We introduce a general framework for modeling functionally diverse problem-solving agents. In this framework, problem-solving agents possess representations of problems and algorithms that they use to locate solutions. We use this framework to establish a result relevant to group composition. We find that when selecting a problem-solving team from a diverse population of intelligent agents, a team of randomly selected agents outperforms a team comprised of the best-performing agents. This result relies on the intuition that, as the initial pool of problem solvers becomes large, the best-performing agents necessarily become similar in the space of problem solvers. Their relatively greater ability is more than offset by their lack of problem-solving diversity. diverse society creates problems and opportunities. In the past, much of the public interest in diversity has focused on issues of fairness and representation. More recently, however, there has been a rising interest in the benefits of diversity. In the legal cases surrounding the University of Michigan's admissions policies and in efforts to curtail affirmative action in California, Texas, and elsewhere, there have been claims that diverse perspectives improve collective understanding and collective problem solving. Coincident with this political and legal wrangling has been an effort on the part of scholars to identify how to exploit this diversity both in solving hard computational problems (1, 2) and in human organizations (3). In the common understanding, diversity in a group of people refers to differences in their demographic characteristics, cultural identities and ethnicity, and training and expertise. Advocates of diversity in problem-solving groups claim a linkage among these sorts of diversity (which we will refer to as identity diversity) and what we might call functional diversity, differences in how people represent problems and how they go about solving them. Given that linkage, they conclude that, because of their greater functional diversity, identity-diverse groups can outperform homogeneous groups (4-6). Building on earlier ideas from the psychology and artificial intelligence literatures (7), we describe a mathematical framework for modeling problem solvers that captures the functional diversity that cognitive psychologists and organizational theorists claim is correlated with identity diversity. In our framework, agents possess internal representations of problems, which we call perspectives, and algorithms that they use to locate solutions, which we call heuristics. Together, a perspective-heuristic pair creates a mapping from the space of possible solutions to itself. A diverse group is one whose agents' mappings are diverse. Our perspective-heuristic framework is not minimal, because we show in an earlier paper (8) that two problem solvers with distinct perspectives and heuristics can act identically in the space of solutions. However, the advantage of the full framework is that it generalizes models in the computer science literature that focus on diverse heuristics (1, 2), and models in the organizational behavior and psychology literature, which often emphasize diverse perspectives (3, 4, 6). The conclusion that identity-diverse groups can outperform homogeneous groups due to their greater functional diversity rests upon a well accepted claim that if agents across groups have equal ability, functionally diverse groups outperform homogeneous groups. It has also been shown that functionally diverse groups tend to outperform the best individual agents, provided that agents in the group are nearly as good (1). These results still leave open an important question: Can a functionally diverse group whose members have less ability outperform a group of people with high ability who may themselves be diverse? The main result of our paper addresses exactly this question. Consider the following scenario: An organization wants to hire people to solve a hard problem. To make a more informed decision, the organization administers a test to 1,000 applicants that is designed to reflect their individual abilities in solving such a problem. Suppose the applicants receive scores ranging from 60% to 90%, so that they are all individually capable. Should the organization hire (i) the person with the highest score, (ii) 20 people with the next 20 highest scores, or (iii) 20 people randomly selected from the applicant pool? Ignoring possible problems of communication within a group, the existing literature would suggest that ii is better than i, because more people will search a larger space, but says little about ii vs. iii. The intuition that agents with the highest scores are smarter suggests that the organization should hire ii, the individually bestperforming agents. The intuition that the randomly selected agents will be functionally diverse suggests that the organization should hire iii, the randomly selected ones. In this paper, we provide conditions under which iii is better than ii. Thus, the focus of our analysis is on the tension between the individual abilities in a group and its functional diversity. Under the set of conditions we identify, as the initial pool of problem solvers becomes large, the functional diversity of the group of individually best-performing agents necessarily becomes very small. Ultimately, the gain in individual abilities is more than offset by the functional diversity of a group of randomly selected people. It is in this sense that we might say diversity trumps ability. This tension is established regardless of the precise nature of group cooperation. Complementary to our study, a computer science literature (2) has been addressing the questions of how to make the diverse group as effective as possible, and how and when the algorithms should share hints, information, and solutions, taking as a given that a diverse group does better than an individual. Organizational theorists have also focused on exploiting diversity. Their challenge has been how to encourage people with diverse identities and backgrounds to work together productively (3). This paper focuses exclusively on functional diversity: differences in how people encode problems and attempt to solve them. The claim that perspectives and heuristics may be influenced by race, geography, gender, or age has much to recommend it, as does the claim that perspectives and tools are shaped by experiences, training, and preferences. However, even when applying our result to those cases when identity diversity has been shown
Using an agent-based model, Hong and Page REF found that diverse groups can outperform groups composed of the best individual problem-solvers.
Groups of diverse problem solvers can outperform groups of high-ability problem solvers
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Computer Science Medicine