  Information Foraging Theory's (IFT) framing of human information seeking
choices as decision-theoretic cost-value judgments has successfully explained
how people seek information among linked patches of information (e.g., linked
webpages). However, the theory has to be adopted and validated in non-patchy
LLM-based chatbot environments, before its postulates can be reliably applied
to the design of such chat-based information seeking environments. This paper
is a thought experiment that applies the IFT cost-value proposition to
LLM-based chatbots and presents a set of preliminary hypotheses to guide future
theory-building efforts for how people seek information in such environments.
