crit ...
Why aren't Evaluators using Digital Media Analytics?CesToronto
Whether it’s through blogs, tweets, or even the comments section of an online newspaper, the world is increasingly talking online. However, the potential uses for the massive amounts of information available on the internet remain largely untapped in the sphere of evaluation.
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• Measure the outcomes of public outreach, advocacy, communications, and information sharing programs;
• Establish current and retroactive baselines;
• Conduct “borderless” data collection to gain insights from other countries, as well as disapora communities in Canada;
• Identify unknown stakeholder groups and create detailed stakeholder maps; and,
• Provide context and insight to inform further data collection.
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In medias res: Fads, Trends and Diffusion in RTW Fashion
1. In Medias Res:
Fads, Trends, Bubbles and
Massively Scaled Analyses in RTW Fashion
Thomas Ball
NYIT
March 8, 2018
2. Highlights
• The importance and relevance of fashion
• Growth is king
• Watersheds in strategic thought
• Massively scaled analyses in the RTW fashion ecosystem
• Modeling of fads and trends: towards a protocol
• The bottom line
3. The Importance And Relevance Of Fashion
Fashion may be the world‘s largest and most important creative industry
• A global business with recent annual U.S. sales of more than $250 billion -- ~2% of GDP --
larger than that of books, movies, and music combined, hardly frivolous
- Has blended into the wider arena of “Entertainment”
• Greenhouse for the analysis of fads and trends
• Provides economists, sociologists and other cultural thinkers and critics with canonical
examples of consumption, conformity and consecration
• RTW fashion data possess all the computational challenges inherent in any massive analysis
Sources: Hemphill and Suk, The Law, Culture and Economics of Fashion, 2009; Aspers and Godart, Sociology of Fashion: Order and Change, 2013
Cattani, Ferriani and Allison, Insiders, Outsiders, and the Struggle for Consecration in Cultural Fields, 2014; Teri Agins, The End of Fashion, 2000
Non Gustibus Disputandum Est
4. Defining Fads And Trends
Some tentative definitions – a few terms with many synonyms
• A fad is a short-term burst in behavior usually starting with explosive growth that rises to a
peak followed by slower ebbing -- analogous to froth from waves breaking on a beach
– Bubbles are closely related to fads but are usually financial in nature and refer to
unrealistic prices detached from intrinsic value -- can be both positive and negative
• A trend is a long-term or enduring influence on behavior -- analogous to open ocean waves
– The Ancients had no concept of a “trend” viewing existence as eternal and static
– Population demographics are among the most important structural drivers of trends
Wave Motion Does Not Change With Water Depth
Sources: Daniel Bell, Personal communication, 2002; Didier Sornette, Financial Crisis Observatory, ETHZ; Robert Shiller, Irrational Exuberance, 1999; Eli Pariser, The Filter Bubble, 2011
Froth
Forms
“Fads”“Trends”
5. Sources: http://www.theworldeconomy.org/MaddisonTables/MaddisontableB-10.pdf, http://kk.org/thetechnium/archives/2008/10/the_expansion_o.php , http://smartregion.org/2011/03/creative-class
Daniel Bell, The Coming of Post-Industrial Society, 1974; Richard Florida, The Rise of the Creative Class, 2014; Deirdre McCloskey, Bourgeois Dignity, 2012; Google Ngrams Analysis
1500 1560 1600 1650 1690 1725 1775 1800 1850 1885 1925 1965 2010
In terms of the forces of history, we find ourselves in medias res…
Growth Is King
• Exponential growth of everything since the Industrial Revolution – GDP to double by 2050
• A “Knowledge Society” emerged when the Service Sector eclipsed Manufacturing in size and
growth of occupations
• The production and flow of ideas is a primary source of growth
Wealth and Population
1-2010 AD
$0
$10,000
$20,000
$30,000
1 1000 1500 1600 1700 1820 1870 1900 1950 1970 2010
0
2
4
6
Population
Year
(Discontinuous)
U.S. Occupational Change
1800-2010
Millions Billions
Wealth
Production of Ideas
1500-2010
Year
# Books Printed
Agriculture
Manufacturing
Low Wage
Service Sector
Knowledge
Workers
Millions Employed70
60
50
40
20
30
10
0
Service
Sector
Year
~Industrial
Revolution ~Knowledge
Society
~Industrial
Revolution
~Knowledge
Society
~Knowledge
Society
6. Swing towards greater uncertainty and disruptions to equilibrium in normative business practices
Quantifiable Risk, Unknowable Uncertainty and The Business Landscape
Watersheds In Strategic Thought
A Clear Enough Future
What can be
known?
Linear forecasts drive strategy
Change is gradual and incremental
Sustainability
Behavior is deterministic and predictable
Conformance with intuition
Six Sigma precision
Alternate Futures
A few options define the future
Discrete choice models assign
likelihoods to outcomes
A Range of Futures
A range of possible outcomes
No natural scenarios
“The Garden of Forking Paths”
From Risk to Greater Complexity and UncertaintyLow High
1
2
3
• Anomalies regarding Porterian assumptions of sustainability of competitive advantage
• Hypercompetition in a widened arena of business operations versus myopic industry silos
• Shift from moment-based, linear models rooted in “normality” and simple iid relationships to
models exploiting nonlinearity, complex dependence, power law distributions, infinite moments,
heavy tails
True Uncertainty
Nonlinear systems dominate
Extreme shifts can occur abruptly, without warning
Transience
Behavior is deterministic but not predictable
Expectancy violations
Arbitrage in ignorance, diffidence, approximation
?
Sources: Courtney, Kirkland and Viguerie, Strategy Under Uncertainty, HBR, 1999; Richard D’Aveni, Hypercompetition, 1991; Rita McGrath, The End of Competitive Advantage, 2012
Frank Knight, Risk, Uncertainty and Profit, 1921; Nissam Taleb, Silent Risk, 2014; Embrechts, et al., Modeling Extremal Events, 1996; John Deighton, The Value of Data, 2013
Arthur de Vany, Hollywood Economics, 2003; Anita Elberse, Blockbusters, 2013; David Hand, The Improbability Principle, 2014; Sarah Kaplan, Beyond Forecasting, 2014
Cosima Shalizi, The Statistical Analysis of Complex Systems Models, 2010; Aswath Damodoran, Living with noise: Investing under uncertainty, 2013
7. Human Curated Decisions
R&D-Tacit Knowledge
Met Gala
Prediction Markets
Continuous Tracking
Competitive Info from Online
Comparison Engines
Web Scraping
Hiring Trends
FEEDBACK/PROPAGATE
3 Years of
Sales
Decline
“Blooming, Buzzing Confusion”
ZeitgeistVisionaries
PlaceBets
Sources and Tools
Long-
Tailed
Product?Growth
Maturity
Decline
Decelerating
Sales
Performance
RDBMS Sales Data
Fast Fashion, e.g., Zara
Marketing Spend
Social Media OSIs
Competitive Info from Online
Comparison Engines
Web Scraping
eBay
Introduction
Accelerating
Sales
0
Innovation Pipeline Sales Curves and the Product Life CycleExecution
Paris’ Premiere Visione
Text and Image Mining
Patents, Academic Papers
Film, Books, Mags, Art, etc.
VC Investments
Demographics (Youth and
Agelessness)
Tech Conferences
Blogs
Go – No Go
Visionaries Originate While Markets Imitate, Diffuse and Consecrate
Rapidly Cycling Fads And Trends, Massive Classification Of
Fashion Styles Drive Need for Massively Scaled Analysis
Evidence-Based Decision-Making
Launch
Time
Scan,Monitor,Originate,Imitate
From Uncertainty to Risk
Data Mining
Predictive Modeling
Network and Diffusion Models
Agent-Based Models
Massively Categorical Models
Machine Learning Algorithms
Recommender Systems
Sources and Tools
Manufacturing
thePortfolio
8. Fads, Trends And Explosive Self-Generating Demand
Unrelated phenomena such as the explosive flow of water out of a breached dam versus “fad”
behaviors in Google search activity can be seen as structural homologues
Water Flow From A Breached Dam vs “Interest” in Justin Bieber
Sources: V. Seshadri, The Inverse Gaussian Distribution, 1994; Google Trends, March 2015
0
2 5
50
75
10 0
Jan-
09
Jul-
09
Jan-
10
Jul-
10
Jan-
11
Jul-11 Jan-
12
Jul-
12
Jan-
13
Jul-
13
Jan-
14
Jul-
14
Jan-
15
“JustinBieber”GTSearchInterest
• There are at least two challenges inherent in this:
- Separating wheat from chaff or epiphenomenal flotsam and jetsam from emerging trends
- Finding scalable computational solutions for massive numbers of “Biebers”
9. Tracking Fads And Trends With Machine Learning Algorithms
*Word-of-mouth, open source indicators
Sources: http://www.jingdaily.com/from-social-status-to-self-expression-the-rapid-evolution-of-chinas-street-style/42059; Renee Dye, The Buzz on Buzz, 1999
Personal communications: Svante Jerling, P1.cn; Karen Moon, Trendalytics.co; Josh Clark, BoazandClark.com; David Wolfe, Doneger Group
IARPA.gov papers on OSIs, in Google search window enter “D12PC00337 OR D12PC00285 OR D12PC00347”
- Structuring unstructured information in the wilderness of data is difficult to do at scale
- “Words slip and slide and never stay in place” and so do images
• The evolution of Chinese fashion styles based on algorithmic image mining of tens of thousands
of pictures taken on the streets of Shanghai and Beijing suggests:
- Tastes may have shifted from conspicuous status statements using big logo brands such
as LVMH to niche brands
- Slowing economic growth as well as crackdowns on corruption and pirating also contributed
Text and image mining of WOM*, social media OSIs* is an analytic “Wild, Wild West”
Tracking Pictures of Handbags With Louis Vuitton Logo in China
2008-2017
10. Modeling Fads And Trends
Fads and trends can be quantified using models of tech and new product innovation, diffusion,
adoption and evolution rooted in the analysis of nonlinear logistic growth
Sources: Jesse Ausubel, DRAMs as Model Organisms for Study of Technological Evolution, 2001; Steven Johnson, Where Good Ideas Come From, 2010; Jonah Berger, Contagious, 2013
Peres, Muller, Mahajan, Innovation diffusion and new product growth models: A critical review and research directions, 2009
Alex Pentland, Social Physics : How good ideas spread, 2014; Julie Cohen, Configuring the Networked Self, 2012; Harrison White, Markets from Networks, 2002
Lee Cooper, Market Share Analysis, 1989; Gelman and Hill, Data Analysis Using Regression and Multi-Level Models, 2007; Singer and Willet, Applied Longitudinal Data Analysis, 2003
“Small World” social networks, e.g., “I shook Frank Sinatra’s hand,” Six Degrees of Kevin Bacon, Six Degrees of Francis Bacon
• Classic models focused on univariate time series and diffusion processes based on cumulants
of new adopters or sales, e.g., Gompertz and Bass-Anderson models, Fisher-Pry transforms
- Aggregates of individual decisions give a normative description of the adoption life cycle
- S-shaped curves identify inflection points and carrying capacities (ceilings or asymptotes)
• Recent research generalizes this framework to more complex, disaggregate, multi-level and
multivariate growth processes leveraging, e.g., pooled time series, marketing mix or multi-level
regression potentially with multiple DVs, network analysis, information theoretic frameworks, etc.
Classic Diffusion and S-Shaped Curves
Eight Generations of DRAM Chips, 1970-2000
CumulativeDRAMUnitShipments(Million)
8000
6000
4000
2000
4K
16K
64K
256K
1M
4M
16M
64M
1970 1975 1980 1985 1990 1995 2000
Year
Social Networks
11. Differentiating Fads From Trends: Towards A Protocol
It is possible to distinguish fads from trends using a hybrid, generalized approach
- Based on pre-determined “burn-in” periods, partition products by phases of the life cycle
- Fads – “go, no go” phase for new products with time “zero” origin and data of short duration
- Use automated, cumulant diffusion models for insight into growth, ceilings or cancellation
- Early stages of an emergent fad have the least information and are the hardest to predict
-How much information (# data points) is needed for “go, no go” decisions?
- Trends – left-censored, pre-existing products with established sales curves
- Use actual sales, not cumulative, for insights into growth rates (slope, 1st derivative) and
acceleration or momentum (Hessian, 2nd derivative, the rate of change in the slope)
0
1,000
2,000
3,000
0
100
200
300
400
500
Time
Product Sales for “Fads” and “Trends”
Left Aligned, Past 12 months Cumulative
“Fad” Products
Cumulative Sales
“Go”
“Go?”
“No Go”
Cumulative
Units
Actual
Units
Actual
“Zero” Time
Origin
Left-
Censoring
“Trend”
Product
Sales
12. 0
25
50
75
100
-21.4
-4.6
-3.2
-2.2
-1.2
-0.2
0.8
1.8
2.8
3.8
4.8
5.8
6.8
7.9
8.9
9.9
11.0
12.2
14.1
15.8
18.8
31.2
0
20
40
60
80
-2.8 -1.3 -0.3 0.7 1.7 2.7 3.7 4.7 5.7 6.7 8.6
Classifying Trajectories Based On Growth And Acceleration
Growth
Product Growth vs Acceleration Rates
Acceleration
A comparison of growth rates (slope or 1st derivative) with the rate of acceleration in those slopes
(Hessian, 2nd derivative) suggests strong association but qualitatively distinct information
- Model accuracy may not be impacted but are the insights greater from adding momentum?
-15
-10
-5
0
5
10
15
20
25
30
35
-4 -2 0 2 4 6 8 10
ILLUSTRATIVE
Distribution of Product Acceleration Rates
(Hessian, Second Derivative)
#
#
Distribution of Product Growth Rates
(Slope, First Derivative)
“0” Points “0” Point
n~2,000
13. Challenges In Developing Generalized Growth Models
Is the trend really your friend?
*Heteroscedasticity, autocorrelation consistent
Sources: Ainslie and Steenburgh, Massively Categorical Variables: Revealing the information in zip codes, Marketing Science, 2002; Anita Elberse, Blockbuster, 2013
Van Den Bulte and Lilien, Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models, 1997; Edward Thorpe, Beat the Dealer, 1964
Ron Gallant, Nonlinear Models, 1988; Wang, Chen, Schifano, Wu and Yan, A Survey of Statistical Methods and Computing for Big Data
Emmert-Streib and Delmer, Information Theory and Statistical Learning, 2009; Bikhchandani, Hirshleifer and Welch, A Theory of Fads, Fashion, Custom, and Cultural Change as Informational
Cascades, 1992; Wang and Zhang, Reasons for Market Evolution and Budgeting Implications, 2008; Personal communications Mike Hanssens, Gary Lilien, Renana Peres, 2015
Chattopadhyay and Lipson, Data smashing: uncovering lurking order in data, 2015; Andreas Brandmaier, pdc: An R Package for Complexity-Based Clustering of Time Series, 2014
- Univariate diffusion models are simplistic
- Don’t capture important factors such as competition or the marketing mix (ex Extended Bass)
- Prima facie issues with HAC* and nonstationarity (cf. Box-Jenkins, “p’s and q’s”)
- Designed and built for successful new products -- but most new products fail(!)
- Hybrid, generalized models leverage multi-level, pooled, marketing mix, etc., robust regression
- Handle extreme value data in the original units of the dependent variable(s)
- Incorporate information related to competitive effects, marketing spend, market or consumer
heterogeneity, social media, social networks, etc., as appropriate and available
- Find nonlinearities in momentum of growth rate (slope, 1st derivative) based on the rate of
change or acceleration in that slope (Hessian, 2nd derivative)
- Exploit endogenous and combinatoric interactions inherent in massively categorical data to
estimate, e.g., the impact on sales in the infinite nuances of colors or button shapes, sizes
- ML algorithms,e.g., Random forests, Divide and Conquer, BLJs (bags of little jacknifes) compiled
on massively parallel CPUs are approximating workarounds for scalable statistical modeling
- Track the advent of featureless, pattern matching, machine learning, complexity-based algorithms,
e.g., permutation distribution clustering or “data smashing”
14. The Bottom Line
How can the analysis of fads, trends and bubbles be used to enhance business performance?
• Models are calibrated on a known world and projected into an unknown, uncertain future
– Model performance can be benchmarked several ways: 1) improvement over an
incumbent method, 2) % correct prediction in a portfolio over and above random
guessing and 3) prospective (not historic) predictive accuracy
• As in Vegas, beating the house 1%-4% of the time is pretty darn good
– That is, if one beats the house at all
– Relate to key corporate metrics such as YAG sales, stock price, financial ratios, etc.
• Stronger strategic planning, analysis and inventory control of new and existing products from
the insights available in hybrid, generalized growth and diffusion analysis
– Widened set of strategic, evaluative and validatory metrics of prospective performance
that go beyond purely data-based, historic predictive accuracy
– Evaluation of impact of marketing mix and other activities on product evolution
– Use in inventory control as an aid to answering questions related to potential market
size, depth of purchase, duration and timing for when to get in or out
– Early warning for explosive product growth, negative revenue surprises
– Track cross-product elasticities and interdependence for cross-sell
• Extreme value, power law nature of information suggests changing emphasis from predicting
averages to predicting quantiles, tantiles or the value of a heavy-tailed distribution
• Shifting views of growth as smooth and linear to recognition that growth is inherently
nonlinear, inefficient, lumpy, messy