dorsal/arxiv
View SchemaA method for representing and developing process models
| Authors | S. R. Borrett, W. Bridewell, P. Langely, K. R. Arrigo |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0605025 |
| URL | https://arxiv.org/abs/q-bio/0605025 |
Abstract
Scientists investigate the dynamics of complex systems with quantitative models, employing them to synthesize knowledge, to explain observations, and to forecast future system behavior. Complete specification of systems is impossible, so models must be simplified abstractions. Thus, the art of modeling involves deciding which system elements to include and determining how they should be represented. We view modeling as search through a space of candidate models that is guided by model objectives, theoretical knowledge, and empirical data. In this contribution, we introduce a method for representing process-based models that facilitates the discovery of models that explain observed behavior. This representation casts dynamic systems as interacting sets of processes that act on entities. Using this approach, a modeler first encodes relevant ecological knowledge into a library of generic entities and processes, then instantiates these theoretical components, and finally assembles candidate models from these elements. We illustrate this methodology with a model of the Ross Sea ecosystem.
{
"annotation_id": "37285a68-e237-4812-9d59-14d2eb8d0cd4",
"date_created": "2026-03-02T18:01:35.655000Z",
"date_modified": "2026-03-02T18:01:35.655000Z",
"file_hash": "6521ce95deaa1fbfcff9b4214aaa369fc6be2bda598bd08245e23afeda53c05d",
"private": false,
"record": {
"abstract": "Scientists investigate the dynamics of complex systems with quantitative\nmodels, employing them to synthesize knowledge, to explain observations, and to\nforecast future system behavior. Complete specification of systems is\nimpossible, so models must be simplified abstractions. Thus, the art of\nmodeling involves deciding which system elements to include and determining how\nthey should be represented. We view modeling as search through a space of\ncandidate models that is guided by model objectives, theoretical knowledge, and\nempirical data. In this contribution, we introduce a method for representing\nprocess-based models that facilitates the discovery of models that explain\nobserved behavior. This representation casts dynamic systems as interacting\nsets of processes that act on entities. Using this approach, a modeler first\nencodes relevant ecological knowledge into a library of generic entities and\nprocesses, then instantiates these theoretical components, and finally\nassembles candidate models from these elements. We illustrate this methodology\nwith a model of the Ross Sea ecosystem.",
"arxiv_id": "q-bio/0605025",
"authors": [
"S. R. Borrett",
"W. Bridewell",
"P. Langely",
"K. R. Arrigo"
],
"categories": [
"q-bio.QM",
"q-bio.PE"
],
"title": "A method for representing and developing process models",
"url": "https://arxiv.org/abs/q-bio/0605025"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "54197dd3-5c32-44b5-96ef-c5631c8d7700",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}