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Record W2061991363 · doi:10.1108/03684921111160313

The design and validation of an artificial personality

2011· article· en· W2061991363 on OpenAlexaff
Faisal L. Kadri

Bibliographic record

VenueKybernetes · 2011
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsOsta Bio Technologies (Canada)
Fundersnot available
KeywordsCyberneticsArtificial intelligenceComputer scienceConsistency (knowledge bases)Personality psychologyEmpirical researchPersonalityA priori and a posterioriVariety (cybernetics)Machine learningPsychologySocial psychologyMathematicsStatisticsEpistemology

Abstract

fetched live from OpenAlex

Purpose A US patent was recently granted for an artificial psychology dialog player with two artificial personalities and aging simulation. The purpose of this paper is to expose the usefulness and scientific roots and support the commercial viability of the patent based on a new cybernetic model of personality. Design/methodology/approach The design of the artificial personality utilizes a new four‐dimensional cybernetic model, where sentences are classified along the dimensions according to their motivational and cognitive content. The model is a dynamic nonlinear system described in box diagram form. The idea behind the model is rooted in the cybernetics concept of requisite variety and the model's characteristics predict systematic variation with age. The model explains phenomena hitherto found vague. Its predictions regarding age dependence of human behaviour over adult age is tested by online humour appreciation surveys. The methodology is to establish the validity of the model by referencing its a priori predictions against a posteriori empirical classification of survey data by applying statistical consistency tests. Findings The prediction was tested using score data from three online humour appreciation surveys. The results from all surveys confirm the prediction and validate the personality model. The model is indeed useful and the statistical consistency tests support the validity of the model. A novel formulation of Maturana's closed nervous system based on the model with the possibility of empirical validation was also found. Research limitations/implications The media of the analysis was only humorous sentences, while the predictions of the model can be applied to other media such as other types of expressions and semiotic symbols, which are outside the scope of this investigation. Only age‐related changes were investigated. Personality‐related preferences are planned for investigation at a later stage. Practical implications A general age link of types of humor was not available until now, the validation of this link brings closer the realization of improvement in man‐machine interaction. Other applications include marketing and the use of the model as an educational tool. Originality/value The model of personality has a new structure represented as a stable continuous nonlinear system. It employs a feedback mechanism to describe homeostasis (multiplier feedback), a cybernetic regulator that is not based on the familiar negative feedback control structure. Its value as an alternative tool of analysis and prediction cuts across wide disciplines such as psychology, animal behaviour, physical science, computing, engineering and commerce.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.128
GPT teacher head0.340
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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