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.
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.
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.
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.
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.
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.
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.
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.
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.
For best results, combine quantitative models (like linear regressions) with human insights, leveraging the strengths of both.
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.
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.
Array your experiences on a latticework of mental models to better understand and interpret the world, drawing from a wide range of frameworks.
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.
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.
Be acutely aware that individual actions or components may not aggregate predictably, and fundamental paradoxes can arise from underlying assumptions.
Recognize that systems often contain internal feedbacks that can lead to logical inconsistencies or prevent universal success if everyone follows the same “formula.”
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.
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.
When selecting models or perspectives, prioritize those that offer pragmatic utility and genuinely help achieve your specific goals or solve the problem at hand.
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.
Prioritize learning core, unchanging models that apply across disciplines, human history, biology, and even physics, as these provide fundamental insights into persistent patterns.
Understand concavity as diminishing returns, where the added value of each successive unit decreases, and convexity as increasing returns, where the added value increases.
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.
To understand power law distributions, recognize that preferential attachment, random walks, and self-organized criticality are key mechanisms driving these “long tail” phenomena.
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.
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.
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.
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.
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.
When facing complex decisions, hire individuals who can bring different disciplinary perspectives (e.g., pharmacological, sociological, organizational, political) to the table.
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.
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.
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.
When a model fails to predict accurately, investigate whether crucial variables, especially qualitative ones like “ugly,” are missing from its construction.
When analyzing decisions, assess the rationality of the decision-maker, considering whether they are optimizing or primarily relying on rules of thumb.
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.
Recognize that organizational decisions can range from careful, rational committee processes for big choices to rigid, rule-based standard operating procedures for routine tasks.
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.
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.
Develop a broad awareness of various models to identify those that particularly resonate or seem relevant, then use this interest to guide deeper exploration.
Convert raw, overwhelming data into structured information by categorizing and summarizing it into meaningful variables, like unemployment or inflation rates.
Progress from information to knowledge by understanding the correlative or causal relationships between different pieces of information, such as force equals mass times acceleration.
Develop wisdom by discerning which specific knowledge to apply to a given problem, which may involve selecting from or combining various pieces of knowledge.
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.
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.
Actively seek opportunities to apply broad concepts and models across various disciplines to deepen understanding and reveal their universal applicability.
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.
Recognize that no single individual’s cognitive capacity is sufficient to fully comprehend the sheer complexity and dimensionality of the modern world.
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.
Evaluate whether your actions are embedded within a larger social system, where you might be unconsciously influenced by cues from that system.
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.
After considering how others make decisions, critically question and validate the correctness of your own underlying logic.