The Computational Challenges of Pursuing Multiple Goals: Network Structure of Goal Systems Predicts Human Performance

Abstract

Extant psychological theories attribute people’s failure to achieve their goals primarily to failures of self-control, insufficient motivation, or lacking skills. We develop a complementary theory specifying conditions under which the computational complexity of making the right decisions becomes prohibitive of goal achievement regardless of skill or motivation. We support our theory by predicting human performance from factors determining the computational complexity of selecting the optimal set of means for goal achievement. Following previous theories of goal pursuit, we express the relationship between goals and means as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. This allows us to map two computational challenges that arise in goal achievement onto two classic combinatorial optimization problems: Set Cover and Maximum Coverage. While these problems are believed to be computationally intractable on general networks, their solution can be nevertheless efficiently approximated when the structure of the network resembles a tree. Thus, our initial prediction was that people should perform better with goal systems that are more tree-like. In addition, our theory predicted that people’s performance at selecting means should be a U-shaped function of the average number of goals each means is relevant to and the average number of means through which each goal could be accomplished. Here we report on six behavioral experiments which confirmed these predictions. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to pursue their goals effectively.

Moving Beyond Two Goals: An Integrative Review and Framework for the Study of Multiple Goals

“…For instance, a simultaneous pursuit can occur when people discover a multifinal means—a subgoal that can satisfy more than one goal at the same time (e.g., I will use a treadmill desk while working; Köpetz et al, 2011). The ability to find such subgoals relies partly on individuals’ ability to process complex information (Reichman et al, 2018; Ren et al, 2019)—which increases their likelihood to combine goals in a way that minimizes conflict and maximizes facilitation—and partly on situational constraints (e.g., what the other goals are and how they relate to each other). As it stands, there remain a lot of open questions in the literature on goal conflict resolution.…”

Moving Beyond Two Goals: An Integrative Review and Framework for the Study of Multiple Goals

“…The ability to find such subgoals relies partly on individuals' ability to process complex information (Reichman et al, 2018;Ren et al, 2019)-which increases their likelihood to combine goals in a way that minimizes conflict and maximizes facilitation-and partly on situational constraints (e.g., what the other goals are and how they relate to each other). As it stands, there remain a lot of open questions in the literature on goal conflict resolution.…”