Memory & Emotions in Data Patterns Can You

Memory & Emotions in Data Patterns Can You

Memory & Emotions in Data Patterns Can You Hear the Shape of a Drum? Patrick A. McNutt, FRSA Visiting Fellow, Manchester Business School, UK & Smurfit Business School, Dublin, Ireland. March 2018 Follow @tuncnunc Note: Work in Progress Slides 1-25 Omega Circles & Meso-Data Can You Hear the Shape of a Drum?

Patrick A. McNutt, FRSA Visiting Fellow, Manchester Business School, UK & Smurfit Business School, Dublin, Ireland. March & April 2018 Follow @tuncnunc Note: Work in Progress Slides 25-35 Modules available 1. Strategy & Competition at Manchester Business School introduces online transaction costs and non-cooperative game theory 2. Business Economics at Smurfit Business School introduces a winning unbeatable strategy set

3. Masterclass on Cognitive Business Strategy introduces thinking about thinking mistake-proofing strategy Introducing meso-data Small data meso-data.. Large Data. Theessenceofmeso-dataisthatyouthinkyouhavememorybutithasyou

2017 Presentations: Fundamental equation: Memory + Emotions = Meso-Data Algorithms track and capture our behavioural patterns 2012: Quidco used GPS to inform you of discounts in nearby stores IndoorAtlas in 2018 goes beyond individual choice in the store. Data with Memory and Emotions goes beyond individual choice Algorithms become sufficiently intelligent because we outsource memory and because we betray our emotions to smart devices. Patterns & Predictions GeoMagnetic

Technology IPS & Innovation BIN price < END price What MesoData Can Do. Geo-tagged: Re-Allocate Resources Anarchy v Altruism

Algorithmic Pricing Hypothesis Data patterns mimic behaviour but meso-data patterns create a manifold Sufficiently Intelligent Algorithms (SIALs) rely on decoding our data patterns can SIALs mimic human behaviour? YES: only if they pass the Turing Test {The Imitation Game} do SIALS create a manifold? YES: only if they seed a random event from a smaller unpredictable pattern {The Daily Routine}

Mimic Pepper the Robot filters habits & routines as cumulatively unfolding processesso as to influence demand (smart strategy) Example: Pepper and banking Manifold Embeds mutual interdependence between data and individual into action-reaction chain of events, and as you get closer to the transaction, in a moment in time, SIAL resembles an individual. philosophically: SIALas something representing something abstract (J.L.Austin) Example: shopping onlineAction plus time to repeat the pattern MUTUAL INTERDEPENDENCE IN A GAME ONSUMER V SIAL LOOK AT YOURSELF IN THE MIRROR AND YOU RECOGNISE YOURSELF. YOU BECOME SELF-AWARE. YOU HAVE A CONSCIOUS, A MIND OF YOUR OWN.

With data-driven strategy, we have a creative type, SIALs, influencing the onsumer via friends or 3rd party machine learning to arrive at the sustainable outcome. SIALs that nudge behaviour towards a predictable [future sustainable] outcome generate a loss in onsumer sovereignty and infiltrate the level of consciousness of the onsumers taking decisions. The Law of Change Inversion Loss aversion refers to a rational individuals preference in avoiding losses to acquiring gains. Would you rather receive a 5 discount or avoid a 5 surcharge?

Change inversion refers to a rational individuals preference to embrace tangible gains (of robotic and machine learning) while conceding intangible losses (of data memory and sovereignty) Monetization of FB, WhatsApp, Instagram, LinkedIn Tradable assets Hypothesis: With all the information available it makes who you are & what you do a tradable asset Online rational onsumer trades and exchanges personal data and personal search patterns at zero transactional cost. The SIAL acquires the tradable asset at zero cost (no

exchange value) but with a NPV that is infinite. Example: On average: FB makes $3 in EU and $13 in US per month from onsumer data patterns / Are You Ready to Betray Your Emotions? Preparing for the Future (via Experience & Belief) Consider your response to a few simple behavioral experiments. First Example SCENARIO: As predicted you are in your favourite Costa

having a coffee. You sense you are being watched. You notice Mr G wearing the new google digital glasses. He is looking at you and a red dot is on. How do you react? A. You think: I should go to the bathroom and freshen up my look B. You approach Mr G and introduce yourself. C. You think: There ought to be a law against it. D. You share your experience on social media. Second example SCENARIO: After listening to a game theorist, you begin to

realise that your favourite loyalty card provider has been capturing your daily routine and buying habits and trading your data with third parties. How do you react? A: You are amazed at the advances in technology and think about your data as a tradable asset. B: You are interested and take a course in data analysis and gaming. C: You are nervous about the privacy issues and become more circumspect about your habits and patterns D. You share your experience on social media Third example SCENARIO: Preparing for the BBQ, you are having a glass of

wine in your garden and you hear a buzzing noise. Oh no, wasps! But soon you realise that the wealthy reclusive neighbour is operating her small drone. How do you react? A: You are amazed at the continued advance in technology but relieved it was not wasps. B: You wave at the drone look up there and continue drinking your wine C: You are annoyed so you decide to invite the neighbour to your BBQ. D. You share your experience on social media Scorecard:

Preparing for the Future (AdaptedfromStanleyBing) If you ticked all As then you are in the future already but it is not what it used to be! If you ticked a combination of As and Bs then you are ready for the future. If you ticked all Cs then you are not ready for the future. Scorecard

Betrayal of Linked Memory If you ticked all Ds then you are beyond individual choice creating the meso-data manifold under which 1. SIAL copy and paste your memory 2. You have betrayed your emotions Two Examples: (a) Bidding Against Yourself With Online Shopping BIN pricing < END pricing (b) Linked memory The Daily Routine BIN price < END price

Bidding Against Yourself! Online prices do not include latent transaction costs (TC) Example: imputed TC calculated at hourly wage x no of hours searching = minimum value of tradable asset. Onsumers cannot interact with each other online at point of transaction. So there is experimentation to find a better price and this leads to change in behavior at the margin This allows SIAL to experiment with a new BIN price converging to the END price and firms online prices move along the MR line not the demand line. This increases the elasticity of demand online with MR pricing. Telco can nudge you from a 4GB to 5GB plan at marginal price of (additional) 5 per month. Value of Tradable asset at 5GB > MC = 0

Algorithmic pricing: BIN price < END price Game SIAL v Onsumer 1. If N players in non-zero sum game Elasticity falls and prices rise 2. If N+1 players in zero-sum game Elasticity increases and net revenues rise Behavioural Signs and Symbols Aumann & Sorins bounded recall - if your repeat your behaviour many times then SIAL attaches a small probability Maximin & Regret (last seat left or 3 other people are looking at this hotel room rate package) =>. Online rational onsumer pays a higher price at the end of an online shopping transaction

than the opening bid price Check with your own experiences: (i) Airline ticket (ii) Hotel room FOMO 1. Rational onsumers interpret missing information in the worst possible way. 2. Rational onsumers acquire a game DNA as they bid against themselves ala fictional story of Ralphs Pretty Good Grocery Law of One Price Violated BIN price < END price Large data provide patterns Small data sets host the signs and signals that can be nudged towards pattern recognition Small data

meso-data.. Large Data The Daily Routine The Hedgehog (large data patterns) v The Fox (small data patterns) Algorithms become sufficiently intelligent because we outsource memory and because we betray our emotions to smart devices by linked memories.

Example: Send TXT Just met Jane, going to Sbux instead or Tweet the message or Instagram the event. Coffee stop 820am Texting, FB and Googling the weekend plans. Latte to go NFC payment at 825am walk to office At office Log in PC

845 NFC at coffee shop 825 Smartphone Wake Up 7am Leave the house 725am Transit Smartcard at 745am Check text messages Social media

At desk: 845am Log On PC, search web. & texting 925 Receive text message confirm lunch 1230 for lunch, CC payment 13.45 1730 Log off PC; Returning home, smart card 1814; send text; at home by 1855 surf the net, final

texts, web browsing. Texting, surfing lights out by 23.55 Proustian moment in time Large data provide patterns Small data sets like The Daily Routine host the signs and signals, the symbols and surprises that link outsourced memory and allow the SIAL to nudge behaviour towards pattern recognition Small data meso-data.. Large Data

Ameso-data set would contain seeds (signs, symbols, signals, surprises) that can be used to code behaviour and decode data patterns The patterns become predictable with meso-data. seed = a chance meeting with Jane linked with Starbucks or IndoorAtlas signalling (explain to audience) Signs & Symbols Signals & Surprises Meso-Data Seeds

At a Proustian moment in time 1. Would you prefer to eat in a virtual restaurant with no kitchen or in a restaurant with a kitchen? 2. Would you prefer a virtual surgeon or a consultant to operate on you? 3. Would you give your private personal data to a stranger passing by on the street? 4. Would you prefer a robot or a pilot to fly the EK A380? ..THE MESO-DATA MANIFOLD Beyond individual sovereignty at a moment in time 1. As your dinner is served you are informed that it was prepared in a virtual

restaurant off premises. 2. At you arrive in the operating theatre, the anaesthetist informs you that a virtual surgeon will operate on you. 3. Your private personal data is encrypted by a stranger passing by on the street. 4. As you take your seat for the long-haul flight you are reliably informed that a robot will fly the EK A380. For business strategy exploit co-existence of online and offline consumer behaviour Co-existence facilitates SIALS as creative rather than as destructive Business should focus on meso-data (i) to extract hidden information & hidden action; (ii) to identify frozen markets; (iii) to define the temporal distance, moment in time before a decision (to buy) is made temporal distance is moment between action at time period t and

consequences at time period t+1 Dear Onsumer Your patterns are g8t. I am always with you of late. That's why we, Will always be, Digital Serf & Master.. Yours 466453.....Al. Gorithm Seek Not Thyself Outside Thyself Ne te quae siveris extra Ralph Emerson

Thank you for listening Habit is a great deadner Samuel Beckett Waiting for Godot Act II Future Research A methodology for meso-data v deep learning. Mathematics of both omega circles & manifold in pattern recognition.

If G1 no nudge and G2 with nudge, then G1 and G2 are equivalent if we can obtain by reflection one or more sub-games. The mirror test: has SIAL purchased what you would have independently selected? At a moment in time is behaviour symmetric? Meso-data as a manifold nplayersinanonzero-sumgame

n+1playersinazero-sumgame Is the meso-data set reachable? Avoid mistake Define object permanence Two forces at work: moving away v reacting Moving away affects the speed of observer behaviour, so reacting is a secondary effect Is moving away (from BIN) equivalent to reacting to SIAL? Are you moving away from BIN or reacting to END Is moving away equivalent to reacting? Mimic or manifold patterns?

Meso-Data Manifold Loops underlying the data manifold are discussed in an original article from University of Xian: https:// rans/tk/2013/02/ttk2013 020337.html The paper discovers features in the data that

fall on the loopy manifold and this is not dissimilar in representation to our idea of meso-data with omega circles. Omega Circles A = {BIN, END} C = {BUY, Mr Al, EXIT} What if: (C, A) = CA? What if: (ex-post) behaviour in smaller circles creates a

pattern that reaches into or reflects the (ex-ante) behaviour in circumcircles? Ask: Decoding patterns from small meso-data to big machine data. As the END prices reaches BIN preferably diverging away from BIN, Mr AL introduces a rationing rule by assigning excess supply in the form of a Dutch auction. So the onsumer buys a concert ticket but not at the preferred seat location in

the arena. Meso-Data With Emotions the linked memories You txt or tweet that you achieved a personal health goal on your FitBit Deep learning attempts to mimic the activity in the brain as an Euclidean action-reaction sequence. 1. Philosophical: Someone that could be someone else SIAL = You, the onsumer SIAL has become You as a person with a viewpoint that you share on social media 2. Mathematical: Mimic or manifold patterns? Meso-data with linked memories attempts to understand the action-reaction order of the brain as a n-sphere space. BEYOND INDIVIDUAL CHOICE: COUNTER-ARGUMENT

INDIVIDUAL CHOICE & CREATIVITY Steve Jobs: Why join the navy when you can be a pirate? Is that sustainable?

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