AI Beyond Rules, Heuristics, and Dreams: Ergative-Absolutive Agents for Participatory Simulations

This paper opens with a critique of the familiar division between rule-based Symbolic AI and data-driven Subsymbolic methods, suggesting that even their neurosymbolic convergence remains shaped by an inherited model of agency in which discrete subjects act upon passive objects, a structure naturalized by Indo-European subject-predicate-object grammars. We propose a linguistic turn in designing AI agents, using typologically diverse alignment systems, particularly ergative-absolutive languages such as Basque, as tools to rethink how agency is modeled and enacted in language-trained systems. We argue that Large Language Models (LLMs), far from being mere predictive tools, function as performative stages where grammars of agency are enacted rather than encoded. This reframing invites a shift: from optimizing systems to express predefined meanings, to interpreting the emergent structures that unfold through interaction. Drawing on the metaphor of the dreaming machine, we treat unpredictability and improvisation not merely as limitations of reasoning, but as openings for enacting alternative ontologies of action. To explore this, we propose a two-step framework. First, we examine how alignment patterns surface in LLM-generated interaction, not as imposed rules, but as constraints enacted by the grammar in context. Second, we stage participatory simulations in which stakeholders co-design agents with contrasting grammatical alignments, testing how such reconfigurations may support more adaptive, negotiated, and accountable forms of agency.