Howdy!

We’ve had a ton of new people join the community since our post last week, so welcome!! 🙂 ..in our weekly newsletter we dive in to a topic around decision making, cover one bias a week, and highlight a tool from the Uncertainty Project.

You can find all of our past posts here.

Last week we talked about goal setting and OKRs from the perspective of decision making.

This week:

• 🔮 Topic: The role of conviction and narrative in decision making

• 🧠 Bias 10/50: Optimism Bias

• 🛠️ Tool: Outcome Trees

The role of conviction and narrative in decision making

If you’ve been following these posts, you may have noticed the word conviction comes up quite a bit. In the particular case of navigating radical uncertainty, it’s the battleground between biases and heuristics (how we make irrational choices and act on flawed judgements) and naturalistic decision making (how these ‘flaws’ enable us to navigate complex, real-world situations)

Before we get into strategic decision making and the role of conviction, lets revisit the ‘confidence and intuition’ debate.

As we briefly covered in a previous thread, we see in this 2010 interview between Daniel Kahneman and Gary Klein that the two are at odds on the topic of confidence - specifically, overconfidence.

They both agree that overconfidence is detrimental, but Gary Klein, echoed by Gerd Gigerenzer and supporters of ‘Ecological Rationality’, makes exceptions for high-validity environments (e.g. medicine, coaching, aviation) where leaders have deep domain expertise. The most common example was highlighted in Malcolm Gladwell’s book ‘Blink’ about a firefighter who intuitively made a snap decision to evacuate a building by ‘just knowing’ the floor might collapse.

Regardless of this disconnect on over-confidence and intuition, these topics have been covered by countless books authored by various researchers across economics and behavioral science - Richard Thaler, Cass Sunstein, Herbert Simon, Tali Sharot, and Richard Zeckhauser to name a few.

Through this research, we know a great deal about how individuals make judgements and choices - even in some limited real-world scenarios where information and time are limited (e.g. the firefighter example), but this still doesn’t effectively represent ‘radically’ uncertain environments.

Other researchers, practitioners, and academics in the overlapping worlds of strategy and decision making warn against directly translating learnings from decision making observed in a lab to real world strategic decision making.

What factors differentiate strategic decision making?

In his book Left Brain, Right Stuff, Phil Rosenzweig argues there are a few primary factors unique to strategic decision making - Influence, Competition, Feedback Loops, and Conviction:

• Influence: How we act and intervene can influence the outcome (He argues we often have more control than we may realize)

• Competition: In this environment, success is relative, not absolute

• Feedback loops: Time to learning is often long and lagging

• Conviction: Perception and credibility are important

He argues these are the primary factors that cannot be duplicated in a lab and are even difficult to observe in real-world situations - especially since the context for each decision if often unique.

Many of these factors feel intuitive - but what is conviction? How is it different from the overconfidence behavioral scientists warn of, and why is it important in this environment?

First, we can try to make a distinction between the environments:

• Normative Environments: where there are clear actions and subsequent outcomes that are designated as good, desirable, or permissible, and others as bad, undesirable, or impermissible. We have a clear idea of what ‘should be’ vs what ‘shouldn’t be’.

• Radically Uncertain Environments: where outcomes and utility are unknown, the relationship between actions and outcomes are not stable, and many potential options/scenarios can’t be effectively modeled or predicted.

We might say that these normative environments are high validity, but uncertainty can vary. For example, poker and warfare are high validity environments (where one right or wrong move influences the next and feedback loops are tight), but also highly uncertain environments - luck plays a significant role.

Most research observes situations on some spectrum of these ‘normative environments’, but these don’t effectively model radical uncertainty - a low validity environment that’s also highly uncertain (business, politics, or something like space exploration)

So how do we think about strategic decision making in these environments of radical uncertainty? Strategy, management, and leadership practices have been widely explored and debated, but as neuroeconomics and the study of decision making is still a nascent field, relatively little work has been done on the subject of strategic decision making in complex, uncertain environments.

In the work that has been done, it’s evident that the conviction plays a major role in our ability to operate in these environments, and inspire others to do the same.

We’ll dig in to a few of these studies that explore this phenomenon, but first, we just covered this foundational argument around confidence (and overconfidence), so how do we differentiate conviction?

Conviction vs Confidence

Because they are easily conflated, it’s helpful to clarify confidence and conviction:

• Confidence: The state of feeling certain about the truth of something.

• Conviction: a firmly held belief of opinion

The primary difference is that confidence is relative to certainty while conviction is based on beliefs - regardless of evidence or probabilities. If we’ve learned anything so far, it’s two things:

1. The pursuit or illusion of certainty is paralyzing and dangerous

2. Strongly calcified beliefs and assumptions are the enemy of progress

In tech especially, we glorify overconfidence - “Strong opinions, loosely held” (a popular quote by Jeff Bezos) exists in some fashion in many ‘company principles’ - but this way of thinking can have detrimental effects if it’s giving a hall pass to overconfidence.

In short, because it assumes we’re good at challenging our beliefs, objectively processing information, and viewing reality for what it is.

But we’re not. By default, we anchor to our initial assumptions, intake and share information through a strongly biased filter, and construct our subjective reality in real time.

So we need to find a sweet spot and put guardrails in place that combat overconfidence, challenge assumptions/beliefs, and actively avoid the comfort of the status quo (or regression towards the mean)

The capacity to hold conviction in the face of uncertainty

Peter Drucker famously said, “Whenever you see a successful business, someone once made a courageous decision.”

There’s something very human about the ability to succeed against all odds. There is, of course, danger in these intoxicating and heroic stories - they often dismiss base rates and fall victim to survivorship bias, but nonetheless, conviction plays an instrumental part in the emergence of innovative ideas and ‘bold’ decisions.

Even the perception of conviction is meaningful. We gravitate towards confidence not only because it’s inspiring, but it’s a herd heuristic that allows us to make fewer, harder decisions on our own. It’s a lighter cognitive load to trust than it is to construct our own convictions.

There are a few interesting studies that suggest a model for thinking about conviction and strategic decision making:

1. Image Theory: When we think strategically, particularly to achieve some goal, we create mental images: The self-image, the trajectory image, the action image, and the projected image.

2. Conviction Narrative Theory: Enables actors to draw on the information, beliefs, causal models, and rules of thumb situated in their social context to identify opportunities worth acting on, to simulate the future outcome of the actions by means of which they plan to achieve those opportunities, and to feel sufficiently convinced about the anticipated outcomes to act.

Image theory has been influential in theorizing how mental images might play a major role in how we model strategic decisions - essentially creating a picture of our beliefs and values, what success looks like, our action plan, and potential ways the plan unfolds.

This is counter to the idea that we analytically reason our way to conclusions and instead, compare new information to these mental images. Below we can see how Lee Roy Beach represents two types of decision candidates (progress or adoption) challenging the mental images and deciding whether or not the model accepts and adopts new updates.

Now layering in Conviction Narrative Theory, we start to see how these mental models build narratives that we then act on (and continue to act on) among a constellation of changing variables and new information.

• Narrative: A process that allows us to construct the everyday meaning of events and happenings along with their causal implications.

• Conviction: The subjective confidence to act (and often to carry collaborators with you)

It’s interesting that both of these theories build on research suggesting it’s incredibly difficult to update the underlying beliefs and assumptions that subconsciously drive our decision making.

We, as individuals, vary on our threshold for willingness to hold convictions in the face of imperfect or contradicting information, as well as our tendencies to seek certainty (or fabricate the feeling of certainty). It’s common to point out individuals that are more ‘confident’ even if we’re conflating the term.

But instead of dismissing conviction, that by definition is often contrarian or counter to existing information, we may think of it as a unique and powerful factor when making decisions in radical uncertainty.

And also deeply human…

“…While a computationally competent outside observer may be unable to identify secure grounds to support a particular narrative of the future in radical uncertainty and so to commit to a particular decision, a human decision-maker can feel sufficient conviction to act.” — David Tuckett, Milena Nikolic: The role of conviction and narrative in decision-making under radical uncertainty

Beware of escalation of commitment

We’ve started to explore the downsides to holding onto convictions too tightly for too long and crossing in to overconfidence and hubris - Annie Duke, in her book ‘Quit’ extensively covers the risks of escalation of commitment and holding on to sunk costs too long.

There’s plenty of nuance in this topic and a lot more to dig into here that we can cover in the future!

Let us know what you think! You can join the conversation on the thread in The Uncertainty Project. Want to build on or challenge these arguments? Submit a thread!

Bias 10/50: Optimism Bias

Optimism bias is the tendency for individuals and groups to overestimate the likelihood of positive outcomes and underestimate the likelihood of negative ones. This bias can manifest in many ways, from underestimating the time and resources required to complete a project to overestimating the potential success of a new initiative.

In group decision making, optimism bias can lead to a phenomenon known as "groupthink," where members of a group become so convinced of the correctness of their initial assumptions that they fail to consider alternative perspectives or options.

This can, of course, be particularly dangerous when the group is making high-stakes decisions, as it can lead to poor outcomes or even catastrophic failure.

One reason for the prevalence of optimism bias in group decision making is that people tend to want to be seen as positive and forward-thinking, and they may be reluctant to voice dissenting opinions. Additionally, group dynamics can create pressure to conform to the opinions of the majority, further exacerbating the bias.

To combat optimism bias in group decision making, it is important to actively encourage dissenting opinions and to create a culture where it is safe to speak up.

Additionally, tools such as a pre-mortem, nominal group collaboration, and scenario planning can be used to help groups consider multiple perspectives and potential outcomes.

Read more:

• Optimism Bias - The Decision Lab

• Optimism bias (Wikipedia)

• The Optimism Bias and Its Impact

• Tali Sharot: The optimism bias | TED Talk

Tool: Outcome Tree

An outcome tree is an exercise for modeling potential outcomes and probabilities of those potential outcomes. Outcome trees help do three things:

• Align on value by being clear on the desired outcome of the decision

• Defining the potential outcomes helps measure risk vs expected value

• Build rigor in documenting your expected outcomes to compare with the actual outcome in order to make better decisions in the future

How to build an Outcome Tree

Building an outcome tree is quite simple. The goal is to understand the possible outcomes of the decision in order to have a conversation about confidence and risk.

If you want to take it a step further, the outcome tree can help you understand how to measure success, and even more importantly, put measures in place that could help provide leading indicators of a potential negative outcome.

1) Identify the potential outcomes

The best place to start is by simply listing out potential outcomes. This can be done as a stream of consciousness or prompted by questions. For example, asking "What area all the potential positive outcomes?" and conversely, "What are the potential negative outcomes?".

What often happens is that the outcomes you want or expect tend to come to mind first. As this list starts to move into negative outcomes, interesting conversations start to emerge. If we decide to build a new feature, it's a very real possibility (no matter how much we think it won't be the case) that no one will use it. If that happens, then what? How might we defend against that outcome?

Building out the visual tree is helpful to see the different tracks potential outcomes can take. Maybe it's simple, but maybe there are domino effects as well.

Keep in mind the goal here is to drive conversations that lead to better risk mitigation and eventually better decision making - this is not meant to be an exhaustive list.

2) Understand the probability of the potential outcomes

After we build out the list, the next step is to group our potential outcomes into ones we believe are positive, negative or neutral. We could take this a step further and rate them on a five star scale to add some depth. For example, we may have two negative outcomes, but one may be a far worse outcome than the other.

For each potential outcome, give it a 'probability' for the likelihood this outcome will occur. Again, this activity is to drive conversation, so roughly right is okay. We can simply say 'high', 'low' or even 'very low'. If we're talking about a critical decision, maybe we do take the time to give percentages to these probabilities based on some research or insight.

Nevertheless, with each of these, have a conversation around why we believe the probability is what it is. Do we believe the positive outcomes are high due to bias? Do we have information that supports our assumptions? If not, think about whether or not all the risks are really known. This may influence your confidence score.

3) Measure, learn and adapt

Over time, decisions need to be revisited to log the actual outcome and retrospect on the process and expectations against reality. If a negative outcome occurred, did we predict it as a possibility? Were our assumptions of the risk or probability wrong?

Much of the value of outcome trees come from the activity of logging the process before the actual outcome happens. Once the actual outcome happens, we're subject to hindsight bias. Looking back, we (and others) believe the outcome was obvious all along. Documenting that process helps to adapt the process.

In the inverse, a bad outcome doesn't necessarily mean a bad decision was made; plenty of factors are involved, including luck. What determines a good or bad decision is based on the process built around that decision to accept or mitigate risk. In the tree above we can see that not a single outcome is certain, we're looking to accept risk relative to the expected value.

Logging the process, even if simple at first, and learning from the outcomes is the single most powerful way to make consistently better decisions as a team or organization.‍

Resources:

• HBR, Decision Trees for Decision Making

• Annie Duke, How to Decide

• Wikipedia, Decision Tree

We hope this post was helpful and interesting! Have feedback? Just reply to this email! It would be great to get in touch!