Bart Gajderowicz delivered a great talk at Machine Intelligence Toronto about how people go through stages in accomplishing a goal [1]. The talk was about homelessness and AI approaches to public policy. I instantly saw a connection to all sorts of tensions that people endure when they set out on a goal.

To distill the concept, let’s start off with the idea that people have goals, people have emotions, and that time moves forward. As people make progress towards their goals, their emotions change over time. They start off in a good mood, in a state of uninformed optimism. Then, as negative information overwhelms their ignorance, they enter into a state of informed pessimism. So much negative information builds up and somebody reaches a valley of despair. Thankfully though, helpful information starts to come in and they enter a state of helpful realism. When this new information starts to raise expectations they enter informed optimism and their mood becomes┬á lot better. Finally, they achieve success and enter into a great mood.

It’s a nicer Gartner Hype Cycle. But for individuals instead of herds.

And it’s a bit better than using some basic consumer journey techniques. There’s no substitute for real ethno. Fake ethno, the kind that’s like “we visited three people that one day and billed the client, haha ethno” kind of ethnography probably isn’t as good as an emotional mapping.

This is all intriguing for a few reasons.

A big reason for the uneven performance in North America are these big booms and busts in optimism. The interaction between time, information, and emotion drives so much behavior.

It’s predictable

What if you just predicted the hype? What would you do differently?

For one, if somebody just isn’t in a state to be receiving constructive feedback, maybe just don’t. Don’t do real feedback. They’re not in a state to collect any sort of feedback. They just want their existing biases confirmed.

That goes for yourself too. If you’re in a state of uninformed optimism, and it feels good, perhaps start collecting as much information as you can to get out of it. It’s harmful to linger.

A lot of ideas die in the pit of hopelessness. And we experience this a lot. If we can see this a mile away, what sort of activities should be planned for that time period? Would you deliberately plan to accelerate the volume of information you’re getting in?

If you’re aware that this happens, what would you change about the goals that you select?

Goal Formation

Sometimes goals just happen to you. To become housed is one example of a goal that may just happen to you. You may be told by the board that you have to do something. Sometimes, you experience peer pressure to accomplish something.

Sometimes your stance guides your goals. The image below is adapted from Roger Martin’s The Opposable Mind.


Even discovering a new model is a goal. Deciding to discover a new model is a decision. This is something you do to yourself every time you set a goal.

How would you plan achieving that goal differently if you knew in advance that you have feelings?

The perspective of a data scientist is directly informed by the stance. They’re going to get as much data as they can. They’re going to try to make it make sense. And when they’re in the pit of hopelessness — they revisit the original mandate and try to make it out.

Wouldn’t it be great if me made out of hopelessness much more often?

What does your stance, what you are, tell you about persevering and accomplishing a goal?

The bigger the goal you’re setting off to accomplish, the more extreme, and longer, this curve is going to be. You can decide on the goal. And you can decide how you’re going to behave at each stage.


[1] Gajderowicz, B., Fox, M. S., & Gru╠łninger, M. (2017). Requirements for Emulating Homeless Client Behaviour. In Proceedings of the AAAI Workshop on Artificial Intelligence and Operations Research for Social Good. San Francisco: AAAI Press.