Random Dice Pool Generator
Roll a bounded pool of caller-sided dice with one advancing source, importing the existing random integer calculation. Each die independent with replacement; no secure or fair physical-roll guarantee.
Description
A dice pool consists of several independent rolls using the same number of sides.
A dice pool consists of several independent rolls using the same number of sides.1
This generator produces one roll per requested die and sums the results with one advancing random source.
When to use Random Dice Pool Generator
Use this tool when you need to produce the reproducible draw or model experiment described below. Prepare a reproducible seed and the inventory, corpus, or experiment parameters stated below.
How to use Random Dice Pool Generator and understand its results
- Prepare a reproducible seed and the inventory, corpus, or experiment parameters stated below.
- Enter seed, count, sides, following the field table and calculation rules below, then run the tool.
- Read result. For result objects, inspect the nested output fields below, including any missing evidence or selected indices.
- Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| seed | Required nonblank deterministic seed; the SDK can replace the source with context.random. | string | Required |
| count | Number of draws, cards, trials, or output entries under the tool-specific rule; safe integer 1–64 with any narrower inventory bound. | number | Required |
| sides | Sides | number | Required |
Require safe-integer count from one through sixty-four and sides from two through one million. Call the existing inclusive integer sampler over one through sides for each die and return rolls and total. Repeated values are allowed because each die uses a fresh independent model draw.2, 3
Use a nonblank seed of at most 256 UTF-16 units and a safe-integer count from 1 to 64, subject to the tool’s narrower inventory bound. By default the seed is hashed into a deterministic pseudorandom state; each run restarts that state, so identical full inputs and implementation reproduce the result. The SDK may provide context.random, which replaces the seeded source and advances across the request; the seed remains required. Every source value must be finite in [0,1), with a request limit of 100000 source calls. Draw an index as floor(U×remainingSize). For indexed inventory draws, with replacement draw from the full inventory each time; without replacement remove each chosen position from an ordered pool and reject counts exceeding the inventory size. Uniform-source draws are over positions, not necessarily distinct values. This is not secure randomness.
roll=1+floor(U×sides); total=Σrolls Read these symbols with the field meanings and branch or reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Composite output; read its nested fields and the worked example. | object |
| result.rolls | rolls | list |
| result.total | total | number |
| result.samplingConvention | sampling Convention | string |
{
"input": {
"seed": "tool.io/batch42",
"count": 1,
"sides": 6
},
"output": {
"result": {
"rolls": [
6
],
"total": 6,
"samplingConvention": "independent-uniform-with-replacement"
}
}
}Limitations and assumptions
The default source is deterministic pseudorandomness, not an independently audited or secure draw. Replacement applies to inventory positions; distinct positions may render identically or yield equal answers. A fixture does not independently verify the supplied answers or reference content.
Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related task, use Random Coin-Toss Experiment Generator: A biased coin model is a sequence of Bernoulli trials with an explicit success probability. This experiment returns the observed sequence and success-run statistics, which need not match the model probability in a finite sample. For a complementary step, use Random Domino Practice Draw Generator: A double-six domino set contains twenty-eight unordered tiles from blank-blank through six-six. This practice generator draws distinct physical tile positions from that set, avoiding backs and duplicate reversed orientations.
References
-
Random Dice Pool Generator — tool.io
-
Calculation or validation: random/number — tool.io
Similar or alternative tools
- Random Coin-Toss Experiment Generator
A biased coin model is a sequence of Bernoulli trials with an explicit success probability. This experiment returns the observed sequence and success-run statistics, which need not match the model probability in a finite sample.
- Random Domino Practice Draw Generator
A double-six domino set contains twenty-eight unordered tiles from blank-blank through six-six. This practice generator draws distinct physical tile positions from that set, avoiding backs and duplicate reversed orientations.