Simpler Grants.gov #DEVCOM-ARL-RFI-24-01-HC

RFI: Theories and Models of Community Influence on Human Judgment for Army Research Laboratory

The U.S. Army Combat Capabilities Development Command Army Research Laboratory (DEVCOM ARL) is seeking information on interdisciplinary theories and models of how communities influence human judgment during information processing tasks. - Government Buyer: - U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL) - Department of the Army, Army Materiel Command - No OEMs or vendors are specified, as this is a scientific inquiry and not a product or service procurement - Products/Services Requested: - Information, white papers, and research insights on: - Theories and models of collective influence on human judgment - Judgment formation in analysts - Cognitive biases and their mitigation - Modalities of information presentation (e.g., text, images, audio, video) - Impact of big data and algorithmically driven information (e.g., large language models, social media) - Human-agent teaming and interactions with virtual agents - Unique/Notable Requirements: - Focus on environments involving human-agent teaming and algorithmic information - Emphasis on understanding both individual and collective cognitive processes - No procurement of products or services; this is for planning and research purposes only - Responses should address specific research questions outlined by ARL - No product or service line items are requested - No contract or award will result from this RFI

Description

The Army Research Laboratory (ARL) is seeking information on interdisciplinary theories and models for collective influences on human judgment during information processing tasks and prior to collective decision making. This RFI is issued solely for information and planning purposes and does not constitute a solicitation. The focus is on understanding how analysts' judgments are influenced by their networks, available information, interactions with virtual agents, and internal predispositions, especially in the context of human-agent teaming and algorithmically driven information. Respondents are invited to address specific questions related to judgment formation, cognitive biases, modalities of information presentation, big data characteristics, and human-agent interactions.

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