ACT-R Modeling to Simulate Information Amalgamation Strategies

Today, military decision-making is dependent on the ability to amalgamate information across sources of varying degrees of agreement. Given the increasing volume of information, automated methods to assist in the identification and prioritization of the most valuable or relevant information has become paramount. Relevant information is not only critical to situational awareness and the military decision-making process, but vital to mission success. Towards this end, the US Army Research Laboratory (ARL) has undertaken a research initiative to model and test how analysts perceive the Value of Information (VoI) in varying military context. The goal of this effort is to develop methodologies useful in the development of automated information agents. As a part of the VoI initiative, ARL conducted an experiment with Subject Matter Experts (SMEs) at the US Army Intelligence Center of Excellence (ICOE), where data was collected on how intelligence analysts’ amalgamate information given information content and source reliability within complementary and contradictory conditional associations. The resulting experimental data was incorporated into an Adaptive Control of Thought-Rational (ACT-R) model. Exercising the ACT-R cognitive model resulted in some interesting response behaviors not observed in the initial experiment. In an effort to better understand the perceptions (cognitive underpinnings) of a military intelligence analyst, this paper extends the previous effort and utilizes a crowdsourced experiment within Amazon Mechanical Turk (Mturk). The experiment captures many of the same conditional ratings encountered by the military analysts. Data gained from the Mturk experiment will be examined using the ACT-R model as a simulation to determine whether the same data distributions exist within a wider audience and as a direct comparison to the analyst’s responses. This paper will examine the Mturk experimental design, discuss the experimental apparatus implementation and provide an overview of the ACT-R model utilized to replicate the amalgamation strategies.