#55 Scott Page: Becoming a Model Thinker
Scott Page, Professor of Complex Systems at the University of Michigan, discusses how mental models help make sense of the world and solve complex problems. He emphasizes using an ensemble of models and cognitive diversity to address challenges too vast for individual understanding.
Deep Dive Analysis
15 Topic Outline
Defining Mental Models and Their Application
The Evolution of Thinking: From Simple to Complex Models
Collective Intelligence and Ensembles of Models
Cognitive Diversity and the Wisdom Hierarchy
Applying Models: Data, Information, Knowledge, Wisdom
The Role of Linear Models vs. Human Judgment
Acquiring and Prioritizing Mental Models
Models as Tools for Surfacing Assumptions and Design
Why Traditional Education Teaches Single Models
Teaching Complexity and Meta-Thinking Skills
Experimenting with Collective Intelligence in Problem Solving
Power Law Distributions: Causes and Implications
Concavity and Convexity: Understanding Returns and Growth
Local Interaction Models and Cultural Coordination
Perspective Taking and the Pragmatic Use of Models
7 Key Concepts
Mental Model
A mental model is a framework used to make sense of the world, mapping messy reality to clean, logical mathematical structures. It involves deciding which variables from reality to connect to an existing mathematical framework.
Collective Intelligence
This concept suggests that one way to make sense of complexity is by applying ensembles of models to problems, as no single individual's mind is big enough to grasp the full complexity of the world. Different models may explain different percentages of variation, and their combined insights lead to a deeper understanding.
Wisdom Hierarchy
This hierarchy describes the progression from raw data to information (structured data), then to knowledge (understanding relationships between information), and finally to wisdom (understanding which knowledge to apply to a specific problem). Mental models are primarily applied at the knowledge stage, with wisdom discerning their relevance.
Cognitive Diversity
This refers to a group of people having different mental models and basic assumptions about how the world works, leading them to see different parts of a problem. It is valuable because it allows for a more holistic understanding of complex issues that no single person can fully grasp.
Power Law Distributions
These are distributions where most events are very small, with occasional huge events, unlike normal distributions where values cluster around an average. They are characterized by a long tail and can be caused by mechanisms like preferential attachment, random walks, or self-organized criticality.
Concavity and Convexity
These terms describe how the value or outcome changes with additional input. Concavity implies diminishing returns, where the added value of each successive unit decreases (e.g., eating more chocolate cake). Convexity implies increasing returns, where the added value of each successive unit increases (e.g., preferential attachment in book sales).
Local Interaction Models
These models describe situations where an individual's behavior depends on the behavior of those around them, often leading to coordination games. The specific action itself may not be inherently important, but coordinating with others on that action is crucial, shaping culture and organizational practices.
9 Questions Answered
Mental models are frameworks used to understand the world, essentially mapping complex reality onto logical mathematical structures. They help us identify relevant variables and relationships to make sense of situations.
Complex problems like the obesity epidemic have multiple contributing dimensions (e.g., infrastructure, food, gut bacteria, work-life balance) and systems-level feedbacks. Fixing only one piece is insufficient due to the interconnectedness and lack of a single 'silver bullet' solution.
Cognitive diversity means having people with different mental models and basic assumptions in a group, allowing them to see different parts of a problem. This collective perspective leads to a deeper, more holistic understanding and better solutions for complex issues.
The hierarchy starts with raw data, which is structured into information. Knowledge is the understanding of relationships between pieces of information, and wisdom is the ability to discern which knowledge (or models) to apply to a specific problem.
If a linear model and human predictions are close, the linear model is often preferred due to its calibration and ability to process more data without bias. However, if predictions are far apart, it's crucial to investigate what variables humans are considering that the linear model isn't, or if the environment has changed, as linear models assume stationarity.
Prioritization depends on the context of the action (individual choice, strategic situation, or social system), the rationality of the decision-maker (rational vs. rule-of-thumb), and whether the logic of the model aligns with the observed reality. Considering these factors helps determine the most relevant models.
Single models were effective in a simpler world where problems could be 'carved at their joints' and explained by simple equations. However, modern decisions are far more complex, involving numerous interconnected dimensions (e.g., environmental impact, talent attraction, brand), making a single model inadequate.
Power law distributions mean that most outcomes are small, with a few extremely large ones (e.g., book sales). This suggests that wild success can be as much due to luck and positive feedback mechanisms (like preferential attachment) as it is to skill, potentially challenging traditional notions of merit and fairness.
Concave functions represent diminishing returns, where each additional unit provides less benefit (e.g., adding workers to a team beyond a certain point). Convex functions represent increasing returns, where each additional unit provides more benefit (e.g., network effects or preferential attachment). Recognizing these helps avoid dangerous linear projections of growth or impact.
53 Actionable Insights
1. Cultivate Cognitive Diversity
To achieve a deeper and more holistic understanding of complex problems, assemble a collection of people with diverse knowledge, overlapping expertise, and different mental models.
2. Employ Ensemble Thinking
To make sense of complex problems, use an ensemble of models, as no single model can fully explain the variation, and combining them offers a deeper understanding.
3. Align Passion, Ability, Purpose
When choosing a path, seek something that combines three elements: a genuine love for the practice, some innate ability, and a connection to something useful, meaningful, or purposeful that makes the world better.
4. Practice Perspective Taking
Cultivate the meta-model of perspective-taking by actively considering how a problem appears through the lens of various stakeholders, including individuals, shareholders, and government, to gain a nuanced view.
5. Strategize Model Acquisition
Decide whether to specialize deeply in a few models, become proficient in a handful, or gain broad awareness of many models, tailoring your approach to your career and cognitive strengths.
6. Balance Individual, Collective Effort
While lifelong learning and amassing models are valuable, recognize that individual capacity is limited for solving large-scale problems; combine individual efforts with collections of people using diverse models for greater impact.
7. Practice Deliberative Modeling
Construct models through a deliberative process: first, identify the general class of the problem (system, decision, game), then write down the model, use mathematics to deduce implications, and revise if they don’t match reality.
8. Use Logic-Structure-Function
When observing a pattern or structure in the world, apply the “Logic-Structure-Function” framework: identify the underlying logic that created it, and then assess its functionality and whether it matters.
9. Design Reality with Models
Recognize that in leadership or policy roles, constructing models is not just about understanding reality but also about actively designing it by defining rules, policies, and strategy spaces.
10. Integrate Models, Human Insight
For best results, combine quantitative models (like linear regressions) with human insights, leveraging the strengths of both.
11. Triangulate Model Predictions
When quantitative models and human predictions are similar, trust the calibrated linear model; if they diverge significantly, engage with the humans to understand their differing perspectives and variables.
12. Expand Relevant Model Toolbox
Accumulate a diverse range of mental models, viewing them as tools in a toolbox, to be better equipped for a wide variety of problems, but prioritize relevance to the task at hand.
13. Build Model Latticework
Array your experiences on a latticework of mental models to better understand and interpret the world, drawing from a wide range of frameworks.
14. Surface Model Assumptions
Utilize models as a tool to surface underlying assumptions, ensure logical consistency, identify key drivers of behavior, understand interactions and aggregation, and inform appropriate responses.
15. Define Problem Actors, Context
When approaching a problem, first identify the relevant actors and the decision context, determining if it’s a single actor decision, a strategic interaction, or a larger system.
16. Understand Aggregation Issues
Be acutely aware that individual actions or components may not aggregate predictably, and fundamental paradoxes can arise from underlying assumptions.
17. Consider Systemic Feedbacks
Recognize that systems often contain internal feedbacks that can lead to logical inconsistencies or prevent universal success if everyone follows the same “formula.”
18. Anticipate Competitor Models
Develop an understanding of the mental models your competitors use, as this allows you to anticipate their responses, avoid surprises, and gain a strategic advantage.
19. Test Models, Avoid Force-Fit
Apply a variety of models but be pragmatic: if a model doesn’t fit the problem (“spaghetti doesn’t stick”), let it go for that specific case, but keep it in your toolbox for future applicability.
20. Prioritize Pragmatic Utility
When selecting models or perspectives, prioritize those that offer pragmatic utility and genuinely help achieve your specific goals or solve the problem at hand.
21. Instill Multi-Model Thinking
Teach children to develop a mental latticework of models, even without deep mathematical understanding, to foster an appreciation for the world’s complexity beyond single explanations.
22. Master Core Universal Models
Prioritize learning core, unchanging models that apply across disciplines, human history, biology, and even physics, as these provide fundamental insights into persistent patterns.
23. Grasp Concavity, Convexity
Understand concavity as diminishing returns, where the added value of each successive unit decreases, and convexity as increasing returns, where the added value increases.
24. Beware Linear Projections
Be cautious of linear thinking and projections, as they can be dangerous and lead to flawed assumptions by failing to account for diminishing or increasing returns.
25. Grasp Power Law Dynamics
To understand power law distributions, recognize that preferential attachment, random walks, and self-organized criticality are key mechanisms driving these “long tail” phenomena.
26. Grasp Local Interaction Models
Understand local interaction models as frameworks where an individual’s behavior is influenced by the behaviors of those immediately around them, often leading to coordination.
27. Balance Luck, Skill Attribution
When evaluating success, especially in power law distributed outcomes, recognize that “big winners” often result from a combination of skill and the natural processes of positive feedback, rather than solely superior ability.
28. Reframe “Weird” Behaviors
Instead of labeling unfamiliar behaviors as “weird” or intrinsic character traits, reframe them as solutions to local coordination problems within a specific cultural or family context.
29. Value Individual Model Diversity
Even if another’s mental model seems superior, retaining your own diverse model is valuable for the collective, as it contributes to overall cognitive diversity and problem-solving.
30. Adapt to Other’s Models
To interact efficiently and predictably within an organization or community, adapt to and coordinate with other people’s mental models and terminology, understanding their worldview.
31. Hire Diverse Perspectives
When facing complex decisions, hire individuals who can bring different disciplinary perspectives (e.g., pharmacological, sociological, organizational, political) to the table.
32. Seek Diverse History Views
Avoid a singular “great man” view of history; instead, seek out and engage with diverse perspectives from all people who experienced an event to gain a more complete understanding.
33. Bridge AI, Real-World Understanding
Cultivate the ability to communicate and translate between sophisticated artificial intelligence models and real-world contexts, acting as a bridge between technical and practical understanding.
34. Interrogate Model Assumptions
When model predictions diverge from human insights, critically examine the model’s variables, coefficients, and whether environmental conditions have changed, while also understanding human-considered variables.
35. Identify Missing Model Variables
When a model fails to predict accurately, investigate whether crucial variables, especially qualitative ones like “ugly,” are missing from its construction.
36. Gauge Decision Rationality
When analyzing decisions, assess the rationality of the decision-maker, considering whether they are optimizing or primarily relying on rules of thumb.
37. Anticipate Rationality Drivers
Expect decision-makers to exhibit more rational behavior in situations that are frequently repeated, allowing for learning, or when the stakes involved are exceptionally high.
38. Distinguish Org Decision Modes
Recognize that organizational decisions can range from careful, rational committee processes for big choices to rigid, rule-based standard operating procedures for routine tasks.
39. Validate Complexity Claims
If you argue for complexity, path dependence, or policy impact, ensure your model accounts for this by demonstrating the creation of new states or fundamental changes in transition probabilities.
40. Gain Model Robustness, Bonus
A variety of models provides robustness, akin to a portfolio, reducing the likelihood of mistakes, and often yields a “bonus” where combined performance exceeds the average of individual models.
41. Use Awareness to Guide Deeper Learning
Develop a broad awareness of various models to identify those that particularly resonate or seem relevant, then use this interest to guide deeper exploration.
42. Structure Raw Data
Convert raw, overwhelming data into structured information by categorizing and summarizing it into meaningful variables, like unemployment or inflation rates.
43. Build Relational Knowledge
Progress from information to knowledge by understanding the correlative or causal relationships between different pieces of information, such as force equals mass times acceleration.
44. Apply Knowledge Wisely
Develop wisdom by discerning which specific knowledge to apply to a given problem, which may involve selecting from or combining various pieces of knowledge.
45. Understand Distribution Impact
Understand the practical implications of different distribution types (e.g., normal vs. power law), as they profoundly affect fairness, predictability, and design challenges in systems.
46. Grasp Distribution Logics
Learn the underlying logic that creates different statistical distributions, such as addition for normal, multiplication for log-normal, and preferential attachment or self-organized criticality for power laws.
47. Apply Ideas Cross-Discipline
Actively seek opportunities to apply broad concepts and models across various disciplines to deepen understanding and reveal their universal applicability.
48. Seek Broader Impact
Strive to make your work interesting and intriguing to a wider audience beyond a small circle, as this increases its value and potential for contribution.
49. Acknowledge Individual Cognitive Limits
Recognize that no single individual’s cognitive capacity is sufficient to fully comprehend the sheer complexity and dimensionality of the modern world.
50. Recognize Model Bias
Be aware that your existing mental models can bias how you filter raw data into information, as they influence which variables you deem relevant and how you perceive their interactions.
51. Consider Systemic Embeddedness of Actions
Evaluate whether your actions are embedded within a larger social system, where you might be unconsciously influenced by cues from that system.
52. Recognize Routine-Based Decisions
Acknowledge that many daily decisions are based on routines and rules of thumb rather than rational optimization, and these routines may adapt slowly over time.
53. Verify Personal Logic
After considering how others make decisions, critically question and validate the correctness of your own underlying logic.
7 Key Quotes
Your brain just isn't going to be big enough. Collections of people, creating a larger ensemble model, actually have a hope of addressing these problems.
Scott Page
What a mental model is, is a framework that you use to make sense of the world.
Scott Page
Munger has this wonderful quote about, you want to sort of array your experiences on the lattice work of models.
Scott Page
No individual's head is big enough to sort of make sense of the complexity of the world.
Scott Page
But if they're far apart, if the linear model and the humans are giving very different predictions, then you want to go talk to them.
Scott Page
Imagine how difficult physics would be if electrons could think.
Murray Gell-Mann (quoted by Scott Page)
But in a complex world, your ability to succeed is going to depend on you sort of filling a niche that's valuable.
Scott Page