Framing

Framers: Human Advantage in an Age of Technology and Turmoil 

Frames are mental models of the world that we use to understand problems, and to come up with new or refined solutions. As a tool, framing has always been with us. But as long as we were focused on traits like memory and reasoning that were more obviously essential to human cognition, framing didn’t get much attention. Now that computers have become better at some of those cognitive tasks, framing stands out as a critical function—one that matters more than ever because it can’t be handed off to the machines.

Framing means dreaming with constraints, letting your mind wander in a methodical and structured way, or wondering how old tools could be applied to new problems. FRAMERS shows how framing will not just be a way to improve how we make decisions in the era of algorithms, but will be a matter of survival for humanity in the coming age of machine prosperity.

 

A guide to working with frames

I. Harness mental models

• Framing happens all the time but can deliberately be used to improve decisions.
• Identify and inspect the assumptions in your mental models.
• Ask “why” and “how” questions: Why did you reach this conclusion? How must the world work if you anticipate this happening?
• Imagine how a wise friend, a historical hero, or a rival might frame a certain challenge.
• Ask yourself what would need to change for you to want to frame a situation differently.
• When your views clash with another’s, try to characterize the underlying way they see the world.

II. Dream with constraints

• Applying a frame is about swiftly and efficiently identifying appropriate options.
• Focus on those elements that are most easily changeable.
• Start by making minimal changes to your constraints, and gradually contemplate more elaborate modifications.
• Be careful to remain consistent by weighing whether the change contradicts any underlying assumptions or beliefs.
• Embed constraints in a physical model if it is too difficult to keep all of them in mind at once.

III. Reframe wisely

• Switching to an alternative frame lets you see the world differently, but it is risky.
• See if you already have a frame in your repertoire that will work.
• Try repurposing a frame that you can apply from a different domain.
• Invent a new frame as a last resort, since it’s the hardest option.
• Keep in mind the trade-offs between tight frames (fast but limited) and broad ones (comprehensive but time-consuming).
• Don’t reframe repeatedly, since it leads to disorientation.

IV. Conditions matter

• We can improve our framing through cognitive diversity.
• Develop a curiosity for the unfamiliar to continually challenge your worldview.
• Be willing to accept tensions among frames: they are less an indication of faulty reasoning than of the complexity of reality.
• Speak the truth, even when it is not comforting for individuals or organizations. The courage is respected by those who matter.
• Seek dissent rather than confirmation.
• When deciding in teams, have each person frame the problem independently before sharing views and deciding as a group.

V. Think beyond yourself

• The role of society is to ensure frame pluralism to produce optimal responses in times of change.
• Strive to see the colorful; don’t be color-blind. Speak openly but respectfully about differences.
• Regard societal friction as an advantage, not a drawback.
• Use education to instill respect for others’ frames.
• Promote a commingling of cultures as a way to foster imagination, innovation, and dynamism in a society.
• Reject anything that presents itself as a single frame to encompass all of reality.

AI everywhere (9)

The Feeling Economy. How Artificial Intelligence Is Creating the Era of Empathy

We have seen that artificial intelligence (AI) is in the process of ushering in a new era that will have profound implications for how humans work and live. The emerging “Feeling Economy” is one in which AI assumes many of the mechanical and thinking tasks, leaving humans to emphasize feeling. Just as many people’s lives were transformed in the 1900s by the industrial revolution and automation, people’s lives are now again being transformed.

The transformation in the last century was from physical and mechanical tasks to thinking tasks. In the twenty-first century, the transformation is from thinking tasks to feeling tasks. Artificial intelligence is developing in the order of (a) mechanical, to (b) thinking, to (c) feeling. Mechanical AI is easiest, and is mostly accomplished already. Thinking intelligence is next easiest, and is an area of strong current innovation. Feeling intelligence is the hardest for AI, and competence in that is probably decades away.

The main thesis of this book is: As AI assumes more thinking tasks, humans will emphasize feeling. Our research, both theoretical and empirical, provides initial support for this thesis.

 

AI everywhere (8)

Doing AI: A Business-Centric Examination of AI Culture, Goals, and Values

A common external goal for artificial intelligence is cognitive plausibility. That is, in order to qualify as “real,” a solution must solve intelligence in much the way humans are intelligent. When it is discovered that a solution is not anthropomorphic enough, many dismiss the accomplishment. In other words, how insiders solve puzzles is as important as how they define puzzles.

 Is AI About Simulating the Brain?The answer is sometimes, but not always. However, many insiders believe that if a solution looks like the brain, then it might actually act like the brain. When a solution doesn’t act like the brain, insiders conclude that the solution teaches them nothing about the brain or intelligence. Simulating the brain effectively requires insiders to reverse engineer it. The so-called inverse problem is the process of calculating from a set of observations the causal factors that produced them. In other words: starting with the answer and working backward to the question. However, reverse engineering the brain and studying intelligence will always be an exercise that is more complex, with much longer payoffs, than identifying and solving real-world problems.

 

Rethinking attitudes towards success and failure

 The Tyranny of Merit. What's Become of the Common Good?

The economist's view (4)

 21st Century Economics_ Economic Ideas You Should Read and Remember

The peril of media capture

 Media Capture. HOW MONEY, DIGITAL PLATFORMS, AND GOVERNMENTS CONTROL THE NEWS

Platforms, a business model (5)

 The Platform Delusion. WHO WINS AND WHO LOSES IN THE AGE OF TECH TITANS

Behavioral Insights (2)

 Behavioral Insights

Key message,

Over the past ten years, there has been increasing interest in the idea of behavioral insights. However, at some point, this interest may start to end and attention may go elsewhere. Other ideas may come along, with their own compelling claims. People may stop deciding to “bring in” behavioral experts, as Rory was for autonomous vehicles. If this is true, then the immediate priority should be to integrate behavioral insights into the standard way that policy is made or organizations are run. That would mean that the practices are resilient to people no longer asking for “behavioral” solutions.

In fact, you could argue that this is the ultimate goal of the behavioral insights approach. The idea of a “behavioral” solution or approach should become meaningless, since the principles will have become absorbed into standard ways of working. Since, as we said in chapter 1, most policies or services concern behavior, then this is just about improving the way that central function is performed—it is not some kind of optional extra. Rather than talking about “behavioral public policy,” we would just refer to public policy (or corporate strategy) done better. In a sense, stopping talking about behavioral insights may actually be a sign that its true promise has been fulfilled. Until then, there is more to do.

The idea of a “behavioral” solution or approach should become meaningless, since the principles will have become absorbed into standard ways of working.

Risk-sharing agreements for drugs (3)

 Characterization of the Pharmaceutical Risk‑Sharing Arrangement Process in Catalonia

Table 1

Uncertainty type, scope, and considered variables for drug assessment

Uncertainty type	Uncertainty scope	Considered variables
Clinical	Efficacy, effectiveness, and safety	Time frame
		Clinical trial phase
		Patient characteristics
		Primary endpoint
		Surrogate endpoints
		Active comparator
		Sensitivity analysis
		Statistical analysis
		Patient subgroup analyses
		Time frames for treatment follow-up
Financial	BI and CE	Indication extension and concretion
		Treatment regimen
		Potentially replaceable treatments
		Net financial impact of treatment inclusion/replacement
		Potential use extensions
		Other modifications in use of resources linked to new treatment
		Availability of CE or CU studies

Adapted from [14]

Risk-sharing agreements for drugs (2)

 Implementing Risk-Sharing Arrangements for Innovative Medicines: The Experience in Catalonia

Risk-sharing agreements for drugs

 Performance-based managed entry agreements for new medicines in OECD countries and EU member states: How they work and possible improvements going forward

Sick individuals and sick populations

Sick individuals and sick populations

 Individus malalts i poblacions malaltes

Una crítica de l’estratègia poblacional de Geoffrey Rose en la medicina preventiva

Rose's Strategy of Preventive Medicine

És part integral de la bona medicina preguntar no tan sols “Quin és el diagnòstic i quin és el tractament?” sinó també “Per què ha succeït això?” i “Es podia haver previngut?”. Aquesta manera de pensar és la que orienta pràcticament tota la recerca clínica i de laboratori sobre les causes i els mecanismes de la malaltia.

Geoffrey Rose 

The economist's view(3)

 Cogs and Monsters. What Economics Is, and What It Should Be

The economist's view (2)

 The New Economics.  A Manifesto