The paper tests whether graded Dark Factor of Personality descriptions (D1-D5) change language-model choices in a binary, one-shot Ultimatum Game. Seventeen Ollama model labels separately generate proposers choosing either a fair EUR20/EUR20 split or a selfish EUR32/EUR8 split, and responders accepting EUR8 or rejecting so both receive zero. The released main data contain 339,956 D-conditioned completions rather than independent agents: 169,981 proposer and 169,975 responder rows across five levels, two temperatures and 17 models. They are descriptively compared with 4,166 reused human decisions from Hilbig and Thielmann (2025), with additional strong-prompt analyses and 800 generations attributed to GPT-4.1 and GPT-5.1.
In the released D1-D5 data, aggregate fair offers fall from 0.912 to 0.168, and all 17 models have a negative association between D and fair proposing. Responding does not follow an equivalent gradient: aggregate acceptance is 0.689, 0.781, 0.916, 0.859 and 0.754, with five models always accepting and others showing positive, negative or flat patterns. The defensible contribution is therefore evidence that explicit personality instructions can create strong regularities in a forced proposer choice, while responder behavior is highly model-dependent and does not reproduce a uniform human gradient. Justification text also shifts lexically, but terms such as fair, unfair, accept and reject are generated with the decision and predict it in the same fitting sample; this supports observable prompt echoing and rationalization, not internal processing or motivation.
Review of the PDF, all five official CSV files and the repository identifies material problems. Figure 1 reports 0.526 for AI proposers, whereas Table 1, the prose and the D-only release give 0.491; the announced neutral observations are not released. Human H2 reverses the sign shown by OR=0.397 and the human data. The human-likeness score appears with incompatible formulas and values, handles constant models inconsistently and depends on notebooks with unrecorded interactive state. Strong-prompt data contain 50,000 proposer but only 32,083 responder rows, omit cells, publish a qwen2.5 responder result absent from the raw data and use the strong responder descriptions in every proposer row. The reproducible paired test uses only three models and yields p=0.1231 even though another cell states p<0.05. The claimed causal decomposition is an order-dependent sequence of R-squared increments with pseudoreplication, incorrect pairings, hard-coded fallbacks and a bootstrap routine that pools groups before resampling.
The findings should be interpreted as prompt compliance in a narrow hypothetical task, not evidence of negotiating minds, latent dark personality or human psychological similarity. There is no negotiation, interaction, real incentive, persistence or learning. Human D is continuously measured in a natural sample; AI D is a balanced linguistic treatment that sometimes directly prescribes behavior, so the scales are not psychometrically equivalent. The current repository postdates publication, has no tag, retains broken entry points and does not freeze models, dependencies or neutral data. The package also redistributes human microdata containing demographics and 98 psychometric responses while stating that no participants or personal data were used; this creates a governance and privacy concern without, by itself, determining a legal conclusion.