Leveraging collective intelligence in organizations

The concept of collective intelligence puts forth a key hypothesis: There is a cognitive surplus in crowds. Attempts to tap this cognitive surplus in science, business, and government are increasingly being employed [Hand 2010]. Yet, while there is relatively scarce evidence of its efficacy [Galton 1907], recent endeavors point to the potential of collective intelligence in improving people’s performance in a wide range of tasks [Woolley et al. 2010]. However, these findings suggest at least one possible paradox: the individuals who contribute to the collective intelligence of the crowd may be unaware that it exists. We investigate the existence of collective intelligence signals in modern organizations that could result in better decisions than were actually made. Unlike crowds, organizations are the most pervasive human infrastructure for making consequential decisions. Moreover, the design of organizations creates new scientific challenges for understanding collective intelligence. The conditions associated with collective intelligence revolve around crowds that are made up of diverse decision-makers acting independently [Lorenz et al. 2011]. By contrast, the scale economics and stable membership of organizations dictate that decision-makers have correlated information and work towards consensual decisions. This points to the need to understand how collective intelligence can operate effectively in non-crowds. In this study, we examine the resource allocation decisions of a large hedge fund. Each decision concerns the movement of funds in and out of stock holdings. Because hedge funds make a large number of these decisions and the accuracy of the decision can be clearly measured, even a single firm provides remarkably dense data to draw valid statistical tests. Our data covers two years during which the organization made approximately 800 decisions per week valued at an average of $700,000 per decision. Financial organizations also have sophisticated communications that are now acquired in their totality and accurately through electronic records. To capture communications dynamics in the organization that may be indicative of collective intelligence mechanisms, we analyze the instant message communications of the organization, which include 22 million IMs exchanged among the 182 personnel of the hedge fund and their network of 8,646 outside contacts. From these IMs we identify two collective intelligence signals that we show to be predictive of the changes in the market’s movement as recorded by public Dow Jones Industrial Average (DJIA) data.