Calculate Your Exact Chance of Falling in Love This Valentine’s Day

Calculate Your Exact Chance of Falling in Love This Valentine’s Day

One way to look at dating and other life choices is to consider them as decision-time problems. Imagine, for example that have a number of candidates for a job, and all can be expected to say yes. You want a recipe that maximizes your chance to pick the best. This might apply to a fabulously wealthy individual picking a secretary or a husband Mr Right in a situation where there are 50 male choices. Under the above restrictions, I mentioned in this previous post that you maximize your chance of finding Mr Right by dating without intent to marry After that, you marry the first fellow who is better than any of the previous. My previous post had a link to a solution using Riemann integrals, but I will now show how to do it with more prosaic math — a series. I present this, not only for the math interest, but because the above recipe is sometimes presented as good advice for real-life dating, e.

The Secretary Problem

Erin, according to skip over the ideal thing to date just the problem is to skip over the first. I’m trying to marry. I learned about solving secretary problem is a scenario involving optimal stopping problem one should you can.

I’ve been thinking about an “inverse secretary problem” for choosing contract jobs​: Well, in the case of something like dating, I’m not so sure.

The following problem is best when not described by me:. Although there are many variations, the basic problem can be stated as follows:. There is a single secretarial position to fill. There are n applicants for the position, and the value of n is known. The applicants, if seen altogether, can be ranked from best to worst unambiguously. The applicants are interviewed sequentially in random order, with each order being equally likely. Immediately after an interview, the interviewed applicant is either accepted or rejected, and the decision is irrevocable.

The decision to accept or reject an applicant can be based only on the relative ranks of the applicants interviewed so far. The objective of the general solution is to have the highest probability of selecting the best applicant of the whole group.

I’m Plagued by This Decades-Old Dating Equation

Tight time frames, local competing projects, and a chronic labor shortage all make hiring one of the hardest parts of your project. Like dating, apartment hunting, and other forms of comparison shopping, you can optimize hiring by using the percent rule. The percent rule is all about spending just the right amount of time to make a decision that results in the best possible outcome.

So one of my good friends is starting to date again (after being out of the country for two years), and I think that it might be helpful, or at least fun, to keep track of her.

I’ve been thinking about an “inverse secretary problem” for choosing contract jobs: 1. I have a limited time in which to secure the next contract 2. Each client has a different, unknown, maximum daily rate MDR they are willing pay. Given my goal is to find the client who will pay the highest daily rate before the deadline, what is the best strategy? My best guess at the moment is to start at a high rate, and gradually decrease it as the deadline approaches.

But how can I use the information I gather about rejected client’s MDRs to decide the best daily rate to quote future potential clients? Is that actually your goal though? Are you sure you wouldn’t prefer a client who will offer repeat business at a decent but not maximal daily rate? How about a client who will offer a more interesting job, or one who will offer you the opportunity to learn something new? In so many of these optimisation problems, the real difficulty is specifying exactly what you want to optimise never mind making sure that the specification is tractable.

In general the solution you get from your algorithm will depend sensitively on your objective: if you’re not completely sure about the objective, you shouldn’t be sure about the solution. BerislavLopac on Mar 11, When it comes to contracting, this is precisely the goal.

Maximizing the chances of finding “the right one” by solving The Secretary Problem

Stop for gas or look for a cheaper gas station? With some details abstracted, these problems share a similar structure. Can we improve on this? The secretary algorithm only uses an ordinal ranking of the options: which option is best, second-best, etc.

At the core of the secretary problem lies the same problem as when dating, apartment hunting (or selling) or many other real life scenarios;.

Are you stumped by the dating game? Never fear — Plus is here! In this article we’ll look at one of the central questions of dating: how many people should you date before settling for something a little more serious? Why is that a good strategy? You don’t want to go for the very first person who comes along, even if they are great, because someone better might turn up later. On the other hand, you don’t want to be too choosy: once you have rejected someone, you most likely won’t get them back.

It’s a question of maximising probabilities. The value of depends on your habits — perhaps you meet lots of people through dating apps, or perhaps you only meet them through close friends and work.

Strategic dating: The 37% rule

Okay, go on. This led me on a rabbit hunt through the internet to understand where that number the 37 percent came from. This is also where the concept of e started to go a little over my head and I stopped Googling. I did enjoy this simplified example of the setup, though, which is also called the Secretary Problem , from Scientific American in Ask someone to take as many slips of paper as he pleases, and on each slip write a different positive number. The numbers may range from small fractions of 1 to a number the size of a googol 1 followed by a hundred 0s or even larger.

In the secretary problem, the optimal strategy is to skip over the first (s − 1) Whether it’s job hunting, dating, or whatever else that you can think of, good luck​.

I was, to put it mildly, something of a mess after my last relationship imploded. I wrote poems and love letters and responded to all of her text messages with two messages and all sorts of other things that make me cringe now and oh god what was I thinking. I learned a few things, though, like when you tell strangers that your long-term relationship has just been bulldozed as thoroughly as the Romans salted Carthage, they do this sorta Vulcan mind-meld and become super empathy machines.

Even older folk, who usually treat me not exactly as a non-person but something sorta like it. Have some Diazepam and relax. Mention heartbreak and everyone has their own private story — maybe more than one.

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Real life is hard. Then yes you should break up. Tough call. Go by your brain; go by your gut. Let me know if this post was helpful or if it worked for you or why not. Please tell me I am wrong, I would rather be wrong than nice, and wrong than vague.

Marry the person you’re with or keep dating? With some details abstracted, these problems share a similar structure. The goal is to pick the best.

This problem can be stated in the following form: Imagine an administrator who wants to hire the best secretary out of n rankable applicants for a position. The applicants are interviewed one by one in random order. A decision about each particular applicant is to be made immediately after the interview. Once rejected, an applicant cannot be recalled. During the interview, the administrator can rank the applicant among all applicants interviewed so far but is unaware of the quality of yet unseen applicants.

The question is about the optimal strategy stopping rule to maximize the probability of selecting the best applicant. Optimal Stopping : In mathematics, the theory of optimal stopping or early stopping is concerned with the problem of choosing a time to take a particular action, in order to maximize an expected reward or minimize an expected cost. If the decision to hire an applicant was to be taken in the end of interviewing all the n candidates, a simple solution is to use maximum selection algorithm of tracking the running maximum and who achieved it and selecting the overall maximum at the end.

The difficult part of this problem is that the decision must be made immediately after interviewing a candidate. If you think carefully, it might seem obvious that one cannot select the first candidate because the first candidate has no one to compare with. A better strategy is to choose a few candidates as a sample to set the benchmark for remaining candidates.

So the sample will be rejected and will only be used for setting benchmark. This article is contributed by Shubham Rana.

How to win at Online Dating


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