Betting as Evidence Amplifier

In her book “The biggest bluff” the author Maria Konnikova describes betting as a “corrective for many of the follies of human reason”. This seems contradictory at first as betting is rarely considered a prudent activity. It is, however, important to distinguish betting (in poker) from gambling. A game like roulette, for example, is based purely on luck. Poker, on the other hand, is a complex game that requires a mix of skills to be successful. Luck plays a role here as well, but as every amateur player who participates in a tournament can confirm, in the long run, the most experienced and skilled players win.

Leaving aside the question of whether betting is a “virtuous” endeavour or not, the point is that introducing a direct cost into our decision making changes how we evaluate a situation. When we make a decision, we are searching for possibilities and evidence. Are the things we believe actually true? The lower the stakes, the less we are inclined to make an effort to question our beliefs. If the costs are high, and in the case of betting in poker, if being wrong leads to an immediate loss, we are much more motivated to scrutinise our assumptions. “Would you bet your house on it?” changes what we claim to be true.

In other words: betting is an evidence amplifier.

We encounter a similar situation in software engineering. The cost of failure determines the stakes of developing software. Do we build a system for the space shuttle or an image sharing app? Will an error cost lives or cause a minor disturbance of a (freemium) user?

Shipping software into production is a bet. Knowing the stakes is a crucial ingredient when determining how much research, testing and rigour we want to invest. In other words, how much evidence we need to seek out before releasing software to the world. When we overestimate the stakes, we may unnecessarily hinder our velocity by over-thinking or over-engineering our solution instead of relying on a shorter release cycle that will lead to actual evidence that the feature works in production. The opposite, however, can be worse. Miscalculating the stakes can lead to underestimating the risk of how much damage shipping a faulty component can cause.

The search-inference framework of thinking

The half-life of a tech-stack

The skills of a software engineer can be split into two groups: those that belong to a tech-stack (A) and those that don’t (B).

Skills in group (A) are directly associated with the ability to use a certain tool, platform or programming language. Skills in group (B), on the other hand, are fuzzy and can’t be defined easily. They are often called soft- or meta-skills.

As a general career strategy, I believe that time invested in honing the skills of group (B) yields a higher return. An engineer needs to have sufficient knowledge of (A) to be useful, but as technology is perpetually changing at an increasing rate, knowledge in that group has a short half-life. In contrast, skills acquired in (B) tend to stay relevant and to grow in value over time. Once you master to communicate well, that skill remains relevant regardless of how the working environment changes.

The most fundamental of all meta-skills is the ability to think (well). As engineers and knowledge workers spend most of their time thinking, doing that as efficient as possible is undoubtedly beneficial. It is a skill that is highly transferable and maximizing it will make everything else better.

Thinking is search and inference

But what does it mean to think well?

In his book “Thinking and Deciding”1 the author Jonathan Baron answers that question by defining a framework that is very much in the wheelhouse of any developer. It is called the search-inference-framework.

We can summarize the framework in the following diagram:

Search-inference framework

We think when we need to resolve doubts or make a decision about what to do or not to do. We base our decisions on what we believe and what goals we have. To form a belief or build an opinion, we think about possibilities and try to find evidence to support our judgement. We search for answers. This search happens within ourselves (our memories or ideas) or outside (other people, books and so on).

Then, to make a decision or come to a conclusion we infer from our findings. We do so by considering our goals and weighing the options we have available.

The objects of thinking are:

  • Possibilities are potential answers to our question that started the search.
  • Goals are criteria by which we evaluate possibilities. The word can be misleading. Usually, a goal is something that is either reached or not. Here, more often, a goal is something that can be gradually achieved on a scale.
  • Evidence is any form of belief that helps determine if a possibility achieves a goal or not.

Goals are not fixed and can change with new possibilities or evidence.

Good thinking is often synonymous with rational thinking. Baron defines it the following way:

Rational thinking is thinking that is in our best interest. To think rationally means to think in ways that help us best to achieve our goals.

What is counter-intuitive of this definition is that it does not dictate in any way how to think. When we imagine a rational thinker, we often mean someone who thinks logically and without emotions. This is not necessarily rational. If being emotional or illogical brings someone closer to their goal, then that kind of thinking is rational.

Better thinking

With the framework and the definition of rational thinking at hand, we can now answer the question of how to think better. There are two ways:

  • improve search
  • improve inference

so that we can reach our goals more efficiently.

There are many ways to get better at either search or inference. Proponents of rational thinking discuss mainly overcoming biases and our struggle to inherently grasp probabilities. But doing certain activities alone will already have a positive impact. For example, writing is a full work-out of the search-inference framework. It starts with a goal (or multiple goals) to explore a subject. Interesting writing requires an extensive search for possibilities that have not been conveyed before. To be convincing, an author has to discuss evidence to form a compelling argument.

Freewriting and how to generate ideas bottom-up

One apparent difference between human and artificial intelligence is that an algorithm needs to be fed with adequate data to be able to do something useful. In contrast, the human brain can be quite happy without external input. Giving the mind nothing to work on or confronting it with nonsensical material can stimulate fresh thinking.

This is what an exercise called “freewriting”1 takes advantage of. I find it one of the most effective tools to generate new ideas. The rules are simple: during a limited time (usually 10-20 mins) write about a subject without ever stopping to move your hand (or to type on your keyboard). The key is to never stop writing. When you are out of things to say, instead of interrupting the writing flow by searching your mind for new ideas, jot down nonsensical words (for example, repeat the last word on the page multiple times) or filler phrases. I like to literally translate my mind onto paper and often use sentences like “I don’t know what to say” or “what else am I thinking here” and so on. As long as you keep putting words on paper, it does not matter what they are or if what you write makes any sense at all.

When you do this, something happens.

The brain is a thought producing machine that continuously spits out opinions, worries, ideas, memories and so on. When you challenge your mind with useless repetition and boredom, it will jump out the rut to something else. Quite often, that leads to uncovering new places or hidden corners.

Being able to come up with a fresh set of ideas whenever stuck is tremendously valuable. There is something about this “writing flow” that is not available to other techniques.

Another advantage is that freewriting reliably delivers a sense of accomplishment. As there are almost no rules (just keep going), it is not difficult to reach the writing goal. What is required is a concentrated effort to keep the mind on paper. That is in some respect, similar to what happens during meditation but simpler to execute. The challenge of meditation is not to get distracted and keep the mind aware of what is happening. In freewriting, distractions are welcome, and all we have to do is to follow them with our pen or keyboard. I find that a couple of intense writing sessions can clear up the mind in a similarly satisfying way as meditation can.

Because leaving the path is allowed and encouraged, the likelihood to encounter something new is high - an idea, direction or novel connection between existing ideas. Freewriting helps to enrich our internal map by adding further details to it. Clarity comes from bringing previously unrelated strain of thoughts into alignment.

This “connection making” happens automatically. What we need to do is to “feed” our mind with explorations of concepts, thoughts and mental images and then let it do its work.

Another way to describe what is going on is to developing ideas bottom-up versus top-down. Classical brainstorming starts top-down. We have a question or subject and try to find as many related ideas as possible. A freewriting session initially starts top-down. But then we allow the mind to digress and explore new directions that might not have anything to do with the original question. We then move into a bottom-up approach where ideas emerge through making novel connections. Bottom-up requires less effort as it is more natural to how we think and our brain functions.

I just scratched the surface of this fascinating subject of creativity and human versus artificial intelligence. More to come soon.

And then Covid happened - Resilience as a blogger

Mike Tyson knows a thing or two about the meaning of disruption when he says that “everyone has a plan until they get punched in the mouth”.

Something similar happened to this blog. I had planned to write consistently on a regular publishing schedule (writing after hours and weekends) and even accounting for a major (but happy) change of welcoming a new family member. I was ready. But the pandemic had turned everything upside-down. I was confronted with a sudden, unexpected disruption that my blogger persona was not prepared to deal with.

Strategic planning involves predicting the future. Unforeseen events are, by definition, excluded from any consideration. If those do happen, something else matters - the inherent ability to bounce back from a sudden change. This is called resilience.

In systems engineering, resilience is defined as

… an ability of the system to withstand a major disruption within acceptable degradation parameters and to recover within an acceptable time 1

Applying this definition to this blog, the service degradation (little to zero article output) and time to recover (months) were both not acceptable.

But like most people, I hold more than one role in life - I’m also an employee, parent, partner and so on. Since this blog doesn’t pay my bills, other services I provide were more important to maintain.

Fast versus slow disruption

Most of the time, when we think about resilience, we are talking about dealing with a sudden disruption - a pandemic, hacker attack, a new competitor, key employees leaving a company and so on.

However, harder to handle is gradual change. Service degradation is a temporary measure to gain time for dealing with a problem. This won’t work if the underlying cause can not be fixed immediately and requires a long-term adjustment.

Skills that slowly become obsolete is a prevalent example. Companies can deal with a skill shortage by hiring workers with the right expertise. But there is a limitation to how quickly it is possible to find and integrate new employees.

Even more restricted are individuals. The worker that has been laid off and finds that their skills are not in demand anymore will need time to acquire new expertise.

A better strategy to deal with slow change is constant gradual adaptation. Companies can train their workers and implement a culture that encourages learning. Workers that keep updating their skills doing their day-to-day job stay employable.

That is not always easy to accomplish. The interest of companies is stability. If in-house technology is mastered and fulfils business needs, there is little incentive for stakeholders to change anything. No new investments will be made just for the sake of helping employees to update their skills.

Some companies understand this and allow employees to spend a certain amount of their time to pursue personal interests. This is a start but doesn’t help if no guidance is provided on how to spend the time wisely. Our education system does not prepare students for a world where constant learning is required. This problem is amplified when it is not clear what technologies will stay relevant.

It is therefore beneficial to invest time into acquiring skills that have a long half-life. The author of this post believes that blogging is such a skill. Writing regularly, and in front of an audience, is an exercise in thinking and clear communication. Those are true meta skills that are useful for most of current and future jobs.

The Pioneer in Software Development

Concepts from the military can often be useful to explain ideas in different areas like business or technology (as it is the case for this blog - the word “strategy” is deeply rooted in warfare).

For example, consider the role of a pioneer in the military.

Wikipedia 1 2 defines this type of soldier as follows:

Historically, the primary role of pioneer units was to assist other arms in tasks such as the construction of field fortifications, military camps, bridges and roads

Pioneers are enablers and fixers. They pave ways, fix broken infrastructure and secure beachheads. They often march in front of an advancing army to secure new terrain.

There are phases in a software development project that require a similar type of pioneering work. Often, this work is done by software architects.

Mostly two types of situations come to mind:

  • The need for a proof-of-concept work
  • Investigation of a notoriously difficult bug

Continuing with the metaphor, when we implement a proof-of-concept project, we are exploring new territory in the hope to secure a (strategically) important landmark. This can mean to make sure a new framework or platform works the way we expect it to do, trying out a new implementation of an algorithm or testing the feasibility of an idea.

Fixing a notoriously difficult bug is the equivalent of helping an army that got stuck by fixing broken infrastructure like bridges or train tracks. In the world of software development, this goes beyond mere bug fixing. I’m talking about a situation where debugging a problem has become so difficult that usual ways don’t work anymore. This can happen, for example, when, for whatever reason, a team had to make so many changes at once that the number of unknowns made narrowing down the problem impossible. In those cases, it can be better (or might be the only feasible option) to re-create the environment with only the minimum dependencies (or even less) and stripping away everything else. Starting new from scratch, or to be more exact, starting from the last known working state and moving towards the desired, non-working state.

Rules of pioneering work

  • Pioneer code is meant to be thrown away. The moment we understand how the new framework works and that it does what we expect from it we are fine and can move on. Pioneer coding is coding to gain knowledge, not to deliver functionality.
  • Speed matters. Code can be as ugly as needed because we only want to get from start to finish as quickly as possible.
  • We need to keep the number of unknowns to a minimum. The goal is to move from one working state to the next.
  • The second rule of pioneering club is … yes, really, the code is meant to be thrown away. It might be tempting to use the code as a base for a new project but as stated above, quick & ugly is the motto and we don’t want that as the basis for any serious development.

The downsides of pioneering work

  • Expect reluctance from management because pioneering work can look like a waste of time and money.
  • Expect reluctance from developers because being deliberately fast and forget rules of clean code can push some people out of their comfort zone
  • Due to the uncertain nature of pioneering work, a higher degree of ownership is required. There is no clear specification or feature request which feel unmotivating for some.

The upsides of pioneering work

  • It can feel like fun and play. It can be liberating not having to worry about code quality with the only goal to gain understanding
  • Starting from scratch with only the bare minimum and everything else stripped away can lead to a significant understanding. Often, we can’t see the forest for the trees and just by starting small things become clear
  • Deeper knowledge leads to deeper developer happiness. And helping others to get unstuck can be very rewarding