This paper proposes an evolutionary model in which an English sentence of roughly ten words acts as a personality “gene” related to cooperation or selfishness. Llama-2-13B-Chat-GPTQ does not play every round as a conversational agent: it is used once per unique sentence to compile that sentence into a deterministic phenotype, a 16-action C/D lookup table covering all possible two-round histories of the iterated Prisoner's Dilemma. If a response contains no identifiable choice, generation is retried up to ten times and then a random action is assigned. The table is cached for every later occurrence of that exact sentence. The LLM re-enters at mutation: with probability .05 it rewrites the gene toward a randomly selected cooperative or selfish tone. The simulation uses 30 individuals, 1,000 generations, 20-round round-robin games, .05 action noise, and payoffs R=4, T=5, S=0, P=1. Seven initial genes generated by GPT-4 span a selfish-to-cooperative gradient. To examine initial mapping, each gene is compiled 50 times at temperature .9; extremes average roughly .1 and .7 cooperation, while intermediate descriptions vary more. That variability is then omitted: each newly encountered gene retains one deterministic sample. Across 15 evolutionary trials, mean cooperation in the language model is .31, with a mode near .18 and a tail toward higher values; mean cosine distance between population-average genes 20 generations apart is .05, with a strong peak near .02. The authors interpret this as long defection-dominated stagnation interrupted by cooperative invasions. One illustrative run stays near .05 cooperation until generation 300, rises to .55, peaks near .75 around generation 850, and drops to .15 near generation 900. A control directly encodes the 16 actions and mutates by flipping one or two; it reaches .50 mean cooperation and .07 cosine distance, with smoother and more similar trajectories across trials. This comparison shows that linguistic rewriting creates irregular mutation sizes and a different evolutionary topology; it does not isolate “personality,” because representation, mutation operator, and genotype-phenotype mapping all change together. The lexical analysis associates frequent words with outcomes: “gently,” “fosters,” “establishes,” and “harmony” occur in high-cooperation genes; “trampling,” “trumps,” “disregard,” “blatant,” and “skepticism” occur with defection. These are associations among words co-occurring in selected sentences, not causal effects of individual tokens; there is no ablation, regression, or context control. The actual contribution is showing that a quantized LLM can act as a semantic operator inside an artificial-life model and produce nontrivial dynamics. It does not demonstrate the evolution of human personality or psychological traits in agents. “Personality” is operationalized as phrases about cooperation and self-interest, without a Big Five instrument, human validation, or correspondence with real behavior; the authors explicitly acknowledge that this correspondence remains unknown. Reproducibility is limited: code and data are not released, only available on request, and the paper supplies no seeds, environment, exact GPTQ model revision or branch, library versions, GPT-4 prompt used to create the initial genes, parsing-failure rate, or fallback rate. The model repository remains accessible and offers multiple quantizations, but the article does not pin the one used. The findings are therefore exploratory evidence about the evolution of linguistic representations compiled by an LLM in one game, not psychological or social evidence about people.
Research question
Can an LLM map linguistic descriptions of cooperation and selfishness to prisoner's dilemma strategies and act as a mutation operator to generate evolutionary dynamics distinct from a direct encoding of actions?