Number of Decision Makers Influences Information Use

  • As the number of decision makers increases, organizations must adapt how they collect, share, and synthesize information to maintain decision quality.
  • In small groups, information aggregation often occurs informally through interpersonal trust, direct communication, and intuitive coordination.
  • In larger groups, cognitive and communication constraints require formal mechanisms—such as voting, structured deliberation, and delegation—to manage dispersed judgments.
  • Academic research shows that as group size grows, coordination costs rise faster than informational benefits, creating diminishing returns from adding more decision makers.
  • Mechanisms such as the Condorcet jury theorem, deliberative pooling, hierarchical aggregation, and epistemic democracy theory help explain why more participants can either improve or degrade collective judgment.
  • Effective decision governance depends on designing aggregation processes that balance inclusiveness, efficiency, and epistemic quality.

Case Study: Use of Information in New Product Decisions

Advanced Machines Inc (AMI), a medium-sized manufacturer of industrial equipment, faces a recurring challenge: deciding which new product ideas to fund each year. The firm operates in a competitive market where innovation determines survival, yet its decision process for selecting ideas has evolved with the number of decision makers involved.

  • Stage 1, One Decision Maker: Initially, AMI’s founder made all investment decisions alone. Information flowed upward informally: engineers and salespeople proposed ideas, but only the founder’s judgment determined which received funding. Decisions were fast and coherent, reflecting a single cognitive model of the market. However, this concentration of authority limited diversity of input and increased the risk of individual bias—anchoring on past successes and overconfidence in personal intuition.
  • Stage 2, Five Executives: As the company grew, the founder delegated decision authority to the top management team: five C-level executives representing operations, finance, marketing, R&D, and strategy. The team met quarterly to evaluate proposals. Each executive brought distinct expertise, expanding the informational base. Deliberation became more structured—requiring agendas, comparative scoring, and presentations. Conflicts arose between technical feasibility and financial viability, leading to compromises. Coordination costs rose moderately, but the quality of information improved. The group used consensus or majority voting when disagreements persisted.
  • Stage 3, Twenty Senior Managers: Later, AMI expanded participation to twenty executives and senior managers to increase legitimacy and cross-functional input. Now, proposals required formal documentation, standardized evaluation criteria, and facilitated workshops. While this broadened the informational base further, it introduced new challenges: meetings were lengthy, information unevenly distributed, and participants hesitant to speak. Informal coalitions formed, leading to “information silos.” The process required structured aggregation mechanisms—scoring models, nominal group techniques, or Delphi rounds—to reconcile judgments.
  • Stage 4, One Hundred Participants: Finally, when AMI sought to democratize innovation and invited all technical directors and project leads (around one hundred participants) to vote on proposals, the process became complex. Most participants lacked full information and relied on reputational cues or departmental loyalties. Voting simplified aggregation but reduced the epistemic depth of deliberation. As the group grew, decision quality became less a function of expertise and more a product of procedural fairness and perceived legitimacy.

The evolution of AMI’s decision process illustrates how the optimal method of aggregating dispersed information depends on group size. Each increase in participants requires a shift—from intuition to deliberation, from discussion to formal structure, from shared understanding to procedural governance.

How Decision Group Size Influences Information Aggregation

Academic research across economics, political science, and organizational behavior consistently finds that group size alters how dispersed information and judgments are combined.

  1. Information diversity vs. coordination cost: Small groups can integrate diverse insights through direct conversation. In larger groups, diversity increases informational potential but also raises coordination and communication costs (March & Simon, 1958). The challenge becomes managing attention and filtering relevant signals without losing accuracy.
  2. Social influence and conformity pressures: As groups expand, social dynamics change. Research in social psychology shows that conformity and “social loafing” increase with group size, leading participants to defer to perceived experts or majority opinions (Latané, Williams & Harkins, 1979). This can distort aggregation by suppressing dissenting but valuable information. 
  3. Decision speed and noise: Larger groups tend to deliberate longer but may not produce proportionally better outcomes. Information overload and diffusion of responsibility slow decision speed (Olson, 1965). Without structured methods, noise overwhelms signal, and decisions drift toward mediocrity.
  4. Procedural formalization: As group size grows, organizations adopt formal rules for aggregating inputs—weighted scoring, voting systems, or hierarchical filters—to manage complexity (Arrow, 1963). These mechanisms substitute for the direct trust and tacit coordination possible in smaller teams. 
  5. Legitimacy and acceptance: Larger decision groups often aim to enhance legitimacy rather than epistemic accuracy. The inclusion of multiple stakeholders may increase acceptance of the decision but dilute analytical rigor (Dryzek, 2000). Information aggregation thus becomes a political as much as a cognitive process.

In AMI’s case, the expansion from one decision maker to one hundred transformed the process from personalized judgment to procedural governance. The firm had to design new ways to surface, filter, and combine knowledge—shifting from individual cognition to collective intelligence.

Mechanisms Explaining the Relationship Between Group Size and Aggregation Strategies

Academic theories offer several mechanisms to explain why the number of decision makers influences how information is aggregated.

  1. Condorcet Jury Theorem: This theorem (Condorcet, 1785) posits that if each decision maker has a better than random chance of being correct, the probability that the majority decision is correct increases with group size. However, the theorem assumes independence of judgments—rare in real organizations. When social influence or correlated errors exist, the benefit of more participants diminishes.
  2. Information Pooling Models: Research in organizational theory and economics (Sah & Stiglitz, 1986) shows that decision structures affect how information is pooled. In parallel structures, many actors make independent evaluations, maximizing diversity but risking redundancy. In hierarchical structures, information passes through filters, preserving efficiency but potentially discarding useful signals. Optimal design depends on trade-offs between noise reduction and information loss.
  3. Deliberative and Epistemic Democracy Theories: Political theorists (List & Goodin, 2001; Landemore, 2013) argue that large groups can outperform individuals if deliberation aggregates dispersed knowledge. Yet deliberation’s epistemic benefits require conditions rarely met in practice: equality of participation, balanced information, and absence of domination. AMI’s experience reflects this: open deliberation among twenty or more managers improved diversity but risked diffusion of responsibility.
  4. Social Choice and Communication Models: Arrow’s impossibility theorem (Arrow, 1963) demonstrates that no aggregation rule perfectly converts individual preferences into a collective choice without violating some rationality condition. Larger groups must therefore accept trade-offs between fairness, coherence, and efficiency. Communication constraints further limit how much dispersed information can be shared before fatigue and cognitive overload set in (Gigone & Hastie, 1993).
  5. Collective Intelligence and Cognitive Diversity: Recent studies (Woolley et al., 2010) show that groups with high social sensitivity and balanced participation can aggregate information more effectively than those dominated by a few voices. Thus, beyond sheer size, the structure of interaction determines whether adding members improves or hinders decision quality.

These mechanisms suggest that as AMI expanded its decision body, each stage demanded different aggregation strategies. With five executives, deliberation and consensus were feasible. With twenty, structured facilitation and multi-criteria scoring became necessary. With one hundred, voting and reputation-based heuristics replaced discussion. Each adaptation traded some epistemic precision for procedural manageability.

Role of Decision Governance

More participants bring more information and legitimacy but also more noise, conflict, and coordination burden. Effective decision governance lies in matching aggregation mechanisms to group size and purpose. For AMI, the lesson is that decision-making systems must evolve as participation expands. A single decision maker can rely on intuition. A small team benefits from deliberation. Larger assemblies require formal rules to ensure that dispersed judgments are not lost in translation. Ultimately, as the number of decision makers grows, governance becomes a key determinant of decision quality.

Another View: Alternative Perspectives and Nuances in Research

While much research supports the idea that more decision makers can enhance information aggregation through diversity of perspectives, several lines of evidence challenge or qualify this conclusion.

First, empirical studies in behavioral economics and social psychology suggest that larger groups can amplify cognitive biases rather than cancel them. Janis’s (1972) concept of groupthink shows how cohesion and conformity pressures lead to collective overconfidence and suppression of dissenting views. Similarly, Sunstein and Hastie (2015) demonstrate that groups often engage in correlation neglect—they overweight shared information and ignore unique insights, causing deliberation to reinforce common errors rather than correct them.

Second, research on collective intelligence indicates that group performance depends less on size than on interaction quality. Woolley and colleagues (2010) found that social sensitivity and equal participation, not the number of participants, predict collective problem-solving ability. Beyond a modest threshold, additional members may dilute accountability and increase coordination noise rather than contribute useful information.

Third, network and communication theories point out that information rarely flows freely in large groups. Lazega (2001) and Borgatti & Cross (2003) show that social networks shape who communicates with whom, and dense hierarchical or political structures often distort signals before they reach collective deliberation. Hence, increasing the number of decision makers may multiply transmission bottlenecks instead of informational gains.

Finally, institutional and cultural contexts moderate these effects. In high-trust, egalitarian cultures, large groups may indeed pool knowledge effectively. In hierarchical or low-trust settings, expanding participation can entrench power dynamics, turning formal inclusiveness into symbolic participation (Mansbridge et al., 2012).

In sum, more decision makers do not automatically mean better decisions. The quality of aggregation depends on the design of deliberative structures, the incentives for truthful information sharing, and the social dynamics that govern how participants listen, interpret, and synthesize one another’s judgments.

References

  • Arrow, K. J. (1963). Social Choice and Individual Values. Yale University Press.
  • Condorcet, M. (1785). Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. Paris.
  • Dryzek, J. S. (2000). Deliberative Democracy and Beyond: Liberals, Critics, Contestations. Oxford University Press.
  • Gigone, D., & Hastie, R. (1993). “The common knowledge effect: Information sharing and group judgment.” Journal of Personality and Social Psychology, 65(5), 959–974.
  • Landemore, H. (2013). Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. Princeton University Press.
  • Latané, B., Williams, K., & Harkins, S. (1979). “Many hands make light the work: The causes and consequences of social loafing.” Journal of Personality and Social Psychology, 37(6), 822–832.
  • List, C., & Goodin, R. E. (2001). “Epistemic democracy: Generalizing the Condorcet jury theorem.” Journal of Political Philosophy, 9(3), 277–306.
  • March, J. G., & Simon, H. A. (1958). Organizations. Wiley. 
  • Olson, M. (1965). The Logic of Collective Action. Harvard University Press.
  • Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). “Evidence for a collective intelligence factor in the performance of human groups.” Science, 330(6004), 686–688.
  • Sah, R. K., & Stiglitz, J. E. (1986). “The architecture of economic systems: Hierarchies and polyarchies.” American Economic Review, 76(4), 716–727.
  • Borgatti, S. P., & Cross, R. (2003). “A relational view of information seeking and learning in social networks.” Management Science, 49(4), 432–445.
  • Janis, I. L. (1972). Victims of Groupthink. Houghton Mifflin. 
  • Lazega, E. (2001). The Collegial Phenomenon: The Social Mechanisms of Cooperation among Peers in a Corporate Law Partnership. Oxford University Press.
  • Mansbridge, J., Bohman, J., Chambers, S., et al. (2012). Deliberative Systems: Deliberative Democracy at the Large Scale. Cambridge University Press.
  • Sunstein, C. R., & Hastie, R. (2015). Wiser: Getting Beyond Groupthink to Make Groups Smarter. Harvard Business Review Press.

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