For years, I have been interested in the science of decision analysis.
Afterall, it is decisions and outcomes that make the world go round.
It is decisions and outcomes that decide the winners and the losers.
And, as such, the people who think about these sorts of things are constantly theorizing and striving to reach optimal outcome(s) so they can come out on top.
One of the theories I have found particularly useful in this regard is Dave Krantz’s goal-based model.
Full disclosure: I have always been the type of person who knew what I wanted and operated with an intense sense of urgency to achieve my desired outcome(s).
I always hated setting goals. I only focused on processes, systems, and inputs.
And, topping my hate list even higher than “setting goals” was my disdain for the usual framework of utility theory and decision trees, which, in agreement with Krantz, is not salvaged by suboptimal quick fixes like nonlinear utilities and prospect theory.
If you’re not familiar with Krantz’s theory, you can check it out here.
It is somewhat of a tricky framework to understand at first, but it essentially attempts to present a constructed-choice model for general decision making, diverging from utility theory and prospect theory, and focuses on the treatment of multiple goals and the ways context can influence choice.
The model is particularly applied to protective decisions, addressing anomalies like insurance against non-catastrophic events, underinsurance against catastrophic risks, and the impact of extraneous factors on insurance and other protective decisions.
There are also a few prescriptions added in that may help improve individual decision-making in a protective measurement context.
It’s probably the only decision-making model I’ve seen that has the potential to make sense.
Here’s the abstract:
We propose a constructed-choice model for general decision making. The model departs from utility theory and prospect theory in its treatment of multiple goals and it suggests several different ways in which context can affect choice. It is particularly instructive to apply this model to protective decisions, which are often puzzling. Among other anomalies, people insure against non-catastrophic events, underinsure against catastrophic risks, and allow extraneous factors to influence insurance purchases and other protective decisions. Neither expected-utility theory nor prospect theory can explain these anomalies satisfactorily. To apply this model to the above anomalies, we consider many different insurance-related goals, organized in a taxonomy, and we consider the effects of context on goals, resources, plans and decision rules. The paper concludes by suggesting some prescriptions for improving individual decision making with respect to protective measures.
If you decide to take a deep dive, you’ll see the classical decision-analysis framework (Table 1) and the new model (Table 2), which I think is an optimal methodology.
You’ll find that the paper proposes a model that deviates from traditional theories like utility or prospect theory (focusing on how decision making is influenced by various goals a person or entity might have, rather than a single utility function), suggesting that decision making is influenced by a construction of choices based on various goals.
It also discusses protective decision anomalies a bit, highlighting how to set goals, and how having multiple goals can lead to better decision-making strategies, especially in high-stakes arenas when faced with complex risk scenarios and uncertainties.
The main focus is on how goals are constructed, influenced, and prioritized in complex decision-making scenarios — and expands the concept of goal setting beyond basic utility maximization.
These days, I still hate convention — aka goal setting season — a time where everyone gets the compulsion to write down big ideas on what they’ll do differently over the next Earth orbit around the Sun.
Read more books!
Eat less sugar!
Drink less caffeine!
Go to bed earlier!
It is all so very tedious.
We all know we should be doing these things.
Despite this (and even though I know the big macro items I want in my head) I’ve begun the exercise of putting what I want down on paper.
In a way, seeing it on paper sort of makes it official.
And I’ve found that when I do this, I have a clearer focus. I can cut out what doesn’t matter and say “no” a lot easier.
If you’re a gamer, it’s like removing the “fog of war” on a map so you know where you’re going.
With that said, here are my goals for 2024:
- Finish our new spacecraft prototype
- Conduct 10 HALO launches
- Pull 2 kilos of gold from the earth
- Acquire 2 new gold mines/claims
- $1 million in year 1 revenue from our Research Subsidiary
- Increase R&D budget 5x
- Grow core team by 150%
- New NASA contract
- Publish 2 new predictive studies
- Acquire 100 acres of land
- Finish new spaceport build
- Penetrate 2 new international markets
- Finish DFAL production on Amazon Prime
- Re-start piano & violin lessons
- Learn Italian
- 30 days of international travel
- Read 50 new books
- Match my personal best college mile time
- Achieve a superhuman strength to weight ratio
- 4-5% bodyfat
- Perfect blood work
I did not spend a lot of time working on this list (20 minutes or so) as most of these are constantly on my mind 24/7 because I know what I want to achieve. But I think there is some usefulness in jotting what you want down on paper to hold yourself accountable.
When I do these lists now (as tedious and generic as they may be) I have found that I am able to discover new insights that may have been buried deep in the hidden files of my brain — that move the dial much deeper than just goal setting.
With that in mind, I have to point out that I do not think there is an optimal path or “strategy” for setting goals, so don’t spend too much time reading goal guru blogs this year.
If you know what you want (even if you just sort of know) I recommend you just find/apply a goal methodology that works for you, and once you do that, it will more than likely work out in your favor, especially if you plan it all out.
Note: one big advantage of having a plan is it enforces a certain logical consistency and clarifies the relationship between each step in the process.
That said, I do think the traditional decisions/utilities/outcomes model is seriously flawed. From the well-known problems such as down-weighting the probability of unforeseen outcomes (these models assume decision-makers operate in a world of complete certainty and information), to the cognitive and emotional constraints of the decision-maker, to the difficulty identifying unbounded problems that are complex in scale/time (that may require knowledge that decision-maker does not have), to the extent that the model is inherently unusable.
Under these conditions, it can be very difficult to be objective and accurate without introducing bias into your decisions.
If you choose to test it, perhaps the Krantz model will offer you better outcomes.
So, what’s on your goals list this year? Let me know on X/Twitter.
Follow me for more shitty analysis: twitter.com/jaminthompson.
Best Decision-Making Books for further study:
- Seeking Wisdom by Peter Bevelin
- Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian
- Superforecasting: The Art and Science of Prediction by Philip Tetlock
- The Model Thinker by Scott Page
- Poor Charlie’s Almanac by Charles Munger
- The Most Important Thing by Howard Marks
- The Personal MBA by Josh Kaufmann
- The Fifth Discipline by Peter Senge
- Thinking in Bets by Annie Duke
- Against the Gods by Peter Bernstein