Judgment and Decision-Making Process

Expected Utility Theories

In general, decisions can be categorized depending on whether the outcomes of the available options are known for sure (decisions under certainty) or whether the outcomes are uncertain and occur with known or uncertain probabilities (decisions under uncertainty). Most research has focused on decisions under uncertainty, because such decisions are more common. Traditional theories of choice under uncertainty, such as subjective expected utility theory (SEUT), posit that choices are derived from only two parameters: (a) the subjective value, or utility, of an option's outcomes and (b) the estimated probability of the outcomes. By multiplying the utilities with the associated probabilities and summing over all consequences, an expected utility is calculated. The option with the highest expected utility is then chosen.

Decision Analysis

This rational model of decision making has been used as a guide to study actual decision behavior and as a prescription to help individuals make better decisions. Multiattribute utility theory (MAUT) is a type of expected utility theory that has been especially influential in decision analysts' attempts to improve organizational and individual decision making. Using MAUT, decision makers carefully analyze each option for its important attributes. For example, a job could be characterized by attributes such as salary, chances for promotion, and location. Decision weights are assigned to attributes according to their importance to the decision maker. Then, each available option is assessed for its expected value on all attributes. The values are then multiplied by the decision weights and summed, and the option with the highest value is selected.

Decision analysts have also developed various decision aids to help individuals and organizations make better decisions. Many of these aids rely heavily on modern information and communication technology, such as management information systems, expert systems, and artificial intelligence. Because few empirical evaluations of the various decision aids have been undertaken, claims about their effectiveness are mostly based more on logical argument than on research and should be considered speculative.

Prospect Theory

Theories of expected utility, such as SEUT, impress through their simplicity, generality, and rational appeal. However, they also place heavy demands on decision makers' knowledge and cognitive abilities and neglect important aspects of the decision process, such as the search and interpretation of information. Under the heuristics and biases approach, JDM researchers have explored various ways in which decision makers deviate from rationality. The most important result of this research program is prospect theory.

Prospect theory (PT) was developed as a descriptive theory of decision making. Prospect theory uses a multiplicative model similar to the one used by expected utility theories. However, instead of utilities and probabilities, PT proposes that decision makers use certain value functions and decision weight functions. The decision weight function differs from a probability function in that low probabilities are overweighted and high probabilities are underweighted. The value function also differs from a typical utility function. Specifically, PT assumes that values are defined relative to a reference point (or the status quo). Further, PT posits that the value function is [Page 425]steeper in the domain of losses (below the reference point) than gains. Finally, the value function is concave above, and convex below, the reference point. This implies that decision makers are risk-averse in the gain domain but risk-seeking in the domain of losses.

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