Systems Engineering’s Role in Addressing Society’s Problems

Guru Madhavan, a National Academy of Engineering senior scholar, has a new book about how engineering can contribute to solving society’s most complex and intractable problems.  He published a related article* on the National Academies website.  The author describes four different types of problems, i.e., decision situations.  Importantly, he advocates a systems engineering** perspective for addressing each type.  We will summarize his approach and provide our perspective on it.

He begins with a metaphor of clocks and clouds.  Clocks operate on logical principles and underlie much of our physical world.  Clouds form and reform, no two are alike, they defy logic, only the instant appearance is real – a metaphor for many of our complex social problems.
 
Hard problems

Hard problems can be essentially bounded.  The systems engineer can identify components, interrelationships, processes, desired outcomes, and measures of performance.  The system can be optimized by applying mathematics, scientific knowledge, and experience.  The system designers’ underlying belief is that a best outcome exists and is achievable.  In our view, this is a world of clocks.

Soft problems

Soft problems arise in the field of human behavior, which is complicated by political and psychological factors.  Because goals may be unclear, and constraints complicate system design, soft problems cannot be solved like hard problems.

Soft problems involve technology, psychology, and sociology and resolving them may yield an outcome that’s not the best (optimal) but good enough.  Results are based on satisficing, an approach that satisfies and suffices.  We’d say clouds are forming overhead.
 
Messy problems

Messy problems emerge from divisions created by people’s differing value sets, belief systems, ideologies, and convictions.  An example would be trying to stop the spread of a pathogen while respecting a culture’s traditional burial practices.  In these situations, the system designer must try to transform the nature of the entity and/or its environment by dissolving the problem into manageable elements and moving them toward a desired state in which the problem no longer arises.  In the example above, this might mean creating dignified burial rituals and promoting safe public health practices.

Wicked problems

The cloudiest problems are the “wicked” ones.  A wicked problem emerges when hard, soft, and messy problems simultaneously exist together.  This means optimal solutions, satisficing resolutions, and dissolution may also co-exist.  A comprehensive model of a wicked problem might show solution(s) within a resolution, and a dissolution might contain resolutions and solutions.  As a consequence, engineers need to possess “competency—and consciousness— . . . to develop a balanced blend of hard solutions, soft resolutions, and messy dissolutions to wicked problems.”

Our perspective

People form their mental models of the world based on their education, training, and lived experiences.  These mental models are representations of how the world works.  They are usually less than totally accurate because of people’s cognitive limitations and built-in biases.

We have long argued that technocrats who traditionally manage and operate complicated industrial facilities, e.g., nuclear power plants, have inadequate mental models, i.e., they are clock people.  Their models are limited to cause-effect thinking; their focus is on fixing the obvious hard problems in front of them.  As a result, their fixes are limited: change a procedure or component design, train harder, supervise more closely, and apply discipline, including getting rid of the bad apples, as necessary.  Rinse and repeat.

In contrast, we assert that problem solving must recognize the existence of complex socio-technical systems.  Fixes need to address both physical issues and psychological and social concerns.  Analysts must consider relationships between hard and soft system components.  Problem solvers need to be cloud people.  

Proper systems thinking understands that problems seldom exist in isolation.  They are surrounded by a task environment that may contain conflicting goals (e.g., production vs. safety) and a solution space limited by company policies, resource limitations, and organizational politics.  The external legal-political environment can also influence goals and further constrain the solution space.

Madhavan has provided some good illustrations of mental models for problem solving, starting with the (relatively) easiest “hard” physical problems and moving through more complicated models to the realm of wicked problems that may, in some cases, be effectively unsolvable.

Bottom line: this is a good refresher for people who are already systems thinkers and a good introduction for people who aren’t.


*  G. Madhavan, “Engineering Our Wicked Problems,” National Academy of Engineering Perspectives (March 6, 2024).  Online only.

**  In Madhavan’s view, systems engineering considers all facets of a problem, recognizes sensitivities, shapes synergies, and accounts for side effects.

A Straightforward Recipe for Changing Culture

Source: COS website

We recently came across a clear, easily communicated road map for implementing cultural change.*  We’ll provide some background information on the author’s motivation for developing the road map, a summary of it, and our perspective on it.

The author, Brian Nosek, is executive director of the Center for Open Science (COS).  The mission of COS is to increase the openness, integrity, and reproducibility of scientific research.  Specifically, they propose that researchers publish the initial description of their studies so that original plans can be compared with actual results.  In addition, researchers should “share the materials, protocols, and data that they produced in the research so that others could confirm, challenge, extend, or reuse the work.”  Overall, the COS proposes a major change from how much research is presently conducted.

Currently, a lot of research is done in private, i.e., more or less in secret, usually with the objective of getting results published, preferably in a prestigious journal.  Frequent publishing is fundamental to getting and keeping a job, being promoted, and obtaining future funding for more research, in other words, having a successful career.  Researchers know that publishers generally prefer findings that are novel, positive (e.g., a treatment is effective), and tidy (the evidence fits together).

Getting from the present to the future requires a significant change in the culture of scientific research.  Nosek describes the steps to implement such change using a pyramid, shown below, as his visual model.  Similar to Abraham Maslow’s Hierarchy of Needs, a higher level of the pyramid can only be achieved if the lower levels are adequately satisfied.

Source: "Strategy for Culture Change"

Each level represents a different step for changing a culture:

•    Infrastructure refers to an open source database where researchers can register their projects, share their data, and show their work.
•    The User Interface of the infrastructure must be easy to use and compatible with researchers' existing workflows.
•    New research Communities will be built around new norms (e.g., openness and sharing) and behavior, supported and publicized by the infrastructure.
•    Incentives refer to redesigned reward and recognition systems (e.g., research funding and prizes, and institutional hiring and promotion schemes) that motivate desired behaviors.
•    Public and private Policy changes codify and normalize the new system, i.e., specify the new requirements for conducting research.
     
Our Perspective

As long-time consultants to senior managers, we applaud Nosek’s change model.  It is straightforward and adequately complete, and can be easily visualized.  We used to spend a lot of time distilling complicated situations into simple graphics that communicated strategically important points.

We also totally support his call to change the reward system to motivate the new, desirable behaviors.  We have been promoting this viewpoint for years with respect to safety culture: If an organization or other entity values safety and wants safe activities and outcomes, then they should compensate the senior leadership accordingly, i.e., pay for safety performance, and stop promoting the nonsense that safety is intrinsic to the entity’s functioning and leaders should provide it basically for free.

All that said, implementing major cultural change is not as simple as Nosek makes it sound.

First off, the status quo can have enormous sticking power.  Nosek acknowledges it is defined by strong norms, incentives, and policies.  Participants know the rules and how the system works, in particular they know what they must do to obtain the rewards and recognition.  Open research is an anathema to many researchers and their sponsors; this is especially true when a project is aimed at creating some kind of competitive advantage for the researcher or the institution.  Secrecy is also valued when researchers may (or do) come up with the “wrong answer” – findings that show a product is not effective or has dangerous side effects, or an entire industry’s functioning is hazardous for society.

Second, the research industry exists in a larger environment of social, political and legal factors.  Many elected officials, corporate and non-profit bosses, and other thought leaders may say they want and value a world of open research but in private, and in their actions, believe they are better served (and supported) by the existing regime.  The legal system in particular is set up to reinforce the current way of doing business, e.g., through patents.

Finally, systemic change means fiddling with the system dynamics, the physical and information flows, inter-component interfaces, and feedback loops that create system outcomes.  To the extent such outcomes are emergent properties, they are created by the functioning of the system itself and cannot be predicted by examining or adjusting separate system components.  Large-scale system change can be a minefield of unexpected or unintended consequences.

Bottom line: A clear model for change is essential but system redesigners need to tread carefully.  


*  B. Nosek, “Strategy for Culture Change,” blog post (June 11th, 2019).

Real Systems Pursue Goals

System Model Control Panel

On March 10, 2023 we posted about a medical journal editorial that advocated for incorporating more systems thinking in hospital emergency rooms’ (ERs) diagnostic processes.  Consistent with Safetymatters’ core beliefs, we approved of using systems thinking in complicated decision situations such as those arising in the ER. 

The article prompted a letter to the editor in which the author said the approach described in the original editorial wasn’t a true systems approach because it wasn’t specifically goal-oriented.  We agree with that author’s viewpoint.  We often argue for more systems thinking and describe mental models of systems with components, dynamic relationships among the components, feedback loops, control functions such as rules and culture, and decision maker inputs.  What we haven’t emphasized as much, probably because we tend to take it for granted, is that a bona fide system is teleological, i.e., designed to achieve a goal. 

It’s important to understand what a system’s goal is.  This may be challenging because the system’s goal may contain multiple sub-goals.  For example, a medical clinician may order a certain test.  The lab has a goal: to produce accurate, timely, and reliable results for tests that have been ordered.  But the clinician’s goal is different: to develop a correct diagnosis of a patient’s condition.  The goal of the hospital of which the clinician and lab are components may be something else: to produce generally acceptable patient outcomes, at reasonable cost, without incurring undue legal problems or regulatory oversight.  System components (the clinician and the lab) may have goals which are hopefully supportive of, or at least consistent with, overall system goals.

The top-level system, e.g., a healthcare provider, may not have a single goal, it may have multiple, independent goals that can conflict with one another.  Achieving the best quality may conflict with keeping costs within budgets.  Achieving perfect safety may conflict with the need to make operational decisions under time pressure and with imperfect or incomplete information.  One of the most important responsibilities of top management is defining how the system recognizes and deals with goal conflict.

In addition to goals, we need to discuss two other characteristics of full-fledged systems: a measure of performance and a defined client.* 

The measure of performance shows the system designers, users, managers, and overseers how well the system’s goal(s) are being achieved through the functioning of system components as affected by the system’s decision makers.  Like goals, the measure of performance may have multiple dimensions or sub-measures.  In a well-designed system, the summation of the set of sub-measures should be sufficient to describe overall system performance.  

The client is the entity whose interests are served by the system.  Identifying the client can be tricky.  Consider a city’s system for serving its unhoused population.  The basic system consists of a public agency to oversee the services, entities (often nongovernmental organizations, or NGOs) that provide the services, suppliers (e.g., landlords who offer buildings for use as housing), and the unhoused population.  Who is the client of this system, i.e., who benefits from its functioning?  The politicians, running for re-election, who authorize and sustain the public agency?  The public agency bureaucrats angling for bigger budgets and more staff?  The NGOs who are looking for increased funding?  Landlords who want rent increases?  Or the unhoused who may be looking for a private room with a lockable door, or may be resistant to accepting any services because of their mental, behavioral, or social problems?  It’s easy to see that many system participants do better, i.e., get more pie, if the “homeless problem” is never fully resolved.

For another example, look at the average public school district in the U.S.  At first blush, the students are the client.  But what about the elected state commissioner of education and the associated bureaucracy that establish standards and curricula for the districts?  And the elected district directors and district bureaucracy?  And the parents’ rights organizations?  And the teachers’ unions?  All of them claim to be working to further the students’ interests but what do they really care about?  How about political or organizational power, job security, and money?  The students could be more of a secondary consideration.

We could go on.  The point is we are surrounded by many social-legal-political-technical systems and who and what they are actually serving may not be those they purport to serve.

  

*  These system characteristics are taken from the work of a systems pioneer, Prof. C. West Churchman of UC Berkeley.  For more information, see his The Design of Inquiring Systems (New York: Basic Books) 1971.

The National Academies on Behavioral Economics

Report cover

A National Academies of Sciences, Engineering, and Medicine (NASEM) committee recently published a report* on the contributions of behavioral economics (BE) to public policy.  BE is “an approach to understanding human behavior and decision making that integrates knowledge from psychology and other behavioral fields with economic analysis.” (p. Summ-1)

The report’s first section summarizes the history and development of the field of behavioral economics.  Classical economics envisions the individual person as a decision maker who has all relevant information available, and makes rational decisions that maximize his overall, i.e. short- and long-term, self-interest.  In contrast, BE recognizes that actual people making real decisions have many built-in biases, limitations, and constraints.  The following five principles apply to the decision making processes behavioral economists study:

Limited Attention and Cognition - The extent to which people pay limited attention to relevant aspects of their environment and often make cognitive errors.

Inaccurate Beliefs - Individuals can have incorrect perceptions or information about situations, relevant incentives, their own abilities, and the beliefs of others.

Present Bias - People tend to disproportionately focus on issues that are in front of them in the present moment.

Reference Dependence and Framing - Individuals tend to consider how their decision options relate to a particular reference point, e.g., the status quo, rather than considering all available possibilities. People are also sensitive to the way decision problems are framed, i.e., how options are presented, and this affects what comes to their attention and can lead to different perceptions, reactions, and choices.

Social Preferences and Social Norms - Decision makers often consider how their decisions affect others, how they compare with others, and how their decisions imply values and conformance with social norms.

The task of policy makers is to acknowledge these limitations and present decision situations to people in ways that people can comprehend and help them make decisions that will serve their own and society’s interests.  In practice this means decision situations “can be designed to modify the habitual and unconscious ways that people act and make decisions.” (p. Summ-3)

Decision situation designers use various interventions to inform and guide individuals’ decision making.  The NASEM committee mapped 23 possible interventions against the 5 principles.  It’s impractical to list all the interventions here but the more graspable ones include:

Defaults – The starting decision option is the designer’s preferred choice; the decision maker must actively choose a different option.

De-biasing – Attempt to correct inaccurate beliefs by presenting salient information related to past performance of the individual decision maker or a relevant reference group.

Mental Models – Update or change the decision maker’s mental representation of how the world works.

Reminders – Use reminders to cut through inattention, highlight desired behavior, and focus the decision maker on a future goal or desired state.

Framing – Focus the decision maker on a specific reference point, e.g., a default option or the negative consequences of inaction (not choosing any option).

Social Comparison and Feedback - Explicitly compare an individual’s performance with a relevant comparison or reference group, e.g., the individual’s professional peers.

Interventions can range from “nudges” that alter people’s behavior without forbidding any options to designs that are much stronger than nudges and are, in effect, efforts to enforce conformity.

The bulk of the report describes the theory, research, and application of BE in six public policy domains: health, retirement benefits, social safety net benefits, climate change, education, and criminal justice.  The NASEM committee reviewed current research and interventions in each domain and recommended areas for future research activity.  There is too much material to summarize so we’ll provide a single illustrative sample.

Because we have written about culture and safety practices in the healthcare industry, we will recap the report’s discussion of efforts to modify or support medical clinicians’ behavior.  Clinicians often work in busy, sometimes chaotic, settings that place multiple demands on their attention and must make frequent, critical decisions under time pressure.  On occasion, they provide more (or less) health care than a patient’s clinical condition warrants; they also make errors.  Research and interventions to date address present bias and limited attention by changing defaults, and invoke social norms by providing information on an individual’s performance relative to others.  An example of a default intervention is to change mandated checklists from opt-in (the response for each item must be specified) to opt-out (the most likely answer for each item is pre-loaded; the clinician can choose to change it).  An example of using social norms is to provide information on the behavior and performance of peers, e.g., in the quantity and type of prescriptions written.

Overall recommendations

The report’s recommendations are typical for this type of overview: improve the education of future policy makers, apply the key principles in public policy formulation, and fund and emphasize future research.  Such research should include better linkage of behavioral principles and insights to specific intervention and policy goals, and realize the potential for artificial intelligence and machine learning approaches to improve tailoring and targeting of interventions.

Our Perspective

We have written about decision making for years, mostly about how organizational culture (values and norms) affect decision making.  We’ve also reviewed the insights and principles highlighted in the subject report.  For example, our December 18, 2013 post on Daniel Kahneman’s work described people’s built-in decision making biases.  Our June 6, 2022 post on Thaler and Sunstein’s book Nudge discussed the application of behavioral economic principles in the design of ideal (and ethical) decision making processes.  These authors’ works are recognized as seminal in the subject report.

On the subject of ethics, the NASEM committee’s original mission included considering ethical issues related to the use of behavioral economics but ethics’ mention is the report is not much more than a few cautionary notes.  This is thin gruel for a field that includes many public and private actors deciding what people should do instead of letting them decide for themselves.

As evidenced by the report, the application of behavioral economics is widespread and growing.  It’s easy to see its use being supercharged by artificial intelligence and machine learning.  “Behavioral economics” sounds academic and benign.  Maybe we should start calling it behavioral engineering.

Bottom line: Read this report.  You need to know about this stuff.


*  National Academies of Sciences, Engineering, and Medicine, “Behavioral Economics: Policy Impact and Future Directions,” (Washington, DC: The National Academies Press, 2023).

A Systems Approach to Diagnosis in Healthcare Emergency Departments

JAMA logo

A recent op-ed* in JAMA advocated greater use of systems thinking to reduce diagnostic errors in emergency departments (EDs).  The authors describe the current situation – diagnostic errors occur at an estimated 5.7% rate – and offer 3 insights why systems thinking may contribute to interventions that reduce this error rate.  We will summarize their observations and then provide our perspective.

First, they point out that diagnostic errors are not limited to the ED, in fact, such errors occur in all specialties and areas of health care.  Diagnosis is often complicated and practitioners are under time pressure to come up with an answer.  The focus of interventions should be on reducing incorrect diagnoses that result in harm to patients.  Fortunately, studies have shown that “just 15 clinical conditions accounted for 68% of diagnostic errors associated with high-severity harms,” which should help narrow the focus for possible interventions.  However, simply doing more of the current approaches, e.g., more “testing,” is not going to be effective.  (We’ll explain why later.)

Second, diagnostic errors are often invisible; if they were visible, they would be recognized and corrected in the moment.  The system needs “practical value-added ways to define and measure diagnostic errors in real time, . . .”

Third, “Because of the perception of personal culpability associated with diagnostic errors, . . . health care professionals have relied on the heroism of individual clinicians . . . to prevent diagnostic errors.”  Because humans are not error-free, the system as it currently exists will inevitably produce some errors.  Possible interventions include checklists, cognitive aids, machine learning, and training modules aimed at the Top 15 problematic clinical conditions. “The paradigm of how we interpret diagnostic errors must shift from trying to “fix” individual clinicians to creating systems-level solutions to reverse system errors.”

Our Perspective

It will come as no surprise that we endorse the authors’ point of view: healthcare needs to utilize more systems thinking to increase the safety and effectiveness of its myriad diagnostic and treatment processes.  Stakeholders must acknowledge that the current system for delivering healthcare services has error rates consistent with its sub-optimal design.  Because of that, tinkering with incremental changes, e.g., the well-publicized effort to reduce infections from catheters, will yield only incremental improvements in safety.  At best, they will only expose the next stratum of issues that are limiting system performance.

Incremental improvements are based on fragmented mental models of the healthcare system.  Proper systems thinking starts with a complete mental model of a healthcare system and how it operates.  We have described a more complete mental model in other posts so we will only summarize it here.  A model has components, e.g., doctors, nurses, support staff, and facilities.  And the model is dynamic, which means components are not fixed entities but ones whose quality and quantity varies over time.  In addition, the inter-relationships between and among the components can also vary over time.  Component behavior is directed by both relatively visible factors – policies, procedures, and practices – and softer control functions such as the level of trust between individuals, different groups, and hierarchical levels, i.e., bosses and workers.  Importantly, component behavior is also influenced by feedback from other components.  These feedback loops can be positive or negative, i.e., they can reinforce certain behaviors or seek to reduce or eliminate them.  For more on mental models, see our May 21, 2021, Nov. 6, 2019, and Oct. 9, 2019 posts.

One key control factor is organizational culture, i.e., the values and assumptions about reality shared by members.  In the healthcare environment, the most important subset of culture is safety culture (SC).  Safety should be a primary consideration in all activities in a healthcare organization.  For example, in a strong SC, the reporting of an adverse event such as an error should be regarded as a routine and ordinary task.  The reluctance of doctors to report errors because of their feelings of personal and professional shame, or fear of malpractice allegations or discipline, must be overcome.  For more on SC, see our May 21, 2021 and July 31, 2020 posts.

Organizational structure is another control factor, one that basically defines the upper limit of organizational performance.  Does the existing structure facilitate communication, learning, and performance improvement or do silos create barriers?  Do professional organizations and unions create focal points the system designer can leverage to improve performance or are they separate power structures whose interests and goals may conflict with those of the larger system?  What is the quality of management’s behavior, especially their decision making processes, and how is management influenced by their goals, policy constraints, environmental pressures (e.g., to advance equity and diversity) and compensation scheme?

As noted earlier, the authors observe that EDs depend on individual doctors to arrive at correct diagnoses in spite of inadequate information or time pressure and doctors who can do this well are regarded as heroes.  We note that doctors who are less effective may be shuffled off to the side or in egregious cases, labeled “bad apples” and tossed out of the organization.  This is an incorrect viewpoint.  Competent, dedicated individuals are necessary, of course, but the system designer should focus on making the system more error tolerant (so any errors cause no or minimal harm) and resilient (so errors are recognized and corrective actions implemented.)          

Bottom line: more systems thinking is needed in healthcare and articles like this help move the needle in the correct direction.


*  J.A. Edlow and P.J. Pronovost, “Misdiagnosis in the Emergency Department: Time for a System Solution,” JAMA (Journal of the American Medical Association), Vol. 329, No. 8 (Feb. 28, 2023), pp. 631-632.

Guiding People to Better Decisions: Lessons from Nudge by Richard Thaler and Cass Sunstein

Safetymatters reports on organizational culture, the values and beliefs that underlie an organization’s essential activities.  One such activity is decision-making (DM) and we’ve said an organization’s DM processes should be robust and replicable.  DM must incorporate the organization’s priorities, allocate its resources, and handle the inevitable goal conflicts which arise.

In a related area, we’ve written about the biases that humans exhibit in their personal DM processes, described most notably in the work by Daniel Kahneman.*  These biases affect decisions people make, or contribute to, on behalf of their organizations, and personal decisions that only impact the decision maker himself.

Thaler and Sunstein also recognize that humans are not perfectly rational decision makers (citing Kahneman’s work, among others) and seek to help people make better decisions based on insights from behavioral science and applied economics.  Nudge** focuses on the presentation of decision situations and alternatives to decision makers on public and private sector websites.  It describes the nitty-gritty of identifying, analyzing, and manipulating decision factors, i.e., the architecture of choice. 

The authors examine the choice architecture for a specific class of decisions: where groups of people make individual choices from a set of alternatives.  Choice architecture consists of curation and navigation tools.  Curation refers to the set of alternatives presented to the decision maker.  Navigation tools sound neutral but small details can have a significant effect on a decider’s behavior. 

The authors discuss many examples including choosing a healthcare or retirement plan, deciding whether or not to become an organ donor, addressing climate change, and selecting a home mortgage.  In each case, they describe different ways of presenting the decision choices, and their suggestions for an optimal approach.  Their recommendations are guided by their philosophy of “libertarian paternalism” which means decision makers should be free to choose, but should be guided to an alternative that would maximize the decider’s utility, as defined by the decision maker herself.

Nudge concentrates on which alternatives are presented to a decider and how they are presented.  Is the decision maker asked to opt-in or opt-out with respect to major decisions?  Are many alternatives presented or a subset of possibilities?  A major problem in the real world is that people can have difficulty in seeing how choices will end up affecting their lives.  What is the default if the decision maker doesn’t make a selection?  This is important: default options are powerful nudges; they can be welfare enhancing for the decider or self-serving for the organization.  Ideally, default choices should be “consistent with choices people would make if they all the relevant information, were not subject to behavioral biases, and had the time to make a thoughtful choice.” (p. 261)

Another real world problem is that much choice architecture is bogged down with sludge - the inefficiency in the choice system – including barriers, red tape, delays, opaque costs, and hidden or difficult to use off-ramps (e.g., finding the path to unsubscribe from a publication).

The authors show how private entities like social media companies and employers, and public ones like the DMV, present decision situations to users.  Some entities have the decider’s welfare and benefit in mind, others are more concerned with their own power and profits.  It’s no secret that markets give companies an incentive to exploit our DM frailties to increase profits.  The authors explicitly do not support the policy of “presumed consent” embedded in many choice situations where the designer has assumed a desirable answer and is trying to get more deciders to end up there. 

The authors’ view is their work has led to many governments around the world establishing “nudge” departments to identify better routes for implementing social policies.

Our Perspective

First, the authors have a construct that is totally consistent with our notion of a system.  A true teleological system includes a designer (the authors), a client (the individual deciders), and a measure of performance (utility as experienced by the decider).  Because we all agree, we’ll give them an A+ for conceptual clarity and completeness.

Second, they pull back the curtain to reveal the deliberate (or haphazard) architecture that underlies many of our on-line experiences where we are asked or required to interact with the source entities.  The authors make clear how often we are being prodded and nudged.  Even the most ostensibly benign sites can suggest what we should be doing through their selection of default choices.  (In fairness, some site operators, like one’s employer, are themselves under the gun to provide complete data to government agencies or insurance companies.  They simply can’t wait indefinitely for employees to make up their minds.)  We need to be alert to defaults that we accept without thinking and choices we make when we know what others have chosen; in both cases, we may end up with a sub-optimal choice for our particular circumstances. 

Thaler and Sunstein are respectable academics so they include lots of endnotes with references to books, journals, mainstream media, government publications, and other sources.  Sunstein was Kahneman’s co-author for Noise, which we reviewed on July 1, 2021.

Bottom line: Nudge is an easy read about how choice architects shape our everyday experiences in the on-line world where user choices exist. 

 

*  Click on the Kahneman label for all our posts related to his work.

**  R.H. Thaler and C.R. Sunstein, Nudge, final ed. (New Haven: Yale University Press) 2021.

A Massive Mental Model: Lessons from Principles for Dealing with the Changing World Order by Ray Dalio

At Safetymatters, we have emphasized several themes over the years, including the importance of developing complete and realistic mental models of systems, often large, complicated, socio-technical organizations, to facilitate their analysis.  A mental model includes the significant factors that comprise the system, their interrelationships, system dynamics (how the system functions over time), and system outputs and their associated metrics.

This post outlines an ambitious and grand mental model: the recurring historical arc exhibited by all the world’s great empires as described in Ray Dalio’s new book.* Dalio examined empires from ancient China through the 20^th century United States.  He identified 18 factors that establish and demonstrate a great society’s rise and fall: 3 “Big Cycles,” 8 different types of power an empire can exhibit, and 7 other determinants.

Three Big Cycles 

The big cycles have a natural progression and are influenced by human innovation, technological development, and acts of nature.  They occur over an empire’s 250 year lifetime of emergence, rise, topping out, decline, and replacement by a new dominant power.

The financial cycle initially supports prosperity but debt builds over time, then governments accommodate it by printing more money** which eventually leads to a currency devaluation, debt restructuring (including defaults), and the cycle starts over.  These cycles typically last about 50 to 100 years so can occur repeatedly over an empire’s lifetime.

The political cycle starts with a new order and leadership, then resource allocation systems are built, productivity and prosperity grow, but lead to excessive spending and widening wealth gaps, then bad financial conditions (e.g., depressions), civil war or revolution, and the cycle starts over.

The international cycle is dominated by raw power dynamics.  Empires build power and, over time, have conflicts with other countries over trade, technology, geopolitics, and finances.  Some conflicts lead to wars.  Eventually, the competition becomes too costly, the empire weakens, and the cycle starts over.

Dimensions and measures of power

An empire can develop and exercise power in many ways; these are manifestations and measures of the empire’s competitive advantages relative to other countries.  The 8 areas are education, cost competitiveness, innovation and technology, economic output, share of world trade, military strength, financial center strength, and reserve currency status.

Other determinants

These include natural attributes and events, internal financial/political/legal practices, and measures of social success and satisfaction.  Specific dimensions are geology, resource allocation efficiency, acts of nature, infrastructure and investment, character/civility/determination, governance/rule of law, gaps in wealth, opportunity and cultural values.

The 18 factors interact with each other, typically positively reinforcing each other, with some leading others, e.g., a society must establish a strong education base to support innovation and technology development.  Existing conditions and determinants propel changes that create new conditions and determinants.

System dynamics

Evolution is the macro driving force that creates the system dynamic over time.  In Dalio’s view “Evolution is the biggest and only permanent force in the universe . . .” (p. 27)  He also considers other factors that shape an empire’s performance.  The most important of these are self-interest, the drive for wealth and power, the ability to learn from history, multi-generational differences, time frames for decision making, and human inventiveness.  Others include culture, leadership competence, and class relationships.  Each of these factors can wax and/or wane over the course of an empire’s lifetime, leading to changes in system performance.

Dalio uses his model to describe (and share) his version of the economic-political history of the world, and the never-ending struggles of civilizations over the accumulation and distribution of wealth and power.  Importantly, he also uses it to inform his worldwide investment strategies.  His archetype models are converted into algorithms to monitor conditions and inform investment decisions.  He believes all financial markets are driven by growth, inflation, risk premiums (e.g., to compensate for the risk of devaluation), and discount rates.

Our Perspective

Dalio’s model is ambitious, extensive, and complicated.  We offer it up an extreme example of mental modeling, i.e., identifying all the important factors in a system of interest and defining how they work together to produce something.  Your scope of interest may be more limited – a power plant, a hospital, a major corporation – but the concept is the same.

Dalio is the billionaire founder of hedge fund Bridgewater Associates.  He has no shortage of ego or self-confidence.  He name-drops prominent politicians and thinkers from around the world to add weight to his beliefs.  We reviewed his 2017 book Principles on April 17, 2018 to show an example of a hard-nosed, high performance business culture. 

He is basically a deterministic thinker who views the world as a large, complex machine.  His modeling emphasizes cause-effect relationships that evolve and repeat over time.  He believes a perfect model would perfectly forecast the future so we assume he views the probabilistic events that occur at network branching nodes as consequences of an incomplete, i.e., imperfect model.  In contrast, we believe that some paths are created by events that are essentially probabilistic (e.g., “surprising acts of nature”) or the result of human choices.  We agree that human adaptation, learning, and inventiveness are keys to productivity improvements and social progress, but we don’t think they can be completely described in mechanical cause-effect terms.  Some system conditions are emergent, i.e., the consequence of a system’s functioning, and other things occur simply by chance. 

This book is over 500 pages, full of data and tables.  Individual chapters detail the history of the Dutch, British, American, and Chinese empires over the last 500 years.  The book has no index so referring back to specific topics is challenging. Dalio is not a scholar and gives scant or no credit to thinkers who used some of the same archetypes long before him.

We offer no opinion on the accuracy or completeness of Dalio’s version of world history, or his prognostications about the future, especially U.S.-China relations.

Bottom line: this is an extensive model of world history, full of data; the analyses of the U.S. and China*** are worth reading.

 

*  R. Dalio, Principles for Dealing with the Changing World Order (New York: Avid Reader Press) 2021.

**  If the new money and credit goes into economic productivity, it can be good for the society.  But the new supply of money can also cheapen it, i.e., drive its value down, reducing the desire of people to hold it and pushing up asset prices.

***  Dalio summarizes the Chinese political-financial model as “Confucian values with capitalist practices . . .” (p. 364)