Reasons and Means to Model Preferences as Incomplete

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences

Stretch goals and the distribution of organizational performance

Many academics, consultants, and managers advocate stretch goals to attain superior organizational performance. However, existing theory speculates that, although stretch goals may benefit some organizations, they are not a “rule for riches” for all organizations. To address this speculation, we use two experimental studies to explore the effects on the mean, median, variance, and skewness of performance of stretch compared with moderate goals. Participants were assigned moderate or stretch goals to manage a widely used business simulation. Compared with moderate goals, stretch goals improve performance for a few participants, but many abandon the stretch goals in favor of lower self-set goals, or adopt a survival goal when faced with the threat of bankruptcy. Consequently, stretch goals generate higher performance variance across organizations and a right-skewed performance distribution. Contrary to conventional wisdom, we find no positive stretch goal main effect on performance. Instead, stretch goals compared with moderate goals generate large attainment discrepancies that increase willingness to take risks, undermine goal commitment, and generate lower risk-adjusted performance. The results provide a richer theoretical and empirical appreciation of how stretch goals influence performance

Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models