Imagine you’re planning a road trip and you have a list of destinations you want to visit. You know that each destination can be reached by different means of transportation, like a car, train, or plane. In this study, scientists explore how humans solve similar problems when faced with multiple goals and means. They investigate two classic optimization problems, Set Cover and Maximum Coverage, which involve selecting the best means to achieve a set of goals. The researchers propose that these problems can be represented as a bipartite graph, where the connections between goals and means form a network structure. By analyzing the structure of this network, they predict that people will perform better when the goal system resembles a tree. They conducted three behavioral experiments that support this prediction. These findings demonstrate that understanding the relationship between goals and means can help both in algorithm design and in explaining why people sometimes struggle to choose between multiple options. If you’re intrigued by the challenges of goal selection and want to learn more about how humans tackle these problems, be sure to check out the full article!
Abstract
We study human performance in two classical NP-hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem-solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree-like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals.
Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.