Chapter 12. Visions and Challenges – Priorities for Professionals

Underspecified Model Mistakes (UMMs)

Perhaps the most common mistakes in the mental models of foresight practitioners is that they are too simple where it counts. They are missing certain key facts, relationships, and drivers that affect the system they are attempting to forecast. What they are missing may constrain their imagination/possibility space, or it may expand it. Let’s explain this claim.

All good foresighters think in systems. They build mental models of relevant causal relationships, stakeholders, and resources, and look for data on trends, cycles, and patterns in system behavior. But it’s easy for these models, even formal ones with lots of funding behind them, to be underspecified (missing factors, incomplete). They can also be impressively complex, quantitative and equation-driven, but still missing the proper weighting and subtlety among the subset of factors that will dominate the system’s behavior.

If a foresighter’s model is complex but still missing or improperly weighting the dominant factors, that is an Underspecified Model Mistake (UMM). This is a more complicated variant of the MEE. To remember this mistake, think: “UMM… you’re still missing something important from your model, future thinker…” Then try to find out what’s missing and bring it into the model.

For a classic example, think of the famous World3 model, a quantitative systems model of global change built by a well-meaning team of environmental scientists led by Donella Meadows and published in Limits to Growth (1972). This team of futurists used their model to predict mass famines by the end of the century. Their model, though it carefully extrapolated many trends, and so was not in any way guilty of MEE, still neglected to include any factors representing the deflationary, efficiency-gaining, and capacity-building effects of technological innovation itself. Because the factors they missed turned out to be accelerating global developmental trends, it made their model rapidly more incorrect over time, and Limits became a very public UMM.

This is not to say the Sustainability-Limits school of foresight has been wrong any more often than its counterpart, the Globalization-Growth school. For examples of how the Globalization-Growth school has been dangerously wrong by minimizing forecasts of ecosystem damage, see The Bet: Paul Ehrlich, Julian Simon, and Our Gamble Over Earth’s Future (2012). Both sides are equally guilty of UMMs. We must always remember, when forecasting complex systems, how easy it is for any model to be wrong — and being too simple, or improperly weighted, is a very common way to be wrong.

One way to reduce UMMs is to have more broad conceptual and technical knowledge prior to getting involved in foresight. Another way is to use the power of the crowd, to have a cognitively diverse set of colleagues and the urge to survey them with enough depth to help you understand the breadth of the mental or formal model you need to build. Having the statistical knowledge to arrange all that survey or Delphi or expert opinion is another great way to reduce bias and oversimplification.

Most important however is to keep an open mind, and be willing to see subtleties and complexities that you previously missed. For example, consider the important question of whether Earth’s resource needs and environmental problems are likely to get worse or better over the next generation. As is described in The Bet (2014), the sustainability foresighter and biologist Paul Ehrlich made a famous bet with the innovation foresighter and economist Julian Simon (see his excellent, The State of Humanity (1996)) that the price of a small collection of raw materials (copper, chromium, nickel, tin, and tungsten) would be more expensive a generation hence. Ehrlich lost that bet, as he greatly underestimated the variables of scientific advance, human ingenuity, process efficiency, market incentives, and resource and technology substitution (the ability to switch from higher priced to lower priced alternative inputs in manufacturing processes). All of this was valuable learning from a failed forecast. We live on an incredibly resource-rich planet, and a little ingenuity and competitive incentive have continually resolved our periodic resource crises. In those years at least, innovation and automation were far more deflationary than increasing regulations, and environmental remediation was inflationary.

Ehrlich then shrewdly offered a second bet that predicted various environmental indicators would be worse in coming years. Simon didn’t take that bet. He was foresighted not to do so, as those particular indicators (pollution, species loss, global warming, and twelve other trends) all did continue to get worse. Ehrlich had learned to focus on the areas where he could win. His mental models had improved. Environmental problems have turned out to be much more problematic long-term issues than our temporary resource scarcities (including energy, water, and other resources).

Continual learning, model making, prediction, and review of those predictions are at the heart of foresight process, and these efforts constantly lead us to better models. Today for example, the Environmental Kuznets curve model (Chapter 4) tells us environmental quality commonly gets worse in early stages of economic development, when we value jobs and innovation over environmental health, then later gets better when social wealth reaches a certain per capita threshold, and sustainability politics and initiatives gain traction and we decide to clean up our messes. But we’ve also learned this particular model doesn’t hold in situations where wealth and power are concentrated in the hands of pro-growth oligarchs, as in Russia and until very recently, China. This tells us that average per capita wealth isn’t the best way to predict environmental quality. Median citizen wealth and other measures of economic democracy are a more predictable (universal) way to predict the relationship between social wealth and local environmental quality, across cultures.

At present, perhaps the most important system affecting global environmental quality over the next few generations is climate change. Here again, even with all the uncertainties, step by step, our data, experiments, models, strategy, and policies continually improve. It’s obvious this is a key problem. Generating the foresight to address it in the most cost-effective ways, including an accelerated transition to solar and electric power, is the main issue at hand.

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Table of Contents


Chapter 2. Personal Foresight – Becoming an Effective Self-Leader

Chapter 2: Personal Foresight

Becoming an Effective Self-Leader

Chapter 4. Models – Foundations for Organizational Foresight

Chapter 4: Models

Foundations for Organizational Foresight

Chapter 7. Acceleration – Guiding Our Extraordinary Future

Chapter 7: Acceleration

Guiding Our Extraordinary Future (In Process)

II. Global Progress: 5 Goals, 10 Values, Many Trends

Innovation: Our Abundant Future
Intelligence: Our Augmented Future
Interdependence: Our Civil Future
Immunity: Our Protected Future
Sustainability: Our Rebalanced Future

III. Universal Accelerating Change

Great Race to Inner Space: Our Surprising Future
Entropy&Information: We’re Running Down & Up
The Puzzle of Meaning: We Have No Einstein Yet
Trees, Funnels & Landscapes: Intro to Evo Devo
Big Picture Change: Five Scales of Accelerating ED
Transcension Hypothesis: Where Acceleratn Ends?
IDABDAK: Social Response to Accel & Developmnt
We’re On a Runaway Train: Being Accelaware

IV. Evo Devo and Exponential Foresight

Seeing It All: Accel., Diverg, Adapt, Convrg, Decel.
Natural (I4S) Innovation: The Evolutionary Drive
Natural (I4S) Intelligence: The Human-AI Partnership
Natural (I4S) Morality: Why Empathy and Ethics Rule
Natural (I4S) Security: Strength from Disruption
Natural (I4S) Sustainability: The Developmental Drive
S-Curves: Managing the Four Constituencies
Pain to Gain: Traversing the Three Kuznets Phases
Hype to Reality: Beyond Hype Cycles to Reality Checks
Exponentials Database: Measuring Accelerations
TINA Trends: Societal Evolutionary Development
Managing Change: STEEPCOP Events, Probs, Ideas
A Great Shift: A Survival to a Sentient Economy

V. Evo Devo and Exponential Activism

Building Protopias: Five Goals of Social Progress
Normative Foresight: Ten Values of Society
Top & STEEPCOP Acceleratns: Positive & Negative
Dystopias, Risks, and Failure States
Three Levels of Activism: People, Tech & Universe
A Great Opportunity: Exponential Empowerment


Chapter 8. Your Digital Self – The Human Face of the Coming Singularity

Chapter 8: Your Digital Self

The Human Face of the Coming Singularity (In Process)

I. Your Personal AI (PAI): Your Digital Self

Digital Society: Data, Mediation, and Agents
Personal AIs: Advancing the Five Goals
PAI Innovation: Abundance and Diversity
PAI Intelligence: Bio-Inspired AI
PAI Morality: Selection and Groupnets
PAI Security: Safe Learning Agents
PAI Sustainability: Science and Balance
The Human Face of the Coming Singularity

II. PAI Protopias & Dystopias in 8 Domains

1. Personal Agents: News, Ent., Education
2. Social Agents: Relat. and Social Justice
3. Political Agents :  Activism & Represent.
4. Economic Agents:  Retail, Finance, Entrep
5. Builder Agents :  Work, Innov. & Science
6. Environ. Agents : Pop. and Sustainability
7. Health Agents :  Health, Wellness, Death
8. Security Agents :  Def., Crime, Corrections

III. PAI Activism & Exponential Empowerment

Next Government: PAIs, Groupnets, Democ.
Next Economy: Creat. Destr. & Basic Income
Next Society: PAI Ent., Mortality & Uploading
What Will Your PAI Contribution Be?

Chapter 10. Startup Ideas – Great Product & Service Challenges for Entrepreneurs

Chapter 10: Startup Ideas

Great Product and Service Challenges for Entrepreneurs (In Process)

I. 4U’s Idea Hub: Building Better Futures

Air Deliveries and Air Taxis: Finally Solving Urban Gridlock
Ballistic Shields and Gun Control: Protecting Us All from Lone Shooters
Bioinspiration Wiki: Biomimetics and Bio-Inspired Design
Brain Preservation Services: Memory and Mortality Redefined
Carcams: Document Thieves, Bad Driving, and Bad Behavior
Competition in Govt Services: Less Corruption, More Innovation
Computer Adaptive Education (CAE): Better Learning and Training
Conversational Deep Learning Devsuites: Millions of AI Coders
Digital Tables: Telepresence, Games, Entertainment & Education
Dynaships: Sustainable Low-Speed Cargo Shipping
Electromagnetic Suspension: Nausea-Free Working & Reading in Cars
Epigenetic Health Tests: Cellular Aging, Bad Diet, Body Abuse Feedback
Fireline Explosives and Ember Drones: Next-Gen Fire Control
Global English: Empowering the Next Generation of Global Youth
Greenbots: Drone Seeders and Robotic Waterers for Mass Regreening
High-Density Housing and Zoning: Making Our Cities Affordable Again
Highway Enclosures and Trail Networks: Green and Quiet Urban Space
Inflatable Packaging: Faster and Greener Shipping and Returns
Internet of Families: Connecting People Over Things
Kidcams: Next-Gen Security for Child Safety and Empowerment
Kidpods: Indoor & Outdoor Parent-Assistive Toyboxes
Microdesalination: Democratizing Sustainable Fresh Water Production
Noise Monitors: Documenting and Reducing Noise Pollution
Oceanside Baths: Sustainable Year Round Beach Enjoyment
Open Blood Scanners: DIY Citizen Health Care Sensor Tech
Open Streaming Radio: User-Centered Audio Creation and Rating
Open Streaming Video: User-Centered Video Creation and Rating
Open Values Filters: Social Rankers, Arg. Mappers, and Consensus Finders
Personal AIs: Your Private Advisor, Activist, and Interface to the World
Pet Empowerment: Next-Gen Rights and Abilities for Our Domestic Animals
Safe Closets: Fire-, Earthquake-, and Intruder-Proof Retreat Spaces
Safe Cars: Reducing Our Insane 1.3M Annual Auto Deaths Today
Safe Motorcycles: Lane Splitting in Gridlock Without Risk of Death
Shared Value Insurance: User-Centered Risk Reduction Services
Sleeperbuses and Microhotels: Demonetized Intercity Travel
Space-Based Solar Power: Stratellite Powering and Weather Management
Stratellites: Next-Gen Urban Broadband, Transparency, and Security
Touch DNA: Next-Gen Home Security and Crime Deterrence
View Towers: Improving Urban Walkability, Inspiration, and Community

Chapter 11. Evo Devo Foresight – Unpredictable and Predictable Futures

Chapter 11: Evo Devo Foresight

Unpredictable and Predictable Futures

Appendix 1. Peer Advice – Building a Successful Foresight Practice