dorsal/arxiv
View SchemaSelf-organization in a simple model of adaptive agents playing 2X2 games with arbitrary payoff matrices
| Authors | H. Fort, S. Viola |
|---|---|
| Categories | |
| ArXiv ID | physics/0312010 |
| URL | https://arxiv.org/abs/physics/0312010 |
| DOI | 10.1103/PhysRevE.69.036110 |
Abstract
We analyze, both analytically and numerically, the self-organization of a system of "selfish" adaptive agents playing an arbitrary iterated pairwise game (defined by a 2X2 payoff matrix). Examples of possible games to play are: the Prisoner's Dilemma (PD) game, the Chicken game, the Hero game, etc. The agents have no memory, use strategies not based on direct reciprocity nor 'tags' and are chosen at random, i.e. geographical vicinity is neglected. They can play two possible strategies: cooperate (C) or defect (D). The players measure their success by comparing their utilities with an estimate for the expected benefits and update their strategy following a simple rule. Two versions of the model are studied: 1) the deterministic version (the agents are either in definite states C or D) and 2) the stochastic version (the agents have a probability c of playing C). Using a general Master Equation we compute the equilibrium states into which the system self-organizes, characterized by their average probability of cooperation c_{eq}. Depending on the payoff matrix, we show that c_{eq} can take five different values. We also consider the mixing of agents using two different payoff matrices an show that any value of c_{eq} can be reached by tunning the proportions of agents using each payoff matrix. In particular, this can be used as a way to simulate the effect a fraction d of "antisocial" individuals -incapable of realizing any value to cooperation- on the cooperative regime hold by a population of neutral or "normal" agents.
{
"annotation_id": "f9fe789c-d06b-4295-967a-855246b3ca22",
"date_created": "2026-03-02T18:00:47.042000Z",
"date_modified": "2026-03-02T18:00:47.042000Z",
"file_hash": "adb549463ec7cad48c9dff90ff15025a8983b17151c3d4671c61f4d9ede72e34",
"private": false,
"record": {
"abstract": "We analyze, both analytically and numerically, the self-organization of a\nsystem of \"selfish\" adaptive agents playing an arbitrary iterated pairwise game\n(defined by a 2X2 payoff matrix). Examples of possible games to play are: the\nPrisoner\u0027s Dilemma (PD) game, the Chicken game, the Hero game, etc. The agents\nhave no memory, use strategies not based on direct reciprocity nor \u0027tags\u0027 and\nare chosen at random, i.e. geographical vicinity is neglected. They can play\ntwo possible strategies: cooperate (C) or defect (D). The players measure their\nsuccess by comparing their utilities with an estimate for the expected benefits\nand update their strategy following a simple rule. Two versions of the model\nare studied: 1) the deterministic version (the agents are either in definite\nstates C or D) and 2) the stochastic version (the agents have a probability c\nof playing C). Using a general Master Equation we compute the equilibrium\nstates into which the system self-organizes, characterized by their average\nprobability of cooperation c_{eq}. Depending on the payoff matrix, we show that\nc_{eq} can take five different values. We also consider the mixing of agents\nusing two different payoff matrices an show that any value of c_{eq} can be\nreached by tunning the proportions of agents using each payoff matrix. In\nparticular, this can be used as a way to simulate the effect a fraction d of\n\"antisocial\" individuals -incapable of realizing any value to cooperation- on\nthe cooperative regime hold by a population of neutral or \"normal\" agents.",
"arxiv_id": "physics/0312010",
"authors": [
"H. Fort",
"S. Viola"
],
"categories": [
"physics.soc-ph",
"physics.gen-ph"
],
"doi": "10.1103/PhysRevE.69.036110",
"title": "Self-organization in a simple model of adaptive agents playing 2X2 games with arbitrary payoff matrices",
"url": "https://arxiv.org/abs/physics/0312010"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "7a5dc580-bed5-44dc-88ae-bc846ff1f600",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}