Seeking the Human Element: Decision-making and Problem-solving Capacity in New Computer Systems

Over the past two decades, questions have surfaced about the effectiveness of contribution of intelligent systems to decision makers in a variety of settings. With the proliferation of such systems in operational settings, such as aerospace, medical, manufacturing, and transportation systems, increased attention to evaluation of such systems, and to resulting software safety, became warranted (Grabowski & Sanborn, 2001). Multiple studies evaluating such systems and creating evaluating criteria for future studies have been produced, and the majority of them find that the old, more simplistic approach of one problem-one solution in such computer system is becoming obsolete. Instead, the systems are evolving into self-learning and sometime self-replicating mechanisms, with a capacity to seek solutions to non-standard problems while encountering them only in theory rather than in reality (Fazlollahi & Vahidov, 2001; Baydar & Saitou, 2001). In other words, the machines are learning to think like humans: solving problems via flexible, rather than rigid, approach; observing various operational errors and how the applied solutions affect them; and acquiring the ability to use theoretical thinking, or even imagination.