Derivation of Gain in a Hierarchical Multiple-Goal Pursuit Model

Abstract

The motivational sciences in organizational psychology have recently focused on goal pursuit as a dynamic process, using computational modeling as a methodological tool. This has resulted in a detailed specification of certain components of the goal-pursuit process, leaving others vague. The current research sheds light on one of these underspecified pieces, gain, through the development of the hierarchical multiple-goal pursuit model (HMGPM). The HMGPM proposes that gain, or a goal’s subjectively evaluated importance, is a function of the importance of higher-order goals to which it is connected in an individual’s goal network, and the strength of those connections. Through computational modeling and simulation, the HMGPM is shown to produce theoretically-plausible patterns of goal choice, replicate previous empirical findings, and advance new topics of future research. The usefulness of the HMGPM as a theory-building tool that integrates organizational and social psychological perspectives of motivation is discussed.