Lliira

Organize the best timeslots for a meeting based on probability scores.

📝✨💃

Input format

Input is a tsv file (for easier copy-pasting from spreadsheets). If input file is not specified, data.tsv is used by default.

Each row stands for one participant preferences, each column stands a single timeslot.

Rows could be prefixed with names.

Default scores definition

• 0 — Impossible, ~0%.
• 1 — Bad, <50%, or going to be late or leave early every time.
• 2 — Inconvenvient, ≥50%.
• 3 — Tolerable, ≥80%.
• 4 — Good, ~100%, perhaps with only minor inconvenience.
• 5 — Perfect, ~100%.

Example input file

Alice	4	5	0	0	5	2
John	4	5	5	4	2	5
Maddy	3	2	5	0	4	4
Mike	1	0	3	5	5	4
Peter	5	5	3	1	2	2

Names are optional, generic names are used if only numbers are present in the input.

Output format

This tool is not ideal — it does not select one magic timeslot or a magic combination to use. Instead, it ranks each possible attempt using several metrics, and outputs a set of several optimal combinations for different utility function balances.

After that you can select the one combination you see better fit (based of metrics).

For maximum number of meetings equal to three (default), there should normally be about 10 entries in the output or less.

Timeslots are numbered from 0.

Example output

times: [ 4 ], participation: { avg: 0.7357, min: 0.479, stdev: 0.2096, fair: 0.8255 }, interaction: { avg: 0.5536, min: 0.2395, stdev: 0.2092, fair: 0.6655 }
times: [ 0 ], participation: { avg: 0.7415, min: 0.1936, stdev: 0.2792, fair: 0.5673 }, interaction: { avg: 0.5478, min: 0.1549, stdev: 0.3082, fair: 0.4745 }
times: [ 5 ], participation: { avg: 0.7317, min: 0.4776, stdev: 0.2075, fair: 0.8244 }, interaction: { avg: 0.5492, min: 0.2388, stdev: 0.2065, fair: 0.6644 }
times: [ 0, 4 ], participation: { avg: 0.7386, min: 0.5518, stdev: 0.1245, fair: 0.9678 }, interaction: { avg: 0.5507, min: 0.3185, stdev: 0.1394, fair: 0.725 }
times: [ 4, 5 ], participation: { avg: 0.7337, min: 0.4783, stdev: 0.158, fair: 0.8476 }, interaction: { avg: 0.5514, min: 0.3466, stdev: 0.1563, fair: 0.7372 }
times: [ 0, 2, 4 ], participation: { avg: 0.7103, min: 0.6128, stdev: 0.0904, fair: 0.9688 }, interaction: { avg: 0.5111, min: 0.3538, stdev: 0.1004, fair: 0.8261 }
times: [ 0, 4, 5 ], participation: { avg: 0.7363, min: 0.6254, stdev: 0.0819, fair: 0.9655 }, interaction: { avg: 0.5502, min: 0.3626, stdev: 0.09, fair: 0.7937 }
times: [ 0, 3, 4 ], participation: { avg: 0.5219, min: 0.4337, stdev: 0.0607, fair: 0.9193 }, interaction: { avg: 0.3765, min: 0.2261, stdev: 0.0817, fair: 0.6501 }
times: [ 1, 4, 5 ], participation: { avg: 0.6973, min: 0.6088, stdev: 0.0708, fair: 0.9527 }, interaction: { avg: 0.4973, min: 0.3072, stdev: 0.0845, fair: 0.7563 }
times: [ 0, 4, 4 ], participation: { avg: 0.7376, min: 0.6226, stdev: 0.122, fair: 0.9585 }, interaction: { avg: 0.5516, min: 0.364, stdev: 0.1283, fair: 0.7653 }

Of that, [ 4 ] looks like the most tolerable time for a single meeting, and [ 0, 4, 5 ] looks to be a decent choice for rotating recurrent meetings.

Metrics

Metric groups

• participation is an analysis of participation probabilites. It is done per-person and measures the distribution of number of meetings visited by a person.

• interaction is an analysis of interaction probabilites. It is done per each unique pair of participants and analyzes the distribution of number of times that interaction happened.

Group scores

• avg is an average number for all entries divided by the theoretical maximum.

  • min.participation is equal to the chance for an average paricipation to happen in an average meeting.
  • min.interaction is equal to the chance for an average interaction to happen in an average meeting.

• min is the average number for the least represented entry.

  • min.participation is equal to the chance that the least represented participant will participate in an average meeting.
  • min.interaction is equal to the chance that the least overall probable interaction will happen in an average meeting.

• stdev is a population standard deviation over a set of average numbers for each entry. The lower stdev is, the more equal each entry is represented (i.e. all are equally good or bad).

• fair is a non-linear metric that first tries to optimize the amount of entries that are represented at least onece, then amount of entries that are represented twice, and so on.

Scale for avg, min, and fair is 0,1, scale for stdev is 0,0.5.

Good numbers for avg, min, and fair are the higher ones, good numbers for stdev are the smaller ones.

Quorum rule

Quorum rule is enabled by default, but can be disabled in config.

Meetings not reaching the quorum (e.g. «more than 50% people present», which is the default value) are considered cancelled and have zero resulting utility.

That is taken into account automatically when metrics are computed — metrics are already corrected by the probability of reaching the quorum at the selected time slots.

Default parameters

Parameters are bundled in config.js.

Default maximum number of meeting slots that could be rotated is equal to 3 — that means that each row will present at much 3 time slots to rotate between those (for regular meetings).

Default probability values for scores are [0.02, 0.2, 0.5, 0.8, 0.94, 0.95].
That never reaches 0 and 1 on a purpose — those are not real-world probabilites. So 0 is not always 0% and 5 can not mean 100% attendance — unexpected things happen.

Default quorum size is 0.5 + 1e-6, which stands for «more than 50%».