Request for Information (RFI): Community Influence on Human Judgment During Information Processing Tasks
ID: 350730Type: Forecasted
Overview

Buyer

Dept of the Army -- Materiel Command (DOD-AMC)

Eligible Applicants

Others

Funding Category

Other

Funding Instrument

Procurement Contract

Opportunity Category

Other

Cost Sharing or Matching Requirement

Yes

Original Source

https://grants.gov/search-results-detail/350730
Timeline
    Description

    The Department of the Army's Materiel Command is issuing a Request for Information (RFI) to gather insights on the influence of community dynamics on human judgment during information processing tasks. This RFI aims to explore interdisciplinary theories and models that address how collective influences affect analysts' decision-making, particularly in the context of algorithmically driven information from sources like social media and intelligent agents. The Army Research Laboratory (ARL) is particularly interested in understanding cognitive biases, the impact of information presentation on diverse teams, and the role of big data in shaping judgment formation. Interested respondents should submit their insights by January 17, 2024, and can direct inquiries to Dr. Edward T. Palazzolo or Dr. Robert St. Amant via email at ARL-CIHJ-RFI@army.mil.

    Point(s) of Contact
    Dr. Edward T. Palazzolo Program Manager: Social and Cognitive Networks Humans in Complex Systems DEVCOM ARL Army Research Office Dr. Robert St. Amant Program Manager: Knowledge Systems Military Information Sciences DEVCOM ARL Army Research Office
    Contact By Email
    ARL-CIHJ-RFI@army.mil
    Files
    Title
    Posted
    DEVCOM-ARL -RFI-24-01-HC Amendment 1.pdf
    DEVCOM-ARL.pdf
    The U.S. Army Combat Capabilities Development Command - Army Research Laboratory (ARL) has issued a Request for Information (RFI) concerning the influence of community dynamics on human judgment during information processing tasks. This RFI seeks interdisciplinary insights into how collective influences shape analysts' decision-making, especially amidst the proliferation of algorithmically curated information from sources such as social media and intelligent agents. Key areas of interest include cognitive biases emerging from technology interaction, the effect of various information presentation modalities on neurodiverse teams, and how big data characteristics influence judgment formation. The ARL encourages multidisciplinary responses that blend theories from social sciences, computer science, and neuroscience, among others. Respondents are tasked with answering specific questions regarding existing theories, cognitive biases, and human-agent teaming impacts on decision-making and belief systems. The submission instructions highlight a clear format and deadline, located at the end of this RFI. The overarching aim is to explore enhanced understanding and methodologies for effective collective decision-making in complex information environments.
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