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- The Worst Argument in the World
- That Alien Message
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- Lawful Uncertainty
- Your Intuitions are Not Magic
- The Planning Fallacy
- The Apologist and the Revolutionary
- Scope Insensitivity
- The Allais Paradox (with two followups)
- We Change Our Minds Less Often Than We Think
- The Least Convenient Possible World
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- The Domain of Your Utility Function
- Newcomb's Problem and Regret of Rationality
- The True Prisoner's Dilemma
- The Tragedy of Group Selectionism
- Policy Debates Should Not Appear One-Sided
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In the home purchase decision use case, I'm currently working with a "utility table" where the columns list serious home purchase candidates, and one column is reserved for my current home as a baseline. (The theory there is I know what my current home feels like, so I can map abstract attribute scores to a tangible example. Also, if a candidate new home fails to score better overall than my current home, there's no sense in moving.)
The rows in the utility table list various functions or services that a home with its land might perform and various attributes related to those functions. Examples:
Each of these gets a set of possible values defined, and the possible values are then ranked from 1 to n, where 1 is less desirable and n is more desirable. A rank of 0 is assigned to outright aversive conditions such as being located in a high wildfire risk zone or in a FEMA 100-year flood zone or a multi-story home (your rankings will vary). I then normalize the rank scores for each row to a value between zero and 1.
One squirrelly feature of my system is that some of the row score ranks are not predefined but dynamic. By that I mean that the actual base value before scoring -- such as the price of the house -- is rank ordered across all the columns rather than placed in a standard price interval that is given a rank relative to other price intervals. This means that the ranks assigned to each of the home candidates can change when a new home is added to the table. (And yes, I have to renormalize when this happens, because n increments by 1.)
Then I sum up the scores for each candidate, plus my baseline existing home, and see which one wins.
It all sounds logical enough, but unfortunately it's not as easy as it sounds. It's hard to optimize across all possible home choices in practice, because candidate homes have a basically random arrival rate on the market and they can't all be compared at once. You can't even wait to make pairwise comparisons, because -- at least in Southern California -- any reasonably affordable, acceptable home is likely to be snapped up for cash by an investor or flipper within days of coming on the market unless you make an offer first, right then.
Another problem with the serial arrival rate of candidate homes is that you can get fixated on the first house you see on Zillow or the first house your real estate agent trots out for you to visit. I've got hacks outside of the utility table (as I'm calling it) for getting around that tendency, but I want the utility table to work as tool for preventing fixation as well.
Just trying to create a utility table has helped me tremendously with figuring out what I want and don't want in a home. That exercise, when combined with looking at real homes, also taught me that most things I thought were absolutes on paper actually were not so when I got to looking at real houses and real yards and real neighborhoods and experiencing what the tradeoffs felt like to occupy. The combination of experience and analysis has been a good tool for updating my perceptions as well as my utility table. Which is why I think this might be a useful tool: it gives me method of recording past experience for use in making rapid but accurate judgments on subsequent, serially presented, one-time-opportunities to make a home purchase.
But I've also had a lot of trouble making the scoring work. First I tried to weight each row by how important I considered it to be, but that made things too easy to cheat on.
Then I tried to weight rows by probability of experiencing the lifestyle function or cost or risk involved. For example, I sleep every night and don't travel much, so a functional bedroom matters basically 99.9% of the time. The risk of wildfire, on the other hand, is lower, but how do I calculate it? This county averages about 8 major fires a year -- but what base to I use to convert that to a percentage? Divide 365 days per year into 8 fire events per year, or into 8 times the average days duration of the fire? Or should I count the number of homes burned per year as a percentage of all homes in the county? These latter statistics are not easily obtained, unlike the count of fires per year. Plus, I plan to live here 30 years, and here has more fires than elsewhere, while sleeping probability is unaffected by location. How do I account for that? And then there's the fact that only one fire would be catastrophic and possibly life-ending, while one night of bad sleep can be shrugged off. In the end, I couldn't find any combination of probabilities and costs that was commensurable across all the rows and not subject to cheating.
I also tried giving negative numbers to aversive situations like high wildfire risk and FEMA flood zones, but all that did was make an always crummy but safe house and an always spectacular but occasionally life-threatening house look exactly the same. This just didn't feel right to me.
So I ended up just taking the total unweighted but normalized rank scores for each house, and supplementing that with a separate count of the negatives. That gives me two ways to score the candidate homes, and if the same house wins on both measures, I consider that an indicator of reliability.
By keeping score on all the homes I seriously consider making an offer on, I think I can make a pretty good serial judgment on the current candidate even if I can't optimize concurrently. Or so I believe.
Is this a reasonable approach? I doubt there's anything Bayesian whatsoever about it, but I really don't care as long as the method is reasonable and doesn't have any obvious self-deception in it.
What do you mean "cheat"? Presumably you want to buy a house you like, not just the one that checks the most boxes in a spreadsheet.
That doesn't look like a reasonable procedure to me. So whether a house has exterior steps gets to be as important as the price? One of the reasons such utility tables have limited utility is precisely the weights. They are hard ... (read more)