I’ve noticed two distinct ways of thinking about work. The first is what I’d call the effort-difficulty model of work, which is concerned about the difficulty of completing a task or achieving an objective. This way of looking at work works well for small tasks with predictable outcomes, like homework assignments, and seems to be the easy default way of approaching work.
The second model is called the info-likelihood model, which is concerned about the quality and quantity of information and decision quality. This way of looking at work is better suited for larger tasks or objectives with less predictable outcomes, like projects or longer-term goals you might have, and can be used in tandem with the effort-difficulty model.
The Effort-Difficulty Model
The main assumption in the effort-difficulty model is the linear relationship between the time and effort you put into a task and the end result. This sort of view on work, I imagine, is relatively intuitive to most people — “the harder I work on something, and the longer I work hard on something, the better the end result will be.” Tasks that require more time and effort are considered more difficult, and the difficulty heuristic is used to compare tasks when someone decides they want to work on something.
Writing out the two points I listed above, we get:
(~ means “is proportional to” )
Outcome ~ Effort x Time
Difficulty ~ Effort x Time
If we assume the above two statements to be true, as the effort-difficulty model does, then it follows that outcome ~ difficulty. In other works, each additional unit of difficulty yields a constant increase in outcome.
This model works best for short, simple tasks, like the kind of tasks you would do in a minimum wage job (take someone’s order, clean the bathroom, wipe down the tables, etc.). It makes sense, then, that your wage is in proportion to the time you spend on the job, keeping difficulty constant.
When the complexity and time horizon for a task grows to a certain point the effort-difficulty model becomes almost entirely useless. At that point you can still break down the task or objective into smaller sub-tasks that can be approached using the effort-difficulty model, but still lack an effective way of managing the operation as a whole. Enter the info-likelihood model.
The Info-Likelihood Model
The key insight for the info-likelihood model is that for longer, more complex tasks, decision quality is the most influential factor in determining outcome quality, and for decisions, the outcome quality is affected primarily by the information you have and the quality of your judgement, not the time and effort you exert while making the decision.
Imagine being asked to draw a sketch of the ceiling of the Sistine chapel. If I give you a blurred image, looking longer and harder at the picture is not going to increase the quality of your sketch as much as increasing the quality of the photo or improving your skills as a sketch artist. In this analogy the quality of the image represents the quality of the information you have, the level of your drawing skills represents the quality of your judgement, and the sketch you draw represents the decision you make.
The focus in the info-likelihood view of work, then, is to increase the quality of your information and the quality of your judgement. How you should go about doing that is mostly beyond the scope of this post, but I will say this: In many cases the quality of your information is correlated with the quantity of information you have, since more data points = greater statistical confidence. You can collect information passively from the environment around you or actively by doing something and then collecting feedback and observing consequences. You can’t really control how much information you gather passively, but you can control how much you gather actively. See Volume and Variance in the Long Game for more thoughts on volume and decision making.
If the effort-difficulty model is the “rise and grind” mindset, then the info-likelihood model is the “poker” mindset. The effort-difficulty model draws a direct link between time and effort spent and outcome, while the info-likelihood model is instead more concerned about gathering information and making good decisions based on calculated likelihoods.
Closing Thoughts
Hourly workers and salaried workers are compensated under the effort-difficulty model, since their pay is tied to the time and effort they spend on the job. CEOs, investors, and C-Suite Executives, on the other hand, are compensated under the info-likelihood model, since their compensation does not depend on their hours worked, but instead on the quality of their decisions.
The key benefit of transitioning from effort-difficulty compensation to info-likelihood compensation is the exposure to non-linear payoffs. In practice, that means that many C-Suite Executives are putting in the same time and energy as their employees but able to take home 100x the money.
In order to transition from the former to the latter, several things need to happen. First, your goals need to ambitious enough to surpass the scope of the effort-difficulty model, where “ambitious” means long-term and complex to execute. Second, you need to start focusing on collecting and refining the information you have instead of “grinding”. Lastly, you need to take chances when you make decisions and accept that progress under the info-likelihood model is probabilistic, not linear.
