Jordan William Suchow

A short story, published in Nature’s Futures series, that retells the Book of Job as the trial of an exemplary machine intelligence named Cron Job.

This study investigates the reproducibility of the social and behavioural sciences through a large-scale systematic replication effort.

This study investigates the analytic robustness of the social and behavioural sciences through a large-scale collaboration examining how different analytical approaches to the same research questions yield different results.

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks.

An investigation into truth neurons in large language models, examining the internal mechanisms by which neural networks represent and process truthfulness.

This study investigates researcher variability in computational reproduction, an activity for which it is least expected.

An agent-based approach to the single cryptocurrency trading challenge at the FinNLP-FNP-LLMFinLegal shared task at COLING 2025.

Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making.

Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications.

The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment’s specific conditions.

An investigation of how mirror reversal affects the perception of faces, examining the asymmetries in facial appearance and their consequences for recognition and judgment.

An approach to the FinNLP-AgentScen-2024 shared task on financial challenges in large language models at ACL 2024.

A method for actively learning a Bayesian matrix fusion model with deep side information, combining Bayesian data fusion with deep learning representations for improved prediction and inference.

Workshop version of FinMem, a performance-enhanced LLM trading agent with layered memory and character design, presented at the LLMAgents Workshop at ICLR 2024.

We introduce FINMEM, a novel Large Language Models (LLM)-based agent framework for financial trading, designed to address the need for automated systems that can transform real-time data into executable decisions.

An exploration of public opinion on responsible AI using cultural consensus theory, examining how shared beliefs about AI governance and ethics emerge across populations.

A continuation patent (U.S.

An examination of how folk theories of sociotechnical systems shape the design and operation of digital platforms, with implications for platform governance and user behavior.

Deep neural network representations of entities can serve as inputs to computational models of human mental representations to predict people’s behavioral and physiological responses to those entities.

A century of experiments on human visual memory have catalogued the many determinants of what people remember about their visual environments.