Nowadays we became quite good at analysing (big) data. However, more often than not, it becomes ‘big data – small insight’. If we only use data, we miss the intuition of our best experts. We say that our most valuable asset is the knowledge of our employees but we only use this knowledge if it fits into neat tables or colourful charts.
There is a way to make use of these intuitions and still keep the neatness and tidiness, only we need to use qualitative rather than quantitative tools. With causal maps, we can represent the structure of issues, how they are linked, and what the effect of changing something would be. With knowledge-based expert systems, we can drill down a particular issue, such as a decision, to evaluate decision alternatives. In both of these, we have powerful (qualitative) analysis that can help us figure out priorities or the rules of decisions. Furthermore, both tools also offer the possibility of presenting transparent arguments.
The approaches to Artificial Intelligence (AI) in the last century may be labelled as (a) trying to understand and copy (human) nature, (b) being based on heuristic considerations, (c) being formal but from the outset (provably) limited, (d) being (mere) frameworks that leave crucial aspects unspecified. This decade has spawned the first theory of AI, which (e) is principled, formal, complete, and general. This theory, called Universal AI, is about ultimate super-intelligence. It can serve as a gold standard for General AI, and implicitly proposes a formal definition of machine intelligence. After a brief review of the various approaches to (general) AI, I will give an introduction to Universal AI, concentrating on the philosophical, mathematical, and computational aspects behind it. I will also discuss various implications and future challenges.
The Epistemological Basis for Resolving the Rigor-Relevance Debate in Management Research.
Since the time of Plato and Aristotle, there has been a debate over how humans can create valid knowledge about the world in which we operate.
Plato argued that abstract models within human cognition can be considered valid even if there is no corresponding instance of the phenomena observable in the external environment.
Aristotle argued that abstract models must have a corresponding instance of the phenomena they represent that is observable within the external environment.
Subsequently, Euclid was one of the first to use the linguistic frame of math to establish a rigorous correspondence between abstract models and real world evidence in his geometric proofs.
Since then, scientific breakthroughs and knowledge have emerged from the precise and accurate representations of the external environment made possible within the rigorous linguistics of basic math and Aristotle's scientific method.
Today, management research as practiced in accredited business schools has taken sides with Plato, not Aristotle. They operate within their own closed loop of investigation and knowledge generation that is based on abstract models of a theoretic world that is disconnected from the realities of practicing managers. Academics argue that the knowledge they generate is valid because it is "rigorous". Practitioners argue that this "knowledge" is not relevant to the real world in which they operate.
Until now, no one has followed the example of Euclid and expanded the frame of math to establish a more rigorous correspondence between the abstract models and the evidence from the external environment.
Basic Social Math is a new framework that seeks to change that by reconnecting management research to the real world!
The Future of AI: Going BeyondDeep Learning, Watson, and the Semantic WebJames Hendler
These slides, based on a presentation at distinguished lecture at IBM Almaden in March, 2017 explore some of the challenges to machine learning and some recent work. It is a newer version of the slides originally presented at IJCAI 2016.
Design and Darkmatter, Connecting Storytelling with Business OutcomesTrip O'Dell
Service design is about all the invisible aspects of a brand or product experience, but designers are frequently tasked with only the superficial parts of a customer problem. How do we break out of a pixel-perfect box?
This talk focuses on the role of storytelling, and new ways to enroll stakeholders into that process as a way of helping them understand that the most important consideration in design is context, and how the most important design decisions are usually invisible and hard to detect.
The approaches to Artificial Intelligence (AI) in the last century may be labelled as (a) trying to understand and copy (human) nature, (b) being based on heuristic considerations, (c) being formal but from the outset (provably) limited, (d) being (mere) frameworks that leave crucial aspects unspecified. This decade has spawned the first theory of AI, which (e) is principled, formal, complete, and general. This theory, called Universal AI, is about ultimate super-intelligence. It can serve as a gold standard for General AI, and implicitly proposes a formal definition of machine intelligence. After a brief review of the various approaches to (general) AI, I will give an introduction to Universal AI, concentrating on the philosophical, mathematical, and computational aspects behind it. I will also discuss various implications and future challenges.
The Epistemological Basis for Resolving the Rigor-Relevance Debate in Management Research.
Since the time of Plato and Aristotle, there has been a debate over how humans can create valid knowledge about the world in which we operate.
Plato argued that abstract models within human cognition can be considered valid even if there is no corresponding instance of the phenomena observable in the external environment.
Aristotle argued that abstract models must have a corresponding instance of the phenomena they represent that is observable within the external environment.
Subsequently, Euclid was one of the first to use the linguistic frame of math to establish a rigorous correspondence between abstract models and real world evidence in his geometric proofs.
Since then, scientific breakthroughs and knowledge have emerged from the precise and accurate representations of the external environment made possible within the rigorous linguistics of basic math and Aristotle's scientific method.
Today, management research as practiced in accredited business schools has taken sides with Plato, not Aristotle. They operate within their own closed loop of investigation and knowledge generation that is based on abstract models of a theoretic world that is disconnected from the realities of practicing managers. Academics argue that the knowledge they generate is valid because it is "rigorous". Practitioners argue that this "knowledge" is not relevant to the real world in which they operate.
Until now, no one has followed the example of Euclid and expanded the frame of math to establish a more rigorous correspondence between the abstract models and the evidence from the external environment.
Basic Social Math is a new framework that seeks to change that by reconnecting management research to the real world!
The Future of AI: Going BeyondDeep Learning, Watson, and the Semantic WebJames Hendler
These slides, based on a presentation at distinguished lecture at IBM Almaden in March, 2017 explore some of the challenges to machine learning and some recent work. It is a newer version of the slides originally presented at IJCAI 2016.
Design and Darkmatter, Connecting Storytelling with Business OutcomesTrip O'Dell
Service design is about all the invisible aspects of a brand or product experience, but designers are frequently tasked with only the superficial parts of a customer problem. How do we break out of a pixel-perfect box?
This talk focuses on the role of storytelling, and new ways to enroll stakeholders into that process as a way of helping them understand that the most important consideration in design is context, and how the most important design decisions are usually invisible and hard to detect.
This is my talk delivered 06/04/2024 at the CUBE event (https://www.uni-corvinus.hu/post/landing-page/cube/?lang=en) at the Gellért Campus of the Corvinus University.
[DSC Europe 23] Danijela Horak - The Innovator’s Dilemma: to Build or Not to ...DataScienceConferenc1
In this talk, we will delve into the evolving landscape of firms with established in-house machine learning teams and explore the critical question of their future role in the ever-changing world of AI. As the remit of these teams undergoes a seismic shift, a fundamental dilemma arises: Should these teams transition into AI engineering roles, leveraging commercially available tools and adapting their strategies around them, or should they continue to pioneer innovations within their specialised domains? This dilemma is unique to organizations with existing ML capabilities, setting them apart from firms without such teams. Furthermore, the talk will address the intricacies of using retrieval-augmented generation (RAG) as a potential solution in knowledge-intensive tasks, and ask whether such a method is a "hack" or a grounded methodology that will withstand the test of time. We will also reflect on the safe and responsible development of LLM-powered tools.
Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be had at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
(Subtitle — User Experience: an Agony in Eight Fits)
Talk given by Chris Atherton at Technical Communication UK, 22nd September 2010.
The idea of this presentation was to introduce some findings from experimental psychology that might influence user experience design. Also, it was fun to see how riled up people can get about shower control design ... :)
The ability to learn things is an essential part of the developer’s toolkit, which is only getting more important as we march into the future. New technologies and new tools are released constantly. Even if you’re on a fixed tech stack on a long-running project, you need to evaluate and adapt to new versions of your tools and new software idioms as they're released.
The thing is, we’re never really taught HOW to learn things - we’re expected to just figure it out ourselves. It is my opinion that this is Really Terrible. If you share that opinion, do something about it by coming to this talk! Do you wonder about learning types? We'll cover those. Do you wonder how learning a new framework is different than learning best practices for that framework? We’ll discuss that, too! We’ll also talk about the neuroscience of learning, how your brain connects cause and effect, the tricks your memory plays on you, and more. By the end, you’ll hopefully have the tools you need to learn anything efficiently and effectively.
A discussion of the nature of AI/ML as an empirical science. Covering concepts in the field, how to position ourselves, how to plan for research, what are empirical methods in AI/ML, and how to build up a theory of AI.
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
Insider Insight: The Importance of BracketingViktor Dörfler
Talk delivered at the Online Seminar "Alternative and innovative research methods: untangling research rhetorics and publishing realities", on 29th June 2022, organised by the British Academy of Management (BAM) – Research Methodology (RM) SIG
This is my talk delivered 06/04/2024 at the CUBE event (https://www.uni-corvinus.hu/post/landing-page/cube/?lang=en) at the Gellért Campus of the Corvinus University.
[DSC Europe 23] Danijela Horak - The Innovator’s Dilemma: to Build or Not to ...DataScienceConferenc1
In this talk, we will delve into the evolving landscape of firms with established in-house machine learning teams and explore the critical question of their future role in the ever-changing world of AI. As the remit of these teams undergoes a seismic shift, a fundamental dilemma arises: Should these teams transition into AI engineering roles, leveraging commercially available tools and adapting their strategies around them, or should they continue to pioneer innovations within their specialised domains? This dilemma is unique to organizations with existing ML capabilities, setting them apart from firms without such teams. Furthermore, the talk will address the intricacies of using retrieval-augmented generation (RAG) as a potential solution in knowledge-intensive tasks, and ask whether such a method is a "hack" or a grounded methodology that will withstand the test of time. We will also reflect on the safe and responsible development of LLM-powered tools.
Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be had at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
(Subtitle — User Experience: an Agony in Eight Fits)
Talk given by Chris Atherton at Technical Communication UK, 22nd September 2010.
The idea of this presentation was to introduce some findings from experimental psychology that might influence user experience design. Also, it was fun to see how riled up people can get about shower control design ... :)
The ability to learn things is an essential part of the developer’s toolkit, which is only getting more important as we march into the future. New technologies and new tools are released constantly. Even if you’re on a fixed tech stack on a long-running project, you need to evaluate and adapt to new versions of your tools and new software idioms as they're released.
The thing is, we’re never really taught HOW to learn things - we’re expected to just figure it out ourselves. It is my opinion that this is Really Terrible. If you share that opinion, do something about it by coming to this talk! Do you wonder about learning types? We'll cover those. Do you wonder how learning a new framework is different than learning best practices for that framework? We’ll discuss that, too! We’ll also talk about the neuroscience of learning, how your brain connects cause and effect, the tricks your memory plays on you, and more. By the end, you’ll hopefully have the tools you need to learn anything efficiently and effectively.
A discussion of the nature of AI/ML as an empirical science. Covering concepts in the field, how to position ourselves, how to plan for research, what are empirical methods in AI/ML, and how to build up a theory of AI.
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
Insider Insight: The Importance of BracketingViktor Dörfler
Talk delivered at the Online Seminar "Alternative and innovative research methods: untangling research rhetorics and publishing realities", on 29th June 2022, organised by the British Academy of Management (BAM) – Research Methodology (RM) SIG
Talk delivered at the international conference ‘The Principle of Subsidiarity from a Transdisciplinary Perspective’, Budapest, Hungary, 8th November 2016
Bridge: ICT for Connecting Knowledge and KnowingViktor Dörfler
These are the slides of my keynote talk at the EcoCom 2013: 2nd Conference on the Economics of Communication, Vertretung des Landes Sachsen-Anhalt beim Bund, Berlin, Germany, 8 November, 2013
Legal Expertise vs. Competence: Knowledge Sharing Theories & PracticesViktor Dörfler
These are the slides of my 'Academic Keynote' talk delivered at the Janders Dean Legal Knowledge Management & Innovation Conference, London, 15th May 2015.
Mesterek és inasok személyes tudása: Mit tanulhatunk a nagymesteri szinttű tu...Viktor Dörfler
Polányi 'személyes tudás' elképzelésének a lényege, hogy a tudás elválaszthatatlan a 'tudó'-tól, és ennek megfelelően jó része szótlan, és minden ami szavakba önthető szintén szótlan alapokon nyugszik. A nagymesterek tudásának nagyon nagy része szótlan. Persze a szótlan tudás nem átadható tanteremben. Hogyan válhat akkor valaki nagymesterré? GaMa projektem keretében a nagymesterek észjárását tanulmányoztam, és ezért elbeszélgettem néhány nagymesterrel, köztük 17 Nobel-díjassal. A nagymesterek tudását jellemzi az intuíció, a harmóniára (szépségre) törekvés, az analogikus gondolkodás és a lényeglátás. Ilyenek most. Emellett, mindannyian átestek valamiféle mester-inas kapcsolaton, ami általában 6-10 évig tartott, attól függően, hogy honnan indultak és mennyi időt töltöttek el mással. A személyes tudáson alapuló mester-inas modell és a Nobel-díjasoknál megfigyelt gondolkodási jellemzők nem csak a tudósokra jellemzőek, hasonló dolgokat figyeltünk meg mesterszakácsok, orvosok és és stratégák esetében is.
Understanding ‘Expert’ Scientists: Implications for Management and Organizati...Viktor Dörfler
These are the slides of the presentation delivered at AoM 2014 (The 74th Annual Meeting of the Academy of Management - 4th August, 2014 - Philadelphia, PA) on a paper written with Colin Eden.
Beyond Systematic Entrepreneurship: The Role of Intuition in Experience Innov...Viktor Dörfler
These are the slides of the presentation Marc Stierand and I delivered as part of the symposium organised by Marta Sinclair at AoM 2014 (The 74th Annual Meeting of the Academy of Management - 4th August, 2014 - Philadelphia, PA).
3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
What are the main advantages of using HR recruiter services.pdfHumanResourceDimensi1
HR recruiter services offer top talents to companies according to their specific needs. They handle all recruitment tasks from job posting to onboarding and help companies concentrate on their business growth. With their expertise and years of experience, they streamline the hiring process and save time and resources for the company.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
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Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Attending a job Interview for B1 and B2 Englsih learnersErika906060
It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
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Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
1. SmArt Strategising for the Knowledge Era
Viktor Dörfler
KnowledgeBrief | Bridging Academia and Business
2. 81% of executives believe that “data should be
at the heart of all decision-making” (EY study)
“Big data can eliminate reliance on ‘gut feel’
decision-making” (EY conclusion)
3. Wicked Problem
(emotion, communication)
Tame Problem
(calculation, logic)
Wicked Mess
(metaphor, story)
Mess
(imagination, experiments)
HIGH
LOW
LOW HIGH
Behavioural complexity
Dynamiccomplexity
Experts may have plausible guesses about the future
6. it is necessary to have a master to become one
both following and abandoning the master’s way is wrong
but in this struggle the new master is forged
even the genius needs it
it is a highly asymmetric setup
several forms
1. traditional master-disciple pairs
2. wandering apprentice journeys
3. mutual apprenticing
4. hot spots: creative workshops
founded by one master
7. A theatre cannot work with all
the actors being Laurence
Oliviers – but it is noticeable if
there is none.
Photo taken by Liz Handy, 2016, Budapest
8. Knowledge is only a potentiality, like the engine of your car.
Thinking is applying knowledge, realising the potentiality, like
the driver’s skill.
9. Professor of Strategy & Management Science
An engineer with great knowledge of
psychology (and even philosophy)
Top 10 strategy scholar in the World
15,000+ Google Scholar citations
A few hundreds of consultancy projects
(using cognitive mapping)
Making Strategy with
Decision Explorer
and there are others as well
10. Professor of Decision Making
An economist with great knowledge of
technology, psychology (and even philosophy)
‘The Mintzberg of Balkan’
20+ books
A few hundreds of consultancy projects
(using knowledge-based system and coaching)
Supporting
Decision Takers
with Doctus
11.
12. Be SmArt
Choose to analyse data where you have data
Use intuition when intuition is needed
Make SmArt
Create an environment where analysts and intuitors co-exist
Develop mutual respect between intuitors and analysts
Do SmArt
Use tools that help intuition be rigorous and transparent
Support master-apprentice relationships