dorsal/arxiv
View SchemaSlowdown and saturation of internal time according to the statistics of information input: a minimal model of response systems
| Authors | Tatsuaki Tsuruyama |
|---|---|
| Categories | |
| ArXiv ID | 2601.06433vv1 |
| URL | https://arxiv.org/abs/2601.06433 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
We consider a response system that updates its internal state in accordance with information input arriving from outside. In this paper, we define as internal time the ``number of kinds'' of codes that have been observed at least once up to a given time, and analyze how the way internal time advances is determined by the statistics of information input (arrival rate and code distribution). When arrivals follow a Poisson process, the average advancing speed of internal time decreases monotonically with time, and if the number of kinds of codes is finite, it eventually approaches an upper limit and saturates. As a result, on long time scales, internal time becomes relatively shorter than physical time. For a uniform code distribution, we provide a closed form for the correspondence between internal time and physical time, and show that the physical time required to ``advance internal time by one step'' increases in later stages. As an ancillary quantity, we quantify by conditional entropy the remaining uncertainty of ``which codes have been observed'' when only internal time is known, and we give unimodality and the maximization time in the uniform case, and upper bounds, equality conditions, and expressions of the difference from the upper bound in the non-uniform case. Finally, we also present a generalization that assigns weights (description lengths) to each code so that internal time is ticked according to the amount of information in the input.
{
"annotation_id": "444cd758-dda2-4b63-9db6-f8c3cc1bfcb1",
"date_created": "2026-02-17T05:53:08.232000Z",
"date_modified": "2026-02-17T05:53:08.232000Z",
"file_hash": "337e135ce78f9546c19bbaabd902933407876562ce2b5a11296daedf43bf08b6",
"private": false,
"record": {
"abstract": "We consider a response system that updates its internal state in accordance with information input arriving from outside. In this paper, we define as internal time the ``number of kinds\u0027\u0027 of codes that have been observed at least once up to a given time, and analyze how the way internal time advances is determined by the statistics of information input (arrival rate and code distribution). When arrivals follow a Poisson process, the average advancing speed of internal time decreases monotonically with time, and if the number of kinds of codes is finite, it eventually approaches an upper limit and saturates. As a result, on long time scales, internal time becomes relatively shorter than physical time. For a uniform code distribution, we provide a closed form for the correspondence between internal time and physical time, and show that the physical time required to ``advance internal time by one step\u0027\u0027 increases in later stages. As an ancillary quantity, we quantify by conditional entropy the remaining uncertainty of ``which codes have been observed\u0027\u0027 when only internal time is known, and we give unimodality and the maximization time in the uniform case, and upper bounds, equality conditions, and expressions of the difference from the upper bound in the non-uniform case. Finally, we also present a generalization that assigns weights (description lengths) to each code so that internal time is ticked according to the amount of information in the input.",
"arxiv_id": "2601.06433",
"authors": [
"Tatsuaki Tsuruyama"
],
"categories": [
"physics.comp-ph"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Slowdown and saturation of internal time according to the statistics of information input: a minimal model of response systems",
"url": "https://arxiv.org/abs/2601.06433",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "e29c935b-625f-4be5-a3b7-af0e3471d017",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}