Simulate processes

Proposes autonomous AI agents ("voting avatars") that debate and vote on behalf of citizens, addressing the cognitive burden of direct democracy in complex societies through compact preference representation. Umberto Grandi argues these systems would leverage AI research in multiagent systems and...

Examines which voting rules are robust to prediction errors in "virtual democracy" systems that learn individual preferences and aggregate predicted votes. The research proves that the classic Borda count rule is robust to prediction errors, whereas any voting rule belonging to the wide family of...

Presents an approach for building AI "general" agents that can predict human behavior in novel settings without requiring extensive setting-specific training data. The agents use theory-grounded natural language instructions combined with existing empirical data and knowledge from language model ...

Examines design choices behind computational frameworks for finding common ground across collective preferences, situating AI-assisted preference elicitation within the historical context of opinion polls. Emphasizes that preferences are shaped by context and seldom objectively captured, explorin...

Policy Priority Inference (PPI) is a research programme and open-source toolkit that models the causal link between government expenditure and policy outcomes using agent-based modeling (a transparent AI approach). It helps governments measure public spending impact on development outcomes and su...

Proposes AI-enabled representatives trained on individual shareholder preferences to vote on their behalf in corporate governance. Addresses the problem that mutual funds concentrate voting power among few asset managers who lack insight into individual preferences. Argues this approach could out...

We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals—applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent. 

Simile is a simulation platform for human behavior. AI-driven simulations show how and why customers, employees, or populations respond to change.

Simulated participants built with imitation learning have been used to provide high-volume feedback on possible income redistribution policies (DeepMind, 2022).

Deliberativa headed 'deliberative labs' ahead of both global citizens' assemblies to test out methodological questions of time e.g. best method for multi-lingual deliberation or data capture.

Paper proposes training LLMs as "digital representatives" that can stand in for individual humans in collective decision-making processes, expressing their preferences in group interactions like consensus-finding. It explains the concept of digital representation, defines metrics for evaluating ...

Introduces Plurals, a Python library for pluralistic AI deliberation using simulated social ensembles with LLM Agents, customizable Structures inspired by deliberative democracy, and Moderators overseeing discussions. The system integrates government datasets to create nationally representative p...

This paper propose a novel research agenda focusing on ‘utopian’ democratization efforts with formal and computational methods as well as with artificial intelligence – this agenda is labelled ‘Artificial Utopia’. Artificial Utopias provide safe testing grounds for new political ideas and economi...