Intelligent affect: rational decision making for socially aligned agents
Bibliographic record
Abstract
Affect Control Theory (ACT) is a mathematical model that makes accurate predictions about human behaviour across a wide range of settings. The predictions, which are derived from statistics about human actions and identities in real and laboratory environments, are shared prescriptive and behaviours that are believed to lead to solutions to everyday cooperative problems. A generalisation of ACT, called BayesAct, allows the principles of ACT to be used for human-interactive agents by combining a probabilistic version of the ACT dynamical model of affect with a utility function encoding external goals. Planning in BayesAct, which we address in this paper, then allows one to go beyond the prescription, and leads to the emergence of more complex interactions between cognitive and affective reasoning, such as deception leading to manipulation and altercasting. We use a continuous variant of a successful Monte-Carlo tree search planner (POMCP) that dynamically discretises the action and observation spaces while planning. We give demonstrations on two classic two-person social dilemmas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".