1. The Many-Model Thinker
To become wise you’ve got to have models in your head. And you’ve got to
array your experience—both vicarious and direct—on this latticework of
models.
—Charlie Munger
This is a book about models. It describes dozens of models in
straightforward language and explains how to apply them. Models are
formal structures represented in mathematics and diagrams that help us to
understand the world. Mastery of models improves your ability to reason,
explain, design, communicate, act, predict, and explore.
This book promotes a many-model thinking approach: the application of
ensembles of models to make sense of complex phenomena. The core idea
is that many-model thinking produces wisdom through a diverse ensemble
of logical frames. The various models accentuate different causal forces.
Their insights and implications overlap and interweave. By engaging many
models as frames, we develop nuanced, deep understandings. The book
includes formal arguments to make the case for multiple models along with
myriad real-world examples.
The book has a pragmatic focus. Many-model thinking has tremendous
practical value. Practice it, and you will better understand complex
phenomena. You will reason better. You exhibit fewer gaps in your
reasoning and make more robust decisions in your career, community
activities, and personal life. You may even become wise.
Twenty-five years ago, a book of models would have been intended for
professors and graduate students studying business, policy, and the social
sciences along with financial analysts, actuaries, and members of the

intelligence community. These were the people who applied models and,
not coincidentally, they were also the people most engaged with large data
sets. Today, a book of models has a much larger audience: the vast universe
of knowledge workers, who, owing to the rise of big data, now find
working with models a part of their daily lives.
Organizing and interpreting data with models has become a core
competency for business strategists, urban planners, economists, medical
professionals, engineers, actuaries, and environmental scientists among
others. Anyone who analyzes data, formulates business strategies, allocates
resources, designs products and protocols, or makes hiring decisions
encounters models. It follows that mastering the material in this book—
particularly the models covering innovation, forecasting, data binning,
learning, and market entry timing—will be of practical value to many.
Thinking with models will do more than improve your performance at
work. It will make you a better citizen and a more thoughtful contributor to
civic life. It will make you more adept at evaluating economic and political
events. You will be able to identify flaws in your logic and in that of others.
You will learn to identify when you are allowing ideology to supplant
reason and have richer, more layered insights into the implications of policy
initiatives, whether they be proposed greenbelts or mandatory drug tests.
These benefits will accrue from an engagement with a variety of models
—not hundreds, but a few dozen. The models in this book offer a good
starting collection. They come from multiple disciplines and include the
Prisoners’ Dilemma, the Race to the Bottom, and the SIR model of disease
transmission. All of these models share a common form: they assume a set
of entities—often people or organizations—and describe how they interact.
The models we cover fall into three classes: simplifications of the world,
mathematical analogies, and exploratory, artificial constructs. In whatever
form, a model must be tractable. It must be simple enough that within it we
can apply logic. For example, we cover a model of communicable diseases
that consists of infected, susceptible, and recovered people that assumes a
rate of contagion. Using the model we can derive a contagion threshold, a
tipping point, above which the disease spreads. We can also determine the
proportion of people we must vaccinate to stop the disease from spreading.
As powerful as single models can be, a collection of models
accomplishes even more. With many models, we avoid the narrowness

inherent in each individual model. A many-models approach illuminates
each component model’s blind spots. Policy choices made based on single
models may ignore important features of the world such as income
disparity, identity diversity, and interdependencies with other systems.1
With many models, we build logical understandings of multiple processes.
We see how causal processes overlap and interact. We create the possibility
of making sense of the complexity that characterizes our economic,
political, and social worlds. And, we do so without abandoning rigor—
model thinking ensures logical coherence. That logic can be then be
grounded in evidence by taking models to data to test, refine, and improve
them. In sum, when our thinking is informed by diverse logically
consistent, empirically validated frames, we are more likely to make wise
choices.

Models in the Age of Data
The appearance of a book on models may seem out of place in the era of
big data. Today, data exists in unprecedented dimensionality and
granularity. Customer purchase data, which used to arrive in monthly
aggregates on printed paper, now streams instantaneously with geospatial,
temporal, and consumer tags. Student academic performance data now
includes scores on every homework, paper, quiz, and exam, as opposed to
semester-end summary grades. In the past, a farmer might mention dry
ground at a monthly Grange meeting. Now, tractors transmit instantaneous
data on soil conditions and moisture levels in square-foot increments.
Investment firms track dozens of ratios and trends for thousands of stocks
and use natural-language processing tools to parse documents. Doctors can
pull up page upon page of individual patient records that can include
relevant genetic markers.
A mere twenty-five years ago, most of us had access to little more than a
few bookshelves’ worth of knowledge. Perhaps your place of work had a
small reference library, or at home you had a collection of encyclopedias
and a few dozen reference books. Academics and government and privatesector researchers had access to large library collections, but even they had
to physically visit the material. As late as the turn of the millennium,
academics could be found shuttling back and forth between card catalog
rooms, microfiche collections, library stacks, and special collections in
search of information.
That has all changed. Content that had been paper-bound for centuries
now flows in tiny packets through the air. So too does the information about
the here and now. News that arrived on our doorsteps on newsprint once a
day now flows in a continuous digital stream into our personal devices.
Stock prices, sports scores, and news of political events and cultural
happenings can all be accessed with a swipe or query.
As impressive as the data may be, it is no panacea. We now know what
has happened and is happening, but, owing to the complexity of the modern
world, we may be less capable of understanding why it happened. Empirical
findings may be misleading. Data on piece-rate work often shows that the

more people are paid per unit of output, the less they produce. A model in
which pay depends on work conditions can explain those data. If conditions
are poor so that producing output is difficult, per unit pay may be high. If
conditions are good, per unit pay may be low. Thus, higher pay does not
lead to less productivity. Instead, more difficult work conditions require
higher per unit pay.2
In addition, most of our social data—that is, data about our economic,
social, and political phenomena—documents only moments or intervals in
time. It rarely tells us universal truths. Our economic, social, and political
worlds are not stationary. Boys may outscore girls on standardized tests in
one decade and girls may outscore boys the next. The reasons people vote
today may differ from the reasons they vote in coming decades.
We need models to make sense of the fire-hose-like streams of data that
cross our computer screens. Thus, it is because we have so much data that
this might also be called the age of many models. Look across the academy,
government, the business world, and the nonprofit sector, and you struggle
to find a domain of inquiry or decision not informed by models. Consulting
giants McKinsey and Deloitte build models to formulate business strategies.
Financial firms such as BlackRock and JPMorgan Chase apply models to
select investments. Actuaries at State Farm and Allstate use models to
calibrate risk when pricing insurance policies. The people team at Google
builds predictive analytic models to evaluate its more than three million job
applicants. College and university admissions officers construct predictive
models to select from among tens of thousands of applicants.
The Office of Management and Budget constructs economic models to
predict the effects of tax policies. Warner Brothers applies data analytics to
create models of audience responses. Amazon develops machine learning
models to make product recommendations. Researchers funded by the
National Institutes of Health build mathematical models of human
genomics to search for and evaluate potential cures for cancer. The Gates
Foundation uses epidemiological models to design vaccination strategies.
Even sports teams use models to evaluate draft prospects and trade
opportunities and to formulate within-game strategies. By relying on
models to select players and strategies, the Chicago Cubs won a World
Series championship after more than a century of failures.

To people who use models, the rise of model thinking has an even
simpler explanation: models make us smarter. Without models, people
suffer from a laundry list of cognitive shortcomings: we overweight recent
events, we assign probabilities based on reasonableness, and we ignore base
rates. Without models, we have limited capacity to include data. With
models, we clarify assumptions and think logically. And, we can leverage
big data to fit, calibrate, and test causal and correlative claims. With
models, we think better. In head-to-head competitions between models and
people, models win.3

Why We Need Many Models
In this book we advocate using not just one model in a given situation but
many models. The logic behind the many-model approach builds on the
age-old idea that we achieve wisdom through a multiplicity of lenses. This
idea traces back to Aristotle, who wrote of the value of combining the
excellences of many. A diversity of perspectives was also a motivation for
the great-books movement, which collected 102 important transferable
ideas in The Great Ideas: A Syntopicon of Great Books of the Western
World. The approach finds a modern voice in the work of Maxine Hong
Kingston, who wrote in The Woman Warrior, “I learned to make my mind
large, as the universe is large, so that there is room for paradoxes.” It is also
the basis for pragmatic actions in the world of business and policy. Recent
books argue that if we want to understand of international relations, we
should not model the world exclusively as a group of self-interested nations
with well-defined objectives, or only as an evolving nexus of multinational
corporations and intergovernmental organizations. We should do both.4
As commonsensical as the many-model approach may seem, keep in
mind that it runs counter to how we teach models and the practice of
modeling. The traditional approach—the one taught in high school—relies
on a one-to-one logic: one problem requires one model. For example: now
we apply Newton’s first law; now we apply the second; now the third. Or:
here we use the replicator equation to show the size of the rabbit population
in the next period. In this traditional approach, the objective is to (a)
identify the one proper model and (b) apply it correctly. Many-model
thinking challenges that approach. It advocates trying many models. Had
you used many-model thinking in ninth grade, you might have been held
back. Use it now, and you will move forward.
Academic papers, for the most part, follow the one-to-one approach as
well, even though they use those single models to explain complex
phenomena: Trump voters in the 2016 election were those who had been
left behind economically. Or: the quality of a child’s second-grade teacher
determines how economically successful that child will be as an adult.5 A

stream of best-selling nonfiction titles present cures for our ills based on
single-model thinking: Educational success depends on grit. Inequality
results from concentrations of capital. Our nation’s poor health is due to
sugar consumption. Each of these models may be true, but none is
comprehensive. To confront the complexity of these challenges, to create a
world of broader educational achievement, will require lattices of models.
By learning the models in this book, you can begin to build your own
lattice. The models originate from a broad spectrum of disciplines,
addressing phenomena as varied as the causes of income inequality, the
distribution of power, the spread of diseases and fads, the conditions that
precede social uprisings, the evolution of cooperation, the emergence of
order in cities, and the structure of the internet. The models vary in their
assumptions and their structure. Some describe small numbers of rational,
self-interested actors. Others describe large populations of rule-following
altruists. Some describe equilibrium processes. Others produce path
dependence and complexity. The models also differ in their uses. Some help
predict and explain. Others guide actions, inform designs, or facilitate
communication. Still others create artificial worlds for our minds to
explore.
The models share three common characteristics: First, they simplify,
stripping away unnecessary details, abstracting from reality, or creating
anew from whole cloth. Second, they formalize, making precise definitions.
Models use mathematics, not words. A model might represent beliefs as
probability distributions over states of the world or preferences as rankings
of alternatives. By simplifying and making precise, they create tractable
spaces within which we can work through logic, generate hypotheses,
design solutions, and fit data. Models create structures within which we can
think logically. As Wittgenstein wrote in his Tractatus LogicoPhilosophicus, “Logic takes care of itself; all we have to do is to look and
see how it does it.” The logic will help to explain, predict, communicate,
and design. But the logic comes at a cost, which leads to their third
characteristic: all models are wrong, as George Box noted.6 That is true of
all models; even the sublime creations of Newton that we refer to as laws
hold only at certain scales. Models are wrong because they simplify. They
omit details. By considering many models, we can overcome the narrowing

of rigor by crisscrossing the landscape of the possible.
To rely on a single model is hubris. It invites disaster. To believe that a
single equation can explain or predict complex real-world phenomena is to
fall prey to the charisma of clean, spare mathematical forms. We should not
expect any one model to produce exact numerical predictions of sea levels
in 10,000 years or of unemployment rates in 10 months. We need many
models to make sense of complex systems. Complex systems like politics,
the economy, international relations, or the brain exhibit ever-changing
emergent structures and patterns that lie between ordered and random. By
definition, complex phenomena are difficult to explain, evolve, or predict.7
Thus, we confront a disconnect. On the one hand, we need models to
think coherently. On the other hand, any single model with a few moving
parts cannot make sense of high-dimensional, complex phenomena such as
patterns in international trade policy, trends in the consumer products
industry, or adaptive responses within the brain. No Newton can write a
three-variable equation that explains monthly employment, election
outcomes, or reductions in crime. If we hope to understand the spread of
diseases, variation in educational performance, the variety of flora and
fauna, the effect of artificial intelligence on job markets, the impact of
humans on the earth’s climate, or the likelihood of social uprisings, we must
come at them with machine learning models, systems dynamics models,
game theory models, and agent-based models.

The Wisdom Hierarchy
To sketch the argument for many-model thinking, we begin with a query
from poet and dramatist T. S. Eliot: “Where is the wisdom we have lost in
knowledge? Where is the knowledge we have lost in information?” To that
we might add, where is the information we have lost in all this data?
Eliot’s questioning can be formalized as the wisdom hierarchy. At the
bottom of the hierarchy lie data: raw, uncoded events, experiences, and
phenomena. Births, deaths, market transactions, votes, music downloads,
rainfall, soccer matches, and speciation events. Data can be long strings of
zeros and ones, time stamps, and linkages between pages. Data lack
meaning, organization, or structure.
Information names and partitions data into categories. Examples clarify
the distinction between data and information. Rain falling on your head is
data. Total rainfall for the month of July in Burlington, Vermont, and Lake
Ontario’s water level are information. The bright red peppers and yellow
corn on farmers’ stands surrounding the capitol in Madison, Wisconsin, on
market Saturdays are data. The farmers’ total sales are information.

Figure 1.1: How Models Transform Data into Wisdom

We live in an age of abundant information. A century and a half ago,
knowing information brought great economic and social status. Jane
Austen’s Emma asks if Frank Churchill is “a young man of information.”
Today she would not care. Churchill, like everyone else, would have a
smartphone. The question is whether he could put that information to use.
As Fyodor Dostoyevsky writes in Crime and Punishment, “We’ve got facts,
they say. But facts aren’t everything; at least half the battle consists in how
one makes use of them!”
Plato defined knowledge as justified true belief. More modern

definitions refer to it as understandings of correlative, causal, and logical
relationships. Knowledge organizes information. Knowledge often takes
model form. Economic models of market competition, sociological models
of networks, geological models of earthquakes, ecological models of niche
formation, and psychological models of learning all embed knowledge.
Those models explain and predict. Models of chemical bonds explain why
metallic bonds prevent us from putting our hands through steel doors while
hydrogen bonds yield to our weight when we dive into a lake.8
Atop the hierarchy lies wisdom, the ability to identify and apply relevant
knowledge. Wisdom requires many-model thinking. Sometimes, wisdom
consists of selecting the best model, as if drawing from a quiver of arrows.
Other times, wisdom can be achieved by averaging models; this is common
when making predictions. (We discuss the value of model averaging in the
next section.) When taking actions, wise people apply multiple models like
a doctor’s set of diagnostic tests. They use models to rule out some actions
and privilege others. Wise people and teams construct a dialogue across
models, exploring their overlaps and differences.
Wisdom can consist of selecting the correct knowledge or model;
consider the following physics problem: A small stuffed cheetah falls from
an airplane’s hold at 20,000 feet. How much damage will it do upon
landing? A student might know a gravity model and a terminal velocity
model. The two models give different insights. The gravity model predicts
that the stuffed animal would tear through a car’s roof. The terminal
velocity model predicts that the toy cheetah’s speed tops out at around 10
mph.9 Wisdom consists of knowing to apply the terminal velocity model. A
person could stand on the ground and catch the soft cheetah in her hands.
To quote the evolutionary biologist J. B. S. Haldane, “You can drop a
mouse down a thousand-yard mine shaft; and, on arriving at the bottom, it
gets a slight shock and walks away, provided that the ground is fairly soft.
A rat is killed, a man is broken, a horse splashes.”
In the stuffed-cheetah problem, arriving at the correct solution requires
information (the weight of the toy), knowledge (the terminal velocity
model), and wisdom (selecting the correct model). Business and policy
leaders also rely on information and knowledge to make wise choices. On
October 9, 2008, the value of Iceland’s currency, the króna, began a free

fall. Eric Ball, then treasurer of software giant Oracle, was faced with a
decision. A few weeks prior he had dealt with the domestic repercussions of
the home mortgage crisis. Iceland’s situations posed an international
concern. Oracle held billions of dollars in overseas assets. Ball considered
network contagion models of financial collapse. He also thought of
economic models of supply and demand in which the magnitude of a price
change correlates with the size of the market shock. In 2008, Iceland had a
GDP of $12 billion, or less than six months’ revenues for McDonald’s
Corporation. Ball recollected thinking, “Iceland is smaller than Fresno. Go
back to work.”10 The key to understanding this event, and many-model
thinking generally, lies in recognizing that Ball did not search among many
models to find one that supported an action that he had already decided to
take. He did not use many models to find one that justified his action.
Instead, he evaluated two models as possibly useful and then chose the
better one. Ball had the right information (Iceland is small), chose the right
model (supply and demand), and made a wise choice.
We next show how to create a dialogue among multiple models by
reconsidering two historical events: the 2008 global financial market
collapse, which reduced total wealth (or what had been thought to be
wealth) by trillions of dollars, resulting in a four-year global recession, and
the 1961 Cuban missile crisis, which nearly resulted in nuclear war.
The 2008 financial collapse has multiple explanations: too much foreign
investment, over-leveraged investment banks, lack of oversight in the
mortgage approval process, blissful optimism among home-flipping
consumers, the complexity of financial instruments, a misunderstanding of
risk, and greedy bankers who knew the bubble existed and expected a
bailout. Superficial evidence aligns with each of these accounts: money
flowed in from China, loan originators wrote toxic mortgages, investment
banks had high leverage ratios, financial instruments were too complex for
most to understand, and some banks expected a bailout. With models we
can adjudicate between these accounts and check the internal consistency of
these accounts: Do they make logical sense? We can also calibrate the
models and test the magnitude of the effects.
The economist Andrew Lo, exercising many-model thinking, evaluates
twenty-one accounts of the crisis. He finds each to be lacking. It does not

make sense that investors would contribute to a bubble that they knew
would lead to a global crisis. Hence, the extent of the bubble must have
been a surprise to many. Financial firms may well have assumed the other
firms had done due diligence when in fact they had not. Second, what were,
in retrospect, clearly toxic (low-quality) bundles of mortgages found
buyers. Had global collapse been a foregone conclusion, the buyers would
not have existed. And while leverage ratios had increased since 2002, they
were not much higher than they had been in 1998. And as for the notion
that the government would bail out the banks, Lehman Brothers collapsed
on September 15, 2008; with over $600 billion in holdings, it was the
largest bankruptcy in US history. The government did not intervene.
Lo finds that each account contains a logical gap. The data, such as it is,
privileges no single explanation. As Lo summarizes: “We should strive at
the outset to entertain as many interpretations of the same set of objective
facts as we can, and hope that a more nuanced and internally consistent
understanding of the crisis emerges in the fullness of time.” He goes on to
say, “Only by collecting a diverse and often mutually contradictory set of
narratives can we eventually develop a more complete understanding of the
crisis.” No single model suffices.11
In Essence of Decision, Graham Allison undertakes a many-model
approach to explain the Cuban missile crisis. On April 17, 1961, a CIAtrained paramilitary group landed on the shores of Cuba in a failed attempt
to overthrow Fidel Castro’s communist regime, increasing tensions between
the United States and the Soviet Union, Cuba’s ally. In response, Soviet
premier Nikita Khrushchev moved short-range nuclear missiles to Cuba.
President John F. Kennedy responded by blockading Cuba. The Soviet
Union backed down, and the crisis ended.
Allison interprets events with three models. He applies a rational-actor
model to show that Kennedy had three possible actions: start a nuclear war,
invade Cuba, or impose a blockade. He chose the blockade. The rationalactor model assumes that Kennedy draws a game tree with each action
followed by the possible responses by the Soviets. Kennedy then thinks
through the Soviets’ optimal response. If, for example, Kennedy launched a
nuclear attack, the Soviets would strike back, resulting in millions dead. If
Kennedy imposed a blockade, he would starve the Cubans. The Soviet

Union could either back down or launch missiles. Given that choice, the
Soviet Union should back down. The model reveals the central strategic
logic at play and provides a rationale for Kennedy’s bold choice to blockade
Cuba.
Like all models, though, it is wrong. It ignores relevant details, allowing
it to initially appear a better explanation than it really is. The model
neglects to add a stage in which the Soviets put the missiles in Cuba. If the
Soviets had been rational, they should have drawn the same tree as
Kennedy and realized that they would have to remove the missiles. The
rational-actor model also fails to explain why the Soviets did not hide the
missiles.
Allison applies an organizational process model to explain these
inconsistencies. A lack of organizational capacity explains the Soviets’
failure to hide the missiles. The same model can explain Kennedy’s choice
to blockade. At the time, the United States Air Force lacked the capacity to
wipe out the missiles in a single strike. If even a single missile remained, it
could kill millions of Americans. Allison deftly combines the two models.
An insight from the organizational model changes the payoffs in the
rational-choice model.
Allison adds a governmental process model. The other two models
reduce countries to their leaders: Kennedy acts for the United States and
Khrushchev for the Soviet Union. The government process model
recognizes that Kennedy had to contend with Congress and that
Khrushchev needed to maintain a political base of support. Thus,
Khrushchev’s placing of the missiles in Cuba signaled strength.
Allison’s book shows the power of models alone and in dialogue. Each
model clarifies our thinking. The rational-actor model identifies possible
actions once the missiles have arrived and allows us to see the implications
of those actions. The organizational model draws our attention to the fact
that organizations, not individuals, carry out those actions. The
governmental process model highlights the political cost of invasion. By
evaluating events through all three lenses, we gain a broader and deeper
understanding. All models are wrong; many are useful.
In both examples, the different models explicate distinct causal forces.
Multiple models can also focus on different scales. In an oft-repeated tale, a
child claims that the Earth rests on the back of a giant elephant. A scientist

asks the child what the elephant stands on, to which the child replies, “A
giant turtle.” Anticipating what’s about to come next, the child quickly
adds, “Don’t even ask. It’s turtles all the way down.”12 If the world were
turtles all the way—if the world were self-similar—then a model of the top
level would apply at every level. But the economy, the political world, and
society are not turtles all the way down, nor is the brain. At the sub-micron
level, the brain is made up of molecules that form synapses, which in turn
form neurons. The neurons combine in networks. The networks overlap in
elaborate ways that can be studied with brain imaging. These neuronal
networks exist on a scale below that of functional systems such as the
cerebellum. Given that the brain differs at each level, we need multiple
models, and those models differ. The models that characterize the
robustness of neuronal networks bear little resemblance to the molecular
biology models used to explain brain cell function, which in turn differ
from the psychological models used to explain cognitive biases.
The success of many-model thinking depends on a degree of
separability. In analyzing the 2008 financial crisis, we rely on separate
models of foreign purchases of assets, of the bundling of assets, and of
increased leverage ratios. Allison drew implications from the game
theoretic model without considering the organizational model. In studying
the human body, doctors separate the skeletal, muscular, limbic, and
nervous systems. That said, many-model thinking does not require that
these distinct models divide the system into independent parts. Confronted
with a complex system, we cannot, to paraphrase Plato, carve the world at
its joints. We can partially isolate the major causal threads and then explore
how they are interwoven. In doing so, we will find that the data produced
by our economic, political, and social systems exhibits coherence. Social
data is more than sequences of incomprehensible hairballs that might have
been spit up by the family cat.

Summary and Outline of the Book
To summarize, we live in a time awash in information and data. The same
technological advances generating those data shrink time and distance.
They make economic, political, and social actors more agile, capable of
responding to economic and political events in an instant. They also
increase connectedness, and therefore complexity. We face a
technologically induced paradox: we know more about the world, but that
world is more complex. In light of that complexity, any single model will be
more likely to fail. We should not though abandon models. To the contrary,
we should privilege logical coherence over intuition and double, triple, and
even quadruple down on models and become many-model thinkers.
Becoming a many-model thinker requires learning multiple models of
which we gain a working knowledge; we need to understand the formal
descriptions of the models and know how to apply them. We need not be
experts. Hence, this book balances accessibility and depth. It can function
both as a resource and as a guide. The formal descriptions are isolated in
stand-alone boxes. It avoids line after line of equations, which overwhelm
even the most dedicated readers. The formalism that remains should be
engaged and absorbed. Modeling is a craft, mastered through engagement;
it is not a spectator sport. It requires deliberate practice. In modeling,
mathematics and logic play the role of an expert coach. They correct our
flaws.
The remainder of the book is organized as follows: Chapters 2 and 3
motivate the many-model approach. Chapter 4 discusses the challenges of
modeling people. The next twenty or so chapters cover individual models or
classes of models. By considering one type of model at a time, we can
better wrap our heads around its assumptions, implications, and
applications. This structure also means that we can pull the book from our
bookshelves or open it in our browsers and find self-contained analyses of
linear models, prediction models, network models, contagion models, and
models of long-tailed distributions, learning, spatial competition, consumer
preferences, path dependence, innovation, and economic growth.
Interspersed throughout the chapters are applications of many-model

thinking to a variety of problems and issues. The book concludes with two
deeper dives into the opioid epidemic and income inequality.