The Housewarming Party: A Parable of Algorithms, Heuristics, and Behavioral Economics

Imagine that you—a 22-year-old, freshly graduated, puffed-up and proper psychology major—have just been hired at a rapidly expanding tech company in Arizona. Despite having all the relevant technological know-how, you got hired to work late nights in the human resources department (HR). Eager to prove your competence, you set out on a mission to make nice with the computer nerds and math aficionados.

Well, a month passes and you find that social skills are good for something after all—the head honcho of the entire applied math department, let’s call her Ally, has just verbally invited you to attend her upcoming housewarming party. You, of course, accept the invitation and ask for an address. Ally smiles devilishly and informs you that she’s onto your game; that she knows how badly you want to be in the big leagues instead of rotting away in HR. “Well”, she teases, “I’m not gonna let you off that easily. If you want to come to my housewarming party, you’re gonna need to come up with a way to find my address on your own, without any help.” She pauses. “By the way, it starts in three hours. Good luck!”

Maintaining an eager and playful facade, you walk back to your cramped office, where the suffocating mire of problem-solving anxiety immediately devours you.


You are absolutely devastated. All your hard work; all your careful social manipulation and maneuvering—it’s all crumbling through your fingers. How can she do this? It isn’t fair. You notice your heart rate has gone up, sweat beads form in the furrows of your forehead. I can’t believe it, I just can’t freaking believe this. I have no idea how she expects me to find her stupid house without any sort of hint. 

You stumble over to the company water cooler in hopes of calming your nerves, but, to your dismay, a balding old man with a few remaining thin tendrils of gray hair is taking his sweet time filling up a cartoonishly large water bottle. Noticing your obvious despondency, he kindly asks you what’s wrong.

“I’m, uh, struggling with a problem that I can’t solve.”

“Ah, I see…math or code?”

“Uh. I’m, uh, not really sure actually.”

“How can you not be sure?”

“Because it wasn’t presented to be as a math or programming problem.”

“I mean, this may be too forward, but I have 20 minutes to kill if you want to run it by me.”

You think about it for all of a millisecond and realize that you’re desperate enough to try anything. Walking back to your HR office, the old man turns and introduces himself as Daniel. “Nice to meet you Daniel, do you work here?” you reflexively ask. He informs you that he is a visiting psychology professor from Princeton University, there to help the advertising department better market the company’s products.

“Oh, I have a friend who just graduated from there…what do you teach?”

“Psychology mostly. Sometimes a little bit of economics…the behavior aspects of it at least.”


Daniel Kahneman, Psychologist & Nobel Laureate in Economics

You stop dead in your tracks.

“You’re not…”


“Daniel Kahneman?!”

“In the flesh…”

Your spirits begin to rise. You know from your old Cognition and Behavior course that if there is anyone on this planet who understands problem-solving, it was the old Israeli standing beside you. Once you both get back to HR, you start explaining the situation. The old man is endearingly receptive, nodding with your every sentence, signaling that he understands exactly what needs to be done.

“Oh, that’s simple. What you need is a heuristic algorithm.”

Too bad you never paid attention in that class, and have no clue what a heuristic algorithm is. He picks up on your confusion immediately, and begins to explain; almost as if he weren’t explaining it to you, but to a huge audience honing in on his every word.

“You see, back in the olden days, there was this great big gaping hole right in the middle of decision theory. Economists and philosophers both repeatedly made the same mistake: they assumed the infallible rationality of humans.”

He pauses for a moment to gather his thoughts. As he does, you note that your three hours has been decapitated down to two.

“Okay, it’s like this. Picture yourself at a grocery store. You’re considering what kind of soup you want to buy. There are a lot of criteria involved here. Do you want Clam Chowder? Tomato? Chicken Noodle? Are you looking for low-fat? Low price? Filling? Gourmet? A particular brand? You get the picture.


As it turns out, the old economic decision theory paradigm assumes—I mean it, just assumes—that in an allocated, reasonably short period of time, you are able to weigh every single option against your criteria, and optimize your purchase to best fit your exact needs…”

You begin to sweat again. Maybe the old man is lying. Maybe he’s not really Daniel Kahneman. What does soup have anything to do with anything?!

“…it turns out that the same type of assumptions we were seeing in economics were intimately connected to some open problems in psychology. Let me ask you a question: Say a random man, Steve, is very shy and withdrawn, invariably helpful, but with little interested in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail. Is Steve a librarian, a jet pilot, or a farmer?”

“A librarian, clearly.” You almost scoff at the obviousness of the question.

“Exactly! But why?! Why is it any more likely that Steve is a librarian than a farmer? The probability is exactly the same for each option—so why do almost all people choose librarian? The answer is that you used an instance of something called the availability heuristic—an idea that my late friend Amos and I came up with in 1973. An availability heuristic is kind of like a mental shortcut for making a decision or passing judgment. It occurs when you or I try to determine the likelihood of certain events or situations being true by how easy it is to think of related examples. It’s the reason why we automatically assume that dark, bearded men wearing turbans at airports are terrorists. Or that all Asian Americans are intelligent. It’s the root of all stereotypes. It was easier for you to think of a timid and shy librarian that you once knew than an outgoing and boisterous one, so you generalized the withdrawn librarian’s personality to all librarians.”

“I’m sorry Professor, I’m interested in your story—I really am. But I don’t see how this relates to my predicament. If I can’t make it on time to this party–“

“Patience, my friend, I am getting to the good part. Then we will be able to solve your problem. Actually, for your benefit, I’ll just cut to the chase. There are two ways to solve any problem. The first way is an algorithm. With algorithms you are absolutely, one-hundred percent, guaranteed a solution. It might take a thousand years to get that solution, but you’ll get it without question. A heuristic is different. With a heuristic you’re not guaranteed a solution by any means. But for some types of problems, heuristics can be extremely helpful in reducing the amount of time needed to solve the problem.” He hesitated a bit, then grinned assuredly. “I’m about to give you an algorithm that will solve your problem. In fact, this algorithm is brand new, and has never been talked about—let alone executed—before. It won’t just work for this lady, it’ll work for every single human being on this planet that has an address.”

He leans in closer to your face, as if he were about to bestow upon you the holy grail of all solutions. You hold your breath as he begins to speak.

“The algorithm is…”

You zone in with lemur-like intensity. The seconds hand on the clock appears to stop. You need this job; you can’t work another day in HR, you just can’t—you won’t!


“Go ring the doorbell of every single house in the world and ask for your boss”.

You deflate in exasperation. I knew it! This geezer has been wasting my time! Pulling my leg, dammit. Now I’m completely screwed!

“Well Professor it’s been really nice talking to you and I wi–”

“OR…or you could use some heuristics.”

Your patience is running dangerously low, and you have half a mind to tell this supposed Nobel Laureate to go and shove it. But you sublimate your urges and decide to give him one last chance.

“First heuristic. Most people you’ve met in your life don’t commute more than a few hours to their place of work, right? That puts this woman’s house within, oh I don’t know, a two hundred mile radius of this place. Probably, not guaranteed. But see what we’ve done? Now we’ve reduced the alternatives from about one hundred fifty million square kilometers to roughly twenty thousand square kilometers. Not so good, but it’s a start. Now, you said she’s the head of the applied math department right? She dresses nice? She drives an expensive car? Well, your instinct, via the availability heuristic, is probably telling you that she lives in a wealthy neighborhood. How many really wealthy neighborhoods are around here in Arizona? Not that many right? You could probably even narrow it down to two or three I bet. And a party isn’t that hard to spot on a Tuesday evening. Who else is going to have that many cars parked outside their house?”

He glanced down at his IDF-issued watch. “We still have an hour and a half. Let’s hop into the car and drive.” He can tell that you are reluctant to go along.

“You know what? Let’s make this interesting. If I don’t get you to this ladies house on time, I’ll give you a job at Princeton as the head of IT department.” You know a good deal when you hear one, so you raise your hands and say “Oh what the hell, fine. Let’s do it.”

Long story short, let’s just say you still live in Arizona, but you definitely don’t work in HR anymore. But more importantly, now you have a ridiculously cool connection to New Jersey. 

6 responses to “The Housewarming Party: A Parable of Algorithms, Heuristics, and Behavioral Economics

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  6. I believe FAST Systematic Approaches Work Best

    Elementary Reduction
    Eliminating Absurdities
    Narrowing Focus
    Task Breakdown
    * Systems Micro – managed
    Applied Solution

    I believe Systems micro – managed
    breed best heuristic algorithms
    breed Quality Solutions
    breed Quantifiable Increase in projected outcomes



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