Intelligent decision support and modeling

Abstract

The influence of cognitive science and psychology on decision theory is bringing about changes to assumptions about decision making, and, as a consequence, the way that decisions should be modeled and supported. The three articles published in this Special Issue on intelligent decision support and modeling reflect an emerging literature that incorporates psychology into decision modeling and decision support. This literature represents only a starting point, and much remains to be done in terms of acknowledging the influence of psychology on engineering decisions. Decision support tools will probably never completely make up for engineers’ lack of the cognitive capacity needed to make the multitude of decisions associated with engineering design in a fully informed, unbiased way. Incorporating rational choice theory and psychology into decision support tools seems to be a fruitful path toward promoting optimal decisions in engineering. The last two to three decades have brought about important changes to the field of decision theory, particularly in response to the generally accepted principle that the act of cognitive representation and framing of decisions should follow the axioms of expected utility theory. The standard theory for decision making is based on subjective expected utility theory (e.g., Savage, 1954; Schmeidler, 1989), which involves the enumeration of possibilities, an analysis of the possible outcomes, and the selection of the utility-maximizing decision (Gilboa & Schmeidler, 2001). In engineering design, decision theory is generally applied as a systematic procedure for selecting design variables when there is uncertainty over the preferences associated with the objectives (Thurston, 1991, 2001). In schools of engineering, operations research, computer science, and business around the world, students continue to be taught a set of methodologies consonant with rational choice theory (Wood, 2004), which is founded upon the analysis of information as the basis of decision. In short, decision support and modeling in engineering design generally follows a framework of selecting design variables that optimize the expected utility of the design, and, in so doing, casts engineering design within a rational process (Hazelrigg, 1998). The perceived increased accuracy and rigor of the decisions and the decision support systems built upon the premise of utility maximization can mask the realities of eliciting preferences from engineers, which may be subject to psychological biases. Kahneman and Tversky (Tversky & Kahneman, 1974; Kahneman & Tversky, 1979) drew attention to the realities of human decision making in describing the heuristics that human beings employ in decision making under uncertainty, which are subject to psychological biases that can lead to systematic and predictable errors. In recent years, Kahneman and colleagues have published a series of articles dealing with strategies to correct for these psychological biases (e.g., Kahneman & Lovallo, 1993; Kahneman et al., 2011) and entire fields of behavioral finance, behavioral economics, and behavioral strategy have grown up around the application of cognitive science and psychology to the theory and practice of decision making under uncertainty in specific contexts. This influence is now being felt in the engineering design domain in the modeling and support of engineering decisions, which is built upon a rich heritage of studies on the cognitive and behavioral strategies of engineers. The three articles published in this Special Issue reflect a shift away from normative subjective utility maximization as the only model for decision making and decision support toward greater consideration to the exigencies of engineering decision making in practice. All three articles contribute to decision support and modeling and treat these as integrated issues rather than as compartmentalized problems. The article “Bayesian Project Diagnosis for the Construction Design Process” by Matthews and Philip is perhaps the most “traditional” of the three articles. The article deals with the problem of forecasting potential problems in construction processes. We use traditional in the sense that Bayesian modeling is an accepted method for estimating the probability of an event or outcome of interest (Marshall & Oliver, 1995). They model the construction process as a Markov chain, with transition probabilities associated with progressing through various Reprint requests to: Andy Dong, Faculty of Engineering and Information Technologies, University of Sydney, Engineering Building (J05), Sydney 2006, Australia. E-mail: andy.dong@sydney.edu.au Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2012), 26, 371–373. # Cambridge University Press 2012 0890-0604/12 $25.00 doi:10.1017/S0890060412000248