You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention

Computer Science > Artificial Intelligence [Submitted on 26 May 2026] You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention Suraj Biswas, Saurav Gupta, Pritam Mukherjee A central puzzle for the behavioural sciences and for human-facing artificial intelligence is the persistence of within-person variability. The same individual, presented with the same observable input, produces different outcomes on different occasions, and different individuals produce divergent outcomes that no observable covariate fully predicts. We argue that this variability belongs in the dynamic latent state of the person, and that human outcomes are controllable in a precise and operational sense through interventions that target the state and its weighting at the moment a decision is being formed. We define a state as the time-indexed weighting vector over the dimensions that govern how an individual's biology, physiology, and neuropsychology process the next event into a decision and an outcome. The relationship between state, decision, and outcome is causal rather than correlational. The weighting vector is dynamic at sub-daily timescales. The conscious channel through which outcomes are reportable is a narrow attentional bottleneck whose contents are themselves state-dependent. Taken together, these claims imply that the outcome of a given event is controllable, conditionally, on the state-trajectory at the time of intervention. We motivate the framework with six strands of established evidence (causal inference, predictive processing, allostasis, attentional bottleneck, chronobiology, computational psychiatry) and a 24-month observational base from a deployed behavioural platform spanning more than 200,000 consented users across four occupational personas (research period 2023 to 2026). We derive seven testable predictions, list six operational requirements for state-aware systems, and discuss implications for digital health, education, AI personalisation, and personal agency. Comments: 20 pages, 12 figures, 37 references. Companion to a prior SSRN preprint on causal architecture for human modelling Subjects: Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC) ACM classes: I.2.0; J.4 Cite as: arXiv:2605.27580 [cs.AI]   (or arXiv:2605.27580v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.27580 Focus to learn more Submission history From: Suraj Biswas [view email] [v1] Tue, 26 May 2026 18:49:41 UTC (15,763 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs q-bio q-bio.NC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)