Human-Agent teaming requires a fundamental understanding of humans’ interaction with information. The current dynamics of Human Information Interaction (HII) are not fully understood or formalized. The dynamics of current and increasingly, future operations will mandate seamless coupling of humans and automated capabilities. Faster decision making and asymmetric views will critically depend on the performance of these human-agent teams. There are various interactions within the HII field of study including how and why humans find, consume, and use information in order to solve problems, make decisions, and carry out other tasks. There are several parallel between HII and biological interactions; one is the concept of energy. No matter the interaction, energy is acquired and expended. We will focus on one interaction, information consumption. The parallel in biology is consumption to the cellular rate of free energy from the Laws of Thermodynamics in a system at chemical equilibrium. Gibbs Standard Free Energy (ΔG° = − RT ln K) represents the maximum amount of work obtained from a process under conditions of fixed temperature and pressure. This equation can represent the idea of level of work within HII. We mapped variables in the equation to concepts within HII, for example the equilibrium constant (K) links to the balance of information units before and after interaction task. For this research, we are developing an Agent Based Model where complex interaction can be constructed and evaluated. We are using Netlogo, an integrated environment for model development, visualization, and analysis as a tool for developing this model. In this paper, we will present details of the current implementation of our model with the Gibbs Standard Free Energy equation and initial results from the Netlogo simulations of our model.
Adrienne Raglin, Somiya Metu, and Christianna Howard, "Understanding theoretical human information interaction, the development of a standard model using an agent based modeling framework
," Proc. SPIE 10653, Next-Generation Analyst VI, 1065302 (Presented at SPIE Defense + Security: April 16, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2304533.
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