Cognitive Dissonance in a Multi-mind Automated Decision System

We address the application of machine learning to the modeling of a specific cognitive behavior that brings two fundamental elements of human cognition into direct conflict: the purely emotional (referred to here as EMOS), and the purely rational (referred to here as NOOS). An extensive development of the machine learning algorithmics for Knowledge-Based Systems (KBS) used for this work is presented. Psychologists refer to the mental experience associated with this conflict as cognitive dissonance. "Cognitive dissonance refers to a situation involving conflicting attitudes, beliefs or behaviors. This produces a feeling of mental discomfort leading to an alteration in one of the attitudes, beliefs or behaviors to reduce the discomfort and restore balance."–Saul Mcleod, University of Manchester, Division of Neuroscience & Experimental Psychology [1] (Fig. 1). Early development efforts undertaken by AI researchers were necessarily focused on modeling very limited domains (e.g., Blocks World, Board Games, etc.) However, the range of possible behaviors in these domains was so narrow that the models developed were viewed as "toy problems" having little relation to the modeling of actual human behavior [2]. Modern learning machines have reached a level of maturity allowing the development of relatively high-fidelity models of group human behavior (e.g., social media traffic, collaborative filtering). This success is facilitated by the fact that group behavior models are actually models of the sampling distribution of the population of behaviors, rather than the behaviors of individuals. The next natural step in the development of high-fidelity models of human behavior is to decrease the size of the groups being modeled, while simultaneously maintaining the scope and complexity of the tasks being performed. As groups become smaller, the mental states and psychology of individual members play a greater role in the process of adjudicating group behavior. A "double-minded" rule-based system was developed consisting of the two components EMOS and NOOS above. These two system components use the same decision-making algorithm, but different heuristics: to assess and combine facts, EMOS applies "soft emotional" factors, while NOOS applies "rigid principled" factors. Not surprisingly, the decisions they produce, given identical inputs, can be quite different. Because the machines' preferences are numerically specified, the level of cognitive dissonance that arises in the double-minded machine as a whole can be quantified using the difference between the components' "commitments" to their separate conclusions. Further, for each preference, each machine has a numerical "psychological inertia" quantifying its reluctance to compromise on each of its preferences. We selected a jury in a criminal trial as the domain for experiments with this system, the task to be performed is to consider facts-in-evidence and apply the rule bases to produce verdicts of "guilty", "not guilty", or "deadlocked". This scenario offers a fundamentally binary decision problem that can be easily understood without special knowledge. To make interpretation of the machine's decision processes transparent, EMOS and NOOS were developed with a conclusion justification capability by which each can express (in natural language) how the pieces of evidence affected their decisions. Results and interpretation of the results of 255 "jury trials" are described. An architecture for incorporating temporal awareness into the machine is given.