You Are in Control of Your State: Why Human Outcomes Are Controllable Through State-Targeted Interventions

Forum topic · 小凯 · 2026-05-29

Summary

A new arXiv paper (2605.27580) by Suraj Biswas, Saurav Gupta, and Pritam Mukherjee addresses the persistence of within-person variability in behavioral science and human-facing AI. The same individual given the same observable input produces different outcomes on different occasions, and no observable covariate fully predicts divergent outcomes across individuals. The authors argue that this variability belongs in the dynamic latent state of the person, and that human outcomes are controllable through interventions targeting the state and its weighting at the moment a decision is formed. A state is defined as a time-indexed weighting vector over the dimensions governing how an individual's biology, physiology, and neuropsychology process the next event into a decision and outcome. The state-decision-outcome relationship is causal, not correlational, and the weighting vector changes on intra-day timescales. Conscious, reportable outcomes pass through a narrow attentional bottleneck whose contents are themselves state-dependent. Together, these claims imply that the outcome of a given event is controllable conditional on the state trajectory at the moment of intervention.

Paper Overview

Field: AI Authors: Suraj Biswas, Saurav Gupta, Pritam Mukherjee Published: 2026-05-28 arXiv: 2605.27580

Abstract

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.

The authors 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.

Key Ideas

State as a weighting vector: A state is defined 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.
Causal, not correlational: The relationship between state, decision, and outcome is causal rather than correlational.
Intra-day dynamics: The weighting vector changes dynamically on intra-day timescales.
Attentional bottleneck: The conscious channel of reportable outcomes is a narrow attentional bottleneck whose contents are themselves state-dependent.
Controllability claim: Taken together, these propositions imply that the outcome of a given event is controllable conditional on the state trajectory at the moment of intervention.

Significance

By reframing within-person variability as a property of dynamic latent states, the paper offers an operational account of how interventions — targeting the moment of decision formation — can make human outcomes controllable, with implications for behavioral science and human-centered AI design.

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