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

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 effects of team members’ behavioral strategies, team size, and team diversity on overall performance. Depending on the landscape complexity and a team’s features a team may 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 effect of moderate risk-taking is substantial. However, risk-taking has a negative effect 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 effect on performance, while hard work decreases the time required to solve the problem. We also find that larger teams generally solved problems more effectively, and that some of this positive effect is due to the increase in diversity. We show more generally that increasing the diversity of teams has a positive impact on the team’s final score, while more diverse teams also require less time to reach their final solution. This work contributes overall to the larger literature on collective problem solving in teams.