Interaction / Intersection of Menger Sponge and Apollonian Sphere in cosmic geometry produces pythagorean triples / triads.
Seven plus one spheres to triads of three.
Apollonian Gasket, Circle and Sphere Packing and Cosmic Geometry
Source: PRECISE CALCULATION OF HAUSDORFF DIMENSION OF APOLLONIAN GASKET
A transfer operator method is proposed to calculate ππ», the Hausdorff dimension of the Apollonian gasket. Compared with previous operator-based methods, we make two improvements in this paper. We adopt an infinite set of contractive MΓΆbius transformations (rather than a finite set of parabolic ones) to generate the Apollonian gasket. We also apply an efficient finite matrix approximation of an infinite sum of infinite-dimensional operators. By using this method, a high precision estimate of ππ» is obtained:
Source: THE FRACTAL DIMENSION OF THE APOLLONIAN SPHERE PACKING
The fractal dimension of the Apollonian sphere packing has been computed numerically up to six trusty decimal digits. Based on the 31 944 875 541 924 spheres of radius greater than 2β19 contained in the Apollonian packing of the unit sphere, we obtained an estimate of 2.4739465, where the last digit is questionable. Two fundamentally different algorithms have been employed. Outlines of both algorithms are given.
Source: THE FRACTAL DIMENSION OF THE APOLLONIAN SPHERE PACKING
Source: What Type of Apollonian Circle Packing Will Appear?
Source: Self-similar space-filling sphere packings in three and four dimensions
Source: Self-similar space-filling sphere packings in three and four dimensions
Sapta Matrikas
Brahmi
Maheswari
KumariΒ
VaishnaviΒ
Varahi
IndraniΒ
Chamunda
Source: Saptamatrikas in Kerala: Iconography and Distribution Pattern
In Varahapurana the Devi, Vaishnavi in account of the creation of the matrika, is doing asceticism on mount Mandara. At one point she losses her concentration. From her disgraced mind, several beautiful attendants were created. They later became Deviβs helpmates on the battlefield when she fights the demon. Although the Matrikas are described as lovely in this account, it is important to note that they are born when Devi losses control of her concentration. This suggests that the matrikas are essentially of uncontrolled natures. Born from lack of mental control, they lack control themselves. Varahapurana relates them to vices or inauspicious emotions; Brahmi of Mada, Maheswari of Krodha, Kumari of Moha, Vaishnavi of Lobha, Indrani of Matsarya, Varahi of Asuya and Chamunda of Paisunya.
Source: Saptamatrikas in Kerala: Iconography and Distribution Pattern
The follower of Tantrasara has an esoteric interpretation of the seven matrikas. According to them, Brahmi represents the primordial Nada, the energy in which even the first throb has not yet appeared. This is the manifest sound, the origin of all creation. It is the same substance or energy represented by the pranava. When Brahmi creates the universe, the power of Vaishnavi gives definite shape. The symmetry, beauty, organization and order in the universe are the work of Vaishnavi. Maheswari stands for the power that gives individuality to the created beings. She resides in the hearts of all and makes them play, as dolls mounted on a machine. Kumari represents the ever present force of aspiration of the evolving soul. She is βGuruguhaβ, the Guru in guha (the cave of the heart, the intellect). Varahi is the all-consuming power of assimilation and enjoyment. Because of her, all living beings get their food and enjoyments. Indrani symbolizes the terrible power that destroys all that opposes the cosmic law. Chamunda is the force of concentrated awareness, the spiritual awakening in the heart that devours that ceaseless activity of the immature mind and uplifts it to the highest level (Harshananda 1981.95-99).
Source: Saptamatrikas in Kerala: Iconography and Distribution Pattern
Source: Saptamatrikas in Kerala: Iconography and Distribution Pattern
Source: Matrikas/Siddha Pedia
Source: The Seven Ancient Mothers
The Pythagoreans considered the figure seven as the image and model of the divine order and harmony in nature. As the harmony of cosmic sound takes place on the space between the seven planets, the harmony of audible sound takes place on a smaller plane within the musical scale of the seven tones. Therefore, the syrinx of the nature god Pan consists of seven pipes, and the lyre of Apollo (the god of music) consists of seven strings. As the number seven is a union between the number three (the symbol of the divine triad) and of four (the symbol of the cosmic forces or elements), the number seven points out symbolically to the union of the divine with the universe.
Source: Internet
Source: Internet
Source: Internet
Source: Regional Variations in MΔtαΉkΔ Conventions
Source: Regional Variations in MΔtαΉkΔ Conventions
Source: Regional Variations in MΔtαΉkΔ Conventions
Pierre Auclairβ Cosmology, Universe and Relativity at Louvain (CURL), Institute of Mathematics and Physics, University of Louvain, 2 Chemin du Cyclotron, 1348 Louvain-la-Neuve, Belgium (Dated: November 15, 2022)
Estimate for the fractal dimension of the Apollonian gasket in d dimensions
R. S. Farr* Unilever R&D, Olivier van Noortlaan 120, AT3133 Vlaardingen, The Netherlands and The London Institute for Mathematical Sciences, 22 S. Audley Street, Mayfair, London, United Kingdom
E. Griffiths
297 Sandy Bay Road, Sandy Bay, Tasmania, Australia
A Study of the Sapta Matrikas’ Origins and Evolution: From the Perspectives of the Art and Literature of Western India Dating from B.C. 1400 to 500 A.D.
Rebecca Sholes 1982
Mother Goddess in Central India
Om Prakash Misra Agam Kala Prakashan, 1985
The Little Goddesses (mΔtrikΔs)
K. C. Aryan RekhΔ, 1980
The Iconography of the Saptamatrikas: Seven Hindu Goddesses of Spiritual Transformation
Katherine Anne Harper E. Mellen Press, 1989
ISBNS 9780889460614, 0889460612 OCLC ocm19921123
SaptamΔtrΜ₯kΔ Worship and Sculptures: An Iconological Interpretation of Conflicts and Resolutions in the Storied BrΔhmanical Icons
Self-similar space-filling sphere packings in three and four dimensions *
D. V. St Μager 1 , β and H. J. Herrmann 1, 2 , β 1 Computational Physics for Engineering Materials, IfB, ETH Zurich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland 2 Departamento de F ΜΔ±sica, Universidade Federal do Cear Μa, 60451-970 Fortaleza, Ceara Μ, Brazil
Scenarios: Frames of Possibilities and Plausibilities
Key Terms
Scenarios
Scenario Planning
Futures
Intuitive Logics method
Shell
GBN
Oxford Scenarios Program
Predetermined Elements
Critical Uncertainty
Weak Signals
SRI International (Stanford Research Institute)
RAND Corporation
Hudson Institute
DNI US MoD
UK MoD
Scenario Quadrant
Multiple Scenarios
Bounded Rationality
Cognitive Biases
Frames
Availability Bias
Overconfidence
Anchoring
Volatile, Uncertain, Complex, and Ambiguous (VUCA)
Key Concepts
Source: UNDP FORESIGHT: THE MANUAL Page 11
Black swans
Rare and discontinuous events that are unprecedented, unexpected and have major effects. They are often inappropriately rationalised after the fact with the benefit of hindsight, but this tendency to see coherence can obscure future threats.
Cognitive bias
A pattern of deviation in judgment that influences the way information is received, processed, retained or called. Cognitive biases influence how inferences, judgements and predictions are drawn.
Cognitive dissonance
The mental stress or discomfort one experiences when confronted with new information or views that contradicts existing values or beliefs. Because humans strive for internal consistency, individuals tend to reduce cognitive dissonance by denying or devaluing new information and views, or rationalising their own values and beliefs.
Complexity
Complex systems are non-linear and diverse networks made up of multiple interconnected elements. Cause and effect relationships within the system are not easily discernable or predictable. Historical extrapolation is futile for predicting emergence (new patterns and behaviours) in complex systems.
Cross-βcutting issues
Issues or challenges that affect more than a single interest area, institution or stakeholder, and that need to be addressed from all points of view. A Whole-of-Government or Networked approach is useful for addressing cross-cutting issues.
Design thinking
An end-user centred approach to problem-solving that places the final experience at the heart of developing solutions. Following an iterative approach, the rapid prototyping component of design thinking allows for quick adaptation in uncertain environments and continual improvement.
Experimentation and prototyping
Experimentation is a process that seeks to test and validate competing hypotheses. Prototyping refers to creating models or sketches to test ideas and spot problems. Experimentation and prototyping are effective ways to navigate and test hypotheses and ideas in complex or rapidly changing environments.
Interdependence
A relationship of mutual reliance between two or more factors within a system such that changes in one area affect the other(s).
Path dependency
Describes the inclination to stick to past practice despite the availability of newer, more efficient practices as a result of cognitive biases such as risk aversion, or concerns over sunk costs. Designing contingency plans with ample space for flexibility can reduce the constraints of path dependency.
Resilience
A systemβs ability to cope with and recover from shocks or disruptions, either by returning to the status quo or by transforming itself to adapt to the new reality. Resilient systems view change as inevitable and failure as opportunities to learn from. Social cohesion, trust in government and national pride can be indicators of resilience.
Retrospective coherence
The act of assigning coherence in hindsight in order to make sense of what is happening. Practicing retrospective coherence presents the danger of making decisions for the future based on the lessons of history that may not apply in similar situations.
Signposts
Milestone markers between a given future and the present day that aid visualisation by breaking up the path to the future into manageable blocks of time. They can help to gauge the extent to which a particular scenario has materialised, and can be events, thresholds or trends and patterns.
Systems thinking
An analytical problem solving approach that looks at a system as a whole rather than in isolation, and that considers the interactions between various elements. The big-picture overview helps decision makers see linkages across different sections within the system and can foster collaboration and shared understanding within an organisation. Systems thinking also helps policymakers identify cause-effect relationships and how they might manifest in the larger system.
Unknown unknowns
Issues and situations in organisations that have yet to surface and which are blind spots for planners who are unaware that they do not know about them.
Whole-βof-βGovernment (WG)
A βjoined-upβ or networked approach to governance that represents a shift from vertical to horizontal decision-making, and which is built on inter-agency collaboration and collective problem-solving. Whole-of-government involves a process of identifying, analysing and managing wide-ranging and cross-cutting issues.
Wicked problems
Large and intractable issues and challenges that have no immediate or obvious solutions and whose causes and influencing factors are not easily determined. Wicked problems are characterised by many agents interacting with each other in often mystifying ways, and involve multiple stakeholders operating with different perspectives and goals.
Purpose of Scenarios
Source: Does the intuitive logics method β and its recent enhancements β produce βeffectiveβ scenarios?
Van der Heijden [15] argues that there is a confusing assortment of reasons as to why one should engage in scenarios. He advocates the importance of clearly identifying the purpose of undertaking scenario work β in order to make the appropriate selection of scenario methodology. Van der Heijden argues that βpurposeβ can be divided along two dimensions; the first dimension is to establish the extent of the scenario work i.e. whether the scenario work is to be a one-off project, or part of on an on-going scenario-based planning process. The second dimension is that of the primary aim of the scenario work, this being either to raise questions, or to answer them β and thus aid decision making.
The combination of these two dimensions results in four purposes of scenario work, namely:
β’ Sense-making: a one-off βexploratory question-raising scenario projectβ; β’ Developing strategy: a one-off βdecision-making scenario projectβ; β’ Anticipation: an βon-going exploratory scenario activityβ; and β’ Action-based organizational learning: an βon-going decision-making activityβ.
Van der Heijden continues by suggesting that these four purposes represent a hierarchy of interconnected aims serving the ultimate goal of βstrategic successβ in which organizational learning is the βoverarching broad organisational skillβ achieved when the scenario work is an on-going decision-making activity [15, page 162].
Benefits of Scenarios
Source: Does the intuitive logics method β and its recent enhancements β produce βeffectiveβ scenarios?
The (mainly practitioner-based) literature contains many testimonials as to the use and organizational benefits of scenarios, which we group under the following headings:
3.1. Enhanced perception
Scenario techniques reportedly enhance corporate and individual perception as they provide a framework for managers to understand and evaluate trends and events as they happen [16], and managers involved in scenario exercises supposedly become better observers of the business environment, more attuned to discerning changes [17]. Porter [18] suggests that scenarios help managers to make explicit their implicit assumptions about the future, and to think beyond the confines of conventional wisdom. This, combined with the fact that scenarios often challenge conventional wisdom and complacency by shifting the βperceptual anchorsβ from which people view the future, reduces the likelihood of managers and organizations making big mistakes in the future and/or of being caught unaware [19,20].
3.2. A structure for dealing with uncertainty
Scenarios provide a structure for thinking aimed at attacking complexity by allowing managers to deal more openly and explicitly with acknowledged uncertainty [21,16], to arrive at a deeper understanding of what is significant, and to identify what needs to be dealt with – and what is transient and can be ignored [11,22]. Bunn and Salo [23] suggest that, by emphasizing that there are a range of possible futures rather than a single-point future, scenarios reduce the bias for underestimating uncertainties. This is echoed by Docherty and McKiernan [24] who state that βthe greatest contribution of scenario planning lies in its active engagement of actors in its process and its power to enable them to think about complexity and uncertainty in external contexts, and then how they might shape the external environment to their own strategic endsβ (p. 10).
3.3. Integration of corporate planning functions
Scenario techniques provide a good middle ground between relying on informal and intuitive techniques, and being bound by the methodological constraints of more formal, quantitative techniques. As a result, a greater variety of information and wider company participation can be incorporated into the forecasting and planning process when scenario planning is used [16]. Other authors [25,26] add that scenarios are also able to combine topical intelligence and structure seemingly disparate environmental factors into a useful framework for decision making in a way that no other planning models can.
3.4. A communications tool
According to Allen [21], the communications qualities of scenarios are overwhelming as they provide a rational and non-threatening framework for discussion, even with those outside of the organization [27]. Durance and Godet [28] state that scenarios are also an effective means of rallying employees and communicating strategy across the organization. Bezhold [29] suggests that the scenarios can be used as a marketing and educational campaign throughout the organization. Ringland [25] adds that, by sharing its scenarios with the outside world, an organization can provide the context for dialog with its stakeholders β enabling it to influence its external environment. An added benefit [30] is that the collegiality which usually emerges in a scenario planning exercise does not evaporate once the scenario exercise is complete. Van der Heijden [15,31] reports that Royal Dutch Shell’s scenarios emerged as a powerful management tool by which senior management was able to influence decision-making at all levels throughout the organization, without becoming directly involved in the process or minutiae of the subsequent, scenario-based, evaluation of decisions. This was achieved by making the scenarios the context for key strategic decisions β thus uniting the geographically dispersed, disparate, and decentralized business units in developing a common strategy [28].
3.5. Organizational learning
Although scenario planning was initially understood as a tool for βthinking the unthinkableβ [32], a body of literature has subsequently developed around the value of scenarios in terms of individual and organizational learning [11]. This is because scenario exercises ostensibly provide a politically-safe team learning environment and a rich learning process that stimulates creativity [11,15,33β37]. As models of future business environments, scenarios provide a vehicle for pseudo-experimentation in terms of formulating strategic options and then examining the consequences of these options in a range of future environments [15,30,31,38]. By having to articulate their assumptions in a scenario exercise, managers can identify inconsistencies in their own thinking and that of their colleagues in a non-threatening environment [25,37]. At the same time, the necessity in scenario work to undertake detailed analysis of environmental driving forces and their causal relationships, forces individuals to examine their perceptions, stretch their mental models and to develop a shared view of uncertainty [15,31]. All of the foregoing leads to an increased confidence in decision-making [16] and moves the organization towards becoming, what has been termed, a βlearning organizationβ [15].
Based upon our consideration of the above purposes and benefits of the use of scenario methods, we distil from the literature three main objectives of the application of scenario approaches, as follows:
1) Enhancing understanding: of the causal processes, connections and logical sequences underlying events β thus uncovering how a future state of the world may unfold;
2) Challenging conventional thinking: to reframe perceptions and change the mindsets of those within organizations; and
3) Improving decision making: to inform strategy development.
Support for this conclusion also comes from the work of Varum and Melo who, after undertaking a comprehensive bibliometric analysis of the literature on scenario planning, argued that there is a consensus in the literature on three benefits of using scenarios, namely an βimprovement of the learning process, improvement of the decision-making process, and identification of new issues and problemsβ [2, page.362].
Our three objectives are interlinked in that: firstly, understanding the connections, causal processes and logical sequences which determine how events may unfold to create different futures, will challenge conventional thinking and will also prove of benefit in improving organizational decision making and strategy; secondly, challenging conventional thinking, reframing perceptions and changing mind-sets should result in collective organizational learning; and, thirdly, collective organization learning should enhance organizational decision making and strategy β which in turn should enhance collective organizational learning.
Types of Scenarios
Source: An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?
Predictive
Trend
Whatif
Explorative
Framed
Unframed
Normative
Preserving
Transformational
Types of Uncertainty
Source: Nine lives of uncertainty in decision-making: strategies for dealing with uncertainty in environmental governance
Source: A Scenario-based Approach to Strategic Planning β Integrating Planning and Process Perspective of Strategy
Multiple Frames of Changes in Contextual Environment on the Transcational Environment
Source: Using Scenario Planning to Reshape Strategy
Source: Multiple Scenario Development: Its Conceptual and Behavioral Foundation
Source: Multiple Scenario Development: Its Conceptual and Behavioral Foundation
Source: Multiple Scenario Development: Its Conceptual and Behavioral Foundation
Institutions and Methods of Scenario Planning
Shell/GBN Intuitive Logics Method
Oxford Scenario Planning Approach
La Prospective / M Godet
Rand Corporation
SRI International
GBN/Monitor/Deloitte/Center for Long View/Market Sensing and Scenario Planning
Source: Plausibility and probability in scenario planning
Source: The current state of scenario development: an overview of techniques
Research Journals and Authors on Scenario Planning
Source: SCENARIOS IN BUSINESS AND MANAGEMENT: THE CURRENT STOCK AND RESEARCH OPPORTUNITIES
Source: SCENARIOS IN BUSINESS AND MANAGEMENT: THE CURRENT STOCK AND RESEARCH OPPORTUNITIES
Source: SCENARIOS IN BUSINESS AND MANAGEMENT: THE CURRENT STOCK AND RESEARCH OPPORTUNITIES
Source: SCENARIOS IN BUSINESS AND MANAGEMENT: THE CURRENT STOCK AND RESEARCH OPPORTUNITIES
More is not always better: Coping with ambiguity in natural resources management
M. Brugnach a, b, *, A. Dewulf c, H.J. Henriksen d, P. van der Keur d
a Faculty of Engineering Technology, University of Twente, The Netherlands b Institute for Environmental Systems Research, University of OsnabrΓΌck, Germany c Public Administration and Policy Group, Wageningen University, The Netherlands d Geological Survey of Denmark and Greenland, Denmark
Journal of Environmental Management xxx (2010) 1e7
The Environmental Goffman: Toward an Environmental Sociology of Everyday Life
BRADLEY H. BREWSTER
Gaylord Nelson Institute of Environmental Studies, University of Wisconsin, Madison, Wisconsin, USA
MICHAEL MAYERFELD BELL
Department of Community & Environmental Sociology, University of Wisconsin, Madison, Wisconsin, USA
Society and Natural Resources, 23:45β57 Copyright # 2010 Taylor & Francis Group, LLC ISSN: 0894-1920 print=1521-0723 online DOI: 10.1080/08941920802653505
An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?
H.R. Maier a, *, J.H.A. Guillaume b, H. van Delden a, c, G.A. Riddell a, M. Haasnoot d, e, J.H. Kwakkel e
a School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide SA 5005, Australia b Water & Development Research Group (WDRG), Aalto University, Tietotie 1E, Espoo 02150, Finland c Research Institute for Knowledge Systems, Hertogsingel 11B, 6211 NC Maastricht, The Netherlands d Deltares, Fresh Water Department, Delft, The Netherlands
e Delft University of Technology, Faculty of Technology Policy and Management, Delft, The Netherlands
Identification and classification of uncertainties in the application of environmental models
J.J. Warmink a, *, J.A.E.B. Janssen a, b, M.J. Booij a, M.S. Krol a
a Department of Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands b Waterboard Rijn and IJssel, P.O. Box 148, 7000 AC Doetinchem, the Netherlands
Risk frames and multiple ways of knowing: Coping with ambiguity in oil spill risk governance in the Norwegian Barents Sea
Tuuli Parviainena,β, Annukka Lehikoinenb, Sakari Kuikkaa, P.ivi Haapasaaria
a University of Helsinki, Finland, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-
00014 Helsinki Finland
b University of Helsinki, Finland, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, Kotka Maritime Research Center,
Thereβs no sense in denying it: interpreting weak signals into useful decision making takes time and focus. These three stages can help you see the peripheryβand act on itβmuch more clearly.
Rather than trying to predict the future, organizations need to strengthen their abilities to cope with uncertainty. A new approach to scenario planning can help companies reframe their long-term strategies by developing several plausible scenarios.
Chapter 10 The Learning Dimension of Adaptive Capacity: Untangling the Multi-level Connections
Alan Diduck
Adaptive Capacity and Environmental Governance
Derek Armitage l Ryan Plummer Editors
Using Trends and Scenarios as Tools for Strategy Development
Shaping the Future of Your Enterprise
by Ulf Pillkahn
ISBN 978-3-89578-304-3
Risk frames and multiple ways of knowing: Coping with ambiguity in oil spill risk governance in the Norwegian Barents Sea
Tuuli Parviainena,β, Annukka Lehikoinenb, Sakari Kuikkaa, P.ivi Haapasaaria
a University of Helsinki, Finland, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014 Helsinki Finland
b University of Helsinki, Finland, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, Kotka Maritime Research Center, Keskuskatu 10, FI-48100 Kotka, Finland
Environmental Science and Policy 98 (2019) 95β111
How Issues Get Framed and Reframed When Different Communities Meet: A Multi-level Analysis of a Collaborative Soil Conservation Initiative in the Ecuadorian Andes
ART DEWULF1*, MARC CRAPS1 and GERD DERCON2
1Centre for Organizational and Personnel Psychology, Katholieke Universiteit, Leuven, Belgium
2International Institute of Tropical Agriculture (IITA), Ibidan, Nigeria
Journal of Community & Applied Social Psychology
J. Community Appl. Soc. Psychol., 14: 177β192 (2004)
Defining Uncertainty
A Conceptual Basis for Uncertainty Management in Model-Based Decision Support
W.E. WALKER1, P. HARREMOβ¬EES2, J. ROTMANS3, J.P. VAN DER SLUIJS5, M.B.A. VAN ASSELT4, P. JANSSEN6 AND M.P. KRAYER VON KRAUSS2
1Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands,
2Environment & Resources DTU, Technical University of Denmark, Denmark,
3International Centre for Integrative Studies (ICIS), Maastricht University, The Netherlands,
4Faculty of Arts and Culture, Maastricht University, The Netherlands,
5Copernicus Institute for Sustainable Development and Innovations, Utrecht University, The Netherlands, and
6Netherlands Environmental Assessment Agency, National Institute of Public Health and the Environment (RIVM), The Netherlands
Integrated Assessment
2003, Vol. 00, No. 0, pp. 000β000
1389-5176/03/0000-000
A Structured Approach to Strategic Decisions
Reducing errors in judgment requires a disciplined process.
Foundations of Scenario Planning: The Story of Pierre Wack
By Thomas J Chermack
2017
ROLE OF SCENARIO PLANNING AND PROBABILITIES IN ECONOMIC DECISION PROBLEMS β LITERATURE REVIEW AND NEW CONCLUSIONS
Helena GASPARS-WIELOCH *
Department of Operations Research, Faculty of Informatics and Electronic Economy, Poznan University of Economics and Business, Al. Niepodleglosci 10, 61-875, PoznaΕ, Poland
1Harvard Forest, Harvard University, Petersham, Massachusetts, 2Harvard Forest, Harvard University and Science Policy Exchange, Petersham, Massachusetts, 3Michigan State University, Department of Forestry, East Lansing, Michigan, 4Climate and Global Warming Solutions, Executive Office of Energy and Environmental Affairs, Boston, Massachusetts, 5Consensus Building Institute, Cambridge, Massachusetts
Global Trends, Scenarios, and Futures: For Foresight and Strategic Management
There are a few Institutions which do general long term trends and scenario analysis.
US DNI NIC
Atlantic Council
UK MOD
Shell International
HP
EY
WEF
There are many institutions both public and private which do issue or industry specific scenarios, trends, and futures analysis.
Water
Food
Energy
Climate Change
Globalization
Urbanization
Governance
Security
Technology
Demographic
Industry specific
Nationalism
Protectionism
Healthcare
Human Development
Why do Scenarios?
Its a way to internalize an organization’s external environment. By doing so, managers and leaders can future-proof their strategy.
Image Source: If only we knew. With scenario planning, we do. Here’s how to prepare better for the next crisis
Image Source: Global Business Network
Image Source: WHY THE SOCIAL SECTOR NEEDS SCENARIO PLANNING NOW
Image Source: Megatrends 2020 and beyond /EY Mega Trends
The article below was published in MIT Sloan Review.
The World in 2030: Nine Megatrends to Watch
Where will we be in 2030?
I donβt usually play the futurist game β Iβm more of a βpresentist,β looking at the data we have right now on fast-moving megatrends that shape the world today. But a client asked me to paint a picture of what the big trends tell us about 2030. And Iβd say we do have some strong indications of where we could be in 11 years.
The directions we go and choices we make will have enormous impacts on our lives, careers, businesses, and the world. Here are my predictions of how nine important trends will evolve by 2030 β listed in order roughly from nearly certain to very likely to hard to say.
Nine Global Trends on the Horizon
Demographics: There will be about 1 billion more of us, and we will live longer. The world should reach 8.5 billion people by 2030, up from 7.3 billion in 2015. The fastest growing demographic will be the elderly, with the population of people over 65 years old at 1 billion by 2030. Most of those new billion will be in the middle class economically, as the percentage of citizens in dire poverty continues to drop (a rare sustainability win). Even as the middle swells, however, the percentage of all new wealth accruing to the very top of the pyramid will continue to be a major, and destabilizing, issue. That said, the other megatrends, especially climate change, could slow or change the outcomes here.
Urbanization: Two-thirds of us will live in cities. The urbanization of our populations will increase, creating more megacities as well as small- and medium-size metropolises. Countervailing forces will include a rising cost of living in the most desirable cities. The effects will include the need for more big buildings with better management technologies (big data and AI that makes buildings much more efficient), and we will need more food moved in from where we grow it to where we eat it β or rapidly expand urban agriculture.
Transparency: Our world will become even more open β and less private.Itβs hard to imagine that the trend to track everything will be going anywhere but in one direction: a radically more open world. The amount of information collected on every person, product, and organization will grow exponentially, and the pressure to share that information β with customers and consumers in particular β will expand. The tools to analyze information will be well-developed and will make some decision-making easier; for instance, it will be easier to choose products with the lowest carbon footprints, highest wages for employees, and fewest toxic ingredients. But all these tools will shatter privacy in the process.
Climate Crisis: The climate will continue to change quickly and feature regular, extreme weather everywhere. Yes, thereβs still uncertainty about how everything will play out exactly, but not about whether the climate is changing dramatically and dangerously. Significant inertia in both atmospheric and economic/human systems allows for a more confident prediction of what will happen in just 11 years. The Intergovernmental Panel on Climate Change (IPCC) has made clear how critical it is to radically alter the path of carbon emissions to hold the world to 1.5 degrees Celsius of warming. But thatβs not likely to happen with current levels of commitment in global governments: The important Paris climate accord of 2015, in theory, agrees to hold warming to 2 degrees Celsius. But in practice, what countries have committed to so far will only hold us to no more than 3 degrees of warming. By 2030, we are very likely to already be at or approaching the 1.5 mark.
The results of climate change will be unrelenting. Many highly populated coastal areas will be in consistent trouble, as sea levels rise. The natural world will be much less rich, with drastic to catastrophic declines in populations of many species and major to total losses of ecosystems like coral. Droughts and floods will stress global breadbasket regions and shift where we grow major crops. The Arctic will be ice-free in the summer (this will allow ships to move freely in this region, which is technically good for shorter supply chains but a Pyrrhic victory at best). Between seas, heat, and shifts in water availability, mass migrations will likely have begun. By 2030, we will have much better clarity on how bad the coming decades after that point will be. We will know whether the melting of the major ice sheets will be literally inundating most coastal cities, and if weβre truly approaching an βUninhabitable Earthβ in our lifetimes.
Resource Pressures: We will be forced to more aggressively confront resource constraints. To keep volumes of major commodities (such as metals) in line with economic growth, we will need to more quickly embrace circular models: sourcing much less from virgin materials, using recycled content and remanufactured products, and generally rethinking the material economy. Water will be a stressed resource, and it seems likely that many cities will be constantly in a state of water shortage. We will need more investment in water tech and desalination to help.
Clean Tech: The transformation of our grid, our roadways, and our buildings to zero-carbon technology will be surprisingly far along. Hereβs some good news: Due to continuing drops in the cost of clean technologies, renewable energy is dramatically on the rise, making up more than half the global new power capacity every year since 2015. By 2030, effectively no new additions of generating capacity will come from fossil-fuel-based technologies.Electric vehicles will be a large part of the transportation equation: While estimates about the share of EVs on the road by 2030 range from the teens to nearly 100% (assuming early retirement of internal combustion engines), nearly all sales of new vehicles will be EVs. This will be driven by dramatic reductions in the cost of batteries and strict legislation banning fossil-fuel engines. We will also see an explosion of data-driven technologies that make buildings, the grid, roadways, and water systems substantially more efficient.
Technology Shifts: The internet of things will have won the day, and every new device will be connected. Proponents of the βsingularityβ have long projected that by around 2030, affordable AI will achieve human levels of intelligence. AI and machine learning will plan much of our lives and make us more efficient, well beyond choosing driving routes to optimize traffic. Technology will manipulate us even more than it does today β Russian interference in U.S. elections may look quaint. AI will create some new kinds of jobs but will also nearly eliminate entire segments of work, from truck and taxi drivers to some high-skill jobs such as paralegals and engineers.
Global Policy: Thereβs an open question about how weβll get important things done. Iβm thinking specifically about whether global governments and institutions will be working in sync to aggressively fight climate change and resource pressures, and tackle vast inequality and poverty β or whether it will be every region and ethnic group for itself. Predicting politics is nearly impossible, and itβs hard to imagine how global policy action on climate and other megatrends will play out. The Paris Agreement was a monumental start, but countries, most notably the U.S., have lately retreated from global cooperation in general. Trade wars and tariffs are all the rage in 2019. It seems likely that, even more than today, it will be up to business to play a major role in driving sustainability.
Populism: The rise of nationalism and radicalism may increase β¦ or it wonβt. Even less certain than policy is the support, or lack thereof, of the mass of people for different philosophies of governing. In recent years, populists have been elected or consolidated power in countries as varied as the U.S., Brazil, and Hungary. And yet, in recent weeks, citizens in countries like Turkey, Algeria, and Sudan have pushed back on autocracy. Will that trend continue?
How Should Business Prepare?
Laying out strategies for companies to navigate this likely future world is a book-length conversation. But let me suggest a few themes of action to consider:
Engage everyone in the sphere of the business world on climate. A dangerously changing climate is the biggest threat humanity has ever faced. But itβs not all set in stone β¦ yet. Companies have an economic incentive and moral responsibility to work hard to reduce the damage as much as possible. Engage employees (stamp out climate denial), talk to consumers and customers about climate issues through your products, and change internal rules on corporate finance to make investment decisions with flexible hurdle rates that favor pro-climate spending. Most importantly, use influence and lobbying power to demand, at all levels of government, an escalating public price on carbon β and publicly admonish industry lobbying groups that donβt.
Consider the human aspect of business more. As new technologies sweep through society and business, the change will be jarring. Those changes and pressures are part of why people are turning to populist leaders who promise solutions. Business leaders should think through what these big shifts mean for the people that make up our companies, value chains, and communities.
Embrace transparency. To be blunt, you donβt have a choice. Each successive generation will expect more openness from the companies they buy from and work for.
Listen to the next generation. By 2030, the leading edge of millennials will be nearing 50, and they and Gen Z will make up the vast majority of the workforce. Listen to them now about their priorities and values.
Predicting the future means projecting forward from whatβs already happening, while throwing in expected inertia in human and natural systems. It can give us an impressionistic picture of the world of the future. Our choices matter a great deal, as individuals and through our organizations and institutions. Business, in particular, will play a large role in where the world goes. Employees, customers, and even investors increasingly demand that the role of business be a positive one.
Look, we could all just wait and see where these historic waves take us. But I prefer that we all work proactively to ensure that a better, thriving future is the one we choose.
Since 2018, HP has started publishing a report titled Megatrends. Β In this report global macro changes are presented.
Macro Forces
Socio Economic
Demographic
Technological
There is so much change happening around us today. How we live, work and play in both developed and developing countries will look very different in the next ten to thirty years. Underlying this change are key trends, many having disruptive implications for people and businesses, including HP. It is vitally important that we do our best to discern what the future may look like, developing our own point-of-view on potential future states and their implications, in terms of threats and opportunities. Understanding Megatrends gives us the ability to frame and make more informed, strategic long-term decisions and avoid surprises we could have anticipated and even exploited.
Megatrends are those global socio-economic, demographic and technological forces that we think will have a sustained, transformative impact on the world in the years ahead. On businesses, societies, economies, cultures and our personal lives. Our objective with Megatrends is to directionally point to where the world is going, the potential future states that may result, and then to frame implications in terms of threats and opportunities for Customers and HP. We use Megatrends work to help inform our long-term strategic planning thinking and to support Customer and HP thought leadership and communications with employees, customers, partners and market influencers around technology Vision for the future.
We have identified four major Megatrends and a wide range of underlying sub-trends. We cover each Megatrend and an illustrative set of the sub-trends in this paper.
Rapid Urbanization
Changing Demographics
Hyper Globalization
Accelerated Innovation
Technological Changes
As we move farther into the 21st Century, we see new technologies converging that, together, will generate the same kind of growth. In the process, they will change how the entire world makes, sells and lives.
– Β BioConvergence: The science of Biology in combination with compute is accelerating. Over the next twoΒ decades, the way we make things will change radically. We are seeing the radical acceleration of biology as AI changes how analysis is done and robotics/sensors increase the speed and precision of testing.
– Β Beyond Human: New sensors and interfaces change the nature of human computer interaction. Over the next decade, the way we do work will be reinvented as computation integrates itself seamlessly into the biological processes of our bodies and cognitive processes of our minds. We are already starting to see the early glimmer of this in wearable sensors, in pace makers, and in voice assistants.
– Frictionless Business: Technology is changing the size and speed at which transaction and coordination are possible in business processes and markets. In the next ten years, the way business is transacted and coordinated will likely change tremendously. Business processes are being reinvented by concurrent innovation in AI, IoT, Blockchain and applications that automate and create smart- streamlined activities managed by software instead of humans. Markets are also being reinvented when these technologies are used in a distributed (vs. centralized) fashion along with innovative business models.
This yearβs report [whatβs new]
The 2019 HP Megatrends Report explores global, regional and metro income trends, urbanizationβs impact on these trends, the resulting rise of new metro-based economic powerhouses, and the role of automation and education in meeting labor market challenges driving changing demographics and growing economies. Additional research explores how increasing incomes are putting a strain on our energy resources and what role technologies such as 3D printing, Software 2.0 and Edge Computing could play in helping to drive to greater efficiencies benefiting customers, industries, and the planet.
Stewart Brand is one of my Hero. Β I admire his work and have deep respect for him.
Check out his books:
The Media Lab
Whole Earth Discipline
How Buildings Learn
Clock of the Long Now
Stewart Brand and his associates are building a 10000 yr clock in west Texas. Β Project is funded by Jeff Bezos. Β Danny Hillis is one of the designer of the clock. Β Prototypes of clock are in display in Museums in UK and here in USA.
Stewart Brand work is about promoting long term thinking—Very Long Term Thinking. Β What we call long term in our day to day conversation is just Now a days in context of Long Term Thinking being promoted by Stewart Brand.
Long Now thinking would be considered equal to thinking associated with climate cycles time scales such as Milankovitch Cycles.
You can check out current status of the clock and other projects of the Long Now Foundation at its website.
How should Humans live and behave in context of Long Term Thinking?
How should the information, knowledge, culture, artifacts, languages, species, ecology be preserved?
What kind of world are we creating for future generations?
What should be preserved?
How should it be preserved?
How would people after 10000 years extract information contained in preserved objects?
These are big and deep questions? Β We need philosophers like Stewart Brand to guide us.
I am ready to learn from him and other visionaries like him.
Time Horizon – Short to Very Long Term
Now – 3 Days
Now a days – 30 years
Long Now – 20000 years
Types of Cycles – slow moving to fast moving
Nature
Culture
Governance
Infrastructure
Commerce
Fashion
Image of Long Now Time
Key Sources of Researches:
Whole Earth comes into focus
To understand how our planet uses energy, we must integrate genetic data from microbial studies with satellite views of our planet.
FromΒ CAUSES AND CONSEQUENCES OF KONDRATIEV’S LONG-WAVE CYCLE
Economic Cycles
Economists recognize four major cycles, or regular fluctuations, in the economy as follows:
(1) Kitchinβs short-wave cycle of average duration 3-5 years, discovered in 1930;
(2) Juglarβs cycle of average duration 7-11 years, discovered in 1862;
(3) Kuznetsβ medium-wave cycle of average duration 15-25 years, discovered in 1923;
(4) Kondratievβs long-wave cycle of average duration 45-60 years, discovered in 1922.
J. Schumpeter, who was born in Austria and came to the United States where he also served as President of the American Economic Society in the 1950’s, was an outstanding student of economic cycles. He believed that the various cycles are inter-dependent, in contrast with the view of others such as Forrester, who believed that the cycles act independently of one another. Schumpeter baptized three of the four cycles by naming them after their discoverers. The exception was Kuznetsβ cycle, which he did not recognize.
FromΒ Long-Wave Economic Cycles: The Contributions of Kondratieff, Kuznets, Schumpeter, Kalecki, Goodwin, Kaldor, and Minsky
Several different theories of the long wave exist. These include Kondratieff’s theory of cycles in production and relative prices; Kuznets’ theory of cycles arising from infrastructure investments; Schumpeter’s theory of cycles due to waves of technological innovation; KeynesβKaldorβKalecki demand and investment oriented theories of cycles; Goodwin’s theory of cyclical growth based on employment and wage share dynamics; and Minsky’s financial instability hypothesis whereby capitalist economies show a genetic propensity to boom-bust cycles.
Writing in the early 1920s Nikolai Kondratieff advanced the idea of the probable existence of long wave cycles in capitalist economies lasting roughly between 48 and 60 years. Within that, there is a period of accumulation of material wealth as productive forces move to a newer, higher, level of development. But at a certain point there commences a decline in economic activity, only to re-start growing again later (Kondratieff 2004 [1922]). This mechanism has been dubbed, in economic literature, as Kondratieff cycles. It should be noted that prior to Kondratieff, some empirical efforts on systematizing the cyclicality of economic crises was carried out by van Gelderen (1913), Buniatian (1915), and de Wolff (1924), which Kondratieff admits to in his publications (see end note in Kondratieff 1935). Though Kondratieff’s ideas were not well accepted by the official Soviet economics he insisted on his main argument and in short time followed up with more rigorous publications. Only few English language translations were available at the time (most notably, Kondratieff 1935). Nevertheless, the potency of his ideas was recognized quickly entering the work of subsequent economists (e.g., Schumpeter 2007 [1934]; Kuznets 1971; Rostow 1975; and others) as we review in the next section.
Simon Kuznets received the Nobel Prize in Economics in 1971 for his empirical analysis of economic growth, where he identified a new era of βmodern economic growthβ. Like Kondratieff, Kuznets relied on empirical analysis and statistical data in his pioneering research. Absorbing his findings on historical development of the industrial nations with initially abstract categories of the national income decomposition, Kuznets developed a concept of long swings, though disputed, now referred to as Kuznets cycles or Kuznets swings (e.g., Korotayev and Tsirel 2010). The Kuznets swings’ period is ranged between 15β25 years and initially connected by Kuznets with demographic cycles. In that analysis, the economist observed and quantified the cyclicality of production and prices, linking with immigrant population flows and construction cycles. Researchers have attempted to connect these cycles with investments in fixed capital or infrastructure investments (see Ibid. for literature review).Β
As mentioned, the work of Kondratieff and Kuznets fostered a systematic approach to modern understanding of long economic swings. Numerous authors have further proposed not only different mechanisms underlying cycles but also cycles on different time scales. An early theory of cycles was put forward by Robert Owen in 1817, who stressed wealth inequality and poverty, originating in industrialization, yielding under-consumption as a reason for economic crises. Sismondi, in the middle of the 19th century took a similar view and developed a theory of periodic crises due to under-consumption. This led to the discussion of the βgeneral glutβ theory of the 19th century, which Marx and other classical economists also extensively contributed to. More specifically, a mechanism of cycles on a shorter times scale, of 8β10 years duration, was developed by Juglar (Juglar cycles), resulting, as he saw it, from the waves in fixed investment. Later, Kitchin, in the 1920s, introduced an inventory cycle of 3β5 years. Later an important contribution was made by Schumpeter (1939), who referred to the βbunchingβ of innovations and their diffusion as a cause for long waves in economic activity. Roughly at the same time, Samuelson (1939), influenced by the Spiethof accelerator and the Keynesian multiplier principle, developed the first mathematically- oriented cycle theory using difference equations.3 Others, such as Rostow (1975), had proposed the theory of stages of growth. Simultaneous with Samuelson, Kalecki (1937) developed his theory of investment implementation cycles where he saw significant delays between investment decisions and investment implementations, formally introducing differential delay systems as tool for studying cycles. Kaldor (1940), rooted in Keynesian theory, developed his famous nonlinear investment-saving cycles, which took into account aggregate demand. Later, Goodwin (1967) proposed a model of growth cycles, which took into account classical growth theory, but was based on unemployment-wage share dynamics, since the overall growth rate, as well as productivity growth, are kept constant in the long run.Β
Next we discuss a Minsky long cycle: a financially-based approach to the long wave theory. Long cycles have historically been interpreted as an interaction of real forces with cost and prices. Kondratieff cycles emphasize secular changes in production and prices; Kuznets cycles are associated with economic development and infrastructure accumulation; Schumpeterian cycles are the result of waves of technological innovation; while Goodwin cycles are based on changes in the functional distribution of income arising from changed bargaining power conditions in a period of high growth rates and Keynesian theories express demand factors.
The work of Hyman Minsky provides an explicitly financially driven theory of business cycles. Minsky’s own writings were largely devoted to exposition of a short-run cycle and a very long-run analysis of stages of development of capitalism. The short-run analysis is illustrated in two articles (Minsky 1957, 1959) that present a financially driven model of the business cycle based on the multiplier-accelerator mechanism with floors and ceilings. A later formalization is the Delli Gatti et al.’s work (1994) in which the underlying dynamic mechanism is increasing leveraging of profit flows, which roughly captures Minsky’s (1992a) hedge-speculative-Ponzi finance transition dynamic that is at the heart of his famous financial instability hypothesis. The very long-run analysis of stages of capitalism’s development is illustrated in Minsky’s (1992b) essay on βSchumpeter and Financeβ. These stages of development perspective have been further elaborated by Whalen (1999) and Wray (2009). Recently, Palley (2010, 2011) has argued Minsky’s (1992a) financial instability hypothesis also involves a theory of long cycles. This long cycle explains why financial capitalism is prone to periodic crises and it provides a financially grounded approach to understanding long wave economics. Β A long cycles perspective provides a middle ground between short cycle analysis and stages of development analysis. Such a perspective was substantially developed by Minsky in a paper co-authored with Piero Ferri (Ferri and Minsky 1992). However, unfortunately, Minsky entirely omitted it in his essay (Minsky 1992a) summarizing his financial instability hypothesis, leaving the relation between the short and long cycle undeveloped.
Key People:
Jay Forrester
John Sterman
J. Schumpeter
Joshua Goldstein
Aleksandr V. Gevorkyan
N. Kondratiev
Kaldor
Kalecki
Hyman Minsky
Goodwin
P. Samuelson
Simon Kuznets
Juglar
Kitchin
Key Sources of Research:
The sixth Kondratieff β long waves of prosperity
A Spectral Analysis of World GDP Dynamics: Kondratieff Waves, Kuznets Swings, Juglar and Kitchin Cycles in Global Economic Development, and the 2008β2009 Economic Crisis
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Viewpoints which Matter 