Summary:
- We live at the border of order and disorder.
- Sufficient order to exploit and sufficient disorder to explore.
- Stability for survival and change and novelty for adaptation.
- Maintaining any level of order requires constant energy.
- We exploit order.
- Certainty, predictability.
- Maximizing efficiency.
- Survival.
- Fixed behavior, biases are productive.
- We explore disorder.
- Uncertainty, unpredictability.
- Reducing uncertainty.
- Adaptation.
- Adjust behavior, actions, biases.
- As we explore and exploit, progress emerges.
- Progress emerges in the form of increasing complexity.
- Continued progress requires some level of stability and cohesion.
- Otherwise we are on shaky ground.
- Threats to continued progress:
- Increasing disorder: excess novelty, environmental change.
- Response: keep it simple, re-think habits, adjust beliefs, social learning.
- No adaptation: overly rigid beliefs.
- Response: temporarily relax beliefs.
- Increasing complexity.
- Environments are becoming more unpredictable (more uncertainty).
- Increased probability of winner-takes-all outcomes (less stability).
- Limits to problem solving capacity (more uncertainty).
- Easier to break stuff than fixing it (less stability).
- Response: smaller scale, better stories (and conversations), updated institutions.
- Increasing disorder: excess novelty, environmental change.
- Other observations:
- Fairness and inequality: people don’t want to lose stuff.
- Maintaining order requires constant energy.
- Economics is not a science.
- It’s not nature versus nurture.
At the border of order and disorder
- Life emerges at critical points. (“Complexity — David Krakauer, Part 3”)
- The border of chaos and stability.
- The right mix of order and chaos, rigidness and adaptability.
- Life is imbalance: (“The Vital Question”)
- The creation of order and resisting decay.
- The interaction between forming structure and heating up the environment.
- Energy generation, capture and usage: electron flow and proton power.
- The most comfortable state.
- Life is surprising. (“The information theory of individuality”)
- Physics and chemistry are universal.
- Biology may not be.
- Working down (reduction) provides certainty: biology breaks down into physics and chemistry – nothing left.
- Working up (emergence) is uncertain: difficult to predict
- Life is self propagation and evolution. (“Complexity Explorer”)
- Life is a self-propagating chemical system capable of undergoing adaptive evolution.
- Self-propagating: growth and reproduction.
- Adaptive evolution: potential to complexify and become more out-of-equilibrium.
- Through mutation and selection.
- Explains how more complex structures can arise over time.
- Life is a self-propagating chemical system capable of undergoing adaptive evolution.
- Life is driven by internal and external constraints. (“Complexity — David Krakauer, Part 3”)
- Regulatory or energetic constraints limit possible outcomes.
- External environmental constraints select fittest from possible outcomes.
- Change in external constraints: explore previously unfit outcomes.
- Change in external constraints: may change regulatory constraints.
Maintaining order requires constant energy
- Order needs: (“Why Information Grows“)
- Constant energy (to emerge).
- The right type of matter (to last).
- Predictable computation (to grow).
- Keeping your house in order requires constant work.
- Institutions, relationships, etc.
Exploiting order = certainty, predictability = efficient survival (biases are good)
- You need regularities in the environment. (“Life’s Information Hierarchy”)
- Low variance, regularity in outcomes.
- No regularities to “exploit” = no complex biological systems.
- All cognition is a process of recognition. (“The Master and His Emissary“)
- We recognize something only if we already have a model in our brain.
- Regularities -> stable internal model -> intuitions, beliefs, knowledge, knowhow.
- Intuitions, gut feelings work best when we can easily recognize stable patterns. (“The Hour between Dog and Wolf”)
- Predictable events, low novelty, you know what to do, no need to think, no need for adjustments. (“The Hour between Dog and Wolf”)
- Knowledge:
- The ability to predict the outcome based on an understanding of relationships / linkages. (“Why Information Grows“)
- Something that is true and useful about the world. (“Making Sense — David Deutsch“)
- Relies on distinctions. (“The Master and His Emissary“)
- We can’t experience something unless there is a change or difference.
- Knowhow: the capacity to perform actions. (“Why Information Grows“)
- Coherent, stable beliefs (habits, biases). (“How to Change Your Mind”)
- You are the thing that maintains constancy across transformations. (“12 Rules for Life“)
- Internal models, beliefs -> prediction (intelligence). (“On Intelligence“)
- A measure of the ability of a system to predict what happens next.
- Not measured by actual behavior or actions.
- Requires structured inputs: need for a patterned world.
- Prediction is the verification of reality and the essence of understanding. (“On Intelligence“)
- The odds that the same patterns occur randomly are low.
- The repeated pattern must therefore exist (at least at our level of observation).
- Predictive coding. (“How to Change Your Mind“) (“REBUS“)
- Used to predict what will happen next.
- Objective: minimize errors between prediction and experience.
- Efficient and optimized for survival.
- Stimulates achievement.
- Learn from the past, plan for the future.
- Learning combines bottom-up classification and top-down pattern prediction. (“On Intelligence“)
- Memory and learning are meaningless if inputs are random, without patterns.
- Parts of our outside world need to have (repeating) structure to be predictable.
- Used by all living systems to exploit the structure of the world.
- Better prediction allows for handling more uncertainty and complexity. (“Prediction Machines“)
- Better prediction drives more efficient survival, faster adaptation and increased complexity. (“Life’s Information Hierarchy”)
- Improve predictions through trial and error, test and update. (“Complexity — David Krakauer, Part 4”)
- Develop good models: (“Complexity — David Krakauer, Part 4”)
- Priors: in order to interpret (noisy) data we need to have an expectation.
- Simulation: in order to have an expectation we need rigorous simulation.
- Priors, derived from experience of regularities over time, drive better prediction. (“Life’s Information Hierarchy”)
- Error correction. (“EconTalk – Doug Lemov”)
- Getting it wrong is the normal state of learning .
- Decision-making is based on anticipated memories: (“Making Sense — Daniel Kahneman“)
- Which experience is likely to produce the best memory.
- Weighing anticipated regret versus anticipated satisfaction.
- Survival and the winner effect. (“The Hour Between Dog and Wolf“)
- Winners emerge with higher levels of testosterone, helping them win yet again.
- Survival and the risk of ruin. (“EconTalk – Nassim Nicholas Taleb“)
- Biases may seem irrational in isolated events, but can be rational over time if they allow you to avoid the risk of ruin in the long run.
Disorder = uncertainty = adaptation (need to adjust your actions, biases)
- Brain is an uncertainty reduction machine. (“How to Change Your Mind“)
- We evolved to pay attention to uncertainty, information. (“The Hour Between Dog and Wolf“)
- Information: things we do not already know, novelty, uncertainty.
- If you do not need to move, you do not need a brain. (“The Hour Between Dog and Wolf“)
- Or, we only think when we are not good at an activity.
- We evolved to seek out novelty. (“The Hour Between Dog and Wolf“)
- Novel things are addictive (dopamine). (“The Hour Between Dog and Wolf“)
- Dopamine: the desire for information and unexpected reward
- The compensation we receive for valuable effort.
- Rewards us for novel physical actions that lead to unexpected reward.
- Pushes us beyond established routines to try new search patterns (optimism).
- Curiosity itself, the need to know, can become a form of addiction.
- We develop a preference for effortful consumption.
- We don’t want to be fed, we want to hunt.
- Explore uncertainty (“REBUS”):
- Curious, novelty seeking behavior, inquiring state of mind.
- Relaxation of priors for the sake of knowledge/learning.
- Reduce uncertainty (“REBUS and the Anarchic Brain”):
- Removing redundant parameters (unconsciously).
- Revealing the underlying core structure.
As we exploit and explore, progress emerges: the world gets more complex
- Complexity is not achieved by the number of parts, but by the organization of them (the structure of the network). (“The Gene“)
- Accumulation of culture, knowledge and knowhow.
- Crystallize information in the form of new objects and products that augment society, drive growth and complexity. (“Why Information Grows”)
- The ever-increasing amount of culture is the biggest source of economic growth. (“Blueprint“)
- The larger the group studying any problem, the faster the knowledge advances. (“Free Solo and Economic Growth“)
- Diversity and complexity of a country’s industry is a strong predictor of its future growth. (“Why Information Grows”)
- Larger wholes allow for increasing complexity.
- Value of ideas: (“EconTalk — Paul Romer“)
- Ideas are insights about how to rearrange the objects in the world.
- More people = more insights = create new objects = more value/person.
- Value of ideas: (“EconTalk — Paul Romer“)
Continued emergence of progress requires stability, cohesion
- Progress is neither automatic nor mechanistic; it is rare. (“Peter Thiel’s Religion“)
- Progress occurs when properties evolve and emerge that make doing the right thing for the group (societal good) the right thing for the individual (self-interest). (EconTalk — Agnes CallardEcon – Callard)
- As individual humans interact in social groups that are sufficiently large and homogeneous, positive social properties may emerge.
- Justice, norms, rights, institutions, culture.
- These emerging properties over time feedback into the social group and affect individual behavior.
- Influence individual decision making.
- Allow for social learning
- If social groups are sufficiently durable, over time complexity increases.
- Social learning allows for cultural evolution.
- If social groups lose their cohesion, emergent properties lose their relevance.
- Abuse by one part of the group to exploit another.
- As differences widen, further loss of cohesion.
- What is good on average is no longer good for the whole.
- Utilitarian principles are not as useful any more.
- An “above zero” decision outcome is no longer a useful indicator of societal well-being if some groups are consistently left out.
Otherwise, we are on shaky ground
- Lack of trust in experts, institutions
- Institutions rely on faith in the strength of error correction mechanisms (to what degree are experts challenged). (“Making Sense — David Deutsch“)
- Institutions require trust among its members (“EconTalk — Yuval Levin”)
- If behavior is different from expectations, we lose trust in the institutions.
- Current institutions were built to leverage mainstream collective action.
- No more mainstream: fragmentation.
- Technology has created many profitable, small worlds (in terms of financial rewards and status).
- No more collective action: individualization.
- Technology allows us to be functional loners (we don’t need to come together as much to achieve things).
- Lack of morals, abuse, easy to cheat.
- Morality is about the problem of what to do next – like science, it evolves by error correction. (“Making Sense — David Deutsch“)
- Corruption, norms are not enforced. (EconTalk — Yuval Levin)
- Cronyism is logical and inevitable in a system driven by self-interest. (“EconTalk — Michael MungerEconTalk — Michael Munger“)
- Lack of cohesion.
- Lack of stability. (“EconTalk — Branko Milanovic”)
- Gig-jobs instead of normal jobs.
- Societal changes.
- Reduced role of family (incentives to form households have gone down).
- Atomization of social life (individuals become private enterprises).
- Lack of balance of pro- and anti-social behaviors. (“Blueprint“)
- Evolving and interacting genetic and cultural forces prime human social behaviors.
- Certain pro-social tendencies and capabilities shape the emergence of certain types of human social order.
- We are capable of both pro- and anti-social behavior.
- Various types of human behavior (cheating, punishing, cooperating, loners, etc.) co-exist and co-evolve, ensuring that no particular type will disappear or dominate the population.
Increasing disorder: excess novelty, changing environment.
- Excess novelty causes stress. (“The Hour Between Dog and Wolf“)
- Novel things can be bad (cortisol and chronic stress). (“The Hour Between Dog and Wolf“)
- Novelty and uncertainty can also signal a threat and elicit a stress response.
- Short-term, moderate stress exposure triggers focus heightened awareness.
- Long-term, chronic stress response impairs cognitive functioning.
- Thinking becomes more emotional, less factual (pessimistic, risk aversion).
Response to novelty: keep it simple, re-think habits, adjust beliefs, social learning
- Certainty is acquired slowly. (“Complexity — David Krakauer, Part 1”)
- Requires trial and error.
- Tension between certainty needed to make decisions and uncertainty of predictions.
- Bad science -> false certainty -> bad predictions -> loss of credibility.
- Be tolerant of simplicity in times of uncertainty. (“Complexity — David Krakauer, Part 1”)
- When data is bad, use simple models.
- When environment changes, re-think your habits. (“Complexity — David Krakauer, Part 1”)
- Habitual thinking exploits regularity.
- If regularity disappears, drop associated automatic behaviors.
- Analyze and build new habits (slowly).
- Limiting novelty: use what you already know to gain insight about what you don’t. (“Complexity — David Krakauer, Part 1“)
- Handling uncertainty is about manipulating beliefs. (“The Laws of Medicine“)
- Predict: use and test your beliefs.
- Be open: adjust your beliefs.
- Be aware: beliefs have limits.
- Be a better belief calibrator. (“Thinking in Bets“)
- Test, adjust, be open.
- Handling novel things as a positive challenge, not a threat (mental resilience). (“The Hour Between Dog and Wolf“)
- Nature: balance of anabolic (testosterone) and catabolic hormones (cortisol), neurotransmitter profile, vagal tone (vagus nerve ability to reduce stress response).
- Nurture: exposure to productive short-term stressors (exercise, cold), limitation of unnecessary stressors (uncertainty, lack of control).
- If the environment changes so frequently that genetic evolution can’t provide the appropriate adaptation fast enough, cultural evolution through (brains capable of) social learning becomes more adaptive. (“Blueprint“)
No adaptation: trapped in rigid beliefs
- We start adaptive (“How to Change Your Mind“).
- Wide focus, disordered, diffused attention.
- Less predictable answers, high error rates.
- Over time, we become more rigid, preservation (“How to Change Your Mind“).
- Spotlight, ordered, narrower focus.
- More probable answers, lower error rates.
- Maladaptive behaviors. (“How to Change Your Mind“)
- Narrow or fixed perspectives, behaviors and emotional repertoires.
- Negative thinking (stuck in the past or worried about the future).
- Inverse learning (repeating the same destructive thoughts or behavior, thereby reinforcing (unproductive) neural connections).
- We get trapped in thoughts. (“Waking Up“)
- Thoughts themselves are not a problem, but being identified with your thoughts is and being lost in thoughts is.
- We constantly create and repair a world that our minds want to be in.
- Distraction is the normal condition of our minds.
- We become overly confident. (“Making Sense – Shane Parrish“)
- The smarter you are, the better story you’re going to tell yourself about why a bias doesn’t apply.
- Biases create overconfidence about outcomes and worse decisions.
- Higher IQ increases the risk of larger blind spots. (“Thinking in Bets“)
- Better at constructing elaborate narratives supporting beliefs.
- We can’t do it all on our own. (“EconTalk — Agnes Callard“)
- Experiental learning limited by biases, prejudices and assumptions.
- Even if you try to mitigate blind-spots, you are still governed by the same biases and assumptions.
- You need others to ask the questions you are not asking.
- Leverage social learning.
Response to no adaptation: temporarily relax your beliefs.
- Change your state of mind. (“How to Change Yousr Mind“)
- Reduction of activity in the brain’s default mode network.
- Reduction of top-down control (beliefs), increased access to bottom-up stimuli.
- Flatten the grooves of habit, create new pathways of thought.
- How: psychedelics, meditation, task-oriented behavior.
- Meditation: (“Making Sense — Stephen Fry“)
- A tool to train your mind.
- Recognize and snap out of in-the-moment unproductive emotions.
- Importance of serotonin. (“REBUS and the Anarchic Brain“)
- Increased flow promotes adaptation to environmental uncertainty.
- Creates “hot state” of cognitive flexibility and neuroplasticity.
- More suggestible to influences from the environment (adaptation).
Environments are becoming more unpredictable (more uncertainty).
- Complex systems: (“Complexity — David Krakauer, Part 6”)
- Compress and accelerate what was already there.
- Higher variation in outcomes (exponential differences).
- Harder to predict outcomes.
- Harder to intervene.
- Increased uncertainty.
- High variance, unpredictable outcomes (Complexity — David Krakauer, Part 6).
- Too much data to process. (EconTalk — Martin Gurri).
- Not enough data to support a decision (Complexity — David Krakauer, Part 5).
- “Things” have multiple, difficult to unwind causes (Complexity — David Krakauer, Part 6).
- Slow to acquire certainty (Complexity — David Krakauer, Part 1).
- Excessive novelty (The Hour Between Dog and Wolf).
- Slow, low-quality feedback (wicked environments) (Range).
- Longer time scale of regularities, slower feedback: = higher required level of complexity to understand and adapt behavior (elaborate model needed for encoding). (“Life’s Information Hierarchy”)
- More information = more uncertainty. (“EconTalk — Martin Gurri”)
- Previous sources of false sense of certainty lose credibility (institutions, people).
- The more complex the underlying process, the more difficult it is to find universal laws. (“The Laws of Medicine“)
- Physics -> chemistry -> biology -> medicine (economics, etc.)
- Uncertainty increases, level of understanding decreases.
Increased probability of winner-takes-all outcomes (less stability).
- Complex problems. (Complexity — David Krakauer, Part 5)
- Broad averages are misleading.
- Outcomes can be much more volatile than expected.
- Need to understand local variation.
- Need to present all possible outcomes.
- The rich get richer, the fit get fitter (Linked.)
- If a system grows and its parts sufficiently differ.
- Most parts have only a few connections (friends).
- Some have many (acquaintances).
- The winner take all effect. (Finance — Geometric Balancing)
- Averages becomes meaningless.
- The few winners drive up the average.
- Most of the outcomes are below the average.
Limits to problem solving capacity (more uncertainty).
- Liberal democratic capitalism is ill equipped to handle major complex challenges. (“Conversations with Tyler — Slavoj Žižek”)
- The world has gotten more connected and complex, harder to interpret or control impact of capitalism. (“How Capitalism Changes Conscience“).
- Human brains evolved for mechanistic pattern recognition, the world is more complex, non-linear now, limited ability to predict and control (“Homeostasis and Gauss Statistics“)
- Large, harder to predict, connected variations (“The Precautionary Principle“)
- Our bias is preservation over adaptation (“REBUS and the Anarchic Brain”).
- We prefer our high-level models and prior beliefs over data.
- We are less efficient in a high change environment, uncertainty.
- We are not built to handle long-term disturbances. (“The Hour Between Dog and Wolf“)
- We are constrained by the limits of human nature / rationality / behavior / cognition. (“EconTalk — John Gray“)
- (Understanding) complexity is restrained by the computing capacity of its elements. (“Why Information Grows”)
- Human restrictions: we have to learn by doing (experiential) or from others (social).
- Network restrictions: transaction costs, ability to form social bonds (trust).
- Limits of (reductive) science.
- Understanding the parts in ever more detail does not necessarily lead to understanding the whole (the limits of specialization and reductionism) (“EconTalk — David Epstein“)
- Science is: which theory provides the best explanation. (“Making Sense — David Deutsch”)
Easier to break stuff than fixing it (less stability).
- Complex problems are solvable, but there is no common, consensus solution. (EconTalk — Agnes Callard)
- Just because a problem is solvable doesn’t mean it can be solved by someone other than you.
- You have to do the work yourself.
- Requires individual trial and error.
- Creates a lot uncertainty.
- For solving complex problems, difficult to trust anyone but yourself.
- Complex problems require complex interventions. (Complexity — David Krakauer, Part 5)
- We tend to collapse causality into single causes.
- Important problems tend to be complex and rarely have simple causes.
- We poorly understand how to resolve complex problems.
- We typically don’t look for complex solutions as we prefer simple ones.
- No unifying solutions. (Complexity — David Krakauer, Part 5)
- The political left will always have difficulties formulating enduring and unifying problems. (“Conversations with Tyler — Jordan Peterson“)
- Broken systems: (“Permanent Record“)
- The people that make the rules have no incentive to act against them (and fix the system).
- More complexity = easier to break than to fix. (“EconTalk — Martin Gurri”)
- Easier to figure out what you are against, than what you are for.
- Easier to break the system down, than to figure out how to fix it.
Response: smaller scale, better stories (and conversations), updated institutions
- Reduce scale.
- Smaller and more local governance makes outcomes become more visible. (EconTalk — Jonah Goldberg)
- Smaller governance units avoid the negative impact of minority rule. (“EconTalk — Nassim Nicholas Taleb (2)“)
- Small communities allow for a better alignment of skills and goals (“EconTalk — Ran Abramitzky“)
- Bottom-up control to set the limits for what is acceptable or desirable. (“EconTalk — Nassim Nicholas Taleb (2)“)
- Small scale experimentation = clearer feedback. (“EconTalk — Martin Gurri”).
- Pursue certainty through trial and error.
- Replicate success, avoid spread of failure.
- Circuit breakers. (“Circuit Breakers”)
- Regulations tend to be optimized for everyday life.
- Not for emergency situations (such as the current pandemic).
- Fast, low-cost adaptation.
- Immediate interruption of normal procedures.
- Achieve maximum results when the costs for doing so are still low.
- Normal regulations may get you to the same place, but much slower (too slow).
- Regulations tend to be optimized for everyday life.
- Better stories. (“Making Sense — Daniel Kahneman“)
- Avoid stories that are remote and abstract.
- Better conversations.
- Ingredients for a healthy conversation: (“Making Sense — Jack Dorsey“)
- Shared attention.
- Shared reality.
- Receptivity.
- Variety of perspective.
- (Risk of) Reputation.
- Civility and compromise (EconTalk — Yuval Levin)
- Compromise: disagree productively – the other side is not always the problem.
- Civility: rooted in the premise that the people that I don’t like aren’t going anywhere.
- Ingredients for a healthy conversation: (“Making Sense — Jack Dorsey“)
- Update institutions (“EconTalk — Yuval Levin”)
- If there are no institutions, people don’t connect.
- People only come together to do something.
- Excessively high expectations of the individual.
- Excessively low expectations of our institutions.
Fairness and inequality: people don’t want to lose stuff.
- Widening gap between middle-class and self-sustaining upper-class. (“EconTalk — Branko Milanovic”)
- Fairness intuition is “don’t take away my stuff”. (“Making Sense — Daniel Kahneman“)
- Fairness intuition is not “I will share my stuff”.
Capitalism, markets: work for everyone in the long run, hurt and displace some in the short run
- Families versus societies: (“EconTalk — Jonah Goldberg“)
- Families function under socialist norms (sharing, top down)
- Societies function under capitalist norms (contractual, bottom up).
- Markets are inventive, not necessarily efficient. (“EconTalk — Rory Sutherland“)
- Solving the same problem for different people in different ways.
- Long-term compounding, short-term aggregation: (“EconTalk — Arnold Kling“)
- Long-term impact of compounding: ultimately, the boat rises for everyone.
- Short-term impact of aggregation: in the short run, some are better off than others.
- “Front row” and “back row”: (“EconTalk — Chris Arnade“)
- Path to financial well-being is narrow, weaving through elite (educational) institutions.
- Growing number of people lack access to this “credential machine”
- Economic change and social disruption: (“EconTalk — Chris Arnade“)
- Tension between the pace of economic change and social displacement.
- Difficult to think through the first, second, third waves of (social) impact of economic changes.
- If the pace of change is too fast, can be socially disruptive.
Economics is not a science
- Economics has difficulties capturing and measuring individual well-being and emergent cultural norms. (“EconTalk — Paul Romer“)
- Best prediction about the future: what was here in the past and is still here in a healthy state. (“EconTalk — Nassim Nicholas Taleb (2)“)
It’s not nature versus nurture
- Start with nature. (“Superintelligence“)
- Innate preferences shaped by natural, sexual and cultural selection.
- Then, experience and interaction.
- “Life events” (experience) shape human values.
- It’s not nature vs. nurture. (“The Gene“)
- Patterns evolve as nature and nurture interact with and influence each other iteratively over time.
Various
- One gene: good and bad traits. (“Cellular Senescence“)
- Antagonistic pleiotropy.
- One gene is associated with good traits (usually early in life) and bad traits (usually later in life).
- Example: senescent cells signal tissue repair early in life, but as they accumulate in old age lead to chronic inflammation
- One trait: good at first, bad later. (“The Drive with Peter Attia — David Sabatini“)
- One trait may be adaptive for survival early in life, but may become a threat to survival later in life.
- Example: mTOR signaling high and productive early in life, but fails to slow down (and may increase) with age.
- Happiness versus satisfaction: (“Making Sense — Daniel Kahneman“)
- In the moment happiness: social, love, etc.
- Retrospective satisfaction: achievements, rewards, success
- Love is wanting the best possible outcome for someone. (“12 Rules for Life“)
- Individuals and organizations: (“The Spy and the TraitorThe Spy and the Traitor“)
- People hurt by fate or nature.
- Crave power and influence, but defeated by unfavorable circumstances.
- Belonging to a powerful organization provides feeling of superiority.
- Totalitarian regimes: (“The Spy and the Traitor“)
- The interests of society come before personal welfare.
- Betrayal for the greater good is the mark of ideological purity.
- When honest failure is punished, do nothing and hope the problem will go away.
- When we pursue optionality, we avoid bold decisions. (“Peter Thiel’s Religion“)
- Friendship: (“Blueprint“)
- Non-conditional exchanges that are tracked over time and build relationships that provide insurance against setbacks and lay the foundation for many other pro-social traits.
- Brands: (“EconTalk — Rory Sutherland)
- Provide certainty of limited downside.
- Technological progress: (“Permanent Record“)
- If something is technologically feasible, it will likely happen.
- Access to information. (“Permanent Record“)
- Within an organization often disproportionate to formal authority and ability to influence decisions.
- Artificial intelligence: all plausible scenarios (counter-intuitive, outliers). (“The Sentient Machine“)
- Fewer size restrictions (ability to scan and hold complete solution landscapes).
- Potential for inputs from a broader range of sensors.
- Higher processing speed.
Quotes:
- “You go after the king, you best not miss.” (unknown, “Bad Blood“)
- “Be polite, be professional, but have a plan to kill everybody you meet.” (James Mattis, “Bad Blood“).