Clearing Fog

Defining What a “Moat” is (1 of n)

October 21, 2018 by Clearing Fog

Moats, competitive advantages, why a customer chooses you, and value propositions are all relatively the same thing (and I use them interchangeably throughout this post): an advantage one company has over another in convincing its customers to buy from them. By definition, any business in existence has some competitive advantage, what differs are the strength or number of those advantages. A business’s success is ultimately predicated on the quantity (more relative advantages are better) and strength of these advantages. This post attempts to define a distinct set of value propositions and elaborate on their effectiveness.

Roughly speaking (mandatory caveat: all categorization is necessarily a simplification of reality), I have found businesses to exhibit necessarily one (and rarely less than two) of the following value propositions and have broadly bucketed them into scalable vs non-scalable, with examples businesses that I elaborate on:

Through the exploration of these value propositions, I will define them, articulate how to evaluate them, and provide a section specific to each one on its real-world messiness. Additionally, as stated, almost all the businesses discussed exhibit multiple value props and they change over time, in analyzing each one it assumes that is the primary reason a customer chooses that business at that time.

To drive home this point, I will lay out the evolution of a bars primary, non-scalable value propositions (parentheticals represent an outside force changing the landscape): A customer chooses xyz bar because Local Market: it is closest one to them (new bar opens next door); Proprietary Product: xyz bar offers new beer (the bar next door replicates the recipe); Proprietary Service: xyz bar hires attractive waitresses and waiters (bar next door hires equally attractive staff); Relationships: staff and customers build relationships, creating loyalty (bar next door hires half your staff); and, finally, xyz bar petitions local govt. to only allow one liquor license.

Local market / geography:

Simple Definition & Examples: The local market value proposition is one of the easiest to understand: a customer chooses to purchase from a business purely due to its proximity. Leveraging the doctor’s office example, if you have what may be the flu, a testable (low variance in error) illness, what matters is getting in and out of the doctor’s office. Driving an hour away doesn’t make sense. Importantly, for a local market value prop to have applicability, it needs to have low variance in outcomes. Using the doctor’s office as counter-example, say you have a xyz ambiguous illness and the doctor 1 hour away has a better track record of identifying and treating xyz illness then obviously you would go to that doctor. Effectively, there is a linear (I think linear) relationship between variance in outcomes and applicability of this value proposition:

How to Evaluate: The evaluation of local market value props is straightforward with only a few variables needed to determine its strength: (i) variance of outcomes, (ii) proximity of competition, and (iii) catchment area (how far a person would “travel”) (i) is the most important aspect to get right – if there is a high variance in outcomes, then proximity doesn’t matter. The evaluation of this is imperfect, but straightforward, analyze and understand what services are provided and put yourself (and/or ask a representative sample of customers) in the customers shoes (another great local markets business are ATMs, the determinant is purely proximity); (ii) is easy to determine but hard to predict, a quick Google search / scoping of the area can quickly tell you how much competition is in the place, what is difficult is ascertaining how that will change over time, the doctor’s office for flu testing with 8,000 people in its catchment area will do well up until someone opens up next door; (iii) the catchment area is predicated on where the competition is and the real time it takes to get to your office or your competitors. What matters is the growth of people within your catchment area or the opening of new transportation, such as a super-highway between your competitions office and your catchment area reducing the time to the office.

Real World Messiness: Value Proposition Erosion & Discovery: The local market value proposition has eroded over time with technology increasing the catchment area of many products and services. Steam, railroads, roads/cars, airplanes, and the internet have dramatically reduced the value of having a local presence. Restaurants, bars, activities (golf), ERs, and apartment rentals are a few that can still effectively capitalize on this value prop, but the problem is competition. As competition increases this value proposition starts to take a back seat to others: economies of scale (drives price down), regulation (make govt mandate you can be only doctor office in town), proprietary product (you have the only flu vaccine), proprietary service (you have nicest doctors), and relationships (you are friends with your patients).

One other note on local: as we increasingly move to digital, the scope of what the local value proposition is defined as increases. I think it would be appropriate to say showing up first in a google search results is also the equivalent of a local value proposition. (note this website is on ~ page 5 for “clearing fog” ?) The local value proposition certainly isn’t drawing in my readers.

Regulation:

Simple Definition & Examples: A regulation-based value proposition is present when a regulatory body (can be govt. based or a pseudo regulatory body) grants special privileges to a business entity. It can run the gamut from a local level liquor license, a government contract, a store being *allowed* to sell on Amazon, to a federally regulated monopoly such as AT&T was at one point. In AT&T’s case, one of the more salient examples was their ability to block add-ons to phones (if still in effect, OtterBox wouldn’t exist) “No equipment…or device not furnished by the Telephone Company shall be attached to or connected with the facilities furnished by the telephone company, whether physically, by induction or otherwise.” This was ultimately overturned but is an interesting aside. (sources: 1, 2, 3)

How to Evaluate: Evaluation of a regulatory value proposition can be difficult due to the intentional lack of transparency (a firm being granted special privileges won’t advertise it and many times neither will the regulatory body). However, there are few things an analyst can do to better understand how strong this moat is: (i) validation of its existence (ii) enforced / enforceability (iii) rate of change (iv) scarcity. (i) locating the specific regulation and understanding it can be surprisingly difficult (I spent 45 minutes trying to validate the FCC tariff 132 that governed the AT&T regulation blocking phone attachments and still couldn’t find a completely satisfactory source). If you have the resources, it pays to consult lawyers and *experts* in the relevant industry. What experts and lawyers allow you to do is shortcut the process and point you in the direction to look; however, you still need to look (don’t outsource the thinking) and understand how the relevant regulation and process works; (ii) some regulations exist that aren’t enforced, if a regulation appears to not be enforced one must understand if that is due to a lack of enforceability (because it is mechanically impossible) or due to a lack of caring (political regime changes). Of these, the former is more valuable than the latter; (iii) rate of change determines the length of this value props relevancy. A low rate of change in regulation is advantageous to the incumbent and it pays to understand how it has evolved historically. In AT&T’s case, the FCC delayed hearing the case and the matter wasn’t resolved for 7 years, that is a distinct advantage (iv) assume a town of 10,000 people can support 20 beer drinking establishments, if the local government grants 50 beer drinking licenses then for all intents and purposes regulation doesn’t come into play.

Real World Messiness: At the Whims of Others: The core problem with a regulatory advantage is that you are explicitly at the whims of others and, more importantly (because everyone is really at the whims of others), many times it presents itself as a go-to-zero risk. Meaning, if the local bar gets its liquor license revoked then that business can no longer function. Similarly, if Amazon *says* a store on its network violated their terms of service, then they can remove it from the marketplace. From a risk perspective, it is crucial to understand how much of a business is truly regulated by others (maybe the bar’s business is 80% food) and how often that regulation changes and is enforced.

Proprietary Technology / Product:

Simple Definition & Examples: Most of the time when people think of why a business exists they anchor towards what that business sells. Hopefully, the above two examples illustrate that is not the case. Nevertheless, the product or technology a company employs can be instrumental (and typically is the primary reason) why a consumer chooses a business to spend money at. One example that is relatable

to me was when I purchased the Apple iPhone over the Blackberry. Yes, in 2007 that was a difficult choice to make. I don’t remember the exact reasons why I chose the iPhone, but it wasn’t because of the brand, network effects, local market or regulation advantages (at least known to me – regulatory advantages as implied above can be hidden to the consumer). It was because I thought it was a superior product. What is encompassed in that product are the manifold hardware and software components. However, the iPhone probably isn’t the best example, because today there are many conflicting reasons why one purchases it. A more appropriate one would be looking at an approved drug. Let’s say xyz illness can only be treated by abc drug. The reason you choose abc drug is because they are the only ones that can cure xyz illness. It is the drug company’s proprietary product that ensures its success.

How to Evaluate: Evaluation of a product-based value proposition is simple in theory and nearly impossible in practice. Products that provide high utility relative to competitors and substitutes and are difficult to replicate will be successful (graph to right). Given this simple axiom, why is it difficult to evaluate? Two reasons: the expert curve and varying utility curves.

Things with low replicability are almost (discussion on patents later) always technically complex. I have no chance of building an iPhone or really understanding what would be necessary to build one. Let alone a cancer-treating drug. More importantly, I have very little ability to know if a competitor could technically build one. Mistakes occur when one gets to the “analyst trickery” (see graph) level of expertise by spending a *decent* amount of time analyzing whether a product is replicable. In any truly proprietary technology or product it is incredibly difficult to know what is or isn’t replicable and, as such, one will have to outsource that thinking to some extent.

Varying utility curves is one of the oldest economic concepts and is quite simple: What I like doesn’t necessarily mean you will like. Due to this, it is important to evaluate how ambiguous a product’s utility may be relative to the rationality of its target market. Ideally, you want a product that has low ambiguity in utility and is targeting rational individuals. Where one plays on that curve determines the strength of the value proposition.

In presenting those two challenges hopefully it simplifies what to focus on: slam dunk utility cases (cancer treating drug; or a robot that makes cars measurably faster) and a baseline replicability advantage until other advantages supersede the tech advantage (Apple’s network effect of devs and branding have replaced its superior hardware. Case in point, how often do you hear the phrase “iPhone killer” anymore? You don’t, because the hardware has become, for the most part, cloned across the industry: https://finance.yahoo.com/news/16-smartphones-that-were-deemed-iphone-killer-114506321304.html

Real World Messiness: Patents & “commodities”

Ignored above, a key component of whether something is commercially replicable is whether the technology is patented. Thus, replicability is a function of technical complexity and patented technology. Technical complexity is > than patents because (i) patents can be ignored and (ii) patents expire. The expert curve also applies to patents, in that even if a company has a patent on something it is difficult to know whether that patent can be worked around such that a competitor could create a viable substitute.

Additionally, technology naturally progresses from proprietary to commodity. As time progresses, almost universally, existing technologies progress towards commodities.

More to come…

To My Younger Self

August 5, 2018 by Clearing Fog

Wrote this to a family friend’s kid who is interested in finance, but through the lens as if I was writing it to my younger self. My background (and his) is far removed from NYC, as such the route to finance is made a bit more difficult. My hope is the advice below incrementally makes it easier for him and others.

[ ],

My email to you is composed of three aspects: (I) philosophical in that I hope the advice is timeless, (ii) books that I have read informing how I think and (iii) mechanical in that it elaborates on specific career paths and trajectories as I know today and gives more concrete advice.

For (i), a core belief of mine is that we live in a chaotic (mathematical definition)/probabilistic and not deterministic world. As such, luck is and always will be a factor in any outcome (my current modicum of success included). What one does have control over is increasing the odds in their favor. A tangible example is going to [target school] vs [school I went to] increases your chances of getting into finance (investment banking) but does not guarantee it. Accordingly, when making a decision one should think along a scale of 0-100% (with neither absolute attainable) and think through does this decision, relative to other options, implicitly increase my odds of what I want to attain and if you don’t know what you want to attain does it maintain the maximum probabilities around a range of outcomes.

Another aspect of a chaotic world is the opacity around the links between what outcomes could have occurred and what actually occurred. To elaborate, I followed xyz path making decisions that ultimately led me to where I am today. While I like to think the decisions I made increased my chances of success it is impossible to know. Said differently, what if 100 me’s made all of the same decisions and only 1 achieved what I have achieved. It follows that I got lucky and my process isn’t replicable. Of course, we can never know, but in thinking that way it makes you question your own success and others: did this person get lucky (1/100) or was his process sound and success was highly likely. To examine that, look at one’s decisions/process more so than the outcome. You’d never take advice from a lottery winner on how to attain wealth. Similarly, you shouldn’t take advice from someone who is successful, and when you examine how they became successful, you realize it was more luck than skill.

For (ii), cognitively, humans are wired in such a way that thinking probabilistically is difficult. We strive for meaning and believe every decision and action was meant to be and that outcomes are the direct result of our inputs with luck being a passive observer that rarely acts. As such, a lot of my early reading was done on how to overcome these biases and are mostly psychology books (broadly defined). I will provide you with a list of five meaningful books and a few fun reads that or more entertaining but give a sense for a couple high performing cultures (finance and tech)

Books

Books by Dan Ariely are easy, intuitive and provide a good introduction to cognitive biases
Blackswan by Taleb is a solid intro to a lot of what I discussed
Thinking fast and slow is a dense book (took me 6 months to read) but is a seminal book on psychology/behavioral economics.
Behave, is also quite dense but very good. I would read concurrently with thinking fast and slow.
Sapiens by Yuval is an interesting read

Fun books

Liars Poker
Monkey Business
Chaos monkies

Good websites: www.Wallstreetoasis.com; https://www.mergersandinquisitions.com/

For (iii), there are many career options that you can pursue within the vague realm of “business”. By no means exhaustive, a few paths that come to mind are accounting, finance, sales, marketing, HR, legal/compliance. Within these fields, I would break it into two aspects: doers and advisors. Generally speaking, the median advisor makes more than the doer, but the highest paid doers make more than the highest paid advisors. Early in your career, I would recommend the “advisor” realm as it provides a great training ground

Field	Doers	Advisors	1st year comp for advisors (rough estimates)
Accounting	CFOs, controllers, AP/AR clerks	Big 4 accounting – audits / tax etc	60-80k
Finance	CFOs, Business Development, FP&A	Investment banking	120-160k
Sales/marketing	Sales managers	Consultants (not that applicable/ really a seperate category)	80-100k

I would try and get an understanding of these roles and think through what you would like to do ultimately. To get into finance or Big 3 consulting it is much easier if you come from a “target” school opposed to a less renown school. If accounting seems more appealing then this is much less relevant. If you are unsure then probably best to keep your options open by going to the best possible school.

For your reference, my path was cold calling and cold emailing into investment banking then private equity. To get into investment banking I was rejected well over 20 times, with numerous calls and emails never responded to. To get into private equity I was rejected a dozen times. I failed in trying to get into a hedge fund. Don’t be afraid of being rejected and always work on improving your odds of success.

The Superiority of Class Pass: Experiencing vs Narrative Selves

June 14, 2018 by Clearing Fog

I am a recent new user of Class Pass. So will caveat this with I am still in the honeymoon phase. However, there are a few distinct differences between Class Pass and the more traditional gym membership model that I believe makes Class Pass superior. At least, it is superior for individuals like me who don’t love going to the gym in the moment but like the idea of going to the gym (or at least the results the gym yields).

The traditional gym model is as such: big upfront fee, monthly recurring revenue model, in some cases not terribly easy to cancel and one of the few businesses that it is somewhat of a good thing for their customers not to use their services (wear and tear on equipment)(1). Now, as a gym member, I have to ask myself every month do I want to continue paying my membership fee and there are really two people within you: your experiencing and narrative self (2). My narrative self tells me that yes I will do better this upcoming month and I will go (also everything before is a sunk cost so focusing on what I do in the future is all that matters), additionally if I do cancel and I want to go eventually I’ll have to pay the sign up fee again. As I experience day to day living I have no intrinsic motivation after 7pm work. My experiencing self fails me. There is no outside force pushing me to go (as stated, gyms are incentivized for you not to go).

For Class Pass, customers pay X a month for credits, the customer uses credits at “classes” with the price fluctuating by demand. Studios get paid when a customer uses the credits at their class/studio (I think, did not validate). The long-term intrinsic value of Class Pass is dependant on a wide breadth of classes and a lot of users. As a result, they are incentivized for their users to use the classes (they want studios to get paid so more studios sign up). So for me, 10 days before the end of the month I have to decide do I want to continue paying for Class Pass. Again my narrating self says yes. However, the big difference is I have to use these credits and schedule. The mere act of scheduling and creating a commitment with direct costs dramatically increases the chances in my experiencing-self attending the Class. Said differently, it is easier for me to follow a pre-determined scheduled class, than it is to cancel and try and get my money/credits back. It is enough of a nudge, where-as in the gym model there is no nudge.

The only reason why you would want individuals to use the gym is for marketing purposes (new applicants get a tour and like to see people using the gym… confirmation bias). Otherwise, ideally, you have a whole bunch of members that never step foot in it.
The narrative self-being the individual you want to be, the one who sets weight loss goals, reading goals, fitness goals, career goals, etc. The experiencing self is the one overcome by chemicals at the moment and wants to sit on the couch because it feels nice right now.

How to Create an Uber (2 of 2)

August 31, 2017 by Clearing Fog

Connective Tissue or T1: The first part of creating a two-sided network is building the connective tissue that brings both sides together.

In Uber’s case that is building the application for Apple’s and Android’s app stores. From the definitional post, a network should treat both the providers and users as customers. Each industry will have different attributes to focus on and those attributes will change as the network matures. Accordingly, the first iteration should be highly focused and completely centered around getting users and providers. To do that, you have to remove the friction around signing up and using it. Examples will help drive this point home:

Uber:

Providers: Sign-up, payments, and use of the app are incredibly simple and allow individuals to become drivers near instantaneously. Certainly, much faster than interviewing for a comparable waged job.

User: The first iteration was a sign-up and then map with your location and availability of drivers nearby. It was simplistic and easy to understand.

eBay:

Providers: Take a few pictures, fill out an “ad”, and boom you have listed your belongings for sale with far greater reach than a garage sale.

Users: It’s a straightforward marketplace website…

Before you complete T1 (it will never truly be completed as the network will evolve and be iterated) its helpful to think through what T2 will look like, as that will inform your decisions around T1.

T2 – First User OR Provider Adoption: In the definitional post there were a few questions that pertained to T2: How do you determine the friction/reward ratio? How much should a company subsidize and what does subsidize mean exactly?

For the first question, think through a user and provider’s use cases. (Additionally, when thinking through where the friction lies, adopt a mindset of one very resistant to change and work.) In the case of Uber:

Providers:

Friction: Signing up for anything is a pain, how will I ultimately get paid, I’ll have to file taxes as a 1099 employee, how will my current employer react, will my car be insured for this, what kind of passengers will I be driving around, is this even legal, new company that doesn’t know what they are doing, etc.

Reward: monetary rewards (job replacement / augmentation level income), more autonomy, working/growing with a startup, other non-monetary based feelings.

Users:

Friction: Signing up for anything is a pain and you will have to trust someone you have never met (in a transaction that is foreign to you) and get into their personal car.

Reward: cheaper, more available, better vehicles, real-time updates, and nicer drivers

While friction and rewards are presented in aggregate, the reality is each individual has unique perspectives on what friction exists and what reward would be necessary to compensate them for that friction. Additionally, it changes over time. “Nicer drivers” isn’t necessarily a reward for a first-time user, they are more likely to focus on “availability” and “cheaper”.

During T2, the point is to develop a view on who has the greatest reward to friction ratio. In Uber’s case, it is clearly drivers. From the above, Drivers have more friction, but their reward is much higher. For a Provider, Uber could become a new job, provide a livelihood. At the end of the day, the max benefit a User will have is saving a few dollars and an increase in convenience.

T3: The Other side of the Network

T2 and T3 have to be nearly simultaneous for a network to be built successfully. In Uber’s case, the Users would be the other side of the network. Users can be acquired through providing a dramatically reduced price or a service that is markedly better than what is currently available. For Uber, price was the main focus with the better service coming from being able to order an on-demand transportation method through your phone (which varied by geography, in cases where there are high concentrations of cabs this is less valuable to the User, so they needed to compete on price).

T4: Maturity

This is defined as when the user and provider network have reached optimal capacity. This capacity shifts and the network cycles back between adding users and providers, but, at T4, the ratio of drivers to users is optimal such that there is enough liquidity for users to get a service and for providers to make money. This optimal ratio is dependent on the population. Meaning, a subset of users may want to have a car (using Uber as an example) in 5 seconds or less, where another subset is ok waiting 5 minutes. Thus, market maturity is a very fluid concept.

How to Create an Uber (1 of 2)

June 30, 2017 by Clearing Fog

Definitions & Timeline

“Network Effects”, one of the latest buzz words in the investing community. The inherent value of a two-sided network can be expressed through the concept of what came first, the chicken or the egg? Nowadays, it is what came first, the Uber driver or the Uber user. Without users, no drivers will sign up to drive people and without drivers, no users will use the service. However, we live in a world with chickens and eggs and Uber drivers and users. So, how’d we get there?

First, let’s more precisely define what a two-sided network is (and do away with the not so applicable chicken and egg conundrum). Put simply, a two-sided network is one in which every incremental user and / or provider increases the utility for all users and providers. Below are characteristics of providers, the network and users.

T1 the connective tissue of the network is created. In Uber’s case, it is the app itself.
Starting at Time 1 (with T0 being idea conception), let’s go through the major milestones.

T2 the first user or provider starts using the network, and here is where we get back to the original question. For networks broadly, the provider or user with the greatest amount of reward to friction ratio comes first. In Uber’s case, what came first were the professionals already doing this job: taxi’s and black cars. The friction is signing up to the network and having the app running while they are currently doing their job and the reward could be significant. To further tip the friction/reward ratio, companies trying to establish networks will subsidize. In Uber’s case, that means giving a sign-up bonus based on rides given.

T3 is when the other side of the network starts using the network. For this to occur the network has to provide better value relative to substitutes. This is done through a superior product/service or a better price. At the start of a network, competing on price is much easier to do (especially with VC money). In Uber’s case, when a user signs up you are/were granted free rides…hard to beat that value.

T4 the network is mature and there is no longer a need for subsidization.

Pretty simple, right? Well, not really, there are a lot of questions that need to be examined: How do you create the connective tissue? What should you focus on? How do you determine the friction/reward ratio? How much should a company subsidize? What exactly does subsidizing mean in this case? How do you know when a market is mature and subsidization is no longer needed? I’ll answer these in a follow-up post.

(1) Card networks, Visa, MasterCard, Amex, etc., are one of the first networks and immensely valuable. Unlike Uber, Handy, Ebay, AirBnB, etc. it is difficult to define who is the User and who is the Provider. I am inclined to say the Provider is the consumer and the User is the merchant. This is due to the merchant having to pay to accept credit cards (its merchant discount rate). Payments processing is a bit too complicated for this post, so I will leave it for another day.

Mental Model: Perfect Information

May 29, 2017 by Clearing Fog

Summary (TL;DR): The perfect information heuristic is a state of mind where you assume you have access to all available information. It allows you to more specifically and narrowly define what decision you are trying to make and allows you to look for data that, at first blush, may seem impossible to get. It is effective in counteracting the availability heuristic. Its shortcomings are that it is more work, many times what is available is what matters, and it can give you overconfidence in the decision you ultimately make.

Lastly, one could argue that this “heuristic” is just sound decision making and doesn’t really need to be elaborated on. I don’t have a great rebuttal to that, but clearly, I think it’s worth discussing.

On the Perfect Information Heuristic…

What is it: There is a quote that I am very fond of by Laplace:

“We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at any given moment knew all of the forces that animate nature and the mutual positions of the beings that compose it, if this intellect were vast enough to submit the data to analysis, could condense into a single formula the movement of the greatest bodies of the universe and that of the lightest atom; for such an intellect, nothing could be uncertain and the future just like the past would be present before its eyes.”

— Marquis Pierre Simon de Laplace

If the premises are valid then I think the logic is correct. Thus, if one had access to all available information in the universe, one could see the future and therefore make the most optimal decision. While that is impossible, it is still a good heuristic to apply to decision making, especially in countering the availability bias. To more specifically define the perfect information heuristic: it is a state of mind where you assume you have access to all available information, as such, what variables would you examine in making the best decision.

Application:

Using the perfect information heuristic for decision making would start as follows:

Pretend as though you have perfect information, specifically define what you are deciding on and determine what variables are the most salient to the decision. …hmm, seems like a “duh” moment. However, the availability bias(1) creeps in due to: specifically defining decisions are nearly impossible, most decisions you make you have some level of information already and, additionally, decision making is circular, as you learn more you redefine what you are looking for and as you redefine you conform to what information is available.

Let’s say you are thinking of going to college (an imprecisely defined decision – for most, college is a means to an end – define what your “ends” are). You have heard over the years that college educated people make way more than non-college educated individuals (people have initial information). So, what happens is you imperfectly define your decision, should I go to school, then you list variables that you know exist: one being do college educated people make more than non-college educated. As you start looking for this information you stumble on all the careers college educated individuals have access to vs non-college educated – this information is available and should be incorporated (decisions are circular). By the end, you confirmed that college educated people make way more than non-college educated and you realized that there are more career opportunities for college educated individuals. So, you may not know what you want to do, but if you go to college you will make more money and have more career options once you ultimately decide on what you want to do. The data supports it and you can safely say you made a deliberate, informed decision.

So, let’s apply the perfect information heuristic to deciding on going to school and why the above is flawed.

Define the decision specifically: Do / should I go to school Perfect information would dictate that you know exactly what you want: I want to be an accountant making $75,000 a year, working at xyz place, living in abc location, etc. Are you going to capture everything, no, but starting with the premise that you could know exactly what you want changes the way you think about the problem. So, you meet halfway:

Imprecisely defined decision	Modified Decision (still flawed but better)	Perfect Information (tons of variables still unaccounted for)
Do / should I go to school?	Living at xyz geography is most important and making $>50k a year (which allows me to have a good-sized house and the ability to go to Florida twice a year is important.) Will school help me get there?	What is the best way for me to become an accountant making $75,000 a year (so I can do xyz with the money), working at xyz place, living in abc location, etc.

Already you can reframe the question from one that society (you need to decide if you are going to college and the answer should be yes) impresses (read: makes available to you) upon you to one that has more relevance to your life. However, let’s say the modified decision you are making is only “I want to make >$50k a year, should I go to school?” In that slight reframing, you now look for ways to make >$50k. From that, you find a stat that most jobs where people make >$50k the individuals are college educated and it lists the careers. You then apply the heuristic to that stat to see what additional information you can get. Start with perfect information and work backward.

Direct, perfect information question: Would I be able to get one of those jobs? (Unanswerable)

Indirect questions, pretending you have access to near perfect information: If they went to college what degree did they get? What companies do they work for? What skills are they performing? Where do they live? How old are they? How long did it take them to get to >$50k? etc. etc. Just throw questions on a page until you can’t think of anything more.

Pair down to importance: Of the above questions, only a few will hold real influence/matter to the decision you are making. Let’s just pick degree and geography as being important factors to you. (after asking those questions you realized you don’t want to be a science major and you don’t want to live in a big city).

Find proxies: The stat listed only showed the careers of college degree holders. It didn’t show the degrees or where those individuals live, so you must find a way to get that information or approximate for that information. Let’s say you trolled LinkedIn and sampled 80 individuals. Of that sample, 65 were science majors and/or lived in a big city. That leaves 15 individuals and maybe three professions that met your criteria.

Re-analyze: Initial stat was way more people with a college education make >$50k a year. Revised, more relevant stat: I would, effectively, be eligible for three professions that require a college education that makes >$50k a year. If I didn’t go to college, I would be eligible for these other five professions that make >$50k a year. Compare those specifically then.

Shortcomings:

It is more work. If the decision you are making doesn’t appear to have much impact on your life then why expend the energy to make a more optimal decision and, in some cases, get to the exact same answer. And in some, if not many cases, you will get to the same answer. There is wisdom embedded in what society impresses upon people. Questioning for the sake of questioning or being contrarian for the sake of being contrarian isn’t in and of itself valuable. Lastly, in going through this analysis, you may lose the humility that knowing every possible outcome is impossible. You can be wrong and you will be wrong. When you are wrong, be flexible and change. By applying this heuristic the goal is to improve the ratio of good to bad decisions, not to eliminate all bad decisions.

Conclusion: See the summary at the top of the page…

(1) The availability bias, in short, means that individuals will only leverage the information that is immediately available to them regardless of its value. People act on what they can see or measure, even if what they are measuring holds no value. I think this manifests itself in both deliberate and indeliberate decisions. For this post, I will focus on how the perfect information heuristic can help in deliberate decisions where the availability bias rears its head.