In Media Res-Trends,Fads,Bubbles,Diffusion

1. In Media Res:Trends, Fads, Bubbles and Massively Scaled Analyses Thomas Ball Marketing Modelers Group April 10, 2014

2. Highlights • Setting the tone • Growth is king • Watersheds in strategic thought • Theories of innovation and diffusion • A few key trends in our current predicament • Homologies between natural phenomena, finance and fashion • Massively scaled analyses • The arbitrage of ignorance

3. Setting The Tone… Nietzsche noted that the seeds of any trend already exist, latent in the cultural sediment Neal Gabler: “We’re a society driven by entertainment. In an entertainment culture, everything must compete with entertainment…” Christopher Hitchens: “The pleasures and rewards of the intellect are inseparable from uncertainty, angst, conflict and even despair” Lao Tzu, The Way of Life (Witter Bynner trans.): “Whether a man dispassionately sees to the core of life or passionately sees the surface, the core and the surface are essentially the same…”

4. Trends Are Ubiquitous • Some tentative definitions: • A trend is a long-term or enduring influence on behavior, analogous to open ocean waves • The Ancients had no concept of “trends” viewing existence as eternal and static • Modern notions are typically credited as originating with Vico’s 1725 Nuova Scienza • A fad is a short-term burst in behavior usually starting with explosive growth that rises to a single peak, followed by slower ebbing, analogous to froth on the beach from a breaker • Bubbles are closely related to fads but are financial in nature and refer to unrealistic prices detached from intrinsic value Wave Motion Does Not Change With Water Depth Trends Fads Froth Forms Source: Daniel Bell, Personal communication, 2002

5. Trends And The Bottom Line How can the analysis of trends, fads and bubbles be used to enhance business performance? • Exploring nonlinear vs linear growth • Can be leveraged as early warning systems for: • Breakout ideas or new products • Potential negative revenue surprises • Expected timing of trends and bubbles • Are directly relatable to key performance metrics, e.g., stock price, financials, YAG sales • Aid in supply chain planning as analyses of this type can answer questions related to the depth of purchase orders and when to get out of a sales fad or trend for an item • Improved predictions and forecasts: direction, magnitude, acceleration and likely ceiling • Evaluated in terms of a prospective hit rate of actual vs predicted outcomes relative to what was previously used • Facilitate “getting ahead of the curve” in hopes of distinguishing between smoke and mirror fads versus more durable trends in the flow of new ideas and products • Text and image mining can be instrumental in facilitating this

6. Growth Is King We find ourselves thrown into the middle of things… • Exponential growth since the Industrial Revolution (~1760+) • The “Knowledge Society” emerged when the Services Sector eclipsed the Industrial Sector in growth (~1920s) • The production and flow of ideas is a primary source of growth Wealth and Population 1-2010 AD U.S. Occupational Change 1800-2010 Production of Ideas 1500-2010 Million Billion Millions Employed # Books 70 $30,000 6 60 Wealth Low Wage Service Sector 50 Services 4 $20,000 40 Knowledge Workers Population 30 Manufacturing 2 $10,000 20 ~Knowledge Society ~Industrial Revolution ~Industrial Revolution Neolithic ~2 million pop 10 Agriculture $0 0 0 1 1000 1500 1600 1700 1820 1870 1900 1950 1970 2010 Year (Discontinuous) Year Year Source: 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

7. Accelerating Rates Of Change In one framework society is composed of layers, each with its own rate of change • Slower layers provide stability while faster layers drive innovation • This hierarchical linear view, while helpful and illuminating, cannot be correct Differential Rates of Change in Social Layers SOCIAL LAYERS Fashion Technology Markets/Commerce E.g., “Competitive advantage” has been reduced to a few months if not weeks Infrastructure Governance Accelerating Change Language Culture Nature Earth Cosmos Source: Adapted from Stewart Brand, Whole Earth Review, 2000

8. From Risk to Greater Uncertainty and Complexity Low High Watersheds In Strategic Thought Widespread shift towards greater uncertainty and disruptions to normative business practices • Anomalies regarding the assumptions of competitive advantage and sustainability of growth • Trend towards hypercompetition in a widened arena of business operations versus an industry-specific focus • Risk is known and quantifiable, uncertainty is neither Uncertainty and The Business Landscape 1 ? 2 3 A Clear Enough Future True Uncertainty Alternate Futures A Range of Futures What can be known? Linear forecasts drive risk-based strategies Intuition works well here Linear systems Change is gradual Behavior is deterministic and predictable A few discrete outcomes define the future A range of possible outcomes No natural scenarios Intuitive, gut decisions less effective Shrinking evidence that forecasts work Nonlinear systems Extreme changes in behavior can occur abruptly and without warning Behavior is deterministic but not predictable Learning to live with uncertainty, doubt, approximate or imprecise answers Source: Courtney and Kirkland, Strategy Under Uncertainty, HBR, 1996 Richard D’Aveni, Hypercompetition, 1994 Rita McGrath, The End of Competitive Advantage, 2013 Olivier Compte and Andrew Postlewaite, Uncertainty, Ignorance and Strategy, 2014 Kate Raworth, Royal Society for the Arts, Growth Is Not Enough, 2014

9. Models Of Innovation And Diffusion Theories of innovation and diffusion are rooted in analysis of nonlinear logistic growth curves • Classic models are built up from an individual time series with models focused on a single diffusion curve based on cumulative data possessing a known origin or zero start value • More recent theorizing focuses on extensions of the classic model to networks, social learning, flows of ideas, crowdsourcing and quantification of virtually everything – with less emphasis on the individual’s role • So, from a “classic” innovation perspective Thomas Edison was a visionary • More recent views as the spotlight grabbing manager of a lab employing hundreds of scientists Classic Diffusion and S-Shaped Curves Eight Generations of DRAM Chips, 1970-2000 Useful Books on Social Networks Year Source: Jesse Ausubel, DRAMs as Model Organisms for Study of Technological Evolution, 2001 Steven Johnson, Where Good Ideas Come From, 2010 Jonah Berger, Contagious, 2013 Alex Pentland, Social Physics : How good ideas spread, 2014

10. From Hierarchies To Heterarchies Hierarchical, top-down, Tayloristic organizational structures were ubiquitous in the 20th c with clear – if static -- lines of control, authority and division of labor • The 21st c organization is increasingly focused on flatter networks and is labeled heterarchical - This structure allows for much greater flexibility: job profiles overlap as talent replaces skill, the corporate “ladder” flattens out or disappears entirely, human capital flows as needed Visualizing the Shift In Social Structure Heterarchical Structures A Hierarchical Structure Social Networks Fractal Heterarchy Note: “Heterarchy” was coined in1945 by Warren McCullough, a neurophysiologist. From Wiki: A heterarchy is a system of organization where the elements of the organization are unranked (non- hierarchical) or where they possess the potential to be ranked a number of different ways. The two kinds of structure are not mutually exclusive. A heterarchy may be parallel to a hierarchy, subsumed in a hierarchy, or it may contain hierarchies. In fact, each level in a hierarchical system is composed of potentially heterarchical groupings which contains its constituent elements.

11. Leading Us to The Many, Many Megatrends… As well as many unknowns, uncertainties and questions with no current answers… Key Demographic Trends: - Increased urbanization - Birth rates decline - Aging of the population - Growth in disposable income creates a world middle class Singularity? Sustainability? Hyperconnectivity IPv6 Nano-Monetization Increasing Complexity/Fragility Quantified Selves, Cities, Societies Surveillance and Control in the Panopticon Internet of Things Ubiquitous Computing HyperData Living Laboratories Wearable Tech Today? Emergence of Connectivity IPv4 Globalization Social Networks Mobile Technology Klondike-like Wealth Bubbles # Measurements per person per unit of time Traditional Information Sources and Media: E.g., print, broad demographics, Self-reported and scanner data Cross-Section Time Series Time Source: Alex Pentland, Social Physics: How good ideas spread, 2014 Duncan Watts, Computational Social Science, 2013 Eli Pariser, The Fikter Bubble, 2011

12. Trends In The US Household Budget Trade-offs in the allocation of household expenditures are a Darwinian, zero-sum game • Along with significant shifts between categories since the turn of the 20th c, expenditures on necessities – housing, food and apparel -- saw a large decline (-39%) as a percent of total expenditures with a corresponding shift into the All Other* category From Linear to Nonlinear Models and Assumptions US Household Expenditures by Category % of total expenditures, 1900-2012, discontinuous years, current $ % Change In Hhold Expenditures 1900-2012 All Other* $3.3 trill 51% of Total +151% All Other* $3.3 trill +151% % Apparel $215.6 bill -76% Necessities $3.1 trill 49% of Total -39% Housing $2.1 trill +41% Food $820.4 bill -70% Year (Discontinuous) Sources: US Bureau of Economic Analysis, 2008, http://www.bls.gov/opub/uscs/home.htm, the Census Bureau revised Stat Abstracts after 2008 making pre-1990 information less accessible *All Other expenditures include (as % of 2012 All Other): Transportation (36%), Insurance and Pensions (22%), Healthcare (14%), Entertainment (10%), Religion and Charities (8%), Personal Care (2%), Alcohol (2%), Tobacco (1%), Miscellaneous All Other (1.7%)

13. Household Size Shrank 50% In The Past Century Technology played a key role in this trend with the adoption of, e.g., telephones, radio and TVs as well as increasing transportation options • This trend flattens out in the late 80s Average Household Size in the US 1890-2010 Average Household Size in the Developed World ~2010 Avg=2.5 Year (Discontinuous) Average # People Source: US Statistical Abstracts, http://hypertextbook.com/facts/2006/StaceyJohnson.shtmlhttp://www.nationmaster.com/graph/peo_ave_siz_of_hou-people-average-size-of-households

14. Where Did The Time Go? Women are working more and spending less time with their families while the opposite is true for men • Free time as a percent of total time has contracted since the 60s % Utilization of Total Time 2000 Overall % Change 1965-2000 % Change: Men vs Women 1965-2000 % % % Women % Men Source: University of Maryland, Scientific Research on the Internet, Base=168 hours per week

15. Did The Internet Change Behavior? A less active, more passive American emerged along with the Internet as dramatic declines are to be seen in arts attendance and leisure pursuits from pre-web days to the present • An overall metric of adult arts attendance declined 18% from 1982 to 2008 from 39% to 33% • - Exercise and Volunteering are the only categories that show increases in participation Total % Change 1982-2008 Participation In Leisure Activities % of Adults, 2008** Declined Increased

16. Foraging, Exploration And Engagement As hives grow or food sources decline, bees will migrate to new locations • New hive location is a kind of exploratory market lottery distinguished by a collaborative and decentralized process leveraging bees unique ability to find nectar How Do Bees Do It? Is There A Human Analogue? • Bees alternate between random foraging and new hive spotting • Once a bee finds a good possibility, they return to the hive and perform a "waggle" dance indicating the precise direction and distance of the new location • Other bees watch the dance and elect to propagate that source • At the risk of anthropomorphizing distinctly nonhuman behavior, the analogy might be to long-run, aggregate human behavior • Human creativity is the wild card in the survival (or not) of the species Source: Thomas Seeley, The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies, 1996

17. Modeling Fads And Explosive Self-Generating Demand Predictive modeling of behaviors from phenomena such as the explosive flow of water out of a breached dam to search activity for the term “Justin Bieber” • An inverse t-distribution fits both phenomena (ex Bieber’s burstiness) Water Flowing From A Breached Dam vs Justin Bieber Keyword Search “Justin Bieber” Keyword Source: Google Trends, Marcch 17, 2014 “Justin Bieber” as a keyword exploded in the 12 months from May 2009 to May 2010 but did not keep pace with the growth in overall Google search activity after. GT output is normalized to mask the real, underlying raw numbers. This process creates a relative, dimensionless, opaque, ipsative metric that ranges between 0 and 100 where “0” does not mean “no activity” but activity below an unknown, inconsistent and arbitrary level that GT does not report and “100” refers to the maximum level of keyword activity for the time window after normalization. The results can and do change constantly for any number of reasons. All of this makes firm interpretation of any GT chart impossible. While one can assume that a rising trend shows increase, a down trend does not imply decline. What one can infer from a down trend is that keyword use has not grown at the same rate as the denominator (all Google search activity). The burstiness is driven by discrete events with wide media coverage in his celebrity/notoriety.

18. Financial Bubbles: Tulip Mania In Renaissance Holland A price bubble occurred during the 17th c Dutch Renaissance or “Golden Age” involving speculation in tulip bulbs • Accounts vary but one sale of 40 bulbs was recorded at 100,000 florins • To put that in mid-17th c Dutch perspective, a ton of butter cost around 100 florins, laborers might earn 150 florins a year and "eight fat swine" cost 240 florins • One story has it that someone cut up a rare tulip bulb, not his own, like a shallot for his breakfast, leaving its real owner apoplectic with rage Dutch Tulip Bulb Prices 1634-1637 Source: http://en.wikipedia.org/wiki/Tulip_mania

19. Financial Bubbles And Shiller’s Irrational Exuberance The past three decades of financial history contain several examples of the destruction in wealth that can be wreaked when asset price bubbles burst • Nobel Laureate Robert Shiller was an early whistle-blower regarding the potential ravages of the Dot Com Bubble as well as the subsequent real estate bubble leading into the Downturn • His book may have “created” the Dot Com Bubble recession with its nearly wholesale adoption by then Federal Reserve chairman, Alan Greenspan, pulling the rate levers The S&P 500 vs the Consumer Price Index Annualized, 1871-2013 Bubbles? CPI, 1983=100 S&P 500 S&P CPI 2013 Year Source: Robert Shiller, Irrational Exuberance, 2001,2006, http://aida.econ.yale.edu/~shiller/data.htm

20. Extreme Value Models Used To Control Flood Risk Much of Holland is below sea level and survives only due to an extensive battery of sea dikes • In the early 50s, the worst sea surge in their history far exceeded then current flood controls, killing thousands • Using a 400+ year record of maximum annual storm surge height, Dutch mathematicians estimated the minimum required sea wall that would protect them against a 1 in 10,000 year event…the levees were then rebuilt to that specification • With global warming, the Dutch are again rebuilding…this time for a 1 in 100,000 year event Landscape Sculpture Commemorating the “Little Dutch Boy” In Madurodam, The Netherlands Source: http://news.nationalgeographic.com/news/2001/08/0829_wiredutch.html D van Dantzig, Economic Decision Problems for Flood Prevention, 1956 Paul Embrechts, Claudia Kluppelberg, Modelling Extremal Events: for Insurance and Finance (Stochastic Modelling and Applied Probability), 2012 Mary Mapes Dodge, Hans Brinker and the Silver Skates, 1865

21. The Importance And Relevance Of Fashion Fashion is one of the world's most important creative industries • It is the major output of a global business with annual U.S. sales of more than $200 billion—larger than those of books, movies, and music combined • Fashion has provided economic thought with canonical examples of consumption, conformity, diffusion, networks, trends and fads • Social thinkers have long treated fashion as a window into social class, change and culture • Cultural theorists have focused on fashion to reflect on its symbolic meaning and social ideals • It is a greenhouse for the analysis of trends and fads Source: Hemphill and Suk, The Law, Culture and Economics of Fashion, 2009

22. Fragmented Market Arenas And Proliferating, Rapidly-Cycling Products Drive Need For Massive-Scale Monitoring And Analysis Visionaries Originate While Markets Imitate, Diffuse and Drive Execution Innovation Pipeline Sales Curves and the Product Life Cycle “Blooming, Buzzing Confusion” in Idea Flows Decisions Analogous to Bees and New Hive Location Evidence-Based Decision-Making Accelerating Sales Decelerating Sales 3 Years of Sales Decline Go – No Go Maturity Production of the Portfolio Decline Long-Tailed Product? Scan, Monitor, Originate, Imitate Performance Visionaries Place Bets Launch Growth Introduction Time 0 Visionaries Sources Tools Tools Sources Welles P K Dick Bell Wintour Jobs Davos Meeker Artists Academics Social Media Patents Hiring Trends Film, Books, Art, etc. VC Investments Demographics (Youth and Agelessness) Tech Conferences Blogs R&D-Tacit Knowledge Text and Image Mining Prediction Markets Continuous Tracking Competitive Info from Comparison Engines Sales Data Marketing Spend Competitive Info from Comparison Engines Market Mix Modeling Data Mining Predictive Modeling Network and Diffusion Models Recommender Systems Machine Learning Test, Learn and Refine FEEDBACK/PROPAGATE

23. Decoding Trends And Fads: Artists As Cultural Antenna Trends and fads are driven by evanescent, black box creative ferment and idea flows that are difficult to capture, quantify and predict Cascades in Fashion Industry Trends Art, Innovation, Imitation Commoditization Monetization Trend Setting Trend Is Set Diffuse: RTW or Pret-a-Porter Emerge: Haute Couture Cascade: Couture Consolidate: Mass Luxe Premiere Vision Summarizes the Latest Ideas Networks Diffuse the Latest Ideas Retail Buyer Purchases Shape Consumer Choice Fashion Week Presents the Latest Designs Haute Couture as the Avant-Garde How decode emergence as well as importance, promise or pretense? Paris’ fabric and textile show is a first look at latest trends based on fabric purchases Social media WOM plays a tacit role in diffusion of the latest designs Buyer purchases play a huge role in shaping what consumers see Runways reflect a surprising degree of consistency Fast Fashion Skips Over Haute Phase Fast Fashion, e.g., Zara, H&M *Word-of-mouth and Ready-to-Wear Source: Teri Agins, Personal communication, The End of Fashion, 2000 David Wolfe, Doneger Group, Personal Communication, 2014

24. Identifying Fads And Trends With Machine Learning Algorithms The evolution of Chinese fashion street styles based on image mining of thousands of pictures taken in Shanghai and Beijing in the last five years suggests: • Tastes may have shifted away from impersonal, conspicuous status statements using “logo” brands such as LVMH to more personal, “niche” brands • Slowing economic growth as well as a crackdown on corruption and pirating are also factors  Tracking Handbags With LVMH Logo in China 2008-2013 Source: http://www.jingdaily.com/from-social-status-to-self-expression-the-rapid-evolution-of-chinas-street-style/42059/?utm_source=twitterfeed&utm_medium=twitter# Svante Jerling, Personal communication, P1.cn, 2014

25. Leveraging Social Media To Predict Emergent Phenomena Open Source Indicators (OSIs) such as text and image mining of social media have seen wide use in the prediction of emergent social phenomena • These include tracking social mobilization and civil unrest, epidemic forecasting, real-time prediction of stock market moves, rate of picture postings on Flickr during Hurricane Sandy that correlated with the storm’s barometric pressure, networks of A-listers and their entourage • - After Currid-Halkett, fashion is part of the celebrity network and could be decoded as such The Fame Game: Celebrity Networks Source: IARPA program on OSIs, enter these search terms into a Google Scholar search window: D12PC00337 OR D12PC00285 OR D12PC00347 Elizabeth Currid-Halkett, Starstruck: The Business of Celebrity, 2012, she posits five tiers in the celebrity system: first, celebrities and aspirants, second, PR reps, agents and handlers working directly for the first tier, third, the supporting machinery of lawyers, chauffeurs, bodyguards, couriers and attendants, fourth, “preppers,” e.g., stylists, beauty salons and fifth, media

26. The Arbitrage Of Ignorance Rediscovering the value inherent in ignorance, uncertainty, diffidence, cultivation of doubt, error and insecurity as modes of learning, motivation and discovery • Sherlock is dead! Long live Sherlock! • It isn’t possible to keep up with everything. Who was the last intellectual that could? • Hype, hubris and disinformation in massive quantities of information: our modern Babbits • Approximation versus false precision • What does it mean to “optimize” inaccurate and incomplete data? • Can some grad student in decision theoretics develop a “Law of Bad Data?” • In the midst of a paradigm shift • Disruptions are everywhere • Anomalies in Porterian assumptions of the sustainability of growth We’re All Drinking From Fire Hoses Now Source: Stuart Firestein, Ignorance: How it drives Science , 2012 Rita McGrath, The End of Competitive Advantage, 2012

27. Appendix

28. Johari’s Window • Developed in the 50s as a framework visualizing the structure of interpersonal knowledge • Still sees wide use in corporate training events • Adapting and extending the original, symmetric boxes to the wider arena of what is known vs not known, a potentially more representative and asymmetric framework emerges Johari’s Window Enhanced Johari’s Window Knowledge Ignorance Known to Self Not Known to Self Competitive Arena Vulnerability, Blind Spot Known to Others Open Arena, Mano a Mano, Trench Warfare Vulnerability, Blind Spot Not Known to Others Hic Leonem, Uncertainty, Black Swans, Exploration, Serendipity, Greatest Potential, Generally Not Quantifiable But Not Unknowable Advantage, Private, Tacit, Façade, Insight Hidden, Façade, Private or Tacit Knowledge, Advantage Unknown to Self or Others, Area with Greatest Potential Façade/Insight Source: JosephLuft and Harrington Ingham, The Johari window, a graphic model of interpersonal awareness, 1955 Lowell Bryan, McKinsey Director, “~80% of a corporation’s knowledge assets are tacit,” 2004 Associate Lunch talk