Hard Work, Risk-Taking, and Diversity in a Model of Collective Problem Solving

Abstract

We studied an agent-based model of collective problem solving in which teams of agents search on an NK landscape and share information about newly found solutions. We analyzed the e ects of team members’ behavioral strategies, team size, and team diversity on overall performance. Depending on the landscape complexity and a team’s features a teammay eventually find the best possible solution or become trapped at a local maximum. Hard-working agents can explore more solutions per unit time, while risk-taking agents inject randomness in the solutions they test. We found that when teams solve complex problems, both strategies (risk-taking and hard work) have positive impacts on the final score, and the positive e ect of moderate risktaking is substantial. However, risk-taking has a negative e ect on how quickly a team achieves its final score. If time restrictions can be relaxed, a moderate level of risk can produce an improved score. If the highest priority is instead to achieve the best possible score in the shortest amount of time, the hard work strategy has the greatest impact. When problems are simpler, risk-taking behavior has a negative e ect on performance, while hard work decreases the time required to solve the problem. We also find that larger teams generally solved problems more e ectively, and that some of this positive e ect is due to the increase in diversity. We showmore generally that increasing the diversity of teams has a positive impact on the team’s final score, while morediverse teamsalso require less time to reach their final solution. Thiswork contributesoverall to the larger literature on collective problem solving in teams.