Knowing your stuff is important. Knowing where to look in order to learn how to know your stuff is important. But at the beginning of any decision, you likely do NOT know what is needed to confidently know you will make a great decision.
Thus -- 'achieving the known' is foundational to a great decision.
I am a choice architect. Choice architects look at information as an input to the decision process. How people curate data to make it available to inform decisions tends to be highly personal. The connection between curated data and good decisions is iron-clad.
To help connect the dots -- Economists believe it is essential to clearly understand your own decision "what is important to me" utility. What economists do not tell you is that understanding your own utility is weirdly difficult. Utility is found at the intersection of your highly personalized and dynamic motivations known as 'self-interest.' Our individual self-interests, a mishmash of both selfish AND selfless motivations, are unique and may change over time. Curated information is the building block of any good decision coming from your unique self-interests. [i]
To help you achieve conviction in your confidence to make the best decisions, this article focuses on data curation as the foundation for your decisions. I hope you find this helpful! - Jeff Hulett
Abstract:
The article starts with an overview of the standard decision process. The HRU framework is then provided to prioritize and rationalize how you go about curating decision information. College decision examples are offered to deepen the understanding of the HRU framework. Information curation tips are furnished. A fun parable from Google is explored encouraging us to look for 'deal killer' risks. Finally, choice architecture tools are suggested to help you curate data and make the best decision.
Table of Contents
Introduction
Data and the Decision Process
Information and using the HRU framework for curating data
Information Curation Tips
The Monkey and the Pedestal
Tools you can use - resources for making the best decision
Notes
About the author: Jeff Hulett is a career banker, data scientist, behavioral economist, and choice architect. Jeff has held banking and consulting leadership roles at Wells Fargo, Citibank, KPMG, and IBM. Today, Jeff is an executive with the Definitive Companies. He teaches personal finance at James Madison University and provides personal finance seminars. Check out his new book -- Making Choices, Making Money: Your Guide to Making Confident Financial Decisions -- at jeffhulett.com.
2. Data and the Decision Process
This article focuses on curated information inputs as the starting point for the decision process. The curation process is how data is transformed into decision information. Uncurated data, by itself, is not particularly useful to the decision. As we will explore, uncurated data may discourage the decision. Transforming data into information is characterized by either a) combining individual pieces of data to make them usefully inform the decision or b) confirming individual data usefully informs the decision. Today, data is found along a data source continuum-- either abundant or scarce.
Data abundance: Today's internet and technology-enabled world is mostly characterized by data abundance. This means the data is readily available, the challenge is to choose which data to curate and to reject irrelevant or wrong data. According to credit behavioral design insights from the Financial Health Network [ii], a significant challenge is that
"People tire from too many options."
Abundant data sources include publicly available information found on the internet, using search engines or Chat GPT.
It was not long ago that data was mostly scarce. In recent decades, the scale has rapidly tipped to data abundance. Today, curation is characterized by subtracting abundant but unnecessary data to develop accurate decision information. This occurs much more often than the need to add scarce data because it was unavailable to develop accurate decision information.
Data scarcity: Scarce private information, particularly from parents or mentors, is also helpful. For example, mentors may have unique, “this is how it felt” information about a new decision, like being a college freshman. Also, scarce private data sources are often easier to curate or even come pre-curated!
Regardless of whether the data source is abundant or scarce, data curation is needed to prepare information for the decision process. To provide relevant context, next is a light touch to ground the reader in the other aspects of the decision process. The author's book covers the fulsome decision process scope in more detail.
Decision process primer: Beyond the information input sources, your decision process makes use of that curated information. You can follow along in the 5-box decision process graphic presented in the earlier system's view. At a high level, the decision process starts with defining the bird's eye view of your overall decision mental map. From there, you will define and weigh your buying preferences known as criteria. Those benefit criteria are modeled, which is also known as your utility function. Your model is then made available to evaluate potential buying alternatives. Costs are also considered. In the end, trade-off reporting is available in the form of a cost-benefit indifference curve. This curve is the basis for prioritizing your purchase alternatives and negotiating a win-win. Because individual self-interests are unique, each of our utility functions is unique as well.
Next, discussed is the framework for classifying your data sources and making them available for data curation.
3. Information and using the HRU framework for curating data
Information, whether from scarce or abundant sources, may be divided into these 4 HRU framework categories. [iii]
The known-known (Happy place)
The known-unknown (Risk)
The unknown-unknown (Uncertainty)
The unknown-known (Ignorance or fooling yourself)
As the graphic shows, the 4 categories are described along 2 dimensions.
The vertical dimension describes how much is known today to identify which decision factors and related situations impact the decision outcome. For example, the criteria we chose for a decision is "known today." The 'how much is known' is found somewhere between the known and unknown ends of this dimension.
The horizontal dimension describes how much is known about how those factors and situations will behave in the future to impact the decision outcome. For example, outcomes with multiple possible outcomes means that future outcome is NOT deterministic. These are found at the unknown end of the horizontal dimension. The more determinist outcomes are found on the known end.
Examples are provided in the following information curation tips section.
To understand the framework, it is easiest to start with the “today” vertical axis. Today, you “know what you know” about a decision. This is a good happy place starting point -- but you will need to add more curated information to this starting point. Next, we explore how to add appropriate risk and uncertainty-based information to complete your total information needs. Later, we will circle back to how to manage the ignorance that may seep into the happy place, risk, or uncertainty categories.
From the Happy Place to Risk
To identify which additional data you need to curate, we look to the “known unknown.” That is, what are some risky but knowable things I need to learn about, but I do not 100% know the answer because those answers are only revealed in the future? Not to worry, examples are provided in the next section!
From the Happy Place to Uncertainty
The ”unknown unknown” is a possible uncertain and unknowable thing that could happen, especially if that thing could cause ruin or otherwise cause "game over" for your decision. Do not worry too much about uncertainty at the beginning of the decision process. You will just drive yourself crazy inventing possible bad scenarios. Initially, this will unnecessarily sap your decision confidence. Uncertainty will be addressed later in the decision process.
From the Happy Place to Ignorance
The “unknown known” is the same as ignorance or fooling ourselves. This is where we believe we have a known future outcome, but today’s understanding has changed. This is typical of when we fail to update our beliefs. Having a consistent, repeatable decision process is certainly helpful to overcome this challenge. Due to the ever-changing nature of the world and our tendency to stick to our beliefs, ignorance is often more prevalent than we might expect.
Building decision confidence with curated information
Decision confidence is a big deal. Confidence is the decision emotion. The grit and fortitude to march forward in the face of risk and uncertainty is critical! A good decision process optimizes decision confidence to help you stay in the decision game! We discuss resources to help you with the decision at the end of the article.
As an information curator, your hunt is to decide, in order:
What total information is needed to support the best decision process, then
How is the total information distributed across the 4 HRU categories, then
How do I avoid ignorance, reduce uncertainty, manage risk, and increase the curated data in the happy place?
It is that easy! Next are some tips to improve the speed and quality of information curation, improve decision confidence, and manage your HRU framework.
4. Information Curation Tips
a – Information curation is a lifetime generative process. Meaning, over time a strong curation process will move more information toward today's desired known happy place and manageable risk HRU categories. A strong data curation process will move away from today’s undesired ignorance and help reveal and better manage the uncertainty HRU categories. You will improve your ability over time. Implementing a consistent, repeatable decision process is the means by which you will improve.
b - Uninspected beliefs are the hobgoblins of ignorance. Once you move curated information into the happy place bucket, you may “believe” you are done. You are not done, you have only just begun! Think of a belief as a convenient aggregation of curated information. That is:
"Because I learned X, Y, and Z in the past, today I BELIEVE A is true!"
But what if the information ( X, Y, or Z) underlying belief (A) changes?! A person's belief inertia [iv-a], or the inability or unwillingness to change beliefs, is the gateway to ignorance. To minimize the chance of a happy place belief transforming into ignorance, we must regularly test those significant happy place beliefs. [iv-b] In the prescient words of political scientist and psychologist Philip Tetlock, “…beliefs are hypotheses to be tested, not treasures to be guarded.”
c- You may start with little information in your happy place. For example, if you are a teenager and considering going to college, you will start with almost no happy place information. That is ok! However, your first reaction may be to dive deep into gathering information and learning about colleges – that is normal but WRONG. Your first reaction SHOULD be to determine the best decision process. The structure of your decision process will inform you as to which information is needed for the decision. Because data is so abundant today, you should strive to only curate data on target for your decision. If you do not start with the decision process, there is a higher chance you will waste your time chasing data you do not need. Without a decision process, you are more likely to reduce your decision confidence. To be clear, this does not mean you should not explore and test different ideas. You should. But when it comes time to decide, rely on your decision process to guide those learnings.
A college example to structure your decision and data curation processes:
Learn a high-level map of essential items necessary for your decision process. College items may include tuition, major, distance from home, college culture, jobs available after college, application essays, etc. The idea is to learn just enough to form a mental map of the decision process. A more detailed investigation will come later.
Define and weigh a starter set of criteria for "what's important to me" about college.
Identify your initial college target list. Those alternatives may include non-college alternatives like working, armed services, technical school, etc.
See the "Tools You Can Use" section at the end for apps to help with data curation.
d - Examples for obtaining curated information. Once the decision process is in place, provide curated information to either put in the happy place bucket or identify for your risk bucket. Then, consider how to manage uncertainty and ignorance. Next are college examples to help illustrate the HRU framework for data curation. Your actual college decision factors will likely be different:
·Happy Place - You may know you want to live close to home. So your college criteria will include a filter that only looks at colleges within a 2-hour drive from home. [v] You know you will need to write a reusable college essay. You will get started on that before your senior year.
Risk – You may have a good idea of what you want to study, but you are less than 100% sure. This means your college major is in the Risk HRU category. You are also passionate about picking the best major. This means that in your college utility function, your college major criterion is weighted higher as compared to other criteria. As you search for colleges, you will consider strong programs aligned with your likely major. You will also consider colleges with a variety of majors that are lower on your list of probable 'best majors." This is the "known unknown" in action! Most of your criteria will fall into this risk bucket. A good decision process will help you weight the criteria. You can use that weighting as a prioritization for seeking curated information. Focus your time on the highest weighted criteria!
Uncertainty – What if something bad happens out of the blue – like you get hurt in a car accident and cannot go to college? Think of uncertainty as something that could cause "game over" for college. Whether you really will get hurt is unknowable when you are making the college decision. This is the "unknown unknown" in action!
Uncertainty is the domain of insurance. You want to protect your chance of financial ruin with medical insurance. At a minimum, a high deductible policy will protect you and your family's wealth and allow you to come back to college after you heal. Also, check with the college to see how late you can withdraw with a full refund. Most colleges allow you to withdraw within a month after the semester starts. Also, many colleges have policies for emergency student withdrawals. This is good to know for MANAGING your uncertainty. [vi]
Ignorance – circling back to the Happy Place example. What if the original belief that you wanted to attend college close to home changes over time? Perhaps you evolve and in the future determine you want to go farther from home. In the HRU framework, ignoring an evolving belief when the underlying belief evidence changes is called ignorance. The mechanism causing one to ignore changing belief evidence is called belief inertia. In this college example, you can always transfer! Learning how many credits typically transfer BEFORE you make the college decision will help you reduce the impact of ignorance. Choose a college with a good transfer rate history. Plus, getting good grades is helpful so you can transfer many classes to the new college. Regular belief updating to avoid belief inertia helps to MANAGE ignorance.
Ignorance prevention requires a personal belief testing PROCESS. This includes periodically testing significant Happy Place beliefs and understanding how you would react if that belief does evolve. Belief inertia is a big deal. It is very subtle and tends to sneak up on people. People tend to be protective of their beliefs and slow to change. The University of Chicago Economist Steve Levitt's change experiment conclusion was that there is "the presence of a substantial bias against making changes when it comes to important life decisions." [vii]
e- Curating data is a process. Keep in mind that it takes time and ongoing inspection to curate data. Even after you are in the decision process, you will learn more and update curated information. For the college example, you will initially set your college "what is important to me" criteria. As you visit colleges and talk to other people with experience, you will learn even more. It is important to update as you learn. Think of the app we suggest at the end of the article as a little decision concierge. As you learn, take the time to capture that learning in the app. Science teaches that our memory is not always dependable. As such, it is best to update the app as close to real-time as practical.
5. The Monkey and the Pedestal
When evaluating risks, be on the lookout for those decision inputs that may be a deal killer. Meaning, if there is something that is a necessary input to the decision and not completing it would ruin the decision, then be hyper-focused on managing that risk. Either eliminate that risk or quickly "kill the deal" if the risk is unable to be eliminated. Salespeople sometimes call deal killers "Fast to No." This is used when pursuing a possible sales lead. If the sales lead is not going to pan out, a good sales person will quickly "kill the deal" by prioritizing other leads. Unfortunately, these kinds of deal killers sometimes get ignored longer than they should.
The obvious college example is the risk of the high school student not graduating from high school. Not graduating from high school would certainly be a college "deal killer." As such, care should be taken to closely manage risks that may keep the high school student from graduating.
The Monkey and the Pedestal is a fun parable to help remember to focus on deal killer risks.
You want to get a monkey to recite Shakespeare while sitting on a pedestal. What do you do first? Train the monkey or build the pedestal? It’s obvious that training the monkey is a much harder task, and it’s quite likely that we are tempted to start with sculpting a beautiful pedestal instead. But if we can’t get the monkey to recite the monologues on the ground, then all the time and energy put into crafting the pedestal are wasted.
- Astro Teller, the head of X, Google's moonshot division
6. Tools you can use - resources for making the best decision
Definitive Choice is a choice architecture app for managing your HRU data curation process, the decision process, and increasing decision confidence.
Definitive Choice: For individual or small organization groups - This app provides a convenient way to enter and weigh your preference criteria, then, enter your potential decision alternatives and their costs. Behind the scenes, it uses decision science to apply your tailored preferences and preference weights to score each of your alternatives. Ultimately, it renders a rank-ordered report to help you understand which alternatives will give you the biggest bang for your buck. Using a decision support app will 1) save you time, 2) optimize your economic value achieved, and 3) increase your decision-making confidence!
A good decision process, which behavioral economists call choice architecture, has the following Decision A-C-T benefits:
Accelerated: faster, less costly decisions. It enables a nimble decision environment.
Confidence-inspired: process causes people to be more confident in the decision, increasing buy-in, and decision up-take.
Transparency-enabled: reporting, documentation, and charts to help communicate the decision.
College Xoice® is specific to the college decision. It has the same technology as Definitive Choice, but is narrowly focused on the college decision. It also has a number of data and educational resources to help with data curation.
Registration Code: “College”
7. Notes
[i] The challenges to curating data and making the best decisions are explored in the article:
Hulett, Solving the Decision-making Crisis: Making the most of our free will, The Curiosity Vine, 2023
[ii] Johnson, Gdalman, Pirani, A Financial Health Approach to Credit Card Design, Financial Health Network, 2023
[iii] Donald Rumsfeld, United States Secretary of Defense under President George W. Bush, coined the various HRU categories in a 2002 speech on the lack of evidence linking the government of Iraq with the supply of weapons of mass destruction to terrorist groups. He said:
"Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don't know we don't know. And if one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones."
Defense.gov News Transcript, DoD News Briefing – Secretary Rumsfeld and Gen. Myers, United States Department of Defense, February 12, 2002.
[iv-a] Hulett, Changing Our Mind, The Curiosity Vine, 2023
[iv-b] Over 200 years ago, The Reverend Thomas Bayes built a process for testing and updating beliefs called Bayesian Inference. Bayesian Inference is still used today.
Hulett, Challenging Our Beliefs: Expressing our free will and how to be Bayesian in our day-to-day life, The Curiosity Vine, 2023
[v] Advanced decision-makers see the world through Benjamin Franklin's perspective - "The only thing certain in life is death and taxes." So, think of the Happy Place as an ideal. But even the most certain things have at least a little risk. Advanced decision-makers tend to appreciate mathematician Bertrand Russell's sage social commentary, "The whole problem with the world is that fools and fanatics are always so certain of themselves and wiser people so full of doubts."
[vi] The difference between risk and uncertainty is nuanced and best understood via the Ergodicity framework:
Hulett, The Regenerative Life: How to be an ergodic pathfinder, The Curiosity Vine, 2023
[vii] Levitt, Heads or Tails: The Impact of a Coin Toss on Major Life Decisions and Subsequent Happiness, National Bureau of Economic Research, WORKING PAPER 22487, 2016
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