Identifying Optimum Performance Trade‐Offs Using a Cognitively Bounded Rational Analysis Model of Discretionary Task Interleaving

Abstract

We report the results of a dual-task study in which participants performed a tracking and typing task under various experimental conditions. An objective payoff function was used to provide explicit feedback on how participants should trade off performance between the tasks. Results show that participants' dual-task interleaving strategy was sensitive to changes in the difficulty of the tracking task and resulted in differences in overall task performance. To test the hypothesis that people select strategies that maximize payoff, a Cognitively Bounded Rational Analysis model was developed. This analysis evaluated a variety of dual-task interleaving strategies to identify the optimal strategy for maximizing payoff in each condition. The model predicts that the region of optimum performance is different between experimental conditions. The correspondence between human data and the prediction of the optimal strategy is found to be remarkably high across a number of performance measures. This suggests that participants were honing their behavior to maximize payoff. Limitations are discussed.

Understanding multitasking through parallelized strategy exploration and individualized cognitive modeling

“…Some studies (e.g., [2,9,10]) have explored strategies that can be numerically parameterized, such as in deciding how many keys to press before switching tasks, by sampling these parameters at regular intervals and executing all of the resulting strategies. Though this method is effective, many strategies that people actually employ include alternatives that cannot be parameterized numerically, such as in deciding whether to move the mouse before or after finding a target.…”

Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle

“…One way to deal with such problems is to study optimal decisionmaking with information-processing constraints. Following the pioneering work of Simon (1955Simon ( , 1972 on bounded rationality, decision-making with limited information-processing resources has been studied extensively in psychology (Gigerenzer and Todd, 1999;Camerer, 2003;Brighton, 2009), economics (McKelvey andPalfrey, 1995;Rubinstein, 1998;Kahneman, 2003;Parkes and Wellman, 2015), political science (Jones, 2003), industrial organization (Spiegler, 2011), cognitive science (Howes et al, 2009;Janssen et al, 2011), computer science, and artificial intelligence research (Horvitz, 1988;Lipman, 1995;Russell, 1995;Russell and Subramanian, 1995;Russell and Norvig, 2002;Lewis et al, 2014). Conceptually, the approaches differ widely ranging from heuristics (Tversky and Kahneman, 1974;Gigerenzer and Todd, 1999;Gigerenzer and Brighton, 2009;Burns et al, 2013) to approximate statistical inference schemes (Levy et al, 2009;Vul et al, 2009Vul et al, , 2014Sanborn et al, 2010;Tenenbaum et al, 2011;Fox and Roberts, 2012;Lieder et al, 2012).…”

Generalized Thompson sampling for sequential decision-making and causal inference

“…This has led to the development of theories of bounded rational decision-making that take the information processing limitations of decisionmakers into account. The modern study of bounded rationality was famously broached by Simon (1956Simon ( , 1972Simon ( , 1984 and has since been extensively investigated in psychology (Gigerenzer and Selten 2001;Camerer 2003), cognitive science (Howes et al 2009;Janssen et al 2011;Lewis et al), economics (Aumann 1997;Rubinstein 1998;Kahneman 2003), game theory Palfrey 1995, 1998;Wolpert 2004), political science (Jones 2003), industrial organization (Spiegler 2011), computer science and artificial intelligence research (Lipman 1995;Russell and Subramanian 1995). Different conceptions of bounded rationality are divided as to whether bounded rational behavior is thought to be fundamentally non-optimizing or whether it can be expressed as a (constrained) optimization problem and as to whether it involves any kind of meta-reasoning (Klein 2001).…”