Sion Weatherhead

2papers

2 Papers

5.5HCJul 8
The Behavioural Reflection Test: A time-efficient measure of reflective reasoning in morally and epistemically charged decisions

Sion Weatherhead, Flora Salim, Aaron Belbasis et al.

How readily people override intuitive conclusions through reflection shapes how they navigate dense information environments with reliable and misleading sources; yet the effectiveness of a prominent measure, the Cognitive Reflection Test (CRT), is eroded by widespread exposure to classic items and leaves open how such tendencies manifest more generally in decision style and linguistic expression. The Behavioural Reflection Test (BRT) addresses these issues with a brief open-ended measure of reasoning in morally and epistemically charged scenarios, alongside a four-item bespoke CRT (bCRT) as a low-exposure anchor. Among 473 online adults, higher bCRT predicted more evidence-sensitive, ethically driven decisions and reliance on high-quality sources, marked by more emotionally engaged, risk-attentive, economical language; associations the familiarity-adjusted CRT did not recover. The bCRT showed convergent validity, added item information above mean ability. Though open-ended, the BRT remained a time-efficient (median 11.8 minutes) behavioural assay of reflection with scope to extend across domains.

11.0AIApr 19
SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization

Yuncheng Hua, Sion Weatherhead, Mehdi Jafari et al.

Automated simulator construction requires distributional fidelity, distinguishing it from generic code generation. We identify two failure modes in long-horizon LLM agents: contextual drift and optimization instability arising from conflating structural and parametric errors. We propose SOCIA-EVO, a dual-anchored evolutionary framework. SOCIA-EVO introduces: (1) a static blueprint to enforce empirical constraints; (2) a bi-level optimization to decouple structural refinement from parameter calibration; and (3) a self-curating Strategy Playbook that manages remedial hypotheses via Bayesian-weighted retrieval. By falsifying ineffective strategies through execution feedback, SOCIA-EVO achieves robust convergence, generating simulators that are statistically consistent with observational data. The code and data of SOCIA-EVO are available here: https://github.com/cruiseresearchgroup/SOCIA/tree/evo.