Adam Sobieski is an experienced researcher, developer, entrepreneur, and consultant. His research interests span disciplines and include artificial intelligence, narratology, and the digital history of ideas.

He is interested in applications of artificial intelligence to education and to the acceleration of scholarly and scientific progress and achievement.

He is interested in artificial-intelligence safety, the alignment problem, machine ethics, moral enhancement, legal informatics, legal information retrieval, and computational law.

While his previous research is available on this website in the form of recent articles and curated bibliographies, his most current research interests include multiple-text comprehension, synthesis writing, narrative abstraction and generalization, narrative prediction, and the modeling and prediction of gameplay and decision-making.

Multiple-text comprehension results from the processes and strategies with which readers make sense of complex topics or issues based on the information presented across multiple texts. These processes and strategies are necessary when readers encounter multiple challenging, conflicting documents on complex issues.

Synthesis writing is a set of processes and strategies through which the contents of multiple texts can be integrated into resultant output texts.

Narrative abstraction occurs when individuals refer to, generalize over, blend, and otherwise reason about sets of narratives.

Modeling systems at multiple levels of abstraction, multiple granularities, both with respect to mental models and computer-simulation models, are topics of interest.

The nature of specificity, e.g., vagueness and crispness, with respect to forecasts and prediction-making are topics of interest.

Both the unpredictability and predictability of games and gameplay are interesting, in particular the modeling of contexts, players, and formal and naturalistic decision-making processes.