Crowd Wisdom Relies on Agents’ Ability in Small Groups with a Voting Aggregation Rule

Abstract

In the last decade interest in the "wisdom of crowds" effect has gained momentum in both organizational research and corporate practice. Crowd wisdom relies on the aggregation of independent judgments. The accuracy of a group's aggregate prediction rises with the number, ability, and diversity of its members. We investigate these variables' relative importance for collective prediction using agent-based simulation. We replicate the "diversity trumps ability" proposition for large groups, showing that samples of heterogeneous agents outperform same-sized homogeneous teams of high ability. In groups smaller than about 16 members, however, the effects of group composition depend on the social decision function employed: Diversity is key only in continuous estimation tasks (averaging) and much less important in discrete choice tasks (voting), in which agents' individual abilities remain crucial. Thus, strategies to improve collective decision-making must adapt to the predictive situation at hand.

Design principles for a hybrid intelligence decision support system for business model validation

“…While an individual decision maker might be prone to biases and errors (such as individual entrepreneurs or mentors in our context), the principle of statistical aggregation minimizes such errors by combining several judgements (Armstrong 2001). Furthermore, aggregating the judgement of several individuals is informative as it aggregates heterogenous knowledge about a certain problem and allows the capture of a fuller understanding of a decision-making problem (Soukhoroukova et al 2012;Keuschnigg and Ganser 2016;Ebel et al 2016). Consequently, we argue that collective intelligence represents a proper way to augment machine learning systems by accessing more diverse domain knowledge, integrating it into an algorithm, and reducing the threat of biased interpretation.…”

Integrating Ecosystem Intelligence with the Hybrid Intelligence Accelerator

“…To receive valid decisional guidance from collective intelligence, the composition of the crowd is highly dependent on the ability of individuals (Keuschnigg and Ganser 2017). We, thus, use a "select crowd" approach to select mentors that have expertise in the specific domain of the business model (e.g.…”

A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment

“…They focus on niche domains instead of providing domainspanning insights due to low generalizability and comparability of results. To overcome this inadequacy of empirical data, researchers use simulation (e.g., Hastie and Kameda 2005;Hammitt and Zhang 2013;Keuschnigg and Ganser 2017). Via simulation, alternating characterizations of the crowd and the environment (i.e., scenarios) are recreated and the performance of aggregation algorithms can be studied.…”