3.1. Two-armed bandit

In our model, agents are faced with a two-armed bandit problem (Berry and Fristedt Reference Berry and Fristedt1985); each agent is attempting to discover the better of two available options by sequentially choosing an option, experimenting with it, and observing the result. While agents are unaware of the objective payoff of each option, they nonetheless have subjective estimates that inform their choice. Each experimentation involves n trials, whose results are represented by a random draw from a binomial distribution with parameters corresponding to the chosen option’s intrinsic payoff and n. The agents are thus active inquirers whose choice of which option to pursue shapes their subsequent observations; if they don’t pursue an option, they won’t learn anything new about it. Unless, that is, they receive that information from their neighbours.

3.2. Social networks

An agent’s neighbours are those with whom the agent has direct social ties. The social interaction among agents is represented using graphical networks, where each agent is depicted as a node in the graph, and communicative pathways between agents are represented by undirected edges between the nodes. Figure 1 shows three typical network topologies that will be of interest to us. Whereas in a complete, fully connected network every agent is in direct communication with every other, in a cycle or ring network, connections are sparser, and each agent is only connected to two neighbours. Despite this difference, in both complete and cycle networks, all agents have an equal number of neighbours. Not so for the wheel graph. Here one agent is more central compared to others, and there is variability in the number of neighbours each agent has.

Figure 1.

Different network topologies representing different types of social interactions.

Two points are worth emphasizing: First, thanks to their social ties, agents may get to “virtually” explore options they themselves did not choose to pursue. But, such social learning also opens up the possibility of being misled by others’ results. Second, we can use different network properties to represent different aspects of group behavior. For instance, some aspects of intergroup dynamics can be captured by modulating the connection weights between nodes.

3.3. Learning

Having gathered new information through observation and testimony, agents revise their estimates by Bayesian updating. We represent agents’ beliefs in terms of continuous random variables with Beta distributions, which are specified by two parameters, α and β, with a mean, μ, that can be calculated as ${\alpha \over {\alpha + \beta }}.$ The Beta distribution is the conjugate prior of the binomial distribution, which is the type of distribution from which our agents’ observations are drawn, rendering the distribution amenable to efficient Bayesian updating (Sutton and Barto Reference Sutton and Barto1998). Specifically, if the agent’s prior is given by $Beta(\alpha ,\beta )$ , and after n trials, the agent observes that an option was successful s times (according to the evidence gathered by individuals as well as by their neighbours), the agent’s posterior is given by $Beta(\alpha + s,\beta + n - s)$ with a mean centred on ${{\alpha + s} \over {\alpha + \beta + n}}.$

3.4. Decision rule

In experiments that follow, we restrict our investigation to agents who are greedy in their choice behavior, always selecting the option with the highest expected utility. The only source of exploration for agents, therefore, is the information they receive from neighbours that followed an alternative option. In the simplest case, the only determinant of an option’s utility is that option’s estimated payoff. To be sure, in real life this way of conducting oneself is reckless; choice behavior is shaped by considerations about a host of issues other than the perceived “efficacy” of actions. To varying extent and depending on the context, our public conduct also reflects norms of etiquette, concerns about the approval and disapproval of others, and so on (Bicchieri Reference Bicchieri2017; Turner Reference Turner1991). Such factors become especially important in section 5 when we look into choice behavior of agents with a sense of belonging to groups with reward and punishment powers.

4.1. Design

Agents in this simulation act on the basis of the simple decision rule discussed in section 3.4; in choosing which option to experiment with, their behavior is fully determined by their beliefs about the payoffs of the two options. ^{Footnote 1}

4.3. Discussion

Simulation 1 supports the view that demographic diversity can improve group performance by reducing the excessive trust individuals place in others. ^{Footnote 6} The fact that this reduction of trust is most beneficial in highly connected networks is telling: information travels quickly and widely across highly connected networks. But so does misinformation. So, the misleading result of any one agent can spread quickly across the network, potentially derailing the epistemic pursuit of all the neighbours. This is precisely why sparser networks such as the cycle and the wheel outperform the complete graph; because of their sparsity, such networks quarantine misleading results, protecting agents from frequently changing their minds in light of such results. Nonetheless, given the longer time it takes for information to travel across sparser networks, their superior performance comes at the cost of slower convergence to a solution (Zollman Reference Zollman2007, Reference Zollman2010b). Indeed, if what we value is swift convergence to a solution, distrust is particularly harmful to sparser networks. ^{Footnote 7}

Angere and Olsson (Reference Angere, Olsson, Boyer-Kassem, Mayo-Wilson and Weisberg2017) show that group performance in complete networks improves if individuals agree to only share novel, high-quality evidence. Zollman Zollman (Reference Zollman2010b) achieves similar results with “highly confident” agents ^{Footnote 8} , whose beliefs are not easily changed. By slowing down the speed of convergence to consensus, however, this reduced sensitivity to misinformation comes at a cost. Our simulation shows that similar benefits and tradeoffs can arise when individuals exhibit differential processing of social information. By reducing the weight assigned to information arriving from a subset of social sources, specifically those seen as dissimilar, diversity protects individuals from hastily revising their views in response to social information in ambiguous decision environments.

5.1. Design

The behavior of agents in the previous simulation was a pure reflection of beliefs that were shaped, in part, by informational group influences. Normative group influences, in contrast, put pressure on individuals’ decision making: individuals are now sensitive to the approval and disapproval of the group, and public conduct is no longer a perfect indicator of private acceptance (Kelley Reference Kelley, Guy, Theodore and Eugene1952). As discussed in section 3.4, taking such influences into account requires modifying agents’ decision rules.

5.1.1. Representing diversity’s impact on normative group influences

There is an extensive psychological literature seeking to mathematically represent the impact of conformity as a function of factors such as majority size (for a review, see Bond Reference Bond2005). Here, we employ the so-called “Other-Total Ratio,” which has been especially useful for capturing conformity pressure due to normative influences (Mullen Reference Mullen1983; Stasser and Davis Reference Stasser and Davis1981). ^{Footnote 9}

Suppose agent i is part of a community of size ${{\cal N}_i}$ , consisting of i’s direct neighbours as well as i itself. If in the previous round, ${\cal N}_i^A$ of the community publicly pursued option A, we can think of the pressure on i to choose A in the next round as a function of ${{\cal N_i^A} \over {{\cal N_i}}}:$ there is minimum incentive to follow the option that wasn’t pursued by anyone and maximum incentive to choose the option chosen by all. The overall utility of pursuing A for agent i is now a function of i’s belief about A’s chance of success, $p_i^A$ , as well as the social incentive of doing so:

$${u_i}(A) = (1 - \kappa ) \times p_i^A + \kappa \times {{\cal N_i^A} \over {{\cal N_i}}}$$

where κ represents agent i’s conformist tendency. When $\kappa = 0$ , agents act on the basis of their personal beliefs, paying no heed to what others are doing or what is normatively expected of a member of the community. In contrast, when $\kappa = 1$ , truth no longer matters; what agents do is a matter of a popularity contest in their group. ^{Footnote 10} In the simulations that follow, we assume that all agents have the same conformist tendency.

However, previous applications of the “Other-Total Ratio” have been to homogeneous groups. Some adaptation, then, is required to apply the measure to heterogeneous groups, as we do here. Our approach is to assume, first, that individuals are only concerned to conform with members of their own groups, and second that the presence of out-group individuals, regardless of what views they express, tends to dilute pressure to conform. Regarding the first of these points, the opinions of in-groups are known to have greater impact when it comes to normative social influences deriving from group belonging concerns (Levine etal. Reference Levine, Cassidy, Brazier and Reicher2002; Turner Reference Turner and Tajfel1982). Likewise, individuals tend to direct their normative expectations to other in-groups (Antonio etal. Reference Antonio, Chang, Kenji Hakuta, Kenny and Milem2004; Phillips Reference Phillips2003). Regarding the second point, there is empirical evidence to suggest that the presence of out-group individuals has a capacity to reduce conformity that is not tied to what views they express (Gaither etal. Reference Gaither, Apfelbaum, Birnbaum, Babbitt and Sommers2018). As Phillips etal. put it, “the mere presence of social diversity in task groups, even when out-group members are not bringing minority viewpoints to the table, can fundamentally change the behavior of the social majority to enhance group performance” (Phillips, Liljenquist, and Neale Reference Phillips, Liljenquist and Neale2009). Reduction of pressure to conform to one’s identity group is one mechanism through which this enhanced performance can occur, for example, because people tend to experience greater irritation when dissenting views are expressed by in-group rather than out-group individuals (Phillips and Loyd Reference Phillips and Lewin Loyd2006). Thus, in a socially diverse group, a person might expect to provoke less annoyance in others when expressing dissenting views and therefore be less inhibited in doing so.

To reflect the research results sketched above, we restrict ${\cal N}_i^A$ to in-group individuals in agent i’s community, while still interpreting ${{\cal N}_i}$ as the total size of the community, which includes all neighbours regardless of group affiliations; ^{Footnote 11} individuals may thus encounter the greatest social pressure in completely homogeneous groups. As the group gets diversified, the maximum possible pressure is reduced to the ratio of in-group community members to total community, reflecting the idea that the mere presence of out-group individuals makes people more willing to express dissenting views. Hence, in our modification of the “Other-Total Ratio,” only the expressed opinions of in-group members generate normative conformity pressures, but those pressures can be mitigated by the presence of out-group individuals. Note that our measure of conformity pressure does not attempt to capture all of the effects found in the Phillips etal. experiment (Phillips, Liljenquist, and Neale Reference Phillips, Liljenquist and Neale2009) discussed in section 2. For example, we do not attempt to model the effect whereby unexpectedly taking sides with an outsider against one’s own group causes anxiety about damaging in-group relationships and thereby prompts closer attention to relevant arguments and evidence. Such omissions make our measure conservative in the sense of being likely to underestimate the positive effects of demographic diversity associated with counteracting normative conformity.

5.2. Results

5.2.1. Conformity and group composition

Overall and regardless of group composition, performance was negatively affected by conformism (see Figure 4a). ^{Footnote 13} At low levels of κ, the presence of out-group agents resulted in improved performance. With increased conformity, there was a qualitative shift in this trend, and homogenous groups outperformed heterogenous collectives.

Figure 4.

Influence of conformity on successful conduct in different complete networks of 10 agents. (a) Performance varies in networks with different proportions of the two groups. (b) A breakdown of the simulation outcomes in terms of the interaction between public consensus, private acceptance, and polarization. Each bar represents a particular value of κ: a: $\kappa = 0$ , b: $\kappa = 0.00625$ , c: $\kappa = 0.0125$ , d: $\kappa = 0.025$ , e: $\kappa = 0.05$ , f: $\kappa = 0.1$ .

5.3. Discussion

Previous simulation studies have found that increased conformity negatively impacts group performance (Weatherall and O’Connor Reference Weatherall and O’Connor2018). Simulation 2 shows that the same pattern holds, when agents in the network identify with different groups. Consider, then, the hypothesis that demographic diversity can counteract normative conformity and thereby lessen its negative effects. Our results support this hypothesis, albeit only for low values of κ. This can be seen in Figure 4a, wherein the homogeneous group (i.e., 0–10) performs worse than demographically mixed groups when κ is small but greater than zero. ^{Footnote 14} The reasons for the diversity-induced improvements are twofold. First, by reducing the conformity pressure on in-group individuals, the introduction of out-group members promotes contestation. As shown in Figure 4b, for a significant portion of runs where homogeneous groups converged on the wrong option, the group included some agents that privately disagreed with the public consensus (“incorrect but” in the graph). Because of conformity pressures, however, these agents did not act in accordance with their personal beliefs, and so their dissenting opinions remained private. With smaller values of κ, the presence of out-group individuals reduced the conformity pressure on dissenting agents, enabling them to pursue the option they believed best. ^{Footnote 15} These publicly observable differences in the option pursued in turn shape the normative influences on neighbours in the subsequent round. As a result, when the group reaches a consensus, it is one that likely reflects agents’ private opinions. As κ grows, however, agents are less likely to deviate from the majority position. Indeed, any benefit from the presence of out-groups is overwhelmed by between-group polarization.

Second, the introduction of out-groups also promotes exploration. The agents in our simulations are greedy, always following the option with the higher estimated value. Exploration is possible in this setup thanks to observing the result of others. In effect, each agent gets to vicariously explore because the community allows the agents to be greedy. By penalizing agents for pursuing less popular options that they nonetheless consider superior, conformity also prevents this mechanism of vicarious exploration. The opportunity to explore via others is restored to some extent by the addition of out-group agents. In this case, even agents whose beliefs were in alignment with the group behavior (i.e., that were not dissenting) may change their belief in light of the new evidence regarding less explored options.

6.1. Limitations of the model

Our models are limited in a number of ways. First, they abstract away from and make a number of simplifying assumptions about various factors that can modulate informational and normative group influences in important ways. For example, the agents are assumed to be indistinguishable in terms of their group-based trust and conformist tendencies. This is, of course, a simplifying idealization. In general, it matters who says what to whom. Yet, the models abstract away from considerations such as status, centrality, and (perceived) expertise, even though these factors are important modifiers of informational and normative group influences (Gilovich etal. Reference Gilovich, Keltner, Chen and Nisbett2018; Raven Reference Raven2008).

Even when certain variables are explicitly represented within the model, the mechanisms relating them are highly simplified or even ignored. For instance, we are assuming that there is no relation between group-based trust and the composition of the collective. In many real-world contexts, however, this is clearly an implausible assumption because high levels of group-based distrust may cause minority individuals to leave, making the collective more homogeneous. Similarly, high conformity pressure on the majority group may be caused by factors that also force minority individuals to “assimilate” to “fit in,” thereby changing the overall dynamics. More generally, in natural settings, such variables are often related to one another by some mechanism (e.g., a common cause) such that variations in one are associated with variations in others. It is important to be clear about these limitations in interpreting our findings. For example, since mechanisms (e.g., asymmetric power relations) that may potentially connect mutual distrust, group composition, and individual performance are not represented in our model, the findings should not be taken as suggesting that a manager, who is interested in improving her team’s performance, should instigate distrust among employees from different demographics. ^{Footnote 16}

One could imagine different ways of relaxing some of these assumptions. We may, for instance, let agents differ with respect to status, perhaps as a function of their centrality in the graph, in a way that impacts their interactions with others. For our purposes, however, the assumptions help focus the experiment on the core hypothesis under investigation that, even when unrelated to cognitive diversity, demographic diversity can benefit group performance by counteracting detrimental pressures to conform. Moreover, there are interesting realistic scenarios where the most prominent factor driving behavior is indeed group affiliation; these include settings in which all group members interact with one another for a relatively short time period and where salient identities have limited connection to task-related expertise or ability. Importantly for our purposes, these are precisely the type of settings often used in human experiments investigating the influence of diversity on conformity and group performance (Gaither etal. Reference Gaither, Apfelbaum, Birnbaum, Babbitt and Sommers2018; Levine etal. Reference Levine, Apfelbaum, Bernard, Bartelt, Zajac and Stark2014; Phillips, Liljenquist, and Neale Reference Phillips, Liljenquist and Neale2009). Our simulation experiments, then, find that demographic diversity can improve group performance by reducing conformity under circumstances similar to those of human experiments yielding the same result. What is more, as discussed below, the simulations are suggestive of further background conditions that might also moderate the influence of diversity.

6.2. Demographic diversity and the uptake of simulation studies

The work presented here is suggestive for simulation-based studies of diversity in two ways. First, in relation to the point about the expressive limits of a given model, the findings highlight the need for caution in the interpretation, presentation, and uptake of simulation results. This point can be best illustrated with respect to Hong and Page’s agent-based model developed to support what they call the “diversity trumps ability” theorem (Reference Hong and Page2004). In this model, agents face the task of finding the maximum of a function, which can be visualized as finding the peak of a circular landscape. Each agent is uniquely represented by a set of heuristics that enables the agent to check different positions ahead of its current location on the landscape. Individual problem solving consists of the ordered application of heuristics. The agent halts when it can find no further improvements. Each agent’s ability or performance is measured in terms of the expected value of its end points, given all possible starting positions. In the serial version of the task, collective problem solving is represented in terms of agents interacting sequentially: The first agent begins at a randomly assigned position on the circle and applies its heuristics until it can find no further improvement. The next agent begins where the previous agent left off and applies its own heuristics. This process cycles through the team members until no further improvements can be found. Within this set up, Hong and Page find that under certain conditions, “a team of randomly selected agents outperforms a team comprised of the best-performing agents”. Assuming that randomly selected teams are likely to be more diverse in terms of their heuristics, cognitive diversity can thus be seen as improving group performance.

Importantly, Hong and Page are circumspect about the interpretation of their results as demonstrating the beneficial effect of demographic diversity on group performance. This caution is completely reasonable. Hong and Page’s model answers the counterfactual question, “what would happen if, all else equal, we made the teams cognitively more diverse?” But, insofar as demographic diversity is not at all represented in the model, it makes little sense to transport the model’s answer to that question to the new question, “what would happen if we made the teams demographically more diverse?” This means, however, that such models cannot support the claim that demographic diversity cannot benefit group performance, independently of cognitive diversity. Unfortunately, this is precisely what Page (Reference Page2017) appears to be asserting in the following passage.

This claim assumes that, when it comes to communication dynamics, demographic diversity can only lead to “more conflict, more problems with communication, and less mutual respect and trust among members” (Hong and Page Reference Hong and Page2004, 16385–16386). So, one can study demographic diversity’s positive impacts by focusing on its correlation with cognitive diversity and abstracting away from its other consequences, much like the way one studies mechanics assuming frictionless planes or electrical networks assuming negligible wire resistance—factors that in any case only hinder velocity or current. The problem with this inference is that it is based on a model that is not rich enough for actually exploring the complex dynamics instigated by these intergroup frictions. For instead of simply hindering performance, these frictions could enhance performance by productively unsettling detrimental interaction dynamics.

The achievement of any task by a group requires eliciting, examining, and integrating the knowledge possessed by individual group members. These interactions are subject to a host of group influences that could perturb the collective’s epistemic performance. As suggested by the empirical hypotheses discussed in section 2 and demonstrated by our simulations, it is precisely by explicitly incorporating these types of epistemic interactions into our model that we can get a fuller assessment of the dependencies between demographic diversity and group performance. In Hong and Page’s model, on the other hand, group interaction merely involves one agent’s resumption of the collective task after another agent has been incapable of furthering this task. Group influences such as conformity pressure are thus not represented in the model.

What makes the situation unfortunate is not so much the restricted understanding afforded by Hong and Page’s model. That is a feature of most, if not all, simulation-based (and indeed experimental) approaches, including our own. The more serious concern is the frequent use of Hong and Page’s results to identify the core rationale for making a “business case” for demographic diversity. Aside from the ethical question of whether demographic diversity should require a business case, it is crucial that such discussions consider the consequences of demographic diversity for group interaction. By explicitly incorporating these considerations, our results offer a further rationale for the epistemic benefits of demographic diversity: diversity can benefit group performance because it promotes mechanisms that support critical and contestable collective inquiry—desirable properties that can be lost even in cognitively diverse but demographically homogeneous groups. This rationale is arguably more fundamental than the one based on cognitive diversity because the quality of group performance ultimately depends on the interaction dynamics of information sharing and processing (cf. Anderson Reference Anderson2006; Medina Reference Medina2013); differences that could be cognitively beneficial remain inert, if not actively pursued and voiced.

6.3. Empirically sensitive robustness in simulation studies

A second suggestion for simulation-based research on diversity has to do with the manner in which robustness is pursued. In practice, the process of modeling involves constructing rather idiosyncratic representations of the target system. Modelers abstract away from certain aspects of the target system and make a variety of simplifying assumptions about certain other aspects (Weisberg Reference Weisberg2007; Woodward Reference Woodward2006). Perhaps inadvertently, they may even neglect important features of the system of interest. When a feature of the phenomenon is selected, one needs to make value judgments about its appropriate formalization. What is more, in studying the target thus represented, modelers investigate what is often only a small subset of the possible parameter space.

Still, one hopes that these idiosyncrasies do not undermine the intended purpose of the model and that the patterns of interest generated by the model would arise even if the target was represented in some other, equally plausible way. Accordingly, one may wish to test the robustness of the generated patterns under changes to the model: would we observe the same kind of patterns, had we explored other regions of the parameter space or altered some choices of formalization or relaxed some of the idealizing assumptions? Understandably, therefore, many simulation-based works on diversity and epistemic division of labour are robustness studies (e.g., Grim etal. Reference Grim, Singer, Aaron Bramson, McGeehan and Berger2019; Rosenstock, Bruner, and O’Connor Reference Rosenstock, Bruner and O’Connor2017; Thoma Reference Thoma2015). Such works delineate the range of parameter settings and assumptions wherein some modeling results hold and so help guard against illicit inferences. Moreover, they further our understanding by highlighting novel ways of representing the target.

Nonetheless, it is important to keep in mind that robustness should be sought as a means of testing whether the particular way in which the target is represented threatens the model’s intended aim. Suppose, for example, that a model seeks to provide insight regarding a class of real-world systems. In that case, what matters is not that the findings are robust per se, but that they are robust in an empirically sensitive way: the model should yield the patterns of interest precisely under those conditions where one expects similar types of effects in the real-world target system. Given the context sensitivity of the vast majority of effects in complex systems, it would be highly surprising if a model of one such system generated patterns that were robust across the board, regardless of the particular context and parameter setting. As a concrete example, consider Zollman’s (Reference Zollman2010b) model. The model suggests that, under certain conditions, sparser networks are conducive to better epistemic performance. Insofar as sparsity of connections induces a diversity of information, we may interpret the result as suggesting that under the conditions identified cognitive diversity is beneficial to group performance. Nonetheless, as Rosenstock etal. (Reference Rosenstock, Bruner and O’Connor2017) rightly note, Zollman’s findings hold only in limited situations where the task facing the collective is difficult ^{Footnote 17} . Sparsity, and so diversity, is no longer beneficial once we explore the regions of the parameter space corresponding to easier tasks.

From a conceptual point of view, however, this lack of robustness is not surprising. Presumably, what makes the diversity of cognitive repertoires beneficial is its contribution to effective exploration of novel alternatives in uncertain and complex task environments. When the answer is obvious (e.g., due to problem simplicity or learned expertise), no exploration is needed and so one should expect the epistemic benefits of diversity to be minimal. Crucially, this is precisely what one finds in empirical research on diversity. In their meta-analysis of empirical literature, for instance, van Dijk etal. (Reference Van Dijk, Van Engen and Van Knippenberg2012) find that the benefits of cognitive diversity are moderated by task complexity and are most pronounced in tasks requiring innovation. Given these conceptual and empirical considerations, we should in fact be suspicious of the empirical plausibility of models according to which cognitive diversity remains beneficial even in easy and routine tasks.

Seeking empirically sensitive robustness results in a natural complementarity between simulations and human experiments. In the context of the current work, the simulations identify certain effects that one would expect given the empirical literature. In our first simulation, for instance, the benefits of demographic diversity are most pronounced when the two groups are evenly balanced, a finding that is in line with empirical results (Bear and Woolley Reference Bear and Williams Woolley2011; Joshi and Roh Reference Joshi and Roh2009). However, this correspondence is not exact, as the empirical research just cited tends to find negative effects when, for instance, a small proportion of women are introduced into a predominantly male field. This may reflect the fact noted above that our model does not represent all mechanisms whereby increased distrust may adversely impact group performance. Moreover, we also identify certain effect modifiers that, to our knowledge, have not been investigated in empirical settings. For example, in our first simulation experiment, demographic diversity made the group more likely to converge on the correct result only in the complete network—a situation approximated in empirical studies when every individual is in contact with every other, either in face-to-face meetings or online. This result suggests that, at least insofar as the referent informational paths are concerned, demographic diversity may be less beneficial insituations where all do not communicate with all. And our second experiment found that demographic diversity lessened adverse effects of normative conformity only when the preference for conformity was relatively slight, suggesting that demographic diversity is more likely to lead to polarization when there are strong within-group pressures to conform. Thus, our simulations suggest hypotheses that could be tested by experiments involving human subjects and online communities.

There have been recent calls to bring agent-based models of social epistemology in closer contact with empirical evidence (Frey and Šešelja Reference Frey and Šešelja2019, Reference Frey and Šešelja2018; Harnagel Reference Harnagel2019; Martini and Pinto Reference Martini and Fernández Pinto2017; Steel etal. Reference Steel, Fazelpour, Crewe and Gillette2019; Thicke Reference Thicke2020). For example, Thicke (Reference Thicke2020) proposes that models be evaluated on the basis of their representational and predictive accuracy: assumptions in models should be plausible idealizations of the phenomena, and predictions derived from the model should match observations. The approach followed in this paper can be interpreted as attempting to attain these criteria but with the twist that the phenomena are primarily derived from experiments involving human subjects. One advantage of this approach is that human experiments are simplified social interactions whose rules and results are better known and are thus more amenable to modeling than messy real-world social life. Moreover, our approach also takes advantage of the complementary strengths and weaknesses of simulation and human experiments. In this way, it suggests a potential aim for simulation experiments that falls between providing “how possibly explanations” and predicting real-life social dynamics in their full complexity. This alternative aim consists of establishing, in conjunction with research involving human subjects, the existence of an experimental phenomenon; for instance, that demographic diversity can improve group performance by counteracting referent informational conformity. Since both simulation and human experiments are a step removed from the social reality that is ultimately of interest, a gap would still remain between an experimental phenomenon and policy recommendations. Our approach, therefore, could be seen as an example of what Harnagel calls “mid-level modeling,” wherein empirically calibrated models linked to controlled experiments mediate between theory and real-life phenomena (Harnagel Reference Harnagel2019).

Philosophers have developed valuable simulation models for investigating group behavior in a variety of epistemic tasks (see Šešelja, Straußer, and Borg Reference Šešelja, Straußer and Borg2020). These models can be usefully modified to address issues of societal concern regarding the impact of demographic diversity. As diversity researchers investigate the impact of diversity in broader settings and in online communities, we believe that such a philosophical engagement will be mutually beneficial to both empirical and simulation studies of the topic. The work presented here takes a step toward facilitating this complementary and multidisciplinary orientation.